Fetching the paper…
Reading the bibliography…
The next generation of force fields for molecular dynamics will be developed using a wealth of data.
Comparison of simple potential functions for simulating liquid water
W L Jorgensen, J Chandrasekhar, J D Madura, R W Impey, and M L Klein · 1983
Earlier work this paper cites.
Computer simulation of local order in condensed phases of silicon
F H Stillinger and T A Weber · 1985
Earlier work this paper cites.
Diaphragm cell for high-temperature diffusion measurements. Tracer Diffusion coefficients for water to 363 K
A J Easteal, W E Price, and L A Woolf · 1989
Earlier work this paper cites.
An Efficient Gradient-Based Algorithm for On-Line Training of Recurrent Network Trajectories
R J Williams and J Peng · 1990
Earlier work this paper cites.
Molecular dynamics and time reversibility
D Levesque and L Verlet · 1993
Earlier work this paper cites.
Temperature Dependence of Oxygen Diffusion in H
P Han and D M Bartels · 1996
Earlier work this paper cites.
Experimental Parameterization of an Energy Function for the Simulation of Unfolded Proteins
A B Norgaard, J Ferkinghoff-Borg, and K Lindorff-Larsen · 2008
Earlier work this paper cites.
Automated Optimization of Potential Parameters
M Di Pierro and R Elber · 2013
Earlier work this paper cites.
The radial distribution functions of water as derived from radiation total scattering experiments: Is there anything we can say for sure?
A K Soper · 2013
Earlier work this paper cites.
On the difficulty of training Recurrent Neural Networks
R Pascanu, T Mikolov, and Y Bengio · 2013
Earlier work this paper cites.
Rational Construction of Stochastic Numerical Methods for Molecular Sampling
B Leimkuhler and C Matthews · 2013
Earlier work this paper cites.
Building Force Fields: An Automatic, Systematic, and Reproducible Approach
L-P Wang, T J Martinez, and V S Pande · 2014
Earlier work this paper cites.
Building Water Models: A Different Approach
S Izadi, R Anandakrishnan, and A V Onufriev · 2014
Earlier work this paper cites.
Gradient-based Hyperparameter Optimization through Reversible Learning
D Maclaurin, D Duvenaud, and R P Adams · 2015
Earlier work this paper cites.
Training Deep Nets with Sublinear Memory Cost
T Chen, B Xu, C Zhang, and C Guestrin · 2016
Earlier work this paper cites.
MDAnalysis: A Python Package for the Rapid Analysis of Molecular Dynamics Simulations
R J Gowers, M Linke, J Barnoud, T J E Reddy, M N Melo, S L Seyler, J Domański, D L Dotson, S Buchoux, I M Kenney, and O Beckstein · 2016
Earlier work this paper cites.
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
J S Smith, O Isayev, and A E Roitberg · 2017
Earlier work this paper cites.
The reversible residual network: Backpropagation without storing activations
A N Gomez, M Ren, R Urtasun, and R B Grosse · 2017
Earlier work this paper cites.
OpenMM 7: Rapid development of high performance algorithms for molecular dynamics
P Eastman, J Swails, J D Chodera, R T McGibbon, Y Zhao, K A Beauchamp, L P Wang, A C Simmonett, M P Harrigan, C D Stern, R P Wiewiora, B R Brooks, and V S Pande · 2017
Earlier work this paper cites.
Julia: A fresh approach to numerical computing
J Bezanson, A Edelman, S Karpinski, and V B Shah · 2017
Earlier work this paper cites.
Molecular Dynamics Simulation for All
S A Hollingsworth and R O Dror · 2018
Earlier work this paper cites.
Developing a molecular dynamics force field for both folded and disordered protein states
P Robustelli, S Piana, and D E Shaw · 2018
Earlier work this paper cites.
Automatic differentiation in machine learning: a survey
A G Baydin, B A Pearlmutter, A A Radul, and J M Siskind · 2018
Earlier work this paper cites.
Reversible architectures for arbitrarily deep residual neural networks
B Chang, L Meng, E Haber, L Ruthotto, D Begert, and E Holtham · 2018
Earlier work this paper cites.
Neural ordinary differential equations
R T Q Chen, Y Rubanova, J Bettencourt, and D Duvenaud · 2018
Earlier work this paper cites.
Assimilating radial distribution functions to build water models with improved structural properties
A D Wade, L-P Wang, and D J Huggins · 2018
Earlier work this paper cites.
Don’t Unroll Adjoint: Differentiating SSA-Form Programs
M Innes · 2018
Cited alongside, same era.
Learning Protein Structure with a Differentiable Simulator
J Ingraham, A Riesselman, C Sander, and D Marks · 2019
Cited alongside, same era.
ANODE: Unconditionally Accurate Memory-Efficient Gradients for Neural ODEs
A Gholami, K Keutzer, and G Biro · 2019
Cited alongside, same era.
Unified efficient thermostat scheme for the canonical ensemble with holonomic or isokinetic constraints via molecular dynamics
Z Zhang, X Liu, K Yan, M E Tuckerman, and J Liu · 2019
Cited alongside, same era.
Toward empirical force fields that match experimental observables
T Fröhlking, M Bernetti, N Calonaci, and G Bussi · 2020
Cited alongside, same era.
Differentiable Molecular Simulations for Control and Learning
W Wang, S Axelrod, and R Gómez-Bombarelli · 2020
Data science techniques in biomolecular force field development
Y Ding, K Yu, and J Huang · 2023
Later among the works it cites.
