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1

2 High-throughput Data and New Representations for Models and Machine Learning Gus L. W. Hart

3 Why am I here? Automatic-FLOW for Materials Discovery

4 This talk is for you, not for me Automatic-FLOW for Materials Discovery

5 High-Throughput Alloy Search Experimental Structures (ICSD) Automatic-FLOW for Materials Discovery

6 High-Throughput Alloy Search Experimental Structures (ICSD) Automatic-FLOW for Materials Discovery

7 High-Throughput Alloy Search Experimental Structures (ICSD) Automatic-FLOW for Materials Discovery

8 High-Throughput Alloy Search Experimental Structures (ICSD) Combinatorial Substitution Automatic-FLOW for Materials Discovery

9 High-Throughput Alloy Search Experimental Structures (ICSD) Combinatorial Substitution Automatic-FLOW for Materials Discovery

10 High-Throughput Alloy Search Experimental Structures (ICSD) Combinatorial Substitution Automatic-FLOW for Materials Discovery

11 High-Throughput Alloy Search Experimental Structures (ICSD) Combinatorial Substitution Automatic-FLOW for Materials Discovery

12 High-Throughput Alloy Search Experimental Structures (ICSD) Combinatorial Substitution Automatic-FLOW for Materials Discovery

13 An Application: 153 Platinum-Group Alloys Gus L. W. Hart, Stefano Curtarolo, Thaddeus B. Massalski, Ohad Levy; Phys. Rev. X, (Dec ). (msg.byu.edu/pubs.php) cutout from viewpoint

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17 But I want to explore a larger space...and go to finite T...

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23 The phenomenon is entirely one of the configuration of atoms on a fixed lattice.

24 The phenomenon is entirely one of the configuration of atoms on a fixed lattice.

25 Automatic-FLOW for Materials Discovery

26 Representation is rotationally, translationally, permutationally invariant Automatic-FLOW for Materials Discovery

27 Genetic Algorithm for Model Building

28 Solving an under-determined problem

29 Solving an under-determined problem

30 Solving an under-determined problem

31 Solving an under-determined problem

32 Solving an under-determined problem

33 Practicality of CS: norm techniques

34 Practicality of CS: norm techniques

35 Practicality of CS: norm techniques CS is practical because there are so many good numerical approaches for enforcing the norm

36 Practicality of CS: norm techniques CS is practical because there are so many good numerical approaches for enforcing the norm

37 Practicality of CS: norm techniques CS is practical because there are so many good numerical approaches for enforcing the norm

38 Practicality of CS: norm techniques CS is practical because there are so many good numerical approaches for enforcing the norm

39 Practicality of CS: norm techniques CS is practical because there are so many good numerical approaches for enforcing the norm

40 Practicality of CS: norm techniques CS is practical because there are so many good numerical approaches for enforcing the norm

41 Practicality of CS: norm techniques CS is practical because there are so many good numerical approaches for enforcing the norm

42 Practicality of CS: norm techniques CS is practical because there are so many good numerical approaches for enforcing the norm

43 Practicality of CS: norm techniques CS is practical because there are so many good numerical approaches for enforcing the 3 2 norm min `1 norm min `2 norm

44 Bayesian Compressive Sensing vs. GA FIG. 7. Comparison between re-weighted Bayesian cluster expansion model for the binary systems Ag-P 17

45 Bayesian Compressive Sensing vs. GA days seconds FIG. 7. Comparison between re-weighted Bayesian cluster expansion model for the binary systems Ag-P 17

46 Bayesian Compressive Sensing vs. GA FIG. 7. Comparison between re-weighted Bayes cluster expansion model for the binary systems Ag BCS results and the 18 solid curves indicate GA resu

47 Bayesian Compressive Sensing Stand-alone Solver

48 But I want to explore a much larger space...and go to finite T...

49 But I want to explore a much larger space...and go to finite T... I d like to go off the lattice...

50 But compressive sensing uncovered DFT problems

51 Remember the rectangle rule from calculus? Automatic-FLOW for Materials Discovery

52 Remember the rectangle rule from calculus? Z 1 0 (x 1/2) 2 dx Automatic-FLOW for Materials Discovery

53 Remember the rectangle rule from calculus? 0.25 Z (x 1/2) 2 dx Automatic-FLOW for Materials Discovery

54 Remember the rectangle rule from calculus? 0.25 Z (x 1/2) 2 dx Relative Error Number of rectangles Automatic-FLOW for Materials Discovery

55 Remember the rectangle rule from calculus? 0.25 Z (x 1/2) 2 dx Relative Error Number of rectangles Automatic-FLOW for Materials Discovery

56 Remember the rectangle rule from calculus? 0.25 Z (x 1/2) 2 dx Relative Error Number of rectangles Automatic-FLOW for Materials Discovery

57 Remember the rectangle rule from calculus? 0.25 Z (x 1/2) 2 dx Relative Error Number of rectangles Relative Error Number of rectangles Automatic-FLOW for Materials Discovery

58 Remember the rectangle rule from calculus? 0.25 Z (x 1/2) 2 dx Relative Error Automatic-FLOW for Materials Discovery Number of rectangles

59 Remember the rectangle rule from calculus? 0.25 Z (x 1/2) 2 dx Automatic-FLOW for Materials Discovery Relative Error Numerical Integration of Periodic Functions: A 10 Few -29 Examples, J. A. C. Weideman,The American Mathematical Monthly, (Jan., ), pp Number of rectangles

60 1976: Teton Dam Disaster and MK Paper Automatic-FLOW Thursday, February 26, 15 for Materials Discovery

61 1976: Teton Dam Disaster and MK Paper Automatic-FLOW Thursday, February 26, 15 for Materials Discovery

62 1976: Teton Dam Disaster and MK Paper Automatic-FLOW Thursday, February 26, 15 for Materials Discovery

INVESTIGATION OF THE CO-RE-TI SYSTEM AS A POTENTIAL SUPERALLOY. Johnathon M. Rackham. A senior thesis submitted to the faculty of

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