Energy of strongly correlated electron systems

Size: px
Start display at page:

Download "Energy of strongly correlated electron systems"

Transcription

1 Avalable onlne at WSN 49(2) (2016) EISSN Energy of strongly correlated electron systems M. A. Reza a, K. A. I. L. Wjewardena Gamalath b Department of Physcs, Unversty of Colombo, Colombo 3, Sr Lana a,b E-mal address: 11sc12708@stu.cmb.ac.l, male@phys.cmb.ac.l ABSRAC he constraned path Monte Carlo method was used to solve the Hubbard model for strongly correlated electrons systems analytcally n arbtrary dmensons for one, two and three dmensonal lattces. he energy varatons wth electron fllng, electron-electron correlaton strength and tme as well as the netc and potental energes of these system were studed. A competton between potental and netc energes as well as a reducton of the rate of ncrease of the potental energy wth ncreasng correlaton were observed. he degenerate states of the lattce systems at zero correlaton and the ncrease n the energy separaton of the states at hgher correlaton strengths were evdent. he varaton of the energy per ste wth correlaton strength of dfferent lattce szes and dmensons were obtaned at half fllng. From these t was apparent that the most stable lattces were the smallest for all the dfferent dmensons. For one dmenson, the convergence of the results of the constraned path method wth the exact non-lnear feld theory results was observed. Keywords: Strongly correlated electron systems; constraned-path Monte Carlo method; Hubbard model; dynamcal mean feld theory 1. INRODUCION ranston metal oxdes such as ncel oxde and cobalt oxde even though havng partally flled d orbtals were reported by De Boer and Verwey n 1937 to have transparent nsulatng propertes, contradctng the mplcatons of conventonal band theory [1]. Mott suggested that the Coulombc repulson between the electrons prevented metallcty n these

2 materals and these materals were called Mott nsulators [2]. In strongly correlated materals, the potental energy arsng from ths Coulombc repulson, competes wth the electron netc energy to gve nterestng propertes, one beng the Mott metal to nsulator transton. In resstvty measurements conducted on transton metal compounds contanng magnetc mpurtes, a mnmum n resstvty was observed at a specfc temperature. Untl Jun Kondo n 1964 showed that ths mnmum arose from the competton between electron-phonon scatterng and spn-spn scatterng, t remaned a paradox [3]. hs type of behavour occurs manly due to strong electron-electron nteractons and correlatons. hese materals called strongly correlated electron systems, led to the emergence of a separate paradgm n condensed matter physcs. Apart from transton metal oxdes and perovstes, ths phenomenon has been found to be prevalent n graphenes, fullerenes and as well as n soft-matter le polymers [4]. he narrower the band, the longer wll an electron stay on the atom and thereby feels the presence of other electrons more. herefore a narrow bandwdth mples a stronger correlaton. In many materals wth partally flled d or f orbtals such as the transton metals, vanadum, ron, ncel and ther oxdes or rare earth materals such as cerum experence strong electron correlatons as the electrons occupy narrow orbtals [5]. he conventonal models faled to explan these unusual propertes due to the fact that the electrons were treated as separate non-nteractng enttes, whch wored for most materals used wdely such as slcon and alumnum. For strongly correlated materals, when the electron-electron nteractons are taen nto consderaton, the problem evolves nto a many-body problem whch s dffcult to solve even for smple lattces. he Hubbard Hamltonan ncludes the electron-electron repulsons and was ntally used to study the transton metal oxdes. Over the years the Hubbard model has been appled to more complex systems usng mean feld approaches and was used to study hgh temperature superconductors n the 1990s. Despte beng smple n form, t was successful n explanng the behavor of these materals to a larger extent. he fact that the Hubbard model cannot be solved analytcally n arbtrary dmensons led to varous numercal approaches such as the Lanczos algorthm at absolute zero and fnte-temperature auxlary feld Monte-Carlo for low temperatures. he Hubbard model n ts pure form s able to explan basc features of correlated electrons but t cannot account for the detaled physcs of real materals. he ndependent electron approxmaton used n densty functonal theory approaches s not used n ths approach, and the only approxmaton s that the lattce self-energy s momentum ndependent [6]. In ths paper, the strongly correlated electron systems were nvestgated by the Hubbard model approach [7] wth constraned path Monte Carlo method [8] n Slater determnant space. A lnear chan of electrons stes n one dmenson, a planar square lattce of 221 n two dmenson and a cubc lattce of 222 electron stes n three dmenson were studed through total energy, netc energy specfed by hoppng ntegrals and potental energy dctatng the form of the Hubbard-Stratonovch transformaton calculated from Hellman-Feynman theorem. her behavour wth tme and electron-electron correlatons were studed by settng the hoppng parameter n all drectons to unty. he number of Slater determnants was set to 100 and the tme step 0.01s. For dfferent lattce szes 2 1 1, and D lattces, 22 1, 32 1, D lattces and 22 2, 32 2, D lattces, the varatons of the energy per ste wth correlaton was obtaned. For -60-

3 the lattce n one dmenson, the results of the constraned path method wth the exact non-lnear feld theory was compared. 2. HUBBARD MODEL FOR SRONGLY CORRELAED SYSEMS he most complete model yet to descrbe strongly correlated systems s the Hubbard model ntroduced by John Hubbard n 1963 [7]. A regular array of fxed nuclear postons n a sold lattce s consdered neglectng the lattce vbratons and the electrons move around n ths lattce. Intally the atom s consdered as havng one energy level open for electron occupaton thereby gvng two electron stes of spn up and spn down possble. he electrons nteract va the screened Coulomb nteracton, and the largest nteracton s between two electrons at the same ste. he nteracton s modeled by consderng an addtonal term, the correlaton strength U when two electrons occupy the same ste and ths s set to zero f a ste s unoccuped or occuped by only one electron. Snce the nteractons between electrons on the same ste s much larger than the nteractons between electrons on neghbourng stes, the nteractons between electrons from neghbourng stes are not consdered. hs electronelectron nteractons gves rse to the potental energy term n the Hubbard Hamltonan. he netc energy term arses from the electron hoppng governed by the hoppng ntegral t, whch s the energy scale n most Hubbard calculatons. he hoppng s determned by the overlap of the two wave functons on a par of atoms. Snce electron wave functons de off exponentally wth dstance, only hoppng to nearest neghbours are consdered. c j s defned as the electron creaton operator, whch creates an electron of spn σ at lattce ste j and c s defned as the electron destructon operator, whch destroys an electron of spn σ at lattce ste. Hubbard hamltonan s defned as: H t c c U n n ( n n ) (1) j, j j j j j j j he frst term of the Hamltonan s the netc energy term whch denotes the destructon of an electron of spn at ste and a creaton of an electron of spn at lattce ste j. he term j, n the summaton denotes the fact that hoppng s allowed only between two adjacent stes. he second term s the potental energy term and t checs all stes and adds energy of U when t fnds a ste s doubly occuped. he thrd term s the chemcal potental controllng the fllng of electrons. Most nterestng phenomena are observed n strongly correlated materals when there s only one electron per ste ntally, whch represents the half fllng state. 3. CONSRAINED-PAH MONE CARLO MEHOD he constraned path Monte Carlo method combnes the concept of the Hubbard Stratonovch transformaton and Slater determnants wth branchng random wals [8]. In ths study t s assumed that the Hamltonan conserves the total z-component of the electron spn -61-

