Machine-learned metrics for predicting the likelihood of success in materials discovery
Source abstract
Abstract
Abstract Materials discovery is often compared to the challenge of finding a needle in a haystack. While much work has focused on accurately predicting the properties of candidate materials with machine learning (ML), which amounts to evaluating whether a given candidate is a piece of straw or a needle, less attention has been paid to a critical question: are we searching in the right haystack? We refer to the haystack as the design space for a particular materials discovery problem (i.e., the set of possible candidate materials to synthesize), and thus frame this question as one of design space selection. In this paper, we introduce two metrics, the predicted fraction of improved candidates (PFIC), and the cumulative maximum likelihood of improvement (CMLI), which we demonstrate can identify discovery-rich and discovery-poor design spaces, respectively. A combined classification system, composed of the CMLI and PFIC metrics, is then used to identify optimal design spaces with high precision, and thus show the potential to significantly accelerate ML-driven materials discovery.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Machine Learning in Materials Science · X-ray Diffraction in Crystallography · Computational Drug Discovery Methods
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (29)
Show source-provided references
1. Meredig, B. et al. Combinatorial screening for new materials in unconstrained composition space with machine learning. Phys. Rev. B 89, 094104 (2014).10.1103/PhysRevB.89.094104 ↗
2. Isayev, O. et al. Materials cartography: representing and mining materials space using structural and electronic fingerprints. Chem. Mater. 27, 735–743 (2015).10.1021/cm503507h ↗
3. Rajan, K. Materials informatics: the materials "gene” and big data. Annu. Rev. Mater. Res. 45, 153–169 (2015).10.1146/annurev-matsci-070214-021132 ↗
4. Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A. & Kim, C. Machine learning in materials informatics: recent applications and prospects. npj Comput. Mater. 3, 54 (2017).10.1038/s41524-017-0056-5 ↗
5. Gómez-Bombarelli, R. et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. Nat. Mater. 15, 1120 (2016).10.1038/nmat4717 ↗
6. Greeley, J. et al. Computational high-throughput screening of electrocatalytic materials for hydrogen evolution. Nature Mater. 5, 909–913 (2006).10.1038/nmat1752 ↗
7. Kim, E., Huang, K., Jegelka, S. & Olivetti, E. Virtual screening of inorganic materials synthesis parameters with deep learning. npj Comput. Mater. 3, 53 (2017).10.1038/s41524-017-0055-6 ↗
8. Borboudakis, G. et al. Chemically intuited, large-scale screening of mofs by machine learning techniques. npj Comput. Mater. 3, 40 (2017).10.1038/s41524-017-0045-8 ↗
9. Snoek, J., Larochelle, H. & Adams, R. P. Practical bayesian optimization of machine learning algorithms. in Advances in Neural Information Processing Systems. 2951–2959 (2012).
10. Ward, L. et al. Matminer: An open source toolkit for materials data mining. Comput. Mater. Sci. 152, 60–69 (2018).10.1016/j.commatsci.2018.05.018 ↗
11. Ward, L., Agrawal, A., Choudhary, A. & Wolverton, C. A general-purpose machine learning framework for predicting properties of inorganic materials. npj Comput. Mater. 2, 16028 (2016).10.1038/npjcompumats.2016.28 ↗
12. Jia, X. et al. Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis. Nature 573, 251–255 (2019).10.1038/s41586-019-1540-5 ↗
13. Kauwe, S., Graser, J., Murdock, R. & Sparks, T. Can machine learning find extraordinary materials? Comput. Mater. Sci. 174, 109498 (2019).10.1016/j.commatsci.2019.109498 ↗
14. Sanchez-Lengeling, B. & Aspuru-Guzik, A. Inverse molecular design using machine learning: generative models for matter engineering. Science 361, 360–365 (2018).10.1126/science.aat2663 ↗
15. Meredig, B. et al. Can machine learning identify the next high-temperature superconductor? examining extrapolation performance for materials discovery. Mol. Syst. Des. Eng. 3, 819–825 (2018).10.1039/C8ME00012C ↗
16. Raccuglia, P. et al. Machine-learning-assisted materials discovery using failed experiments. Nature 533, 73 (2016).10.1038/nature17439 ↗
17. Ling, J., Hutchinson, M., Antono, E., Paradiso, S. & Meredig, B. High-dimensional materials and process optimization using data-driven experimental design with well-calibrated uncertainty estimates. Integr. Mater. Manuf. Innov. 6, 207–217 (2017).10.1007/s40192-017-0098-z ↗
18. Jain, A. et al. Commentary: the materials project: a materials genome approach to accelerating materials innovation. Apl. Mater. 1, 011002 (2013).10.1063/1.4812323 ↗
19. Hutchinson, M. Lolo library, v2.2.5, https://github.com/CitrineInformatics/lolo (2018).
20. Granda, J. M., Donina, L., Dragone, V., Long, D.-L. & Cronin, L. Controlling an organic synthesis robot with machine learning to search for new reactivity. Nature 559, 377 (2018).10.1038/s41586-018-0307-8 ↗
21. Brandt, R. E. et al. Rapid photovoltaic device characterization through bayesian parameter estimation. Joule 1, 843–856 (2017).10.1016/j.joule.2017.10.001 ↗
22. Efron, B. Model Selection Estimation and Bootstrap Smoothing (Division of Biostatistics, Stanford University, 2012).
23. Christoph Steinbeck, D. G. & Egon, W. Chemistry development kit library, v2.3, https://cdk.github.io/ (2018).
24. Ward, L. et al. A general-purpose machine learning framework for predicting properties ofinorganic materials. npj Comput. Mater. 2, 16028 (2016).10.1038/npjcompumats.2016.28 ↗
25. Hachmann, J. et al. The harvard clean energy project: large-scale computational screening and design of organic photovoltaics on the world community grid. J. Phys. Chem. Lett. 2, 2241–2251 (2011).10.1021/jz200866s ↗
26. Bradley, J.-C., Williams, A. & Lang, A. Jean-Claude Bradley open melting point dataset. FigShare 10, m9 (2014).
27. Michel, K. Superconductor critical temperatures on Citrination. (2016). https://citrination.com/datasets/2210/.
28. Gaultois, M. W. et al. Data-driven review of thermoelectric materials: performance and resource considerations. Chem. Mater. 25, 2911–2920 (2013).10.1021/cm400893e ↗
29. Strehlow, W. & Cook, E. Compilation of energy band gaps in elemental and binary compound semiconductors and insulators. J. Phys. Chem. Ref. Data 2, 163–200 (1973).10.1063/1.3253115 ↗