Materials Expert-Artificial Intelligence for Materials Discovery
arXiv:2312.02796 · doi:10.1038/s43246-025-00928-7
Abstract
The advent of material databases provides an unprecedented opportunity to uncover predictive descriptors for emergent material properties from vast data space. However, common reliance on high-throughput ab initio data necessarily inherits limitations of such data: mismatch with experiments. On the other hand, experimental decisions are often guided by an expert's intuition honed from experiences that are rarely articulated. We propose using machine learning to "bottle" such operational intuition into quantifiable descriptors using expertly curated measurement-based data. We introduce "Materials Expert-Artificial Intelligence" (ME-AI) to encapsulate and articulate this human intuition. As a first step towards such a program, we focus on the topological semimetal (TSM) among square-net materials as the property inspired by the expert-identified descriptor based on structural information: the tolerance factor. We start by curating a dataset encompassing 12 primary features of 879 square-net materials, using experimental data whenever possible. We then use Dirichlet-based Gaussian process regression using a specialized kernel to reveal composite descriptors for square-net topological semimetals. The ME-AI learned descriptors independently reproduce expert intuition and expand upon it. Specifically, new descriptors point to hypervalency as a critical chemical feature predicting TSM within square-net compounds. Our success with a carefully defined problem points to the "machine bottling human insight" approach as promising for machine learning-aided material discovery.
arXiv comment8 pages main text, 4 figs, 8 pages Supplementary material
References in corpus (15)
- On representing chemical environments
- Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
- A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials
- Topological quantum chemistry
- The Computational 2D Materials Database: High-Throughput Modeling and Discovery of Atomically Thin Crystals
- Big Data of Materials Science - Critical Role of the Descriptor
- The Joint Automated Repository for Various Integrated Simulations (JARVIS) for data-driven materials design
- Materials Cloud, a platform for open computational science
- Topology-Scaling Identification of Layered Solids and Stable Exfoliated 2D Materials
- Materials Cartography: Representing and Mining Material Space Using Structural and Electronic Fingerprints
- Topological Semimetals in Square-Net Materials
- A flat band-induced correlated kagome metal
- The role of delocalized chemical bonding in square-net-based topological semimetals
- Large Nernst Power Factor over a Broad Temperature Range in Polycrystalline Weyl Semimetal NbP
- A systematic study of stacked square nets: from Dirac fermions to material realizations