4 s z and that there s no transfer of spns. herefore the number of electrons wth each spn up and down component s fxed. he number of lattce stes n the Hubbard model was consdered as M and the th sngle-partcle bass state as. If N s the number of electrons n the system, then N s the number of electrons wth spn σσ ( = or ) and N M. For a sngle partcles wave functon, the coeffcents of the expanson n the sngle partcle bass s gven as the M-dmensonal vector : c 0 (2) he many-body wave functon whch s wrtten as a Slater determnant s formed from the N-dfferent sngle partcle orbtals by ther symmetrzed product: ˆ ˆ ˆ N (3) where the operator ˆ m c, m creates and electron n the m th sngle-partcle orbtal descrbed by equaton 1. s an M representng the coeffcents of the orbtals. he many-body ground state 0 and the N matrx referred to as a Slater determnant many-body wave functon are not necessarly a sngle Slater determnant. he constraned path Monte Carlo method wors wth a one-partcle bass n Slater determnant space. When the energy s taen n terms of the hoppng parameter t, t s useful to wrte the Hubbard Hamltonan n the form: ( H / t) c jc U / t n n ( / t) ( n n ) j j j j j, j j (4) he coulomb repulson from electrons on the same ste s U. It s useful to gve all energy related measurements n terms of the hoppng parameter t snce we are focusng on the correlatons whch depend on the strength of the nteracton between the electrons and not on the hoppng of the electrons. herefore the hoppng parameter t whch s a unt of energy s set to unty and the parameter U / t s the correlaton strength. he dfference n the Hubbard Hamltonan and the general electronc Hamltonan s the structure of the matrx elements of netc energy ˆK and potental energy V ˆ and ths dfference captures the propertes of strong correlaton. ˆK s specfed by hoppng ntegrals of the form K j whle the elements of potental energy V ˆ dctates the form of the Hubbard- Stratonovch transformaton. he ground-state wave functon 0 can be obtaned from a tral wave functon not orthogonal to 0 by repeated applcatons of the ground-state projecton operator -62-

5 p gs e ( Hˆ E ) (5) where s E s the best guess of the ground-state energy. If the wave functon at the n th tme step n, the wave functon at the next tme step s gven by n ( Hˆ E ) n e (6) 1 wth a small tme step. he second order rotter approxmaton: Hˆ ( Kˆ Vˆ ) Kˆ /2 Vˆ Kˆ /2 e e e e e (7) he netc energy, the one-body propagator e Vˆ Bˆ K /2 Kˆ e /2 and the potental energy propagator does not have the same form. A Hubbard Stratonovch (HS) transformaton transform the potental energy propagator to the desred form. In the Hubbard model, we can use the followng: e Un ( )/2 x ( n n ) n U n n e p( x ) e x 1 (8) where s gven by cosh exp( U / 2). px ( ) 1/ 2 s nterpreted as a dscrete probablty densty functon wth x 1. he exponent on the left, comes from the nteracton term V ˆ on the th ste s quadratc n n, ndcatng the nteracton of two electrons. he exponents on the rght, on the other-hand, are lnear n n, ndcatng two non-nteractng electrons n a common external feld characterzed by x. hus an nteractng system has been converted nto a non-nteractng system n fluctuatng external auxlary felds x, and the summaton over all such auxlary-feld confguratons recovers the many-body nteractons. he lnearzed operator on the rght hand sde n equaton s the spn ( n n ) on each ste. he frst component of the constraned path Monte Carlo method s the reformulaton of the projecton process as branchng, open-ended random wals n Slater determnant space nstead of updatng a fxed-length path n auxlary-feld space. We defne ˆ ( ) ˆ B x B ( x). Applyng the HS-transformed propagator: V V n1 E ( )[ ˆ ˆ ( ) ˆ n e P x B B x B ] (9) x K /2 V K /2 In the Monte Carlo realzaton of ths teraton, the wave functon at each stage s represented by a fnte ensemble of Slater determnants: -63-

6 n (10) n where labels the Slater determnants and an overall normalzaton factor of the wave functon has been omtted. hese Slater determnants wll be referred to as random walers. he teraton n equaton 9 s acheved stochastcally by Monte Carlo samplng of x. hat s, for each random waler n, an auxlary-feld confguraton x s chosen accordng to the probablty densty functon Px ( ) and propagate the determnant to a new determnant n1 ˆ ˆ ( ) ˆ n B B x B. hs procedure s repeated for all walers n the populaton. K /2 V K /2 hese operatons accomplsh one step of the random wal. he new populaton represents n1 n1. hese steps are terated untl suffcent data has been collected. After an equlbraton phase, all walers thereon are Monte Carlo (MC) samples of the ground-state wave functon and ground-state propertes can be computed. We wll refer to ths type of 0 approach as free projecton. In practce, branchng occurs because of the reorthonormalzaton of the Slater determnants. Computng the mxed estmator of the groundstate energy: E mxed Hˆ 0 (11) 0 requres estmatng the denomnator by where are random walers after equlbraton. Snce these walers are sampled wth no nowledge of, terms n the summaton over can have large fluctuatons that lead to large statstcal errors n the MC estmate of the denomnator, thereby n that of E mxed. o elmnate the decay of the sgnal-tonose rato, we mpose the constraned path approxmaton. It requres that each random waler at each step to have a postve overlap wth the tral wave functon : 0 (12) ( n) he constraned path approxmatons easly mplemented by redefnng the mportance functon: O ( ) max,0 (13) he mxed estmator for the ground-state energy for an ensemble s gven by -64-

7 Emxed wel[, ] w (14) where the local energy E L s: E L[, ] Hˆ (15) hs quantty can be easly evaluated for any waler (Slater determnant) as follows. For any par of Slater determnants Green's functon as: c c j 1 j ( ) c c and, we can calculate the one-body equal-tme (16) hs mmedately enables the computaton of the netc energy term t c c. j j he potental energy term U c c c c 16, but can be reduced to that form by an applcaton of Wc's theorem: j does not have the form of equaton c c c c c c c c c c c c c c c c (17) and he reducton occurs because the up and down spn sectors are decoupled n both. hs s not the case n a parng or generalzed Hartree-Foc wave functons. he former s the desred form for U < 0. he latter can be used to mprove the qualty of the tral wave functon for U > 0, and s necessary f the Hamltonan contans spn-orbt couplng. he potental energy value U c c c c when combned wth the netc energy value j j t c c provdes an unbased estmate for the total energy, but the potental and netc energy terms alone are based. From the netc energy term t mples that the netc energy has no varaton wth correlaton strength, but t s nown to vary wth the correlaton. herefore the potental energy s calculated usng an alternatve method, the Hellman- Feynman theorem: dh de PE ( U) U ( U) U (18) du du -65-

8 Here de / du s calculated from the fnte-dfference formula. he netc energy value s then obtaned by deductng the potental energy from the total energy. Snce both the total energy and potental energy are unbased estmators, the netc energy s then unbased too. he dervatve was obtaned usng the fve-pont dfference formula: de E( U 2 U ) 8 E( U U ) 8 E( U U ) E( U 2 U ) du 12U (19) 4. OAL ENERGY AND ELECRON FILLING he total energy of the electron system wth tme for full fllng and half fllng were calculated for one (1D), two (2D) and three (3D) dmensonal lattces. A lnear chan of stes ( L 2, L 1, L 1) for x y z the 1D case, a planar square lattce of 22 1 electron stes ( L 2, L 2, L 1) x y z for the 2D case and a cubc lattce of 22 2 stes ( L 2, L 2, L 2) for x y z 1(a): Half-fllng the 3D case were consdered. L denotes the number of stes n the th drecton. he hoppng parameter was set to unty n all drectons. he number of Slater determnants was set to 100 and the tme step to 0.01s. he nterval between energy 1(b): Full fllng measurements was set to 20 Fgure 1. otal Energy E/ t aganst tme for the 1D 21 1 blocs. An energy measurement s made every 0.2 seconds. he varaton of total energy as a functon of tme for the half fllng and full fllng cases obtaned for the 1D, 2D and 3D lattces. hese are shown n fgures 1, 2 and 3. From these fgures we can see that for 1D, 2D and 3D lattces, the total -66-

