Opportunities from Accurate and Efficient Density Functional Theory Calculations for Large Systems
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1 Seminar CENTRE FOR PREDICTIVE MODELLING, WARWICK Opportunities from Accurate and Efficient Density Functional Theory Calculations for Large Systems Luigi Genovese L_Sim CEA Grenoble October 30, 2017
2 version 1.8.x A code both for Solid-State and Quantum Chemistry 3D periodic, Surfaces and Free BC Very high precision (analytic KS operators) Usage of analytic HGH pseudopotentials ( Poisson Solver) AE accuracy, benchmarked in G2-1, S22, DeltaTest Present functionalities Traditional functionalities for GS Kohn-Sham DFT (including metals, Hybrid Functionals), LR-TDDFT, empirical VdW Exhaustive library of Structural Prediction, calculations A code used in production since 2008 A new formalism: Opens the path towards new opportunities
3 Scaling of Traditional code We can reach systems containing up to a few hundred electrons thanks to wavelet properties and efficient parallelization: (MPI + OpenMP + GPU) Varying the number of atoms N DFT operations scale differently: O(N log N): Poisson solver O(N 2 ): convolutions O(N 3 ): linear algebra and have different prefactors: c O(N3 ) c O(N2 ) c O(N logn) Time / iteration (s) Number of atoms For bigger systems the O(N 3 ) will dominate (first) motivation for a new approach
4 Local orbitals and linear scaling KS orbitals Linear combinations of support functions φ α (r): Ψ i (r) = c α i φ α (r) α localized around atoms expanded in wavelets optimized in-situ Density Matrix Defined via the kernel K αβ in the φ α (r) basis: ρ(r,r ) = f i Ψ i (r)ψ i (r ) i = φ α (r)k αβ φ β (r ) α,β Localized form for the Density and the Hamiltonian Localization sparse matrices O (N)
5 Comparison with the cubic version Energy and forces with accuracy of a systematic approach time (seconds) total runtime linear total runtime cubic linear extrapolation, reference 3000 atoms number of atoms Different levels of precision cutoff radii 20 min for atoms CPU Time and memory number of atoms Precise DFT computing for thousands atoms at Institute-Scale ( CPU cores) Without fine-tuning converges to absolute energy differences of the order of 10 mev/atom, and almost exact forces. High flexibility, like the cubic code Charged systems, various BC (free, surfaces, periodic) System sizes: K atoms 100 k Basis functions
6 Features of the basis set Ideal properties for DFT at many thousand atoms scale Accurate results with good localization (high sparsity) Low No. of degrees of freedom (very!) Low condition number (quasi-orthogonality) Small Spectral Width (PSP) system (#atoms) sparsity κ sparsity SW (AU) DNA (15613) 99.57% % 1.81 bulk pentacene (6876) 98.96% % 1.55 perovskite (768) 90.34% % 1.73 Si nanowire (706) 93.24% % 1.53 H 2 O droplet (1800) 96.71% % 1.41 S H
7 Reference paper for implementation Algorithm is robust and reliable on a variety of systems Accurate and efficient linear scaling DFT calculations with universal applicability S. Mohr, L. E. Ratcliff, L. Genovese, D. Caliste, P. Boulanger, S. Goedecker and T. Deutsch Phys. Chem. Chem. Phys., 2015, 17, 47, DOI: /c5cp00437c Included in the Real-space numerical grid methods in quantum chemistry themed issue of PCCP Guest-edited by Luca Frediani (The Arctic University of Norway) and Dage Sundholm (University of Helsinki)
8 Why Large Scale DFT? Present-day situation O (N ) Why O (N ) Laboratoire de Simulation Atomistique Luigi Genovese
9 Review of DFT calculations New calculation paradigms are emerging
10 Express localized information Support Functions describe exactly KS Density Matrix φ α can be used to map quantum information to localized DoF Extract reliable localized quantities provides also ideal set up to condense information coming from large-scale (many thousands atoms) QM calculations. Partial Density of States Each atom can be associated a subset of basis functions Hamiltonian and density matrices Direct consequence of nearsightedness information can be used for Express efficiently and accurately the QM results (DFT) Define a hierarchy of various levels of theory
11 Nearsightedness implications in Simulation setups Localized basis set helps in understanding systems behaviour Locality of the density matrix Identify fundamental building blocks of the system (s, residues) Understand and model the interaction between them (electrostatic embedding) Manipulate fragment quantities to extract excited-state properties (Constrained-DFT) Actions needed for such investigations Build/Optimize basis functions for different systems Inspect the Density Matrix in this basis Manipulate the Hamiltonian matrix elements
12 Building blocks identification DNA snippet in H2 O 11 base pairs + Na / H2 O solution: atoms Complete DFT calculation: 2h15m (800 MPI, 8 OMP) Let us perform a atomic Mulliken Population analysis Different questions arise O (N ) Why O (N ) Laboratoire de Simulation Atomistique Luigi Genovese
13 Arbitrariness of the results Some atomic charges seem sound (e.g. Na), other look strange (e.g. P)... why? If we use more complete basis results change considerably (known problem of MPA and LPA) However, we know that the basis functions represent (very) well the Ground State normalized occurence H O C N P O* Na O H2 O H H2 O atomic net charge Definition of a meaningful quantity Atomic charges are (in general) not physical observables. Is it possible to identify fragment-related pseudo-observables? meaningful fragmentation
14 ation procedure: the purity indicator Suppose that a QM system, identified by ˆF ψ ψ can be split into M perfect fragments F. We indicate with ψ F the s physical state. There must exists a projection operator Ŵ F s.t. Ŵ F ψ = ψ F. If the fragments are independent, i.e. ψ F ψ F = δ FF, the density matrix satisfies ˆF F ˆFŴ F = ψ F ψ F F = (ˆF ) 2 When expressing this quantities in a localized basis: ˆF = αβ φ α K αβ φ β, Ŵ F = φ α R αβ F φ β αβ we obtain that a good fragment should satisfy Π = 1 ) tr ((KS F ) 2 KS F 0, S F SR F S Q F
15 Meaning of the If the above conditions are satisfied, the expectation value of any observable Ô may also be associated to the fragment F: Ô F = tr (ˆF F Ô ) = tr(ksr F O) The PI is not an observable Explicit functional of { φ α } and of the choice of R F Π 0 is a necessary condition. If it is not satisfied, the basis and/or the projection method do not single out F as a meaningful fragment. Interplay bw the basis set and the projection method Different Realizations Pioneering atomic population analysis might be generalized to fragments. For instance: Mulliken: R F = T F S 1 Loewdin: R F = S 1/2 T F S 1/2 where T F selects the indices α F.
