Abstract
Contemporary materials science has seen an increasing application of various artificial intelligence techniques in an attempt to accelerate the materials discovery process using forward modeling for predictive analysis and inverse modeling for optimization and design. Over the last decade or so, the increasing availability of computational power and large materials datasets has led to a continuous evolution in the complexity of the techniques used to advance the frontier. In this Review, we provide a high-level overview of the evolution of artificial intelligence in contemporary materials science for the task of materials property prediction in forward modeling. Each stage of evolution is accompanied by an outline of some of the commonly used methodologies and applications. We conclude the work by providing potential future ideas for further development of artificial intelligence in materials science to facilitate the discovery, design, and deployment workflow. Graphical abstract: [Figure not available: see fulltext.]
| Original language | English (US) |
|---|---|
| Pages (from-to) | 754-763 |
| Number of pages | 10 |
| Journal | MRS Communications |
| Volume | 13 |
| Issue number | 5 |
| DOIs | |
| State | Published - Oct 2023 |
Funding
This work was performed under the following financial assistance award 70NANB19H005 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD). Partial support is also acknowledged from NSF award CMMI-2053929 and DOE awards DE-SC0019358, DE-SC0021399, and Northwestern Center for Nanocombinatorics.
ASJC Scopus subject areas
- General Materials Science