9 energy vares wth tme n the half-fllng cases only, whle t s a constant n tme for the full fllng case. he energy oscllates n a specfc regon for the half-fllng case and at correlaton strength of U / t 4, the total energy of the system at full fllng s postve, whch mples that the system s not bounded and s unstable for full fllng under ths formulaton. Around half-fllng s where the strange propertes of the strongly correlated materals maybe found, whch s confrmed from most expermental results. herefore from here on n the study, only half fllng cases were consdered for all the lattce systems. 2(a): Half-fllng 2(b): Full fllng Fgure 2. otal Energy E/t aganst tme for the 2D 221lattce -67-

10 3(a): Half-fllng 3(b): Full fllng Fgure 3. otal Energy E/t aganst tme for the 3D 222lattce -68-

11 5. OAL ENERGY, CORRELAION SRENGH AND IME he total energy of the electron system wth tme and the electron-electron correlaton strength for the half fllng case was obtaned for the same one, two and three dmensonal lattces. he hoppng parameters n x, y and z- drectons were set to unty ( t 1, t 1, t 1). he x y z number of Slater determnants was set to 100 and the tme step 0.01s. he on-ste repulson strength U/t was set to ncrease from 0 to 8.0 n steps of 0.5. he magnary tme was set to run from 0 to 6 s n the 1D case and 0 to 12 s n the 2D case and 0 to 18 s n the 3D case n steps of 0.01 s. he total energy n terms of hoppng parameter and correlaton strength as well as tme for one, two and three dmensonal lattces are shown n fgures 4(a), 4(b) and 4(c) respectvely. hese fgures show that for 1D, 2D and 3D systems, the total energy of the system ncreases wth the correlaton strength whle oscllatng wth tme. he varaton wth correlaton strength s more sgnfcant than the varaton wth tme. he oscllaton of the total energy of the system wth tme s more promnent at hgher correlatons strengths. hs explans the fact that when there s zero correlaton strength wth tme, f an electron from a sngly occuped ste jumps to another sngly occuped ste, gvng a double occuped ste and an empty ste snce the correlaton strength s zero, there s no change n energy. herefore although the confguraton of Fgure 4(a): For 1D 2x1x1 lattce Fgure 4(b): For 2D 2x2x1 lattce Fgure 4(c): For 3D 2x2x2 lattce Fgure 4. otal Energy E/t, correlaton strength U/t and tme for 1D (2x1x1), 2D (2x2x1) and 3D (2x2x2) lattces -69-

12 the system has changed, the energy remans the same and the two states are degenerate n energy. But when the correlaton strengths are non-zero and ncreasng, the doubly occuped state has more energy than the state wth two sngly occuped stes, therefore when the electron jumps to a sngly occuped ste gvng that ste double occupancy, the energy of the system ncreases and t goes to a hgher energy state, and when the electron jumps bac, the system goes to a lower energy state, thus gvng oscllatons n the total energy of the system wth tme as correctly predcted for the three cases n the above plots. 6. POENIAL ENERGY AND KINEIC ENERGY AGAINS CORRELAION he varaton of the potental energy (PE) and netc energy (KE) of the electron systems wth correlaton strength were calculated for the same 1D, 2D and 3D lattce systems at half fllng. he hoppng parameters were set to unty ( t 1, t 1, t 1). he x y z number of Slater determnants was set to 100 and the tme step 0.01s. he on-ste repulson strength U/t was set to ncrease from 0 to 8.0 n steps of he energy measurements were taen 10 tmes and the standard error was taen as the error n the energy values. Second order polynomals were ftted to the data. he netc energes and potental energes as a functon of correlaton strength U/t for 1D, 2D and 3D are shown n fgures 5, 6 and 7 respectvely. From the above fgures t can be seen that the potental energy ntally ncreases wth the correlaton strength, but as the system enters the strong couplng regme, the potental energy saturates and decreases gradually. It s seen that netc energy contnues to ncrease n the wea, Fgure 5(a): Potental enery aganst correlaton n terms of hoppng parameter t for 1D 2x1x1 lattce Fgure 5(b): Knetc enery aganst correlaton n terms of hoppng parameter t for 1D 2x1x1 lattce -70-

13 ntermedate and strong couplng regmes. It can also be seen that the errors n the potental energy s slghtly hgher than that of the netc energy. Also t shows that the potental energy and netc energy values agree sgnfcantly wth the second order polynomal, therefore we can say that the energes are of the second order wth correlaton. he netc energy contnues to ncrease n the wea, ntermedate and strong couplng regmes. Fgure 6(a): Potental enery aganst correlaton n terms of hoppng parameter t for 2D 2x2x1 lattce Fgure 6(b): Knetc enery aganst correlaton n terms of hoppng parameter t for 2D 2x2x1 lattce -71-

14 Fgure 7(a): Potental enery aganst correlaton n terms of hoppng parameter t for 3D 2x2x2 lattce Fgure 7(b): Knetc enery aganst correlaton n terms of hoppng parameter t for 3D 2x2x2 lattce 7. ENERGY PER LAICE SIE VS. CORRELAION SRENGH he energy of the 1D, 2D and 3D lattce systems are governed by the number of electrons, the fllng, and the correlaton strength, when the temperature and hoppng parameter are fxed. Snce the energy depends on the number of electrons or the number of lattce stes, the energy per lattce ste for a system s a better measure for the comparson of the energy n lattce systems. he varaton of the energy per lattce ste wth correlaton strength both n terms of the hoppng parameter were compared for the 1D 2 1 1, 2D 22 1 and 3D 22 2 lattce systems. he hoppng parameter was set to unty n all drectons. he number of Slater determnants were set to 100 and the tme step 0.01s. -72-

15 he on-ste repulson strength U/t was set to ncrease from 0 to 8.0 n steps of 0.5. he energy values were calculated for 10 seconds and averaged for each correlaton value. he standard error was taen as the error n energy. A second order polynomal was ftted to the data. hese are shown n the fgure 8 wth 1D n blue, 2D n green and 3D n red. Fgure 8. Energy per ste aganstcorrelaton per hoppng parameter for 1D, 2D,3D lattces he above plot shows that the energy per ste of the 3D system s more negatve than the 2D system whch s more negatve than the 1D lattce system. hs shows that the 3D system s the most stable when compared to the 2D and 1D systems. Also t shows that the 1D, 2D and 3D energy values strongly agreed wth the ftted second order polynomals. Fgure 9. Energy per ste aganstcorrelaton n terms of hoppng parameter for 21 1(blue), 411 (green) and 81 1 (red) 1D lattces -73-

16 Fgure 10. Energy per ste aganst correlaton n terms of hoppng parameter for 22 1(blue), 32 1(green), and 42 1(red)2D lattces Fgure 11. Energy per ste aganst correlaton n terms of hoppng parameter for 22 2 (blue), 32 2 (green) and 32 2 (red) 3D lattces he energy varatons per ste wth correlaton was obtaned for dfferent szes of the latces namely, 2 1 1, 4 1 1, D lattces, 22 1, 32 1, D lattces and 22 2, 32 2, D lattces. A second order polynomal was ftted to the data and these are presented n fgure 9, 10 and 11 respectvely. he comparson of 1D lattces showed that the lattce (blue) was more stable than the (red) and the (green) lattces. here s no fxed relatonshp between the magntude of the energy and the length of the 1D chan. For the 2D lattces the 22 1 lattce was the most stable, followed by the 32 1 lattce. he smallest lattce was more stable and the lattces had smlar -74-

