4.6 Article

JAX, MD A framework for differentiable physics

Related references

Note: Only part of the references are listed.
Article Multidisciplinary Sciences

Improved protein structure prediction using potentials from deep learning

Andrew W. Senior et al.

NATURE (2020)

Article Multidisciplinary Sciences

The frontier of simulation-based inference

Kyle Cranmer et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2020)

Article Physics, Multidisciplinary

Unveiling the predictive power of static structure in glassy systems

V Bapst et al.

NATURE PHYSICS (2020)

Article Nanoscience & Nanotechnology

Inverse Design of Photonic Crystals through Automatic Differentiation

Momchil Minkov et al.

ACS PHOTONICS (2020)

Article Biochemistry & Molecular Biology

End-to-End Differentiable Learning of Protein Structure

Mohammed AlQuraishi

CELL SYSTEMS (2019)

Article Computer Science, Software Engineering

Taichi: A Language for High-Performance Computation on Spatially Sparse Data Structures

Yuanming Hu et al.

ACM TRANSACTIONS ON GRAPHICS (2019)

Article Chemistry, Multidisciplinary

Automatic Differentiation in Quantum Chemistry with Applications to Fully Variational Hartree-Fock

Teresa Tamayo-Mendoza et al.

ACS CENTRAL SCIENCE (2018)

Article Physics, Multidisciplinary

Machine Learning a General-Purpose Interatomic Potential for Silicon

Albert P. Bartok et al.

PHYSICAL REVIEW X (2018)

Article Multidisciplinary Sciences

Relationship between local structure and relaxation in out-of-equilibrium glassy systems

Samuel S. Schoenholz et al.

PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2017)

Article Chemistry, Physical

Representations in neural network based empirical potentials

Ekin D. Cubuk et al.

JOURNAL OF CHEMICAL PHYSICS (2017)

Article Multidisciplinary Sciences

Quantum-chemical insights from deep tensor neural networks

Kristof T. Schuett et al.

NATURE COMMUNICATIONS (2017)

Article Biochemical Research Methods

OpenMM 7: Rapid development of high performance algorithms for molecular dynamics

Peter Eastman et al.

PLOS COMPUTATIONAL BIOLOGY (2017)

Article Materials Science, Multidisciplinary

Developing empirical potentials from ab initio simulations: The case of amorphous silica

Antoine Carre et al.

COMPUTATIONAL MATERIALS SCIENCE (2016)

Article Materials Science, Multidisciplinary

An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2

Nongnuch Artrith et al.

COMPUTATIONAL MATERIALS SCIENCE (2016)

Article Physics, Multidisciplinary

A structural approach to relaxation in glassy liquids

S. S. Schoenholz et al.

NATURE PHYSICS (2016)

Article Computer Science, Interdisciplinary Applications

Strong scaling of general-purpose molecular dynamics simulations on GPUs

Jens Glaser et al.

COMPUTER PHYSICS COMMUNICATIONS (2015)

Review Chemistry, Physical

Gaussian approximation potentials: A brief tutorial introduction

Albert P. Bartok et al.

INTERNATIONAL JOURNAL OF QUANTUM CHEMISTRY (2015)

Article Multidisciplinary Sciences

Human-level concept learning through probabilistic program induction

Brenden M. Lake et al.

SCIENCE (2015)

Article Chemistry, Physical

Atom-centered symmetry functions for constructing high-dimensional neural network potentials

Joerg Behler

JOURNAL OF CHEMICAL PHYSICS (2011)

Article Engineering, Aerospace

Using Automatic Differentiation to Create a Nonlinear Reduced-Order-Model Aerodynamic Solver

Jeffrey P. Thomas et al.

AIAA JOURNAL (2010)

Article Computer Science, Interdisciplinary Applications

General purpose molecular dynamics simulations fully implemented on graphics processing units

Joshua A. Anderson et al.

JOURNAL OF COMPUTATIONAL PHYSICS (2008)

Article Engineering, Mechanical

Automatic differentiation of the general-purpose computational fluid dynamics package FLUENT

Christian H. Bischot et al.

JOURNAL OF FLUIDS ENGINEERING-TRANSACTIONS OF THE ASME (2007)

Article Operations Research & Management Science

Automatic differentiation of explicit Runge-Kutta methods for optimal control

Andrea Walther

COMPUTATIONAL OPTIMIZATION AND APPLICATIONS (2007)

Article Computer Science, Interdisciplinary Applications

On the performance of discrete adjoint CFD codes using automatic differentiation

JD Müller et al.

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN FLUIDS (2005)