Machine learning force fields
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 …
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 …
present contribution discusses applications ranging from small molecule reaction dynamics …
Machine learning of reactive potentials
In the past two decades, machine learning potentials (MLPs) have driven significant
developments in chemical, biological, and material sciences. The construction and training …
developments in chemical, biological, and material sciences. The construction and training …
CHARMM at 45: Enhancements in accessibility, functionality, and speed
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 …
computational biochemistry and biophysics. Commensurate with the developments in …
[HTML][HTML] Learning intermolecular forces at liquid–vapor interfaces
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 …
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
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 …
potentials. For example, hybrid functionals or wave-function-based quantum chemical …
Transfer learning for affordable and high-quality tunneling splittings from instanton calculations
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 …
higher level of electronic structure theory together with ring-polymer instanton (RPI) theory is …
[HTML][HTML] Machine learning meets chemical physics
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 …
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
Constructing accurate, high-dimensional molecular potential energy surfaces (PESs) for
polyatomic molecules is challenging. Reproducing kernel Hilbert space (RKHS) …
polyatomic molecules is challenging. Reproducing kernel Hilbert space (RKHS) …
Transfer learning to CCSD (T): Accurate anharmonic frequencies from machine learning models
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 …
experimentally measured frequencies to the corresponding type of molecular motions is …