Accurate global machine learning force fields for molecules with hundreds of atoms
Global machine learning force fields, with the capacity to capture collective interactions in
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …
molecular systems, now scale up to a few dozen atoms due to considerable growth of model …
Exploring the mechanism of catalysis with the unified reaction valley approach (URVA)—A review
The unified reaction valley approach (URVA) differs from mainstream mechanistic studies,
as it describes a chemical reaction via the reaction path and the surrounding reaction valley …
as it describes a chemical reaction via the reaction path and the surrounding reaction valley …
Reaction mechanism–explored with the unified reaction valley approach
E Kraka, JJ Antonio, M Freindorf - Chemical Communications, 2023 - pubs.rsc.org
One of the ultimate goals of chemistry is to understand and manipulate chemical reactions,
which implies the ability to monitor the reaction and its underlying mechanism at an atomic …
which implies the ability to monitor the reaction and its underlying mechanism at an atomic …
Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
Modern machine learning force fields (ML-FF) are able to yield energy and force predictions
at the accuracy of high-level ab initio methods, but at a much lower computational cost. On …
at the accuracy of high-level ab initio methods, but at a much lower computational cost. On …
Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature
Nuclear quantum effects (NQE) tend to generate delocalized molecular dynamics due to the
inclusion of the zero point energy and its coupling with the anharmonicities in interatomic …
inclusion of the zero point energy and its coupling with the anharmonicities in interatomic …
Dual-level training of Gaussian processes with physically inspired priors for geometry optimizations
Gaussian process (GP) regression has been recently developed as an effective method in
molecular geometry optimization. The prior mean function is one of the crucial parts of the …
molecular geometry optimization. The prior mean function is one of the crucial parts of the …
Electrostatic embedding machine learning for ground and excited state molecular dynamics of solvated molecules
The application of quantum mechanics (QM)/molecular mechanics (MM) models for studying
dynamics in complex systems is nowadays well established. However, their significant …
dynamics in complex systems is nowadays well established. However, their significant …
Learning to decouple complex systems
A complex system with cluttered observations may be a coupled mixture of multiple simple
sub-systems corresponding to latent entities. Such sub-systems may hold distinct dynamics …
sub-systems corresponding to latent entities. Such sub-systems may hold distinct dynamics …
Electronic and Nuclear Quantum Effects on Proton Transfer Reactions of Guanine–Thymine (GT) Mispairs Using Combined Quantum Mechanical/Molecular …
Y Tao, TJ Giese, DM York - Molecules, 2024 - mdpi.com
Rare tautomeric forms of nucleobases can lead to Watson–Crick-like (WC-like) mispairs in
DNA, but the process of proton transfer is fast and difficult to detect experimentally. NMR …
DNA, but the process of proton transfer is fast and difficult to detect experimentally. NMR …
Error bounds of the invariant statistics in machine learning of ergodic Itô diffusions
H Zhang, J Harlim, X Li - Physica D: Nonlinear Phenomena, 2021 - Elsevier
This paper studies the theoretical underpinnings of machine learning of ergodic Itô
diffusions. The objective is to understand the convergence properties of the invariant …
diffusions. The objective is to understand the convergence properties of the invariant …