Machine-learning discovery of descriptors for Topological Semimetals

The accumulation of massive amounts of materials data motivates data-based machine learning(ML) approaches. 

However, an extensive database of materials relying on high-throughput density functional theory (DFT) can be unreliable for emergent properties. Much needed is an approach that can articulate and build on expert human researchers' insights. The tolerance factor introduced in Refs [1-2] articulates a chemical insight for identifying topological semimetals among square-net materials and presents an opportunity to develop such a human-machine synergy. Hence, we developed a supervised-unsupervised hybrid approach combining non-linear Gaussian Process (GP) regression [3] with supervised metric learning to discover descriptors for topological semimetals. Simultaneously, we curated a database containing 1279 square-net materials featuring different physical and chemical attributes and the binary label for the topological property associated with each material. Application of the GP model to the database rediscovers the tolerance factor and offers new theoretical insight. 

 

[1] Klemenz, et al, J. Am. Chem. Soc. 2020, 142, 13, 6350–6359 

[2] Klemenz, et al, Annual Review of Materials Research 2019 49:1, 185-206 

[3] D. Milios, et al, Advance in Neural Information Processing Systems, page 11, 2018