17 energes ntally, but as the correlaton strength ncreases, the energes of the three lattces too dfferent values. In the 3D case the smallest lattce s agan the most stable, and the larger lattces are of hgher energes and less stable. Also the ntal value of energy s not the same at zero correlaton, unle the 2D case. Further as n the prevous cases, the data values agreed wth the second order polynomal ft. herefore the energy per ste for all the lattces consdered are strongly second order. 8. CONVERGENCE OF HE CONSRAINED-PAH MONE CARLO MEHOD It s mportant to chec whether the constraned-path Monte Carlo method, the method used n ths study, converges to the actual values, so that ts fndngs may be consdered as accurate. here s no exact soluton to the Hubbard model usng dynamcal mean feld theory except n one dmenson. he exact soluton for the energy of a D lattce system wth one spn up and one spn down electron obtaned from nonlnear feld theory [10] s: E U U 2 ( 64) / 2 (20) herefore the total energy results obtaned for the D lattce system can be compared wth the exact soluton above to test the convergence of the constraned-path Monte Carlo method. he number of Slater determnants were set to 100 and the tme step Δτ = 0.01s. he nterval between energy measurements was set to 20 blocs. An energy measurement s made every 0.2 seconds. Repeated measurements were taen and the average value and Monte Carlo standard error was taen as the energy and error n energy. he on-ste repulson strength U/t was set to ncrease from 0 to 8.0 n steps of he exact calculatons obtaned from equaton 21 and the values calculated from constraned-path Monte Carlo method are presented n fgure 12 by blue and red respectvely. Even wth 100 Slater determnants, the total energy values obtaned from the constraned path method agree very closely wth the energy values obtaned from the exact non-lnear feld theory method. Fgure 12. otal energy E/taganst correlaton strength U/t from exact soluton (blue) and the constraned-path Monte Carlo method (red)for the 1D 21 1 lattce. -75-

18 9. CONCLUSIONS For the 1D, 2D and 3D lattces at fxed temperature and at correlaton strength of 4 tmes the hoppng parameter, the total energy was negatve and the systems were bound at half fllng. But at full fllng, the total energy of the system was constant wth tme and postve ndcatng an unbounded nature of the system n ths formulaton. he 1D lattce at fxed temperature approached ferromagnetc orderng at hgh correlaton strengths. It was also conclusve that for the same 1D, 2D and 3D lattces at half fllng and fxed temperature the varaton of the system wth correlaton was more sgnfcant than the varaton wth tme and the oscllaton of the total energy of the systems wth tme was more promnent at hgher correlatons strengths. At correlaton strengths of four to eght tmes the hoppng parameter, the oscllatons of the total energy wth tme was more sgnfcant. he degeneracy wth respect to the sngly and doubly occuped states were present at zero correlaton and were partally removed at hgher correlaton strengths. A competton between the potental energy and netc energy s promnent n the 1D, 2D and 3D lattces at fxed temperature n the range of correlaton strengths of zero to eght tmes the hoppng parameter, although the potental energy contrbuton was less n magntude to the netc energy. he varaton n netc and potental energes wth correlaton strength can be ftted to a second order polynomal. herefore the netc and potental energes of the 1D, 2D and 3D lattces were of second order n correlaton strength, but wth a certan degree of dsagreement. Further t was observed that the energy per ste was the most negatve that s most stable for the smaller lattces and as the lattce szes ncreased, the negatvty of the energes s reduced. he varaton of the total energy of the 1D system obtaned from the constraned path method and from the exact non-lnear feld theory method agreed well even wth only 100 Slater determnants, ndcatng that the constraned path Monte Carlo method can be used for correlated systems. References [1] J. Boer, E. J. W. Verwey, Sem-conductors wth partally and wth completely flled 3d lattce bands, Proc. Phys. Soc., vol. 49, no. 4S, pp , [2] N. F. Mott, Metal-Insulator ranston, Rev. Mod. Phys., vol. 40, no. 4, pp , [3] J. Kondo, Resstance Mnmum n Dlute Magnetc Alloys, Prog. heo. Phys. vol. 32, no. 1, pp , [4] H. Barman, Dagrammatc perturbaton theory based nvestgatons of the Mott transton physcs, (Ph.D. hess, heoretcal scence unt, Jawaharlal Nehru centre for advanced scentfc research, Bangalore, Inda, 2013) [5] V. N. Antonov, L. V. Beenov, and A. N. Yareso, Electronc Structure of Strongly Correlated Systems, Advances Cond. Matt. Phys , pp.1-107, [6] D. Vollhardt, Dynamcal mean-feld theory for correlated electrons. Annalen Der Phys, vol. 524, no. 1, pp. 1-19,

19 [7] G. Kotlar, A. Georges, Hubbard model n nfnte dmensons, Phys. Rev. B, vol. 45, no. 12, pp , [8] A. omas, QUES: QUantum Electron Smulaton oolbox. Avalable at: [9] H. Nguyen, H. Sh, J. Xu, and S. Zhang, CPMC-Lab: A Matlab pacage for Constraned Path Monte Carlo calculatons, Comp. Phys. Comm., vol. 185, no. 12, pp , [10] F. D. M. Haldane, Nonlnear Feld heory of Large-Spn Hesenberg Antferromagnets: Semclasscally Quantzed Soltons of the One-Dmensonal Easy-Axs Néel State. Phys. Rev. Lett. 50(15) (1983) ( Receved 10 May 2016; accepted 26 May 2016 ) -77-

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the

More information

The Feynman path integral

The Feynman path integral The Feynman path ntegral Aprl 3, 205 Hesenberg and Schrödnger pctures The Schrödnger wave functon places the tme dependence of a physcal system n the state, ψ, t, where the state s a vector n Hlbert space

More information

CHAPTER 14 GENERAL PERTURBATION THEORY

CHAPTER 14 GENERAL PERTURBATION THEORY CHAPTER 4 GENERAL PERTURBATION THEORY 4 Introducton A partcle n orbt around a pont mass or a sphercally symmetrc mass dstrbuton s movng n a gravtatonal potental of the form GM / r In ths potental t moves

More information

STABILITY OF METALLIC FERROMAGNETISM: CORRELATED HOPPING OF ELECTRONS IN Mn 4 N

STABILITY OF METALLIC FERROMAGNETISM: CORRELATED HOPPING OF ELECTRONS IN Mn 4 N STABILITY OF METALLIC FERROMAGNETISM: CORRELATED HOPPING OF ELECTRONS IN Mn 4 N EUGEN BIRSAN 1, COSMIN CANDIN 2 1 Physcs Department, Unversty Lucan Blaga, Dr. I. Ratu str., No. 5 7, 550024, Sbu, Romana,

More information

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems

Chapter 5. Solution of System of Linear Equations. Module No. 6. Solution of Inconsistent and Ill Conditioned Systems Numercal Analyss by Dr. Anta Pal Assstant Professor Department of Mathematcs Natonal Insttute of Technology Durgapur Durgapur-713209 emal: anta.bue@gmal.com 1 . Chapter 5 Soluton of System of Lnear Equatons

More information

Probabilistic method to determine electron correlation energy

Probabilistic method to determine electron correlation energy Probablstc method to determne electron elaton energy T.R.S. Prasanna Department of Metallurgcal Engneerng and Materals Scence Indan Insttute of Technology, Bombay Mumba 400076 Inda A new method to determne

More information

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng

More information

Chapter 13: Multiple Regression

Chapter 13: Multiple Regression Chapter 13: Multple Regresson 13.1 Developng the multple-regresson Model The general model can be descrbed as: It smplfes for two ndependent varables: The sample ft parameter b 0, b 1, and b are used to

