Fetching the paper…
Reading the bibliography…
The accuracy of atomistic simulations depends on the precision of force fields.
A unified formulation of the constant temperature molecular dynamics methods
Shuichi Nosé · 1984
Earlier work this paper cites.
Canonical dynamics: Equilibrium phase-space distributions
William G. Hoover · 1985
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.
Ab initio molecular dynamics for liquid metals
G. Kresse and J. Hafner · 1993
Earlier work this paper cites.
Projector augmented-wave method
P. E. Blöchl · 1994
Earlier work this paper cites.
Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set
G. Kresse and J. Furthmüller · 1996
Earlier work this paper cites.
Generalized gradient approximation made simple
John P. Perdew, Kieron Burke, and Matthias Ernzerhof · 1996
Earlier work this paper cites.
Environment-dependent interatomic potential for bulk silicon
Martin Z. Bazant, Efthimios Kaxiras, and J. F. Justo · 1997
Earlier work this paper cites.
Interatomic potential for silicon defects and disordered phases
João F. Justo, Martin Z. Bazant, Efthimios Kaxiras, V. V. Bulatov, and Sidney Yip · 1998
Earlier work this paper cites.
From ultrasoft pseudopotentials to the projector augmented-wave method
G. Kresse and D. Joubert · 1999
Earlier work this paper cites.
Comparison of automatic and symbolic differentiation in mathematical modeling and computer simulation of rigid-body systems
Axel Dürrbaum, Willy Klier, and Hubert Hahn · 2002
Earlier work this paper cites.
Liquid–liquid phase transition in supercooled silicon
Srikanth Sastry and C Austen Angell · 2003
Earlier work this paper cites.
Finite difference schemes and partial differential equations
John C Strikwerda · 2004
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.
Development of an interatomic potential for the ni-al system
GP Purja Pun and Y Mishin · 2009
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.
Interatomic potentials
Iam Torrens · 2012
Earlier work this paper cites.
First principles phonon calculations in materials science
A Togo and I Tanaka · 2015
Cited alongside, same era.
Representations in neural network based empirical potentials
Ekin D. Cubuk, Brad D. Malone, Berk Onat, Amos Waterland, and Efthimios Kaxiras · 2017
Cited alongside, same era.
Sympy: symbolic computing in python
Aaron Meurer, Christopher P Smith, Mateusz Paprocki, Ondřej Čertík, Sergey B Kirpichev, Matthew Rocklin, AMiT Kumar, Sergiu Ivanov, Jason K Moore, Sartaj Singh, et al · 2017
Cited alongside, same era.
Automatic differentiation in quantum chemistry with applications to fully variational hartree–fock
Teresa Tamayo-Mendoza, Christoph Kreisbeck, Roland Lindh, and Alán Aspuru-Guzik · 2018
Cited alongside, same era.
A new transferable interatomic potential for molecular dynamics simulations of borosilicate glasses
Mengyi Wang, NM Anoop Krishnan, Bu Wang, Morten M Smedskjaer, John C Mauro, and Mathieu Bauchy · 2018
Cited alongside, same era.
Unravelling the performance of physics-informed graph neural networks for dynamical systems
Abishek Thangamuthu, Gunjan Kumar, Suresh Bishnoi, Ravinder Bhattoo, NM Krishnan, and Sayan Ranu · 2022
Later among the works it cites.
Differentiable Multiscale Molecular Simulations
Wujie Wang · 2022
Later among the works it cites.
A foundation model for atomistic materials chemistry
Ilyes Batatia, Philipp Benner, Yuan Chiang, Alin M Elena, Dávid P Kovács, Janosh Riebesell, Xavier R Advincula, Mark Asta, William J Baldwin, Noam Bernstein, et al · 2023
Later among the works it cites.
Discovering symbolic laws directly from trajectories with hamiltonian graph neural networks
Suresh Bishnoi, Ravinder Bhattoo, Sayan Ranu, and NM Anoop Krishnan · 2023
Later among the works it cites.
Scientific uses of automatic differentiation
Michael P Brenner and Ella M King · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Machine learning interatomic potentials as emerging tools for materials science
Volker L Deringer, Miguel A Caro, and Gábor Csányi · 2019
Cited alongside, same era.
Difftaichi: Differentiable programming for physical simulation
Yuanming Hu, Luke Anderson, Tzu-Mao Li, Qi Sun, Nathan Carr, Jonathan Ragan-Kelley, and Frédo Durand · 2019
Cited alongside, same era.
Machine learning for molecular simulation
Frank Noé, Alexandre Tkatchenko, Klaus-Robert Müller, and Cecilia Clementi · 2020
Cited alongside, same era.
Jax m.d. a framework for differentiable physics
Samuel S. Schoenholz and Ekin D. Cubuk · 2020
Cited alongside, same era.
Differentiable molecular simulations for control and learning
Wujie Wang, Simon Axelrod, and Rafael Gómez-Bombarelli · 2020
Cited alongside, same era.
Differentiable thermodynamic modeling
Pin-Wen Guan · 2021
Cited alongside, same era.
Differentiable sampling of molecular geometries with uncertainty-based adversarial attacks
Daniel Schwalbe-Koda, Aik Rui Tan, and Rafael Gómez-Bombarelli · 2021
Cited alongside, same era.
Understanding molecular simulation: from algorithms to applications
Daan Frenkel and Berend Smit · 2023
Later among the works it cites.
Scaling deep learning for materials discovery
Amil Merchant, Simon Batzner, Samuel S Schoenholz, Muratahan Aykol, Gowoon Cheon, and Ekin Dogus Cubuk · 2023
Later among the works it cites.
Hybrid classical/machine-learning force fields for the accurate description of molecular condensed-phase systems
Moritz Thürlemann and Sereina Riniker · 2023
Later among the works it cites.
Egraffbench: evaluation of equivariant graph neural network force fields for atomistic simulations
Vaibhav Bihani, Sajid Mannan, Utkarsh Pratiush, Tao Du, Zhimin Chen, Santiago Miret, Matthieu Micoulaut, Morten M Smedskjaer, Sayan Ranu, and NM Anoop Krishnan · 2024
Closest in time.
Brognet: Momentum-conserving graph neural stochastic differential equation for learning brownian dynamics
Suresh Bishnoi, Jayadeva Jayadeva, Sayan Ranu, and NM Anoop Krishnan · 2024
Closest in time.
Learning force field parameters from differentiable particle-field molecular dynamics
Manuel Carrer, Henrique Musseli Cezar, Sigbjørn Løland Bore, Morten Ledum, and Michele Cascella · 2024
Closest in time.
Sanjeev Raja, Ishan Amin, Fabian Pedregosa, and Aditi S Krishnapriyan · 2024
Closest in time.
Collaboration on machine-learned potentials with ipsuite: A modular framework for learning-on-the-fly
Fabian Zills, Moritz René Schäfer, Nico Segreto, Johannes Kästner, Christian Holm, and Samuel Tovey · 2024
Closest in time.
Designing athermal disordered solids with automatic differentiation
Mengjie Zu and Carl P. Goodrich · 2024
Closest in time.
Machine learning coarse-grained potentials of protein thermodynamics
Maciej Majewski, Adrià Pérez, Philipp Thölke, Stefan Doerr, Nicholas E. Charron, Toni Giorgino, Brooke E. Husic, Cecilia Clementi, Frank Noé, and Gianni De Fabritiis · 2041
Closest in time.
Accurate machine learning force fields via experimental and simulation data fusion
Sebastien Röcken and Julija Zavadlav · 2057
Closest in time.