A transferable recommender approach for selecting the best density functional approximations in chemical discovery

descriptionPublicationkeyboard_double_arrow_right Article , Preprint 22 Dec 2022Embargo end date: 01 Jul 2022 English Publisher:Springer Science and Business Media LLCJournal:Nature Computational Science, volume 3, pages 38-47 (eissn: 2662-8457,

Copyright policy )Funded by:NSF | Graduate Research Fellows..., NSF | Graduate Research Fellows...

Authors: Chenru Duan; Aditya Nandy; Ralf Meyer; Naveen Arunachalam; Heather J. Kulik;

doi: 10.1038/s43588-022-00384-0 , 10.5281/zenodo.7350957 , 10.48550/arxiv.2207.10747 , 10.5281/zenodo.7347727 , 10.5281/zenodo.7347728 , 10.5281/zenodo.7350958

pmid: 38177951

arXiv: 2207.10747

A transferable recommender approach for selecting the best density functional approximations in chemical discovery

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Abstract

Supplementary files accompanied with the density functional recommender paper (https://arxiv.org/abs/2207.10747) This zip file contains: 1) Checkout of the dfa_recommender git repo (https://github.com/hjkgrp/dfa_recommender). 2) All optimized structures in the data set used to train machine learning models in xyz coordinates. 3) All reference energies from DLPNO-CCSD(T) and DFAs used to train machine learning models.

Related Organizations

Massachusetts Institute of Technology
United States

Keywords

Chemical Physics (physics.chem-ph), FOS: Computer and information sciences, Condensed Matter - Materials Science, Computer Science - Machine Learning, Physics - Chemical Physics, Materials Science (cond-mat.mtrl-sci), FOS: Physical sciences, Machine Learning (cs.LG)

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