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Molecular dynamics simulations use statistical mechanics at the atomistic scale to enable both the elucidation of fundamental mechanisms and the engineering of matter for desired tasks.
Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials
Xie, T., France-Lanord, A., Wang, Y., Shao-Horn, Y. & Grossman, J. C · 1902
Earlier work this paper cites.
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Ceriotti, M · 1902
Earlier work this paper cites.
Differentiable Programming Tensor Networks
Liao, H. J., Liu, J. G., Wang, L. & Xiang, T · 1903
Earlier work this paper cites.
Principal component analysis of nonequilibrium molecular dynamics simulations
Post, M., Wolf, S. & Stock, G · 1903
Earlier work this paper cites.
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Durumeric, A. E. & Voth, G. A · 1904
Earlier work this paper cites.
Neural networks-based variationally enhanced sampling
Bonati, L., Zhang, Y. Y. & Parrinello, M · 1904
Earlier work this paper cites.
A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
Mailoa, J. P. et al · 1905
Earlier work this paper cites.
Controlled exploration of chemical space by machine learning of coarse-grained representations
Hoffmann, C., Menichetti, R., Kanekal, K. H. & Bereau, T · 1905
Earlier work this paper cites.
Neural-Network-Based Path Collective Variables for Enhanced Sampling of Phase Transformations
Rogal, J., Schneider, E. & Tuckerman, M. E · 1905
Earlier work this paper cites.
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Greydanus, S., Dzamba, M. & Yosinski, J · 1906
Earlier work this paper cites.
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Yang, G. et al · 1906
Earlier work this paper cites.
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Dolezal, J. & Jack, R. L · 1906
Earlier work this paper cites.
Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning (2019)
Lu, P. Y., Kim, S. & Soljačić, M · 1907
Earlier work this paper cites.
DeepXDE: A deep learning library for solving differential equations (2019)
Lu, L., Meng, X., Mao, Z. & Karniadakis, G. E · 1907
Earlier work this paper cites.
Symplectic ODE-Net: Learning Hamiltonian Dynamics with Control (2019)
Zhong, Y. D., Dey, B. & Chakraborty, A · 1909
Earlier work this paper cites.
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Sanchez-Gonzalez, A., Bapst, V., Cranmer, K. & Battaglia, P · 1909
Earlier work this paper cites.
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Das, A. & Limmer, D. T · 1909
Earlier work this paper cites.
Learning Compositional Koopman Operators for Model-Based Control (2019)
Li, Y., He, H., Wu, J., Katabi, D. & Torralba, A · 1910
Earlier work this paper cites.
DiffTaichi: Differentiable Programming for Physical Simulation (2019)
Hu, Y. et al · 1910
Earlier work this paper cites.
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Li, S.-H., Dong, C.-X., Zhang, L. & Wang, L · 1910
Earlier work this paper cites.
JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python (2019)
Schoenholz, S. S. & Cubuk, E. D · 1912
Earlier work this paper cites.
On the Determination of Molecular Fields. II. From the Equation of State of a Gas
Jones, J. E · 1924
Earlier work this paper cites.
The mathematical theory of optimal processes (1962)
Pontryagin, L. S., Mishchenko, E. F., Boltyanskii, V. G. & Gamkrelidze, R. V · 1962
Earlier work this paper cites.
A mechanism for the light-driven proton pump of halobacterium halobium
Schulten, K. & Tavan, P · 1978
Earlier work this paper cites.
Strain fluctuations and elastic constants
Parrinello, M. & Rahman, A · 1982
Earlier work this paper cites.
A unified formulation of the constant temperature molecular dynamics methods
Nosé, S · 1984
Earlier work this paper cites.
Nosé-Hoover chains: The canonical ensemble via continuous dynamics
Martyna, G. J., Klein, M. L. & Tuckerman, M · 1992
Earlier work this paper cites.
Teaching lasers to control molecules
Judson, R. S. & Rabitz, H · 1992
Earlier work this paper cites.
Coherent control of quantum dynamics: the dream is alive
Warren, W. S., Rabitz, H. & Dahleh, M · 1993
Earlier work this paper cites.
Coherent laser control of the product distribution obtained in the photoexcitation of HI
Zhu, L. et al · 1995
Earlier work this paper cites.
Observation of coherently controlled photocurrent in unbiased, bulk gaas
Haché, A. et al · 1997
Earlier work this paper cites.
Nonequilibrium equality for free energy differences
Jarzynski, C · 1997
Earlier work this paper cites.
Equilibrium free-energy differences from nonequilibrium measurements: A master-equation approach
Jarzynski, C · 1997
Earlier work this paper cites.
Annealed Importance Sampling
Neal, R. M · 1998
Earlier work this paper cites.
Entropy production fluctuation theorem and the nonequilibrium work relation for free energy differences
Crooks, G. E · 1999
Earlier work this paper cites.
Quantum-mechanical modeling of the femtosecond isomerization in rhodopsin
Hahn, S. & Stock, G · 2000
Earlier work this paper cites.
