Roadmap on Machine learning in electronic structure
Name
Kulik_2022_Electron._Struct._4_023004.pdf
Description
Published version
Size
13.03 MB
Format
Adobe PDF
Checksum (MD5)
3d6afaa7879b3c8c91d642bd59d034cd
Date Issued
August 19, 2022
Journal
Electronic Structure
Publisher
IOP Publishing
Citation
2022. "Roadmap on Machine learning in electronic structure." Electronic Structure, 4 (2).
Version
Final published version
Abstract
In recent years, we have been witnessing a paradigm shift in computational materials science. In fact, traditional methods, mostly developed in the second half of the XXth century, are being complemented, extended, and sometimes even completely replaced by faster, simpler, and often more accurate approaches. The new approaches, that we collectively label by machine learning, have their origins in the fields of informatics and artificial intelligence, but are making rapid inroads in all other branches of science. With this in mind, this Roadmap article, consisting of multiple contributions from experts across the field, discusses the use of machine learning in materials science, and share perspectives on current and future challenges in problems as diverse as the prediction of materials properties, the construction of force-fields, the development of exchange correlation functionals for density-functional theory, the solution of the many-body problem, and more. In spite of the already numerous and exciting success stories, we are just at the beginning of a long path that will reshape materials science for the many challenges of the XXIth century.
MIT Department
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
10.1088/2516-1075/ac572f
