Linear Atomic Cluster Expansion Force Fields for Organic Molecules: Beyond RMSE

Challenges for machine learning force fields in reproducing potential energy surfaces of flexible molecules

(2021) Valentin Vassilev-Galindo et al.   JOURNAL OF CHEMICAL PHYSICS

Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning

(2021) Gregory Fonseca et al.   JOURNAL OF CHEMICAL PHYSICS

Quantitative interpretation explains machine learning models for chemical reaction prediction and uncovers bias

(2021) Dávid Péter Kovács et al.   Nature Communications

Performant implementation of the atomic cluster expansion (PACE) and application to copper and silicon

(2021) Yury Lysogorskiy et al.   npj Computational Materials

FCHL revisited: Faster and more accurate quantum machine learning

(2020) Anders S. Christensen et al.   JOURNAL OF CHEMICAL PHYSICS

The ANI-1ccx and ANI-1x data sets, coupled-cluster and density functional theory properties for molecules

(2020) Justin S. Smith et al.   Scientific Data

The ORCA quantum chemistry program package

(2020) Frank Neese et al.   JOURNAL OF CHEMICAL PHYSICS

Extending the Applicability of the ANI Deep Learning Molecular Potential to Sulfur and Halogens

(2020) Christian Devereux et al.   Journal of Chemical Theory and Computation

Gaussian Moments as Physically Inspired Molecular Descriptors for Accurate and Scalable Machine Learning Potentials

(2020) V. Zaverkin et al.   Journal of Chemical Theory and Computation

QUBEKit: Automating the Derivation of Force Field Parameters from Quantum Mechanics

(2019) Joshua T. Horton et al.   Journal of Chemical Information and Modeling

sGDML: Constructing accurate and data efficient molecular force fields using machine learning

(2019) Stefan Chmiela et al.   COMPUTER PHYSICS COMMUNICATIONS

PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges

(2019) Oliver T. Unke et al.   Journal of Chemical Theory and Computation

De novo exploration and self-guided learning of potential-energy surfaces

(2019) Noam Bernstein et al.   npj Computational Materials

DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics

(2018) Han Wang et al.   COMPUTER PHYSICS COMMUNICATIONS

Fixed-Charge Atomistic Force Fields for Molecular Dynamics Simulations in the Condensed Phase: An Overview

(2018) Sereina Riniker   Journal of Chemical Information and Modeling

Escaping Atom Types in Force Fields Using Direct Chemical Perception

(2018) David L. Mobley et al.   Journal of Chemical Theory and Computation

Force field development phase II: Relaxation of physics-based criteria… or inclusion of more rigorous physics into the representation of molecular energetics

(2018) A. T. Hagler   JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN

ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost

(2017) J. S. Smith et al.   Chemical Science

Quantum-chemical insights from deep tensor neural networks

(2017) Kristof T. Schütt et al.   Nature Communications

Machine learning unifies the modeling of materials and molecules

(2017) Albert P. Bartók et al.   Science Advances

Machine learning of accurate energy-conserving molecular force fields

(2017) Stefan Chmiela et al.   Science Advances

Moment Tensor Potentials: A Class of Systematically Improvable Interatomic Potentials

(2016) Alexander V. Shapeev   MULTISCALE MODELING & SIMULATION

The ReaxFF reactive force-field: development, applications and future directions

(2016) Thomas P Senftle et al.   npj Computational Materials

Building Force Fields: An Automatic, Systematic, and Reproducible Approach

(2014) Lee-Ping Wang et al.   Journal of Physical Chemistry Letters

Characterization of Biaryl Torsional Energetics and its Treatment in OPLS All-Atom Force Fields

(2013) Markus K. Dahlgren et al.   Journal of Chemical Information and Modeling

On representing chemical environments

(2013) Albert P. Bartók et al.   PHYSICAL REVIEW B

Systematic Parametrization of Polarizable Force Fields from Quantum Chemistry Data

(2012) Lee-Ping Wang et al.   Journal of Chemical Theory and Computation

Implementation of the CHARMM Force Field in GROMACS: Analysis of Protein Stability Effects from Correction Maps, Virtual Interaction Sites, and Water Models

(2010) Pär Bjelkmar et al.   Journal of Chemical Theory and Computation

Current Status of the AMOEBA Polarizable Force Field

(2010) Jay W. Ponder et al.   JOURNAL OF PHYSICAL CHEMISTRY B

Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons

(2010) Albert P. Bartók et al.   PHYSICAL REVIEW LETTERS

Permutationally invariant potential energy surfaces in high dimensionality

(2009) Bastiaan J. Braams et al.   INTERNATIONAL REVIEWS IN PHYSICAL CHEMISTRY

Systematic optimization of long-range corrected hybrid density functionals

(2008) Jeng-Da Chai et al.   JOURNAL OF CHEMICAL PHYSICS