Organic reactivity from mechanism to machine learning
As more data are introduced in the building of models of chemical reactivity, the mechanistic
component can be reduced until 'big data'applications are reached. These methods no …
component can be reduced until 'big data'applications are reached. These methods no …
LAMMPS-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
Since the classical molecular dynamics simulator LAMMPS was released as an open source
code in 2004, it has become a widely-used tool for particle-based modeling of materials at …
code in 2004, it has become a widely-used tool for particle-based modeling of materials at …
Best practices in machine learning for chemistry
Best practices in machine learning for chemistry | Nature Chemistry Skip to main content
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Thank you for visiting nature.com. You are using a browser version with limited support for …
Navigating through the maze of homogeneous catalyst design with machine learning
The ability to forge difficult chemical bonds through catalysis has transformed society on all
fronts, from feeding the ever-growing population to increasing life expectancies through the …
fronts, from feeding the ever-growing population to increasing life expectancies through the …
[HTML][HTML] PSI4 1.4: Open-source software for high-throughput quantum chemistry
PSI4 is a free and open-source ab initio electronic structure program providing
implementations of Hartree–Fock, density functional theory, many-body perturbation theory …
implementations of Hartree–Fock, density functional theory, many-body perturbation theory …
Development and benchmarking of open force field 2.0. 0: the Sage small molecule force field
We introduce the Open Force Field (OpenFF) 2.0. 0 small molecule force field for drug-like
molecules, code-named Sage, which builds upon our previous iteration, Parsley. OpenFF …
molecules, code-named Sage, which builds upon our previous iteration, Parsley. OpenFF …
[HTML][HTML] Perspective on integrating machine learning into computational chemistry and materials science
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 …
electronic structure theory and molecular simulation. In particular, ML has become firmly …
Development and benchmarking of open force field v1. 0.0—the parsley small-molecule force field
We present a methodology for defining and optimizing a general force field for classical
molecular simulations, and we describe its use to derive the Open Force Field 1.0. 0 small …
molecular simulations, and we describe its use to derive the Open Force Field 1.0. 0 small …
RMG database for chemical property prediction
The Reaction Mechanism Generator (RMG) database for chemical property prediction is
presented. The RMG database consists of curated datasets and estimators for accurately …
presented. The RMG database consists of curated datasets and estimators for accurately …
End-to-end differentiable construction of molecular mechanics force fields
Molecular mechanics (MM) potentials have long been a workhorse of computational
chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety …
chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety …