The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic-scale structure of matter and its …
D Yu, Z Xue, T Mu - Chemical Society Reviews, 2021 - pubs.rsc.org
Various eutectic systems have been proposed and studied over the past few decades. Most of the studies have focused on three typical types of eutectics: eutectic metals, eutectic salts …
Electronically excited states of molecules are at the heart of photochemistry, photophysics, as well as photobiology and also play a role in material science. Their theoretical description …
Kohn-Sham density functional theory (DFT) is a standard tool in most branches of chemistry, but accuracies for many molecules are limited to 2-3 kcal⋅ mol− 1 with presently-available …
Abstract Machine learning (ML) is becoming a method of choice for modelling complex chemical processes and materials. ML provides a surrogate model trained on a reference …
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 …
PO Dral - The journal of physical chemistry letters, 2020 - ACS Publications
As the quantum chemistry (QC) community embraces machine learning (ML), the number of new methods and applications based on the combination of QC and ML is surging. In this …
Rational design of compounds with specific properties requires understanding and fast evaluation of molecular properties throughout chemical compound space—the huge set of …
Machine learning (ML) methods are being used in almost every conceivable area of electronic structure theory and molecular simulation. In particular, ML has become firmly …