16 , 100 Molecule Water droplet Different basis (SF and AO) qualities. Correlation between H2 O dipoles value of Π Mulliken-Type Projector spd/sp - type sp/s - type sp/s - type sp/s spd/sp sp/s Mulliken-Type Projector Loewdin-Type Projector Mulliken-Type Projector Loewdin-Type Projector spdf/spd - type sp/s - type spdf/spd spd/sp spd/sp - type 1 0 spdf/spd - type spdf/spd spd/sp - type Loewdin-Type Projector Norm of water Dipole moment (D) spdf/spd occurence 0 occurence -0.6 Mulliken-Type Projector spdf/spd - type occurence occurence spdf/spd - type -0.8 energy (ha) occurence Why O (N ) -1 Loewdin-Type Projector occurence 0 Fermi energy energy (ha) O (N ) -0.2 reference sp/s spd/sp spdf/spd occurence -1 non-optimized atomic orbitals Fermi energy spd/sp - type sp/s - type Norm of water Dipole moment (D) Laboratoire de Simulation Atomistique spd/sp sp/s Norm of water Dipole moment (D) spdf/spd occurence reference sp/s - type spd/sp - type spdf/spd - type DoS (ha-1) DoS (ha-1) optimized quasi-orthogonal support functions spd/sp sp/s Norm of water Dipole moment (D) Luigi Genovese 5
17 Advantages of minimal bases Atomic description might be replaced by molecular one Molecules are good fragments for minimal basis sp/s optimized sp/s atomic orbitals H 2 O O H H 2 O O H Mulliken 0.02(1) 0.16(1) 0.45(0) 0.03(1) 0.16(1) 0.46(1) Löwdin 0.03(1) 0.16(1) 0.45(0) 0.03(1) 0.17(1) 0.48(0) quality Meaningful fragmentation even with simple projections optimized atomic orbitals sp/s spd/sp spdf/spd sp/s spd/sp spdf/spd DoS non-purity H 2 O dipole /
18 Coming back to the original case study Atomic descritption is useless with this projection (MPA) The only meaningful atomic fragment is the ionized Na of the solvent normalized occurence H O C N P O* Na O H2 O H H2 O atomic net charge H C N O Os Na P Π s are - unsurprisingly - DNA residues They can be identified a posteriori as the relevant quantities normalized occurence fragment net charge H 2 O PO 4 Cytosine nucleotide Guanine nucleotide PO 4 Cyt Gua H 2 O Π
19 Potentialities for large systems DNA snippet in H2 O Interpreting QM properties of the solute with DFT Inspection of the Density Matrix allows to identify fragments (DNA residues in this case) Electrostatic multipoles on such fragments can be considered as observable quantities A general technique which takes benefit from a optimized minimal basis (JCCP 2017, 13, ) O (N ) Cytosine Why O (N ) nucleotide net charge Guanine nucleotide net charge Laboratoire de Simulation Atomistique Luigi Genovese
20 Interplay between fragmentation and embedding A good fragmentation may also be employed to study the influence of explicit vs. electrostatic solvation Gua nucleotides Cyt nucleotides O (N ) dipole (Debye) Why O (N ) 5 of QM region net charge dipole (Debye) net charge Value (x 10 ) full QM shell 0 Å shell 2 Å shell 4 Å shell 6 Å Explicit solvent shell size (A) Laboratoire de Simulation Atomistique 7 8 * Reduce overpolarization Luigi Genovese
21 Different modelling of the environment Poisson solver for implicit solvents JCP 144, (2016) Allows an efficient and accurate treatment of implicit solvents The cavity can be rigid (PCM-like) determined from the Electronic Density (SCCS approach) Can treat various BC (here TiO 2 surface) Can be used in conjunction with
22 basis set in approach We can duplicate the Support Functions for similar portions of large systems considerably reduces the cost * Enables manipulation of optimized basis sets (database) Impact of the environment in OLEDs charge transport Realistic host-guest morphology: 6192 atoms, 100 molecules Host molecule O (N ) 4,4 -N,N -dicarbazole-biphenyl Why O (N ) Guest molecule tris(2-phenylpyridine)iridium Laboratoire de Simulation Atomistique Luigi Genovese
23 Future scientific directions with codes Linear-Scaling DFT calculations based on wavelets Robust convergence, high accuracy and flexibility (BC) Reduction in degrees of freedom large systems via moderate sized machines ( TFlop/s) Lab-scale Optimal mapping between KS DoF and atoms Different level of descriptions (controlling the precision) QM s Atomic charges Opens up new possibilities and future directions Explore interplay environment electronic excitations (CDFT, QM/MM, statistics... ) Provide high quality back end for extraction of atomic multipoles from QM calculations Towards a control of the level of theory (QM/QM)
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