More information

Non-interacting Spin-1/2 Particles in Non-commuting External Magnetic Fields

Non-interacting Spin-1/2 Particles in Non-commuting External Magnetic Fields EJTP 6, No. 0 009) 43 56 Electronc Journal of Theoretcal Physcs Non-nteractng Spn-1/ Partcles n Non-commutng External Magnetc Felds Kunle Adegoke Physcs Department, Obafem Awolowo Unversty, Ile-Ife, Ngera

More information

5.03, Inorganic Chemistry Prof. Daniel G. Nocera Lecture 2 May 11: Ligand Field Theory

5.03, Inorganic Chemistry Prof. Daniel G. Nocera Lecture 2 May 11: Ligand Field Theory 5.03, Inorganc Chemstry Prof. Danel G. Nocera Lecture May : Lgand Feld Theory The lgand feld problem s defned by the followng Hamltonan, h p Η = wth E n = KE = where = m m x y z h m Ze r hydrogen atom

More information

Kernel Methods and SVMs Extension

Kernel Methods and SVMs Extension Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general

More information

Inductance Calculation for Conductors of Arbitrary Shape

Inductance Calculation for Conductors of Arbitrary Shape CRYO/02/028 Aprl 5, 2002 Inductance Calculaton for Conductors of Arbtrary Shape L. Bottura Dstrbuton: Internal Summary In ths note we descrbe a method for the numercal calculaton of nductances among conductors

More information

Lecture 14: Forces and Stresses

Lecture 14: Forces and Stresses The Nuts and Bolts of Frst-Prncples Smulaton Lecture 14: Forces and Stresses Durham, 6th-13th December 2001 CASTEP Developers Group wth support from the ESF ψ k Network Overvew of Lecture Why bother? Theoretcal

More information

One-sided finite-difference approximations suitable for use with Richardson extrapolation

One-sided finite-difference approximations suitable for use with Richardson extrapolation Journal of Computatonal Physcs 219 (2006) 13 20 Short note One-sded fnte-dfference approxmatons sutable for use wth Rchardson extrapolaton Kumar Rahul, S.N. Bhattacharyya * Department of Mechancal Engneerng,

More information

EPR Paradox and the Physical Meaning of an Experiment in Quantum Mechanics. Vesselin C. Noninski

EPR Paradox and the Physical Meaning of an Experiment in Quantum Mechanics. Vesselin C. Noninski EPR Paradox and the Physcal Meanng of an Experment n Quantum Mechancs Vesseln C Nonnsk vesselnnonnsk@verzonnet Abstract It s shown that there s one purely determnstc outcome when measurement s made on

More information

Amplification and Relaxation of Electron Spin Polarization in Semiconductor Devices

Amplification and Relaxation of Electron Spin Polarization in Semiconductor Devices Amplfcaton and Relaxaton of Electron Spn Polarzaton n Semconductor Devces Yury V. Pershn and Vladmr Prvman Center for Quantum Devce Technology, Clarkson Unversty, Potsdam, New York 13699-570, USA Spn Relaxaton

More information

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 30 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 2 Remedes for multcollnearty Varous technques have

More information

Lecture 4. Macrostates and Microstates (Ch. 2 )

Lecture 4. Macrostates and Microstates (Ch. 2 ) Lecture 4. Macrostates and Mcrostates (Ch. ) The past three lectures: we have learned about thermal energy, how t s stored at the mcroscopc level, and how t can be transferred from one system to another.

More information

This chapter illustrates the idea that all properties of the homogeneous electron gas (HEG) can be calculated from electron density.

This chapter illustrates the idea that all properties of the homogeneous electron gas (HEG) can be calculated from electron density. 1 Unform Electron Gas Ths chapter llustrates the dea that all propertes of the homogeneous electron gas (HEG) can be calculated from electron densty. Intutve Representaton of Densty Electron densty n s

More information

Polynomial Regression Models

Polynomial Regression Models LINEAR REGRESSION ANALYSIS MODULE XII Lecture - 6 Polynomal Regresson Models Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur Test of sgnfcance To test the sgnfcance

More information

Electron-Impact Double Ionization of the H 2

Electron-Impact Double Ionization of the H 2 I R A P 6(), Dec. 5, pp. 9- Electron-Impact Double Ionzaton of the H olecule Internatonal Scence Press ISSN: 9-59 Electron-Impact Double Ionzaton of the H olecule. S. PINDZOLA AND J. COLGAN Department

More information

Frequency dependence of the permittivity

Frequency dependence of the permittivity Frequency dependence of the permttvty February 7, 016 In materals, the delectrc constant and permeablty are actually frequency dependent. Ths does not affect our results for sngle frequency modes, but

More information

The non-negativity of probabilities and the collapse of state

The non-negativity of probabilities and the collapse of state The non-negatvty of probabltes and the collapse of state Slobodan Prvanovć Insttute of Physcs, P.O. Box 57, 11080 Belgrade, Serba Abstract The dynamcal equaton, beng the combnaton of Schrödnger and Louvlle

More information

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM An elastc wave s a deformaton of the body that travels throughout the body n all drectons. We can examne the deformaton over a perod of tme by fxng our look

More information

International Journal of Pure and Applied Sciences and Technology

International Journal of Pure and Applied Sciences and Technology Int. J. Pure Appl. Sc. Technol., 4() (03), pp. 5-30 Internatonal Journal of Pure and Appled Scences and Technology ISSN 9-607 Avalable onlne at www.jopaasat.n Research Paper Schrödnger State Space Matrx

More information

A new Approach for Solving Linear Ordinary Differential Equations

A new Approach for Solving Linear Ordinary Differential Equations , ISSN 974-57X (Onlne), ISSN 974-5718 (Prnt), Vol. ; Issue No. 1; Year 14, Copyrght 13-14 by CESER PUBLICATIONS A new Approach for Solvng Lnear Ordnary Dfferental Equatons Fawz Abdelwahd Department of

More information

Robert Eisberg Second edition CH 09 Multielectron atoms ground states and x-ray excitations

Robert Eisberg Second edition CH 09 Multielectron atoms ground states and x-ray excitations Quantum Physcs 量 理 Robert Esberg Second edton CH 09 Multelectron atoms ground states and x-ray exctatons 9-01 By gong through the procedure ndcated n the text, develop the tme-ndependent Schroednger equaton

More information

V.C The Niemeijer van Leeuwen Cumulant Approximation

V.C The Niemeijer van Leeuwen Cumulant Approximation V.C The Nemejer van Leeuwen Cumulant Approxmaton Unfortunately, the decmaton procedure cannot be performed exactly n hgher dmensons. For example, the square lattce can be dvded nto two sublattces. For

More information

Negative Binomial Regression

Negative Binomial Regression STATGRAPHICS Rev. 9/16/2013 Negatve Bnomal Regresson Summary... 1 Data Input... 3 Statstcal Model... 3 Analyss Summary... 4 Analyss Optons... 7 Plot of Ftted Model... 8 Observed Versus Predcted... 10 Predctons...

