Decomposing Chemical Space: Applications to the Machine Learning of Atomic Energies.
Sign Up to like & getrecommendations! Published in 2022 at "Journal of chemical theory and computation"
DOI: 10.1021/acs.jctc.2c01290
Abstract: We apply a number of atomic decomposition schemes across the standard QM7 data set─a small model set of organic molecules at equilibrium geometry─to inspect the possible emergence of trends among contributions to atomization energies from… read more here.
Keywords: chemical space; machine learning; atomic energies; decomposing chemical ... See more keywords
Exploring the configurational space of amorphous graphene with machine-learned atomic energies
Sign Up to like & getrecommendations! Published in 2022 at "Chemical Science"
DOI: 10.1039/d2sc04326b
Abstract: Two-dimensionally extended amorphous carbon (“amorphous graphene”) is a prototype system for disorder in 2D, showing a rich and complex configurational space that is yet to be fully understood. Here we explore the nature of amorphous… read more here.
Keywords: configurational space; machine learned; atomic energies; amorphous graphene ... See more keywords