Data-driven materials discovery and understanding

photo of Janine George
Janine George
Federal Institute for Materials Research and Testing, Berlin, Germany

Developments in density functional theory (DFT) calculations, their automation and therefore easier access to materials data have enabled ab initio high-throughput searches for new materials for numerous applications. [1–3] These studies open up exciting opportunities to find new materials in a much faster way than based on experimental work alone. However, performing density functional theory calculations for several thousand materials can still be very time consuming. The use of, for example, faster chemical heuristics and machine-learned interatomic potentials would allow to consider a much larger number of candidate materials.[4–6] In addition to DFT based high-throughput searches, the seminar will discuss two possible ways to accelerate high-throughput searches.

Using data analysis on the structures and coordination environments of 5000 oxides, we were able to investigate a chemical heuristic – the famous Pauling rules – regarding its usefulness for the fast prediction of stable materials.[7–9]

We have also investigated how machine-learned interatomic potentials can be used to accelerate the prediction of (dynamically) stable materials.[10] The use of these potentials makes vibrational properties accessible in a much faster way than based on DFT. Our results based on a newly developed potential for silicon allotropes showed excellent agreement with DFT reference data (agreement of the frequencies within 0.1-0.2 THz).

In addition, we have successfully used high-throughput calculations in the search for new candidate materials for spintronic applications and ferroelectrics.[11,12]

References

[1] A. Jain, S. P. Ong, G. Hautier, W. Chen, W. D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, K. A. Persson, APL Mater. 2013, 1, 011002.

[2] K. Mathew, J. H. Montoya, A. Faghaninia, S. Dwarakanath, M. Aykol, H. Tang, I. Chu, T. Smidt, B. Bocklund, M. Horton, J. Dagdelen, B. Wood, Z.-K. Liu, J. Neaton, S. P. Ong, K. Persson, A. Jain, Comput. Mater. Sci. 2017, 139, 140–152.

[3] G. Hautier, Comput. Mater. Sci. 2019, 163, 108–116.

[4] J. Schmidt, M. R. G. Marques, S. Botti, M. A. L. Marques, npj Comput Mater 2019, 5, 1–36.

[5] J. George, G. Hautier, Trends in Chemistry 2021, 3, 86–95.

[6] V. L. Deringer, M. A. Caro, G. Csányi, Adv. Mater. 2019, 31, 1902765.

[7] L. Pauling, J. Am. Chem. Soc. 1929, 51, 1010.

[8] J. George, D. Waroquiers, D. Di Stefano, G. Petretto, G. Rignanese, G. Hautier, Angew. Chem. Int. Ed. 2020, 59, 7569–7575.

[9] D. Waroquiers, J. George, M. Horton, S. Schenk, K. A. Persson, G.-M. Rignanese, X. Gonze, G. Hautier, Acta Cryst B 2020, 76, 683–695.

[10] J. George, G. Hautier, A. P. Bartók, G. Csányi, V. L. Deringer, J. Chem. Phys. 2020, 153, 044104.

[11] W. Chen, J. George, J. B. Varley, G.-M. Rignanese, G. Hautier, Npj Comput. Mater. 2019, 5, 72.

[12] M. Markov, L. Alaerts, H. P. C. Miranda, G. Petretto, W. Chen, J. George, E. Bousquet, P. Ghosez, G.-M. Rignanese, G. Hautier, arXiv:2011.09827 [cond-mat, physics:physics] 2020.