More information

Prof. Dr. I. Nasser Phys 630, T Aug-15 One_dimensional_Ising_Model

Prof. Dr. I. Nasser Phys 630, T Aug-15 One_dimensional_Ising_Model EXACT OE-DIMESIOAL ISIG MODEL The one-dmensonal Isng model conssts of a chan of spns, each spn nteractng only wth ts two nearest neghbors. The smple Isng problem n one dmenson can be solved drectly n several

More information

This model contains two bonds per unit cell (one along the x-direction and the other along y). So we can rewrite the Hamiltonian as:

This model contains two bonds per unit cell (one along the x-direction and the other along y). So we can rewrite the Hamiltonian as: 1 Problem set #1 1.1. A one-band model on a square lattce Fg. 1 Consder a square lattce wth only nearest-neghbor hoppngs (as shown n the fgure above): H t, j a a j (1.1) where,j stands for nearest neghbors

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

Global Sensitivity. Tuesday 20 th February, 2018

Global Sensitivity. Tuesday 20 th February, 2018 Global Senstvty Tuesday 2 th February, 28 ) Local Senstvty Most senstvty analyses [] are based on local estmates of senstvty, typcally by expandng the response n a Taylor seres about some specfc values

More information

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system Transfer Functons Convenent representaton of a lnear, dynamc model. A transfer functon (TF) relates one nput and one output: x t X s y t system Y s The followng termnology s used: x y nput output forcng

More information

Advanced Quantum Mechanics

Advanced Quantum Mechanics Advanced Quantum Mechancs Rajdeep Sensarma! sensarma@theory.tfr.res.n ecture #9 QM of Relatvstc Partcles Recap of ast Class Scalar Felds and orentz nvarant actons Complex Scalar Feld and Charge conjugaton

More information

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4)

Econ107 Applied Econometrics Topic 3: Classical Model (Studenmund, Chapter 4) I. Classcal Assumptons Econ7 Appled Econometrcs Topc 3: Classcal Model (Studenmund, Chapter 4) We have defned OLS and studed some algebrac propertes of OLS. In ths topc we wll study statstcal propertes

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

Linear Approximation with Regularization and Moving Least Squares

Linear Approximation with Regularization and Moving Least Squares Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...

More information

Snce h( q^; q) = hq ~ and h( p^ ; p) = hp, one can wrte ~ h hq hp = hq ~hp ~ (7) the uncertanty relaton for an arbtrary state. The states that mnmze t

Snce h( q^; q) = hq ~ and h( p^ ; p) = hp, one can wrte ~ h hq hp = hq ~hp ~ (7) the uncertanty relaton for an arbtrary state. The states that mnmze t 8.5: Many-body phenomena n condensed matter and atomc physcs Last moded: September, 003 Lecture. Squeezed States In ths lecture we shall contnue the dscusson of coherent states, focusng on ther propertes

More information

Electronic Quantum Monte Carlo Calculations of Energies and Atomic Forces for Diatomic and Polyatomic Molecules

Electronic Quantum Monte Carlo Calculations of Energies and Atomic Forces for Diatomic and Polyatomic Molecules RESERVE HIS SPACE Electronc Quantum Monte Carlo Calculatons of Energes and Atomc Forces for Datomc and Polyatomc Molecules Myung Won Lee 1, Massmo Mella 2, and Andrew M. Rappe 1,* 1 he Maknen heoretcal

More information

CSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography

CSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography CSc 6974 and ECSE 6966 Math. Tech. for Vson, Graphcs and Robotcs Lecture 21, Aprl 17, 2006 Estmatng A Plane Homography Overvew We contnue wth a dscusson of the major ssues, usng estmaton of plane projectve

More information

Workshop: Approximating energies and wave functions Quantum aspects of physical chemistry

Workshop: Approximating energies and wave functions Quantum aspects of physical chemistry Workshop: Approxmatng energes and wave functons Quantum aspects of physcal chemstry http://quantum.bu.edu/pltl/6/6.pdf Last updated Thursday, November 7, 25 7:9:5-5: Copyrght 25 Dan Dll (dan@bu.edu) Department

More information

1. Mean-Field Theory. 2. Bjerrum length

1. Mean-Field Theory. 2. Bjerrum length 1. Mean-Feld Theory Contnuum models lke the Posson-Nernst-Planck equatons are mean-feld approxmatons whch descrbe how dscrete ons are affected by the mean concentratons c and potental φ. Each on mgrates

More information

Rate of Absorption and Stimulated Emission

Rate of Absorption and Stimulated Emission MIT Department of Chemstry 5.74, Sprng 005: Introductory Quantum Mechancs II Instructor: Professor Andre Tokmakoff p. 81 Rate of Absorpton and Stmulated Emsson The rate of absorpton nduced by the feld

More information

SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION do: 0.08/nature09 I. Resonant absorpton of XUV pulses n Kr + usng the reduced densty matrx approach The quantum beats nvestgated n ths paper are the result of nterference between two exctaton paths of

More information

Statistics Chapter 4

Statistics Chapter 4 Statstcs Chapter 4 "There are three knds of les: les, damned les, and statstcs." Benjamn Dsrael, 1895 (Brtsh statesman) Gaussan Dstrbuton, 4-1 If a measurement s repeated many tmes a statstcal treatment

More information

Feb 14: Spatial analysis of data fields

Feb 14: Spatial analysis of data fields Feb 4: Spatal analyss of data felds Mappng rregularly sampled data onto a regular grd Many analyss technques for geophyscal data requre the data be located at regular ntervals n space and/or tme. hs s

More information

Numerical Heat and Mass Transfer

Numerical Heat and Mass Transfer Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and

More information

Supporting Information

Supporting Information Supportng Informaton The neural network f n Eq. 1 s gven by: f x l = ReLU W atom x l + b atom, 2 where ReLU s the element-wse rectfed lnear unt, 21.e., ReLUx = max0, x, W atom R d d s the weght matrx to

More information

A particle in a state of uniform motion remain in that state of motion unless acted upon by external force.

A particle in a state of uniform motion remain in that state of motion unless acted upon by external force. The fundamental prncples of classcal mechancs were lad down by Galleo and Newton n the 16th and 17th centures. In 1686, Newton wrote the Prncpa where he gave us three laws of moton, one law of gravty,

More information

Problem Set 9 Solutions

Problem Set 9 Solutions Desgn and Analyss of Algorthms May 4, 2015 Massachusetts Insttute of Technology 6.046J/18.410J Profs. Erk Demane, Srn Devadas, and Nancy Lynch Problem Set 9 Solutons Problem Set 9 Solutons Ths problem

More information

Difference Equations

Difference Equations Dfference Equatons c Jan Vrbk 1 Bascs Suppose a sequence of numbers, say a 0,a 1,a,a 3,... s defned by a certan general relatonshp between, say, three consecutve values of the sequence, e.g. a + +3a +1

More information

Temperature. Chapter Heat Engine

Temperature. Chapter Heat Engine Chapter 3 Temperature In prevous chapters of these notes we ntroduced the Prncple of Maxmum ntropy as a technque for estmatng probablty dstrbutons consstent wth constrants. In Chapter 9 we dscussed the

More information

NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS

NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS IJRRAS 8 (3 September 011 www.arpapress.com/volumes/vol8issue3/ijrras_8_3_08.pdf NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS H.O. Bakodah Dept. of Mathematc

More information

2016 Wiley. Study Session 2: Ethical and Professional Standards Application

2016 Wiley. Study Session 2: Ethical and Professional Standards Application 6 Wley Study Sesson : Ethcal and Professonal Standards Applcaton LESSON : CORRECTION ANALYSIS Readng 9: Correlaton and Regresson LOS 9a: Calculate and nterpret a sample covarance and a sample correlaton

More information

Section 8.3 Polar Form of Complex Numbers

Section 8.3 Polar Form of Complex Numbers 80 Chapter 8 Secton 8 Polar Form of Complex Numbers From prevous classes, you may have encountered magnary numbers the square roots of negatve numbers and, more generally, complex numbers whch are the

More information

Chapter Newton s Method

Chapter Newton s Method Chapter 9. Newton s Method After readng ths chapter, you should be able to:. Understand how Newton s method s dfferent from the Golden Secton Search method. Understand how Newton s method works 3. Solve

More information

Chapter 2 - The Simple Linear Regression Model S =0. e i is a random error. S β2 β. This is a minimization problem. Solution is a calculus exercise.

