1. Naturally-meaningful and efficient descriptors: machine learning of material properties based on robust one-shot ab initio descriptors
- Author
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Tawfik, Sherif Abdulkader and Russo, Salvy P.
- Subjects
FOS: Computer and information sciences ,Condensed Matter - Materials Science ,Computer Science - Machine Learning ,Materials Science (cond-mat.mtrl-sci) ,FOS: Physical sciences ,Library and Information Sciences ,Physical and Theoretical Chemistry ,Computer Graphics and Computer-Aided Design ,Machine Learning (cs.LG) ,Computer Science Applications - Abstract
Establishing a data-driven pipeline for the discovery of novel materials requires the engineering of material features that can be feasibly calculated and can be applied to predict a material's target properties. Here we propose a new class of descriptors for describing crystal structures, which we term Robust One-Shot Ab initio (ROSA) descriptors. ROSA is computationally cheap and is shown to accurately predict a range of material properties. These simple and intuitive class of descriptors are generated from the energetics of a material at a low level of theory using an incomplete ab initio calculation. We demonstrate how the incorporation of ROSA descriptors in ML-based property prediction leads to accurate predictions over a wide range of crystals, amorphized crystals, metal-organic frameworks and molecules. We believe that the low computational cost and ease of use of these descriptors will significantly improve ML-based predictions., Comment: 13 pages, accepted in Journal of Cheminformatics
- Published
- 2022