Learning to Control PDEs with Differentiable Physics (2020)
Holl, P., Koltun, V. & Thuerey, N · 2001
Earlier work this paper cites.
Rabi oscillations of excitons in single quantum dots
Stievater, T. et al · 2001
Earlier work this paper cites.
Targeted free energy perturbation
Jarzynski, C · 2001
Earlier work this paper cites.
Principles of the quantum control of molecular processes
Shapiro, M. & Brumer, P · 2003
Earlier work this paper cites.
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Balzer, B., Hahn, S. & Stock, G · 2003
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Handbook of photosensory receptors (John Wiley & Sons, 2005)
Briggs, W. R. & Spudich, J. L · 2005
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Coherent control of retinal isomerization in bacteriorhodopsin
Prokhorenko, V. I. et al · 2006
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Generalized neural-network representation of high-dimensional potential-energy surfaces
Behler, J. & Parrinello, M · 2007
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Escorted free energy simulations: Improving convergence by reducing dissipation
Vaikuntanathan, S. & Jarzynski, C · 2008
Cited alongside, same era.
Adaptive biasing force method for scalar and vector free energy calculations
Darve, E., Rodríguez-Gómez, D. & Pohorille, A · 2008
Deep dynamical modeling and control of unsteady fluid flows
Morton, J., Witherden, F. D., Kochenderfer, M. J. & Jameson, A · 2018
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SPNets: Differentiable Fluid Dynamics for Deep Neural Networks (2018)
Schenck, C. & Fox, D · 2018
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Automatic Differentiation in Quantum Chemistry with Applications to Fully Variational Hartree-Fock
Tamayo-Mendoza, T., Kreisbeck, C., Lindh, R. & Aspuru-Guzik, A · 2018
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Townshend, R. J. L., Bedi, R., Suriana, P. A. & Dror, R. O · 2018
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Zhang, L., E, W. & Wang, L · 2018
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Brinks, D. et al · 2010
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Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Bartók, A. P., Payne, M. C., Kondor, R. & Csányi, G · 2010
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Design principles for self-assembly with short-range interactions
Hormoz, S. & Brenner, M. P · 2011
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Chemistry and biology of vision
Palczewski, K · 2012
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Mechanisms in environmentally assisted one-photon phase control
Pachón, L. A. & Brumer, P · 2013
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Excitation of biomolecules with incoherent light: Quantum yield for the photoisomerization of model retinal
Tscherbul, T. V. & Brumer, P · 2014
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Han, J., Jentzen, A. & Weinan, E · 2018
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VAMPnets for deep learning of molecular kinetics
Mardt, A., Pasquali, L., Wu, H. & Noé, F · 2018
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Deep Generative Markov State Models
Wu, H., Mardt, A., Pasquali, L. & Noe, F · 2018
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Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics
Wehmeyer, C. & Noé, F · 2018
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Variational encoding of complex dynamics
Hernández, C. X., Wayment-Steele, H. K., Sultan, M. M., Husic, B. E. & Pande, V. S · 2018
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Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design
Chen, W., Tan, A. R. & Ferguson, A. L · 2018
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The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics
Yao, K., Herr, J. E., Toth, D., Mckintyre, R. & Parkhill, J · 2018
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Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics
Zhang, L., Han, J., Wang, H., Car, R. & Weinan, E · 2018
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Learning mechanism of chromatin domain formation with big data
Xie, W. J. & Zhang, B · 2018
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Model Reduction with Memory and the Machine Learning of Dynamical Systems
Ma, C., Wang, J. & E, W · 2018
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Machine Learning of coarse-grained Molecular Dynamics Force Fields
Wang, J. et al · 2018
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DeePCG: Constructing coarse-grained models via deep neural networks
Zhang, L., Han, J., Wang, H., Car, R. & E, W · 2018
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Machine-Learned Coarse-Grained Models
Bejagam, K. K., Singh, S., An, Y. & Deshmukh, S. A · 2018
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Boltzmann Generators-Sampling Equilibrium States of Many-Body Systems with Deep Learning
Noé, F. & Wu, H · 2018
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Adaptive enhanced sampling by force-biasing using neural networks
Guo, A. Z. et al · 2018
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Optimal thermodynamic control in open quantum systems
Cavina, V., Mari, A., Carlini, A. & Giovannetti, V · 2018
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Variational approach to the optimal control of coherently driven, open quantum system dynamics
Cavina, V., Mari, A., Carlini, A. & Giovannetti, V · 2018
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Using a system’s equilibrium behavior to reduce its energy dissipation in nonequilibrium processes
Tafoya, S., Large, S. J., Liu, S., Bustamante, C. & Sivak, D. A · 2019
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Differentiable Cloth Simulation for Inverse Problems
Liang, J., Lin, M. & Koltun, V · 2019
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Considerations regarding one-photon phase control
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ChainQueen: A real-time differentiable physical simulator for soft robotics
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Learning data-driven discretizations for partial differential equations
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Generative models for graph-based protein design
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Past–future information bottleneck for sampling molecular reaction coordinate simultaneously with thermodynamics and kinetics
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Characterizing chromatin folding coordinate and landscape with deep learning
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