Chapter 2 - The Simple Linear Regression Model S =0. e i is a random error. S β2 β. This is a minimization problem. Solution is a calculus exercise. Chapter - The Smple Lnear Regresson Model The lnear regresson equaton s: where y + = β + β e for =,..., y and are observable varables e s a random error How can an estmaton rule be constructed for the

More information

where the sums are over the partcle labels. In general H = p2 2m + V s(r ) V j = V nt (jr, r j j) (5) where V s s the sngle-partcle potental and V nt

where the sums are over the partcle labels. In general H = p2 2m + V s(r ) V j = V nt (jr, r j j) (5) where V s s the sngle-partcle potental and V nt Physcs 543 Quantum Mechancs II Fall 998 Hartree-Fock and the Self-consstent Feld Varatonal Methods In the dscusson of statonary perturbaton theory, I mentoned brey the dea of varatonal approxmaton schemes.

More information

Design and Optimization of Fuzzy Controller for Inverse Pendulum System Using Genetic Algorithm

Design and Optimization of Fuzzy Controller for Inverse Pendulum System Using Genetic Algorithm Desgn and Optmzaton of Fuzzy Controller for Inverse Pendulum System Usng Genetc Algorthm H. Mehraban A. Ashoor Unversty of Tehran Unversty of Tehran h.mehraban@ece.ut.ac.r a.ashoor@ece.ut.ac.r Abstract:

More information

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,

More information

π e ax2 dx = x 2 e ax2 dx or x 3 e ax2 dx = 1 x 4 e ax2 dx = 3 π 8a 5/2 (a) We are considering the Maxwell velocity distribution function: 2πτ/m

π e ax2 dx = x 2 e ax2 dx or x 3 e ax2 dx = 1 x 4 e ax2 dx = 3 π 8a 5/2 (a) We are considering the Maxwell velocity distribution function: 2πτ/m Homework Solutons Problem In solvng ths problem, we wll need to calculate some moments of the Gaussan dstrbuton. The brute-force method s to ntegrate by parts but there s a nce trck. The followng ntegrals

More information

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix

Lectures - Week 4 Matrix norms, Conditioning, Vector Spaces, Linear Independence, Spanning sets and Basis, Null space and Range of a Matrix Lectures - Week 4 Matrx norms, Condtonng, Vector Spaces, Lnear Independence, Spannng sets and Bass, Null space and Range of a Matrx Matrx Norms Now we turn to assocatng a number to each matrx. We could

More information

Physics 181. Particle Systems

Physics 181. Particle Systems Physcs 181 Partcle Systems Overvew In these notes we dscuss the varables approprate to the descrpton of systems of partcles, ther defntons, ther relatons, and ther conservatons laws. We consder a system

More information

e i is a random error

e i is a random error Chapter - The Smple Lnear Regresson Model The lnear regresson equaton s: where + β + β e for,..., and are observable varables e s a random error How can an estmaton rule be constructed for the unknown

More information

Linear Regression Analysis: Terminology and Notation

Linear Regression Analysis: Terminology and Notation ECON 35* -- Secton : Basc Concepts of Regresson Analyss (Page ) Lnear Regresson Analyss: Termnology and Notaton Consder the generc verson of the smple (two-varable) lnear regresson model. It s represented

More information

Introduction to Density Functional Theory. Jeremie Zaffran 2 nd year-msc. (Nanochemistry)

Introduction to Density Functional Theory. Jeremie Zaffran 2 nd year-msc. (Nanochemistry) Introducton to Densty Functonal Theory Jereme Zaffran nd year-msc. (anochemstry) A- Hartree appromatons Born- Oppenhemer appromaton H H H e The goal of computatonal chemstry H e??? Let s remnd H e T e

More information

A how to guide to second quantization method.

A how to guide to second quantization method. Phys. 67 (Graduate Quantum Mechancs Sprng 2009 Prof. Pu K. Lam. Verson 3 (4/3/2009 A how to gude to second quantzaton method. -> Second quantzaton s a mathematcal notaton desgned to handle dentcal partcle

More information

The equation of motion of a dynamical system is given by a set of differential equations. That is (1)

The equation of motion of a dynamical system is given by a set of differential equations. That is (1) Dynamcal Systems Many engneerng and natural systems are dynamcal systems. For example a pendulum s a dynamcal system. State l The state of the dynamcal system specfes t condtons. For a pendulum n the absence

More information

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law:

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law: CE304, Sprng 2004 Lecture 4 Introducton to Vapor/Lqud Equlbrum, part 2 Raoult s Law: The smplest model that allows us do VLE calculatons s obtaned when we assume that the vapor phase s an deal gas, and

More information

= = = (a) Use the MATLAB command rref to solve the system. (b) Let A be the coefficient matrix and B be the right-hand side of the system.

= = = (a) Use the MATLAB command rref to solve the system. (b) Let A be the coefficient matrix and B be the right-hand side of the system. Chapter Matlab Exercses Chapter Matlab Exercses. Consder the lnear system of Example n Secton.. x x x y z y y z (a) Use the MATLAB command rref to solve the system. (b) Let A be the coeffcent matrx and

More information

Chapter 11: Simple Linear Regression and Correlation

Chapter 11: Simple Linear Regression and Correlation Chapter 11: Smple Lnear Regresson and Correlaton 11-1 Emprcal Models 11-2 Smple Lnear Regresson 11-3 Propertes of the Least Squares Estmators 11-4 Hypothess Test n Smple Lnear Regresson 11-4.1 Use of t-tests

More information

Structure and Drive Paul A. Jensen Copyright July 20, 2003

Structure and Drive Paul A. Jensen Copyright July 20, 2003 Structure and Drve Paul A. Jensen Copyrght July 20, 2003 A system s made up of several operatons wth flow passng between them. The structure of the system descrbes the flow paths from nputs to outputs.

More information

Lecture 10. Reading: Notes and Brennan Chapter 5

Lecture 10. Reading: Notes and Brennan Chapter 5 Lecture tatstcal Mechancs and Densty of tates Concepts Readng: otes and Brennan Chapter 5 Georga Tech C 645 - Dr. Alan Doolttle C 645 - Dr. Alan Doolttle Georga Tech How do electrons and holes populate

More information

Binding energy of a Cooper pairs with non-zero center of mass momentum in d-wave superconductors

Binding energy of a Cooper pairs with non-zero center of mass momentum in d-wave superconductors Bndng energ of a Cooper pars wth non-zero center of mass momentum n d-wave superconductors M.V. remn and I.. Lubn Kazan State Unverst Kremlevsaa 8 Kazan 420008 Russan Federaton -mal: gor606@rambler.ru

More information

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction ECONOMICS 5* -- NOTE (Summary) ECON 5* -- NOTE The Multple Classcal Lnear Regresson Model (CLRM): Specfcaton and Assumptons. Introducton CLRM stands for the Classcal Lnear Regresson Model. The CLRM s also

More information

Physics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1

Physics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1 P. Guterrez Physcs 5153 Classcal Mechancs D Alembert s Prncple and The Lagrangan 1 Introducton The prncple of vrtual work provdes a method of solvng problems of statc equlbrum wthout havng to consder the

More information

EEE 241: Linear Systems

EEE 241: Linear Systems EEE : Lnear Systems Summary #: Backpropagaton BACKPROPAGATION The perceptron rule as well as the Wdrow Hoff learnng were desgned to tran sngle layer networks. They suffer from the same dsadvantage: they

