Uncertainty estimation for molecular dynamics and sampling

Inexpensive modeling of quantum dynamics using path integral generalized Langevin equation thermostats

(2020) Venkat Kapil et al.   JOURNAL OF CHEMICAL PHYSICS

On-the-fly active learning of interpretable Bayesian force fields for atomistic rare events

(2020) Jonathan Vandermause et al.   npj Computational Materials

3D Ordering at the Liquid–Solid Polar Interface of Nanowires

(2020) Mahdi Zamani et al.   ADVANCED MATERIALS

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

Simulating Solvation and Acidity in Complex Mixtures with First-Principles Accuracy: The Case of CH3SO3H and H2O2 in Phenol

(2020) Kevin Rossi et al.   Journal of Chemical Theory and Computation

Committee neural network potentials control generalization errors and enable active learning

(2020) Christoph Schran et al.   JOURNAL OF CHEMICAL PHYSICS

Evidence for supercritical behaviour of high-pressure liquid hydrogen

(2020) Bingqing Cheng et al.   NATURE

High Throughput Screening of Millions of van der Waals Heterostructures for Superlubricant Applications

(2020) Marco Fronzi et al.   Advanced Theory and Simulations

Fast and Accurate Uncertainty Estimation in Chemical Machine Learning

(2019) Félix Musil et al.   Journal of Chemical Theory and Computation

Ab initio thermodynamics of liquid and solid water

(2019) Bingqing Cheng et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Library-Based LAMMPS Implementation of High-Dimensional Neural Network Potentials

(2019) Andreas Singraber et al.   Journal of Chemical Theory and Computation

Quantifying Chemical Structure and Machine-Learned Atomic Energies in Amorphous and Liquid Silicon

(2019) Noam Bernstein et al.   ANGEWANDTE CHEMIE-INTERNATIONAL EDITION

Parallel Multistream Training of High-Dimensional Neural Network Potentials

(2019) Andreas Singraber et al.   Journal of Chemical Theory and Computation

Phase Transitions of Hybrid Perovskites Simulated by Machine-Learning Force Fields Trained on the Fly with Bayesian Inference

(2019) Ryosuke Jinnouchi et al.   PHYSICAL REVIEW LETTERS

Using Gaussian process regression to simulate the vibrational Raman spectra of molecular crystals

(2019) Nathaniel Raimbault et al.   NEW JOURNAL OF PHYSICS

Assessment of Approximate Methods for Anharmonic Free Energies

(2019) Venkat Kapil et al.   Journal of Chemical Theory and Computation

Iterative Unbiasing of Quasi-Equilibrium Sampling

(2019) F. Giberti et al.   Journal of Chemical Theory and Computation

Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm

(2018) Nongnuch Artrith et al.   JOURNAL OF CHEMICAL PHYSICS

Less is more: Sampling chemical space with active learning

(2018) Justin S. Smith et al.   JOURNAL OF CHEMICAL PHYSICS

Towards exact molecular dynamics simulations with machine-learned force fields

(2018) Stefan Chmiela et al.   Nature Communications

i-PI 2.0: A universal force engine for advanced molecular simulations

(2018) Venkat Kapil et al.   COMPUTER PHYSICS COMMUNICATIONS

Chemical shifts in molecular solids by machine learning

(2018) Federico M. Paruzzo et al.   Nature Communications

Transferable Machine-Learning Model of the Electron Density

(2018) Andrea Grisafi et al.   ACS Central Science

The BioFragment Database (BFDb): An open-data platform for computational chemistry analysis of noncovalent interactions

(2017) Lori A. Burns et al.   JOURNAL OF CHEMICAL PHYSICS

Advanced capabilities for materials modelling with Quantum ESPRESSO

(2017) P Giannozzi et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

Addressing uncertainty in atomistic machine learning

(2017) Andrew A. Peterson et al.   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

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

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

Bypassing the Kohn-Sham equations with machine learning

(2017) Felix Brockherde et al.   Nature Communications

Machine learning unifies the modeling of materials and molecules

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

Accurate molecular dynamics and nuclear quantum effects at low cost by multiple steps in real and imaginary time: Using density functional theory to accelerate wavefunction methods

(2016) V. Kapil et al.   JOURNAL OF CHEMICAL PHYSICS

Ab Initio Quality NMR Parameters in Solid-State Materials Using a High-Dimensional Neural-Network Representation

(2016) Jérôme Cuny et al.   Journal of Chemical Theory and Computation

How van der Waals interactions determine the unique properties of water

(2016) Tobias Morawietz et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Constructing high-dimensional neural network potentials: A tutorial review

(2015) Jörg Behler   INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY

Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

(2015) Raghunathan Ramakrishnan et al.   Journal of Chemical Theory and Computation

Beyond static structures: Putting forth REMD as a tool to solve problems in computational organic chemistry

(2015) Riccardo Petraglia et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Molecular Dynamics with On-the-Fly Machine Learning of Quantum-Mechanical Forces

(2015) Zhenwei Li et al.   PHYSICAL REVIEW LETTERS

Parameterization of DFTB3/3OB for Sulfur and Phosphorus for Chemical and Biological Applications

(2014) Michael Gaus et al.   Journal of Chemical Theory and Computation

Computing Gibbs free energy differences by interface pinning

(2013) Ulf R. Pedersen et al.   PHYSICAL REVIEW B

Parametrization and Benchmark of DFTB3 for Organic Molecules

(2012) Michael Gaus et al.   Journal of Chemical Theory and Computation

Neural network interatomic potential for the phase change material GeTe

(2012) Gabriele C. Sosso et al.   PHYSICAL REVIEW B

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

(2012) Matthias Rupp et al.   PHYSICAL REVIEW LETTERS

A generalized-gradient approximation exchange hole model for dispersion coefficients

(2011) Stephan N. Steinmann et al.   JOURNAL OF CHEMICAL PHYSICS

Comprehensive Benchmarking of a Density-Dependent Dispersion Correction

(2011) Stephan N. Steinmann et al.   Journal of Chemical Theory and Computation

Effect of the damping function in dispersion corrected density functional theory

(2011) Stefan Grimme et al.   JOURNAL OF COMPUTATIONAL CHEMISTRY

Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations

(2011) Jörg Behler   PHYSICAL CHEMISTRY CHEMICAL PHYSICS

The inefficiency of re-weighted sampling and the curse of system size in high-order path integration

(2011) M. Ceriotti et al.   PROCEEDINGS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES

A System-Dependent Density-Based Dispersion Correction

(2010) Stephan N. Steinmann et al.   Journal of Chemical Theory and Computation

Colored-Noise Thermostats à la Carte

(2010) Michele Ceriotti et al.   Journal of Chemical Theory and Computation

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

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

PLUMED: A portable plugin for free-energy calculations with molecular dynamics

(2009) Massimiliano Bonomi et al.   COMPUTER PHYSICS COMMUNICATIONS

QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials

(2009) Paolo Giannozzi et al.   JOURNAL OF PHYSICS-CONDENSED MATTER

A refined ring polymer contraction scheme for systems with electrostatic interactions

(2008) Thomas E. Markland et al.   CHEMICAL PHYSICS LETTERS

Accurate determination of crystal structures based on averaged local bond order parameters

(2008) Wolfgang Lechner et al.   JOURNAL OF CHEMICAL PHYSICS