Exploring the Materials Hyperspace

Introductory overview of materials data and the scale of the chemical landscape for new materials.
Slides to accompany graduate class workshop module 5 Jupyter notebook https://github.com/WMD-group/yonsei17

Transcript

1. Yonsei University Graduate Class Energy Materials: Design, Discovery and Data Exploring the Materials Hyperspace Dan Davies PhD Student University of Bath https://wmd-group.github.io @danwdavies
2. About Me •  MChem University of Bath •  PhD student at the CSCT •  Interested in high-throughput computational screening and design of materials Sustainable Centre for Chemical Technologies
3. About Me
4. I Talk Outline: The Materials Hyperspace 1.  Data for Materials Discovery 2.  Exploring Chemical Space 3.  Interacting with Databases II III
5. Question How were the vast majority of materials we use today discovered?
6. Traditional Materials Discovery •  Trial and error •  Trial and improvement “I have not failed, I have just found 10,000 ways that won’t work.”
7. Traditional Materials Discovery Teflon® Silly putty Safety glass
8. Traditional Materials Discovery Teflon® “A white solid material was obtained, which was supposed to be a polymerized product.” www.whitetrout.net/Chuck/Teflon/teflon.htm
9. Traditional Materials Discovery Teflon® “It is insoluble in cold and hot water, acetone, Freon 113, ether, petroleum ether, alcohol, pyridine, toluene ethyl acetate, concentrated sulfuric acid, glacial acetic acid, nitrobenzene, isoanyl alcohol, ortho-dichlorobenzene, sodium hydroxide, and concentrated nitric acid.” www.whitetrout.net/Chuck/Teflon/teflon.htm
10. Getting Materials to Market Time between invention and widespread commercialisation of materials:
11. Modern Materials Discovery A. Agrawal & A. Choudhary, APL Materials, 2016, 4 •  Data from experiment and calculation is now being generated at an incredibly fast rate •  This has allowed for the emergence of “Big Data” driven science
12. The 4 Vs of Big Data K. Rajan, Annu. Rev. Mater. Res., 2015, 153-169
13. The ICSD – Experimental Data Inorganic Crystal Structure Database •  Large database crystallographic data •  ~187,000 crystal structures
14. The ICSD – Experimental Data
15. Experimental vs. Computed Properties “Real”/measured properties of materials No great databases useful for data-driven approach L (with exception of crystallographic data) Simulated/predicted properties of materials Some good emerging databases to choose from J
16. Data From Calculations
17. The NoMaD Repository https://nomad-coe.eu/ Novel Materials Discovery •  Contains input and output files from electronic structure calculations •  3,300,000 entries •  Anyone can upload à Inhomogeneous data
18. The Materials Project •  Uses ICSD as primary input source •  67,000 entries •  All calculations similar à homogeneous data https://materialsproject.org/
19. Using the Available Data Calculated From Database
20. Using the Available Data Machine learning A type of artificial intelligence that provides computers with the ability to learn without being explicitly programmed.
21. Using the Available Data Machine learning – Total Energies Using element properties such as atomic mass, # valence e-, ionisation potential etc. along with connectivity within material Machine learning algorithm from https://arxiv.org/pdf/1608.04782.pdf
22. Using the Available Data Machine learning – Band gaps Using only element composition as input – no other information
23. Talk Outline: The Materials Hyperspace 1.  Data for Materials Discovery 2.  Exploring Chemical Space 3.  Interacting with Databases
24. Question How many unique materials* are known today? *Inorganic compounds
25. Counting Known Compounds ~187,000 Hard to quantify – no definitive list exists ~3,300,000 ~67,000 Lots of duplicates Many hypothetical Uses ICSD as input But not exclusively Not finished All ‘real’ (mostly) Some duplicates
26. Counting Possible Compounds http://hackingmaterials.com/2013/11/14/the-scale-of-materials-design/ •  You have a list of 50 chemical elements and a 10x10x10 grid •  You can put 30 atoms of any element anywhere on the grid to make a unit cell H He Li Be B C N O F Ne Na Mg Al Si P S Cl Ar K Ca Sc Ti V Cr Mn Fe Co Ni Cu Zn Ga Ge As Se Br Kr Rb Sr Y Zr Nb No Tc Ru Rh Pd Ag Cd In Sn I
27. Counting Possible Compounds print(’the value of x is {0}’.format(x)) Format statements: Q. What is the purpose of using the second type of format statement? (Try replacing a {:.2E} with a simple {0} in your notebook to see.) print(’the value of x is {:.2E}’.format(x)) I
28. Mapping Out Chemical Space •  Clearly this approach is a non-starter; we need to simplify things •  One way is to combine elements in their known oxidation states to make binary, ternary, quaternary combinations…
29. Mapping Out Chemical Space SMACT Semiconducting Materials by Analogy and Chemical Theory •  Combine elements together in their known oxidation states exhaustively •  Only allows certain combinations based on certain rules e.g. charge neutrality Sn2+ O2- I- True Ratios = [(2,1,2), (3,2,2)] Charge neutral combinations possible? (stoichiometry threshold = 3)
30. Mapping Out Chemical Space SMACT Semiconducting Materials by Analogy and Chemical Theory •  Combine elements together in their known oxidation states exhaustively •  Only allows certain combinations based on certain rules e.g. charge neutrality Mn7+ F- I- False Ratios = [] Charge neutral combinations possible? (stoichiometry threshold = 3) II
31. Mapping Out Chemical Space all_elements = smact.element_dictionary() Setting up a dictionary Dictionary of element objects Built in smact function Q. Can you spot the other smact function that appears in the same cell? II
32. Mapping Out Chemical Space for i, ele_a in enumerate(elements): Using the enumerate function is the same as for i in range(len(elements)): ele_a = elements[i] II
33. Mapping Out Chemical Space for j, ele_b in enumerate(elements[i+1:]): List slicing [where to start]:[where to stop] Q. What does putting this within our inner loop achieve? II
34. Talk Outline: The Materials Hyperspace 1.  Data for Materials Discovery 2.  Exploring Chemical Space 3.  Interacting with Databases
35. Question How can we access materials data?
36. Accessing Materials Data •  Web Browser e.g. Materials Project •  Data dump e.g. Computational Materials Repository •  Restful API e.g. Materials Project MAPI
37. RESTful API •  Representational State Transfer Application Programming Interface •  Built around resources and how they are accessed •  Resources == objects in object oriented programming!
38. Materials API •  Register: MaterialsProject.org •  Copy and paste your API key: •  m = MPRester(" ") III
39. Materials API General usage III data = m.query(criteria, properties) API query function Criteria of the entries we’re interested in Properties we want to get back Returns a list of dictionaries [ {property_1 : value, property_2: value}, {property_1 : value, property_2: value} ] Entry 1 Entry 2
40. Materials API Getting clever with criteria III criteria = { ’nelements’: 2, ‘elements’: {‘$in’: [‘Co’, ‘Fe’]} } Simple Python dictionary usage MongoDB operator usage (as python strings) Also accepts $gt, $lt, $eq, $all, $nin, $exists etc.
41. Summary – Key Points •  Materials data is being generated at very fast rate •  Emerging efforts to store and organise the data can speed up materials discovery •  Python + modern databases = huge amounts of materials data at your fingertips •  There are vast areas of the chemical landscape that remain totally unexplored