Implicitly perturbed Hamiltonian as a class of versatile and general-purpose molecular representations for machine learning.

ABSTRACT: Unraveling challenging problems by machine learning has recently become a hot topic in many scientific disciplines. For developing rigorous machine-learning models to study problems of interest in molecular sciences, translating molecular structures to quantitative representations as suitable machine-learning inputs play a central role. Many different molecular representations and the state-of-the-art ones, although efficient in studying numerous molecular features, still are suboptimal in many challenging cases, as discussed in the context of the present research. The main aim of the present study is to introduce the Implicitly Perturbed Hamiltonian (ImPerHam) as a class of versatile representations for more efficient machine learning of challenging problems in molecular sciences. ImPerHa

SUBMITTER: Alibakhshi A 

PROVIDER: S-EPMC8913769 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature