From emergent material descriptions to estimation of properties stemming from structures to optimization of process parameters for achieving best performance – all key facets of materials science and related fields have experienced tremendous growth with the introduction of data-driven models. This gradual progression goes at par with developments of machine learning workflows, from purely data-driven shallow models to those that are well-capable in encoding more complex graphs, symbolic representations, invariances, and positional embeddings. Furthermore, this perspective aims at summarizing strategic aspects of such transitions while providing insights into the requirements of bringing in explainable, interpretable predictive models, and causal learning to aid in materials design and discovery. Although the focus remains on a variety of functional materials by providing a handful of case studies, the applications of such integrated methodologies are universal to facilitate fundamental understandings of materials physics while enabling autonomous experiments.
Ghosh, Ayana (2023). Towards physics-informed explainable machine learning and causal models for materials research. Computational Materials Science, 233(1). https://doi.org/10.1016/j.commatsci.2023.112740
@article{osti_2281147,
author = {Ghosh, Ayana},
title = {Towards physics-informed explainable machine learning and causal models for materials research},
annote = {From emergent material descriptions to estimation of properties stemming from structures to optimization of process parameters for achieving best performance – all key facets of materials science and related fields have experienced tremendous growth with the introduction of data-driven models. This gradual progression goes at par with developments of machine learning workflows, from purely data-driven shallow models to those that are well-capable in encoding more complex graphs, symbolic representations, invariances, and positional embeddings. Furthermore, this perspective aims at summarizing strategic aspects of such transitions while providing insights into the requirements of bringing in explainable, interpretable predictive models, and causal learning to aid in materials design and discovery. Although the focus remains on a variety of functional materials by providing a handful of case studies, the applications of such integrated methodologies are universal to facilitate fundamental understandings of materials physics while enabling autonomous experiments.},
doi = {10.1016/j.commatsci.2023.112740},
url = {https://www.osti.gov/biblio/2281147},
journal = {Computational Materials Science},
issn = {ISSN 0927-0256},
number = {1},
volume = {233},
place = {United States},
publisher = {Elsevier},
year = {2023},
month = {12}}