Machine learning force fields

OT Unke, S Chmiela, HE Sauceda… - Chemical …, 2021 - ACS Publications
In recent years, the use of machine learning (ML) in computational chemistry has enabled
numerous advances previously out of reach due to the computational complexity of …

Machine learning for chemical reactions

M Meuwly - Chemical Reviews, 2021 - ACS Publications
Machine learning (ML) techniques applied to chemical reactions have a long history. The
present contribution discusses applications ranging from small molecule reaction dynamics …

Machine learning of reactive potentials

Y Yang, S Zhang, KD Ranasinghe… - Annual Review of …, 2024 - annualreviews.org
In the past two decades, machine learning potentials (MLPs) have driven significant
developments in chemical, biological, and material sciences. The construction and training …

CHARMM at 45: Enhancements in accessibility, functionality, and speed

W Hwang, SL Austin, A Blondel… - The Journal of …, 2024 - ACS Publications
Since its inception nearly a half century ago, CHARMM has been playing a central role in
computational biochemistry and biophysics. Commensurate with the developments in …

[HTML][HTML] Learning intermolecular forces at liquid–vapor interfaces

SP Niblett, M Galib, DT Limmer - The Journal of chemical physics, 2021 - pubs.aip.org
By adopting a perspective informed by contemporary liquid-state theory, we consider how to
train an artificial neural network potential to describe inhomogeneous, disordered systems …

Predicting properties of periodic systems from cluster data: A case study of liquid water

V Zaverkin, D Holzmüller, R Schuldt… - The Journal of Chemical …, 2022 - pubs.aip.org
The accuracy of the training data limits the accuracy of bulk properties from machine-learned
potentials. For example, hybrid functionals or wave-function-based quantum chemical …

Transfer learning for affordable and high-quality tunneling splittings from instanton calculations

S Käser, JO Richardson… - Journal of Chemical Theory …, 2022 - ACS Publications
The combination of transfer learning (TL) a low-level potential energy surface (PES) to a
higher level of electronic structure theory together with ring-polymer instanton (RPI) theory is …

[HTML][HTML] Machine learning meets chemical physics

M Ceriotti, C Clementi… - The Journal of Chemical …, 2021 - pubs.aip.org
Over recent years, the use of statistical learning techniques applied to chemical problems
has gained substantial momentum. This is particularly apparent in the realm of physical …

Permutationally invariant, reproducing kernel-based potential energy surfaces for polyatomic molecules: From formaldehyde to acetone

D Koner, M Meuwly - Journal of chemical theory and computation, 2020 - ACS Publications
Constructing accurate, high-dimensional molecular potential energy surfaces (PESs) for
polyatomic molecules is challenging. Reproducing kernel Hilbert space (RKHS) …

Transfer learning to CCSD (T): Accurate anharmonic frequencies from machine learning models

S Kaser, ED Boittier, M Upadhyay… - Journal of Chemical …, 2021 - ACS Publications
The calculation of the anharmonic modes of small-to medium-sized molecules for assigning
experimentally measured frequencies to the corresponding type of molecular motions is …