Fetching the paper…
Reading the bibliography…
We introduce JAX MD, a software package for performing differentiable physics simulations with a focus on molecular dynamics.
Difftaichi: Differentiable programming for physical simulation
Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, and Frédo Durand · 1910
Earlier work this paper cites.
Diatomic molecules according to the wave mechanics. ii. vibrational levels
Philip M Morse · 1929
Earlier work this paper cites.
Lattice relaxation at a metal surface
Raju P Gupta · 1981
Earlier work this paper cites.
Embedded-atom method: Derivation and application to impurities, surfaces, and other defects in metals
Murray S Daw and Michael I Baskes · 1984
Earlier work this paper cites.
Computer simulation of local order in condensed phases of silicon
Frank H. Stillinger and Thomas A. Weber · 1985
Earlier work this paper cites.
Flocks, herds and schools: A distributed behavioral model
Craig W Reynolds · 1987
Earlier work this paper cites.
Force fields for silicas and aluminophosphates based on ab initio calculations
BWH Van Beest, Gert Jan Kramer, and RA Van Santen · 1990
Earlier work this paper cites.
Nosé–hoover chains: The canonical ensemble via continuous dynamics
Glenn J Martyna, Michael L Klein, and Mark Tuckerman · 1992
Earlier work this paper cites.
Fast parallel algorithms for short-range molecular dynamics
Steve Plimpton · 1995
Earlier work this paper cites.
Testing mode-coupling theory for a supercooled binary lennard-jones mixture i: The van hove correlation function
Walter Kob and Hans C Andersen · 1995
Earlier work this paper cites.
Sensitivity analysis for atmospheric chemistry models via automatic differentiation
Gregory R Carmichael, Adrian Sandu, et al · 1997
Earlier work this paper cites.
Efficient adjoint derivatives: application to the meteorological model meso-nh
Isabelle Charpentier and Mohammed Ghemires · 2000
Earlier work this paper cites.
Torch: a modular machine learning software library
Ronan Collobert, Samy Bengio, and Johnny Mariéthoz · 2002
Earlier work this paper cites.
Miles Cranmer, Sam Greydanus, Stephan Hoyer, Peter Battaglia, David Spergel, and Shirley Ho · 2003
Earlier work this paper cites.
Jamming at zero temperature and zero applied stress: The epitome of disorder
Corey S O’hern, Leonardo E Silbert, Andrea J Liu, and Sidney R Nagel · 2003
Earlier work this paper cites.
On the performance of discrete adjoint cfd codes using automatic differentiation
J-D Müller and P Cusdin · 2005
Earlier work this paper cites.
Structural relaxation made simple
Erik Bitzek, Pekka Koskinen, Franz Gähler, Michael Moseler, and Peter Gumbsch · 2006
Earlier work this paper cites.
IPython: a system for interactive scientific computing
Fernando Pérez and Brian E. Granger · 2007
Earlier work this paper cites.
Automatic differentiation of the general-purpose computational fluid dynamics package fluent
Christian H Bischof, H Martin Bücker, Arno Rasch, Emil Slusanschi, and Bruno Lang · 2007
Earlier work this paper cites.
Automatic differentiation of explicit runge-kutta methods for optimal control
Andrea Walther · 2007
Earlier work this paper cites.
Generalized neural-network representation of high-dimensional potential-energy surfaces
Jörg Behler and Michele Parrinello · 2007
Earlier work this paper cites.
General purpose molecular dynamics simulations fully implemented on graphics processing units
Joshua A Anderson, Chris D Lorenz, and Alex Travesset · 2008
Earlier work this paper cites.
Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
Albert P Bartók, Mike C Payne, Risi Kondor, and Gábor Csányi · 2010
Earlier work this paper cites.
Using automatic differentiation to create a nonlinear reduced-order-model aerodynamic solver
Jeffrey P Thomas, Earl H Dowell, and Kenneth C Hall · 2010
Earlier work this paper cites.
Fast greeks by algorithmic differentiation
Luca Capriotti · 2010
Cited alongside, same era.
High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
Nongnuch Artrith, Tobias Morawietz, and Jörg Behler · 2011
Cited alongside, same era.
The numpy array: a structure for efficient numerical computation
Stefan Van Der Walt, S Chris Colbert, and Gael Varoquaux · 2011
Cited alongside, same era.
Atom-centered symmetry functions for constructing high-dimensional neural network potentials
Jörg Behler · 2011
Cited alongside, same era.
Learning invariant representations of molecules for atomization energy prediction
Grégoire Montavon, Katja Hansen, Siamac Fazli, Matthias Rupp, Franziska Biegler, Andreas Ziehe, Alexandre Tkatchenko, Anatole V Lilienfeld, and Klaus-Robert Müller · 2012
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, and Skye Wanderman-Milne · 2018
Later among the works it cites.
Compiling machine learning programs via high-level tracing, 2018
Roy Frostig, Matthew James Johnson, and Chris Leary · 2018
Later among the works it cites.
Automatic differentiation in machine learning: a survey
Atilim Gunes Baydin, Barak A Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind · 2018
Later among the works it cites.
