Prediction of Materials Synthesizability

In the pursuit of discovering materials with desirable properties, extending the available material libraries is crucial. High-throughput simulations have become an integral part in designing new materials in the past decades. However, there is no straightforward way of distinguishing synthesizable materials from all the proposed candidates. This project focuses on employing AI-driven methods to estimate synthesizability of materials.

Up to now, material scientists and engineers have relied on domain knowledge as well as empirical heuristics to guess the stability and synthesizability of molecules and crystals. The famous Pauling rules [1] of crystal stability are an example of such heuristics. However, after the accelerating material discovery in all the years since Pauling, these rules now fail to account for the stability of most known crystals [2]. A new predictive set of heuristics for crystal stability/synthesizability is unlikely to be uncovered by human perception, given the magnitude and dimensionality of crystallographic data. Hence, a data-driven approach should be proposed to find a predictive model or set of heuristics which differentiate synthesizable crystal structures from the rest. The main challenge of this research problem is the lack of a negative set for classification. Here, there are two classes of data: the positive class which contains synthesizable materials and the negative class which contains materials which are not synthesizable. While the data from the positive class is simply the data of crystals which have been experimentally synthesized, we do not have access to data points which are certainly unsynthesizable. Strictly speaking, if an attempt of synthesizing a crystal fails, it does not necessarily follow that the crystal is not synthesizable. Also, there is no database available which contains the intended crystal structures of unsuccessful synthesis attempts.

This project proposes a semi-supervised learning scheme to predict crystal synthesizability. The ML model is trained on experimental and theoretical crystal data. The initial featurization focuses on local environments which is inspired by the Pauling Rules. The experimental data points are downloaded through the Pymatgen API [3] from the Materials Project database [4] which contains relaxed structures recorded in Inorganic Crystal Structure Database – ICSD [5]. The theoretical data is queried from select databases accessible through the Optimade project’s API [6].

References.

[1] L. Pauling, ‘THE PRINCIPLES DETERMINING THE STRUCTURE OF COMPLEX IONIC CRYSTALS’, J. Am. Chem. Soc., vol. 51, no. 4, pp. 1010–1026, Apr. 1929, doi: 10.1021/ja01379a006.

[2] J. George, D. Waroquiers, D. Di Stefano, G. Petretto, G. Rignanese, and G. Hautier, ‘The Limited Predictive Power of the Pauling Rules’, Angew. Chem., vol. 132, no. 19, pp. 7639–7645, May 2020, doi: 10.1002/ange.202000829.

[3] D. Waroquiers et al., ‘ChemEnv: a fast and robust coordination environment identification tool’, Acta Crystallogr. Sect. B Struct. Sci. Cryst. Eng. Mater., vol. 76, no. 4, pp. 683–695, Aug. 2020, doi: 10.1107/S2052520620007994.

[4] A. Jain et al., ‘Commentary: The Materials Project: A materials genome approach to accelerating materials innovation’, APL Mater., vol. 1, no. 1, p. 011002, Jul. 2013, doi: 10.1063/1.4812323.

[5] M. Hellenbrandt, ‘The Inorganic Crystal Structure Database (ICSD)—Present and Future’, Crystallogr. Rev., vol. 10, no. 1, pp. 17–22, Jan. 2004, doi: 10.1080/08893110410001664882.

[6] C. W. Andersen et al., ‘OPTIMADE, an API for exchanging materials data’, Sci. Data, vol. 8, no. 1, p. 217, Aug. 2021, doi: 10.1038/s41597-021-00974-z.

Sasan AmariAmir (M.Sc.)