Top-down machine learning of coarse-grained protein force fields
C Navarro, M Majewski, and G De Fabritiis · 2023
Later among the works it cites.
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations
X Fu, Z Wu, W Wang, T Xie, S Keten, R Gómez-Bombarelli, and T S Jaakkola · 2023
Later among the works it cites.
MACE-OFF23: Transferable Machine Learning Force Fields for Organic Molecules
D P Kovács, J H Moore, N J Browning, I Batatia, J T Horton, V Kapil, W C Witt, I-B Magdău, D J Cole, and G Csányi · 2023
Later among the works it cites.
Differentiable Simulations for Enhanced Sampling of Rare Events
M Šípka, J C B Dietschreit, L Grajciar, and R Gómez-Bombarelli · 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…
Cited alongside, same era.
JAX, M.D. A Framework for Differentiable Physics
S S Schoenholz and E D Cubuk · 2020
Cited alongside, same era.
Discretize-Optimize vs. Optimize-Discretize for Time-Series Regression and Continuous Normalizing Flows
D Onken and L Ruthotto · 2020
Cited alongside, same era.
On second order behaviour in augmented neural odes
A Norcliffe, C Bodnar, B Day, N Simidjievski, and P Lió · 2020
Cited alongside, same era.
Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules
J Gasteiger, S Giri, J T Margraf, and S Günnemann · 2020
Cited alongside, same era.
Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients
W S Moses and V Churavy · 2020
Cited alongside, same era.
BioStructures.jl: read, write and manipulate macromolecular structures in Julia
J G Greener, J Selvaraj, and B J Ward · 2020
Cited alongside, same era.
F Kümmerer, S Orioli, and K Lindorff-Larsen · 2023
Later among the works it cites.
Understanding Automatic Differentiation Pitfalls
J Hückelheim, H Menon, W Moses, B Christianson, P Hovland, and L Hascoët · 2023
Later among the works it cites.
Julia for biologists
E Roesch, J G Greener, A L MacLean, H Nassar, C Rackauckas, T E Holy, and M P H Stumpf · 2023
Later among the works it cites.
A transferable double exponential potential for condensed phase simulations of small molecules
J T Horton, S Boothroyd, P K Behara, D L Mobley, and D J Cole · 2023
Later among the works it cites.
Machine-learned molecular mechanics force fields from large-scale quantum chemical data
K Takaba, A J Friedman, C Cavender, P K Behara, I Pulido, M M Henry, H MacDermott-Opeskin, C R Iacovella, A M Nagle, A M Payne, M R Shirts, D L Mobley, J D Chodera, and Y Wang · 2024
Closest in time.
Machine Learning Potentials with the Iterative Boltzmann Inversion: Training to Experiment
S Matin, A E A Allen, J Smith, N Lubbers, R B Jadrich, R Messerly, B Nebgen, Y W Li, S Tretiak, and K Barros · 2024
Closest in time.
Differentiable simulation to develop molecular dynamics force fields for disordered proteins
J G Greener · 2024
Closest in time.
Force field optimization by end-to-end differentiable atomistic simulation
A S Gangan, S S Schoenholz, E D Cubuk, M Bauchy, and N M A Krishnan · 2024
Closest in time.
Programming patchy particles for materials assembly design
E M King, C X Du, Q-Z Zhu, S S Schoenholz, and M P Brenner · 2024
Closest in time.
Learning Force Field Parameters from Differentiable Particle-Field Molecular Dynamics
M Carrer, H M Cezar, S L Bore, M Ledum, and M Cascella · 2024
Closest in time.
Structural Coarse-Graining via Multiobjective Optimization with Differentiable Simulation
Z Wu and T Zhou · 2024
Closest in time.
Integrating physics in deep learning algorithms: a force field as a PyTorch module
G Orlando, L Serrano, J Schymkowitz, and F Rousseau · 2024
Closest in time.
Optimizing molecular potential models by imposing kinetic constraints with path reweighting
P G Bolhuis, Z F Brotzakis, and B G Keller · 2024
Closest in time.
Differentiable Programming for Differential Equations: A Review
F Sapienza, J Bolibar, F Schäfer, B Groenke, A Pal, V Boussange, P Heimbach, G Hooker, F Pérez, P-O Persson, and C Rackauckas · 2024
Closest in time.
Efficient, Accurate and Stable Gradients for Neural ODEs
S McCallum and J Foster · 2024
Closest in time.
Tuning Potential Functions to Host-Guest Binding Data
J Setiadi, S Boothroyd, D R Slochower, D L Dotson, M W Thompson, J R Wagner, L-P Wang, and M K Gilson · 2024
Closest in time.
Accurate machine learning force fields via experimental and simulation data fusion
S Röcken and J Zavadlav · 2024
Closest in time.
chemtrain: Learning deep potential models via automatic differentiation and statistical physics
P Fuchs, S Thaler, S Röcken, and J Zavadlav · 2025
Closest in time.
Refining potential energy surface through dynamical properties via differentiable molecular simulation
B Han and K Yu · 2025
Closest in time.
On the design space between molecular mechanics and machine learning force fields
Y Wang, K Takaba, M S Chen, M Wieder, Y Xu, T Zhu, J Z H Zhang, A Nagle, K Yu, X Wang, D J Cole, J A Rackers, K Cho, J G Greener, P Eastman, S Martiniani, and M E Tuckerman · 2025
Closest in time.