More information

LOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin

LOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin Proceedngs of the 007 Wnter Smulaton Conference S G Henderson, B Bller, M-H Hseh, J Shortle, J D Tew, and R R Barton, eds LOW BIAS INTEGRATED PATH ESTIMATORS James M Calvn Department of Computer Scence

More information

More metrics on cartesian products

More metrics on cartesian products More metrcs on cartesan products If (X, d ) are metrc spaces for 1 n, then n Secton II4 of the lecture notes we defned three metrcs on X whose underlyng topologes are the product topology The purpose of

More information

Lecture 7: Boltzmann distribution & Thermodynamics of mixing

Lecture 7: Boltzmann distribution & Thermodynamics of mixing Prof. Tbbtt Lecture 7 etworks & Gels Lecture 7: Boltzmann dstrbuton & Thermodynamcs of mxng 1 Suggested readng Prof. Mark W. Tbbtt ETH Zürch 13 März 018 Molecular Drvng Forces Dll and Bromberg: Chapters

More information

Numerical Solution of Ordinary Differential Equations

Numerical Solution of Ordinary Differential Equations Numercal Methods (CENG 00) CHAPTER-VI Numercal Soluton of Ordnar Dfferental Equatons 6 Introducton Dfferental equatons are equatons composed of an unknown functon and ts dervatves The followng are examples

More information

Open Systems: Chemical Potential and Partial Molar Quantities Chemical Potential

Open Systems: Chemical Potential and Partial Molar Quantities Chemical Potential Open Systems: Chemcal Potental and Partal Molar Quanttes Chemcal Potental For closed systems, we have derved the followng relatonshps: du = TdS pdv dh = TdS + Vdp da = SdT pdv dg = VdP SdT For open systems,

More information

MATH 5630: Discrete Time-Space Model Hung Phan, UMass Lowell March 1, 2018

MATH 5630: Discrete Time-Space Model Hung Phan, UMass Lowell March 1, 2018 MATH 5630: Dscrete Tme-Space Model Hung Phan, UMass Lowell March, 08 Newton s Law of Coolng Consder the coolng of a well strred coffee so that the temperature does not depend on space Newton s law of collng

More information

j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1

j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1 Random varables Measure of central tendences and varablty (means and varances) Jont densty functons and ndependence Measures of assocaton (covarance and correlaton) Interestng result Condtonal dstrbutons

More information

Formal solvers of the RT equation

Formal solvers of the RT equation Formal solvers of the RT equaton Formal RT solvers Runge- Kutta (reference solver) Pskunov N.: 979, Master Thess Long characterstcs (Feautrer scheme) Cannon C.J.: 970, ApJ 6, 55 Short characterstcs (Hermtan

More information

Markov Chain Monte Carlo Lecture 6

Markov Chain Monte Carlo Lecture 6 where (x 1,..., x N ) X N, N s called the populaton sze, f(x) f (x) for at least one {1, 2,..., N}, and those dfferent from f(x) are called the tral dstrbutons n terms of mportance samplng. Dfferent ways

More information

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers Psychology 282 Lecture #24 Outlne Regresson Dagnostcs: Outlers In an earler lecture we studed the statstcal assumptons underlyng the regresson model, ncludng the followng ponts: Formal statement of assumptons.

More information

Physics 5153 Classical Mechanics. Principle of Virtual Work-1

Physics 5153 Classical Mechanics. Principle of Virtual Work-1 P. Guterrez 1 Introducton Physcs 5153 Classcal Mechancs Prncple of Vrtual Work The frst varatonal prncple we encounter n mechancs s the prncple of vrtual work. It establshes the equlbrum condton of a mechancal

More information

Dynamics of a Superconducting Qubit Coupled to an LC Resonator

Dynamics of a Superconducting Qubit Coupled to an LC Resonator Dynamcs of a Superconductng Qubt Coupled to an LC Resonator Y Yang Abstract: We nvestgate the dynamcs of a current-based Josephson juncton quantum bt or qubt coupled to an LC resonator. The Hamltonan of

More information

Bézier curves. Michael S. Floater. September 10, These notes provide an introduction to Bézier curves. i=0

Bézier curves. Michael S. Floater. September 10, These notes provide an introduction to Bézier curves. i=0 Bézer curves Mchael S. Floater September 1, 215 These notes provde an ntroducton to Bézer curves. 1 Bernsten polynomals Recall that a real polynomal of a real varable x R, wth degree n, s a functon of

More information

UNIVERSITY OF TORONTO Faculty of Arts and Science. December 2005 Examinations STA437H1F/STA1005HF. Duration - 3 hours

UNIVERSITY OF TORONTO Faculty of Arts and Science. December 2005 Examinations STA437H1F/STA1005HF. Duration - 3 hours UNIVERSITY OF TORONTO Faculty of Arts and Scence December 005 Examnatons STA47HF/STA005HF Duraton - hours AIDS ALLOWED: (to be suppled by the student) Non-programmable calculator One handwrtten 8.5'' x

More information

x = , so that calculated

x = , so that calculated Stat 4, secton Sngle Factor ANOVA notes by Tm Plachowsk n chapter 8 we conducted hypothess tests n whch we compared a sngle sample s mean or proporton to some hypotheszed value Chapter 9 expanded ths to

More information

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS Avalable onlne at http://sck.org J. Math. Comput. Sc. 3 (3), No., 6-3 ISSN: 97-537 COMPARISON OF SOME RELIABILITY CHARACTERISTICS BETWEEN REDUNDANT SYSTEMS REQUIRING SUPPORTING UNITS FOR THEIR OPERATIONS

More information

This column is a continuation of our previous column

This column is a continuation of our previous column Comparson of Goodness of Ft Statstcs for Lnear Regresson, Part II The authors contnue ther dscusson of the correlaton coeffcent n developng a calbraton for quanttatve analyss. Jerome Workman Jr. and Howard

More information

Supplementary Notes for Chapter 9 Mixture Thermodynamics

Supplementary Notes for Chapter 9 Mixture Thermodynamics Supplementary Notes for Chapter 9 Mxture Thermodynamcs Key ponts Nne major topcs of Chapter 9 are revewed below: 1. Notaton and operatonal equatons for mxtures 2. PVTN EOSs for mxtures 3. General effects

More information

College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009

College of Computer & Information Science Fall 2009 Northeastern University 20 October 2009 College of Computer & Informaton Scence Fall 2009 Northeastern Unversty 20 October 2009 CS7880: Algorthmc Power Tools Scrbe: Jan Wen and Laura Poplawsk Lecture Outlne: Prmal-dual schema Network Desgn:

More information

University of Washington Department of Chemistry Chemistry 453 Winter Quarter 2015

University of Washington Department of Chemistry Chemistry 453 Winter Quarter 2015 Lecture 2. 1/07/15-1/09/15 Unversty of Washngton Department of Chemstry Chemstry 453 Wnter Quarter 2015 We are not talkng about truth. We are talkng about somethng that seems lke truth. The truth we want

More information

Grover s Algorithm + Quantum Zeno Effect + Vaidman

Grover s Algorithm + Quantum Zeno Effect + Vaidman Grover s Algorthm + Quantum Zeno Effect + Vadman CS 294-2 Bomb 10/12/04 Fall 2004 Lecture 11 Grover s algorthm Recall that Grover s algorthm for searchng over a space of sze wors as follows: consder the

More information

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution

Department of Statistics University of Toronto STA305H1S / 1004 HS Design and Analysis of Experiments Term Test - Winter Solution Department of Statstcs Unversty of Toronto STA35HS / HS Desgn and Analyss of Experments Term Test - Wnter - Soluton February, Last Name: Frst Name: Student Number: Instructons: Tme: hours. Ads: a non-programmable

More information