Automatic differentiation in quantum chemistry with applications to fully variational hartree–fock
Teresa Tamayo-Mendoza, Christoph Kreisbeck, Roland Lindh, and Alán Aspuru-Guzik · 2018
Later among the works it cites.
End-to-end differentiable physics for learning and control
Filipe de Avila Belbute-Peres, Kevin Smith, Kelsey Allen, Josh Tenenbaum, and J Zico Kolter · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, James Bergstra, Ian Goodfellow, Arnaud Bergeron, Nicolas Bouchard, David Warde-Farley, and Yoshua Bengio · 2012
Cited alongside, same era.
Sparse representation for a potential energy surface
Atsuto Seko, Akira Takahashi, and Isao Tanaka · 2014
Cited alongside, same era.
Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
Cited alongside, same era.
Strong scaling of general-purpose molecular dynamics simulations on gpus
Jens Glaser, Trung Dac Nguyen, Joshua A Anderson, Pak Lui, Filippo Spiga, Jaime A Millan, David C Morse, and Sharon C Glotzer · 2015
Cited alongside, same era.
G aussian approximation potentials: A brief tutorial introduction
Albert P Bartók and Gábor Csányi · 2015
Cited alongside, same era.
Autograd: Effortless gradients in numpy
Dougal Maclaurin, David Duvenaud, and Ryan P Adams · 2015
Cited alongside, same era.
Identifying structural flow defects in disordered solids using machine-learning methods
Ekin D Cubuk, Samuel Stern Schoenholz, Jennifer M Rieser, Brad Dean Malone, Joerg Rottler, Douglas J Durian, Efthimios Kaxiras, and Andrea J Liu · 2015
Cited alongside, same era.
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
Later among the works it cites.
Machine learning and the physical sciences
Giuseppe Carleo, Ignacio Cirac, Kyle Cranmer, Laurent Daudet, Maria Schuld, Naftali Tishby, Leslie Vogt-Maranto, and Lenka Zdeborová · 2019
Closest in time.
Hamiltonian graph networks with ode integrators
Alvaro Sanchez-Gonzalez, Victor Bapst, Kyle Cranmer, and Peter Battaglia · 2019
Closest in time.
Learning symbolic physics with graph networks
Miles D Cranmer, Rui Xu, Peter Battaglia, and Shirley Ho · 2019
Closest in time.
End-to-end differentiable learning of protein structure
Mohammed AlQuraishi · 2019
Closest in time.
Neural reparameterization improves structural optimization
Stephan Hoyer, Jascha Sohl-Dickstein, and Sam Greydanus · 2019
Closest in time.
Evaluating analytic gradients on quantum hardware
Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, and Nathan Killoran · 2019
Closest in time.
Deep Learning for the Life Sciences
Bharath Ramsundar, Peter Eastman, Patrick Walters, Vijay Pande, Karl Leswing, and Zhenqin Wu · 2019
Closest in time.
Zygote: A differentiable programming system to bridge machine learning and scientific computing
Mike Innes, Alan Edelman, Keno Fischer, Chris Rackauckus, Elliot Saba, Viral B Shah, and Will Tebbutt · 2019
Closest in time.
Machine learning forcefield for silicate glasses
Han Liu, Zipeng Fu, Yipeng Li, Nazreen Farina Ahmad Sabri, and Mathieu Bauchy · 2019
Closest in time.
Learning to simulate complex physics with graph networks
Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter W Battaglia · 2020
Closest in time.
Unveiling the predictive power of static structure in glassy systems
Victor Bapst, Thomas Keck, A Grabska-Barwińska, Craig Donner, Ekin Dogus Cubuk, SS Schoenholz, Annette Obika, AWR Nelson, Trevor Back, and Demis Hassabis · 2020
Closest in time.
Improved protein structure prediction using potentials from deep learning
Andrew W Senior, Richard Evans, John Jumper, James Kirkpatrick, Laurent Sifre, Tim Green, Chongli Qin, Augustin Žídek, Alexander WR Nelson, Alex Bridgland, et al · 2020
Closest in time.
High accuracy protein structure prediction using deep learning
John Jumper et al · 2020
Closest in time.
Inverse design of photonic crystals through automatic differentiation
Momchil Minkov, Ian AD Williamson, Lucio C Andreani, Dario Gerace, Beicheng Lou, Alex Y Song, Tyler W Hughes, and Shanhui Fan · 2020
Closest in time.
Hoomd-tf: Gpu-accelerated, online machine learning in the hoomd-blue molecular dynamics engine
Rainier Barrett, Maghesree Chakraborty, Dilnoza B Amirkulova, Heta A Gandhi, Geemi P Wellawatte, and Andrew D White · 2020
Closest in time.
Efficient training of ann potentials by including atomic forces via taylor expansion and application to water and a transition-metal oxide
April M Cooper, Johannes Kästner, Alexander Urban, and Nongnuch Artrith · 2020
Closest in time.
Haiku: Sonnet for JAX, 2020
Tom Hennigan, Trevor Cai, Tamara Norman, and Igor Babuschkin · 2020
Closest in time.
Unifying framework for strong and fragile liquids via machine learning: a study of liquid silica
Ekin D Cubuk, Andrea J Liu, Efthimios Kaxiras, and Samuel S Schoenholz · 2020
Closest in time.