Heather J. Kulik
Making machine learning a useful tool in the accelerated discovery of transition metal complexes
Massachusetts Institute of Technology
abstract machine learning ml matured opened new frontier theoretical computational chemistry offering promise simultaneous paradigm shifts accuracy efficiency nowhere advance needed also challenging achieve discovery openshell transition metal complexes localized f electrons exhibit variable bonding challenging capture even computationally demanding methods thus despite great promise clear obstacles remain constructing ml models supplement even replace explicit electronic structure calculations article outline recent advances building ml models transition metal chemistry including ability approach subkcalmol accuracy range properties tailored representations discover enumerate complexes large chemical spaces reveal opportunities design analysis feature importance discuss unique considerations essential enabling ml openshell transition metal chemistry including relationship data set sizediversity model complexity representation choice b importance quantitative assessments theory model domain applicability c need enable autonomous generation reliable large data sets ml model training active learning discovery contexts finally summarize next steps toward making ml mainstream tool accelerated discovery transition metal complexes article categorized electronic structure theory gt density functional theory software gt molecular modeling computer information science gt chemoinformatics