Organic reactivity from mechanism to machine learning

K Jorner, A Tomberg, C Bauer, C Sköld… - Nature Reviews …, 2021 - nature.com
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 …

LAMMPS-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales

AP Thompson, HM Aktulga, R Berger… - Computer Physics …, 2022 - Elsevier
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 …

Best practices in machine learning for chemistry

N Artrith, KT Butler, FX Coudert, S Han, O Isayev… - Nature …, 2021 - nature.com
Best practices in machine learning for chemistry | Nature Chemistry Skip to main content
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Navigating through the maze of homogeneous catalyst design with machine learning

G dos Passos Gomes, R Pollice, A Aspuru-Guzik - Trends in Chemistry, 2021 - cell.com
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 …

[HTML][HTML] PSI4 1.4: Open-source software for high-throughput quantum chemistry

DGA Smith, LA Burns, AC Simmonett… - The Journal of …, 2020 - pubs.aip.org
PSI4 is a free and open-source ab initio electronic structure program providing
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

S Boothroyd, PK Behara, OC Madin… - Journal of chemical …, 2023 - ACS Publications
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 …

[HTML][HTML] Perspective on integrating machine learning into computational chemistry and materials science

J Westermayr, M Gastegger, KT Schütt… - The Journal of Chemical …, 2021 - pubs.aip.org
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 …

Development and benchmarking of open force field v1. 0.0—the parsley small-molecule force field

Y Qiu, DGA Smith, S Boothroyd, H Jang… - Journal of chemical …, 2021 - ACS Publications
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 …

RMG database for chemical property prediction

MS Johnson, X Dong, A Grinberg Dana… - Journal of Chemical …, 2022 - ACS Publications
The Reaction Mechanism Generator (RMG) database for chemical property prediction is
presented. The RMG database consists of curated datasets and estimators for accurately …

End-to-end differentiable construction of molecular mechanics force fields

Y Wang, J Fass, B Kaminow, JE Herr, D Rufa… - Chemical …, 2022 - pubs.rsc.org
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 …