Fetching the paper…
Reading the bibliography…
Learning pair interactions from experimental or simulation data is of great interest for molecular simulations.
W. L. Jorgensen, J. Chandrasekhar, J. D. Madura, R. W. Impey, and M. L. Klein, “Comparison of simple potential functions for simulating liquid water,” J. Chem. Phys. 79
1983
Earlier work this paper cites.
D. Chandler, J. D. Weeks, and H. C. Andersen, “Van der waals picture of liquids, solids, and phase transformations,” Science 220
1983
Earlier work this paper cites.
S. Nosé, “A unified formulation of the constant temperature molecular dynamics methods,” J. Chem. Phys. 81
1984
Earlier work this paper cites.
J. R. Magnus, “On differentiating eigenvalues and eigenvectors,” Econometric theory 1
1985
Earlier work this paper cites.
W. G. Hoover, “Canonical dynamics: Equilibrium phase-space distributions,” Phys. Rev. A 31
1985
Earlier work this paper cites.
H. J. C. Berendsen, J. R. Grigera, and T. P. Straatsma, “The missing term in effective pair potentials,” J. Phys. Chem. 91
1987
Earlier work this paper cites.
K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural networks 2
1989
Earlier work this paper cites.
S. L. Mayo, B. D. Olafson, and W. A. Goddard, “DREIDING: a generic force field for molecular simulations,” J. Phys. Chem. 94
1990
Earlier work this paper cites.
G. J. Martyna, M. L. Klein, and M. Tuckerman, “Nosé–hoover chains: The canonical ensemble via continuous dynamics,” J. Chem. Phys. 97
1992
Earlier work this paper cites.
F. Ercolessi and J. B. Adams, “Interatomic Potentials from First-Principles Calculations: The Force-Matching Method,” Europhys. Lett. 26
1994
Earlier work this paper cites.
S. Plimpton, “Fast Parallel Algorithms for Short-Range Molecular Dynamics,” J. Comput. Phys. 117
1995
Earlier work this paper cites.
W. L. Jorgensen, D. S. Maxwell, and J. Tirado-Rives, “Development and Testing of the OPLS All-Atom Force Field on Conformational Energetics and Properties of Organic Liquids,” J. Am. Chem. Soc. 118
1996
Earlier work this paper cites.
W. Tschöp, K. Kremer, J. Batoulis, T. Bürger, and O. Hahn, “Simulation of polymer melts. i. coarse-graining procedure for polycarbonates,” Acta Polymerica 49
1998
Earlier work this paper cites.
J. Baxter, “A model of inductive bias learning,” J. Artif. Intell. Res. 12
2000
Earlier work this paper cites.
F. Müller-Plathe, “Coarse-Graining in Polymer Simulation: From the Atomistic to the Mesoscopic Scale and Back,” ChemPhysChem 3
2002
Earlier work this paper cites.
D. Reith, M. Pütz, and F. Müller-Plathe, “Deriving effective mesoscale potentials from atomistic simulations: Mesoscale Potentials from Atomistic Simulations,” J. Comput. Chem. 24
2003
Earlier work this paper cites.
D. C. Rapaport, The art of molecular dynamics simulation (Cambridge University Press, Cambridge, UK; New York, NY, 2004) oCLC: 928698870
2004
Earlier work this paper cites.
S. Izvekov and G. A. Voth, “A Multiscale Coarse-Graining Method for Biomolecular Systems,” J. Phys. Chem. B 109
2005
Earlier work this paper cites.
W. G. Noid, J.-W. Chu, G. S. Ayton, and G. A. Voth, “Multiscale Coarse-Graining and Structural Correlations: Connections to Liquid-State Theory,” J. Phys. Chem. B 111
2007
Earlier work this paper cites.
L. Cheng and J. Yang, “Modified morse potential for unification of the pair interactions,” J. Chem. Phys. 127
2007
Earlier work this paper cites.
M. E. Johnson, T. Head-Gordon, and A. A. Louis, “Representability problems for coarse-grained water potentials,” J. Chem. Phys. 126
2007
Earlier work this paper cites.
H.-J. Qian, P. Carbone, X. Chen, H. A. Karimi-Varzaneh, C. C. Liew, and F. Müller-Plathe, “Temperature-Transferable Coarse-Grained Potentials for Ethylbenzene, Polystyrene, and Their Mixtures,” Macromolecules 41
2008
Earlier work this paper cites.
J. W. Mullinax and W. G. Noid, “Extended ensemble approach for deriving transferable coarse-grained potentials,” J. Chem. Phys. 131
2009
Earlier work this paper cites.
V. Rühle, C. Junghans, A. Lukyanov, K. Kremer, and D. Andrienko, “Versatile Object-Oriented Toolkit for Coarse-Graining Applications,” J. Chem. Theory Comput. 5
2009
Earlier work this paper cites.
H. A. Karimi-Varzaneh, F. Müller-Plathe, S. Balasubramanian, and P. Carbone, “Studying long-time dynamics of imidazolium-based ionic liquids with a systematically coarse-grained model,” Phys. Chem. Chem. Phys. 12
2010
Earlier work this paper cites.
R. W. Pastor and A. D. MacKerell, “Development of the CHARMM Force Field for Lipids,” J. Phys. Chem. Lett. 2
2011
Earlier work this paper cites.
A. Chaimovich and M. S. Shell, “Coarse-graining errors and numerical optimization using a relative entropy framework,” J. Chem. Phys. 134
2011
Cited alongside, same era.
S. J. Plimpton and A. P. Thompson, “Computational aspects of many-body potentials,” MRS Bull. 37
2012
Cited alongside, same era.
J. F. Rudzinski and W. G. Noid, “The role of many-body correlations in determining potentials for coarse-grained models of equilibrium structure,” The Journal of Physical Chemistry B 116
2012
Cited alongside, same era.
R. Potestio, “Is henderson’s theorem practically useful?” JUnQ 3
2013
Cited alongside, same era.
D. Vlachakis, E. Bencurova, N. Papangelopoulos, and S. Kossida, “Current state-of-the-art molecular dynamics methods and applications,” in Adv. Protein Chem. Struct. Biol. , Vol. 94 (Elsevier, 2014) pp. 269–313
2014
Cited alongside, same era.
J. Wang, S. Olsson, C. Wehmeyer, A. Pérez, N. E. Charron, G. de Fabritiis, F. Noé, and C. Clementi, “Machine Learning of Coarse-Grained Molecular Dynamics Force Fields,” ACS Cent. Sci. , acscentsci.8b00913 (2019)
2019
Later among the works it cites.
H.-J. Liao, J.-G. Liu, L. Wang, and T. Xiang, “Differentiable programming tensor networks,” Phys. Rev. X. 9
2019
Later among the works it cites.
B. Kanungo, P. M. Zimmerman, and V. Gavini, “Exact exchange-correlation potentials from ground-state electron densities,” Nat. Commun. 10
2019
Later among the works it cites.
A. Moradzadeh and N. R. Aluru, “Transfer-learning-based coarse-graining method for simple fluids: Toward deep inverse liquid-state theory,” The journal of physical chemistry letters 10
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2014
Cited alongside, same era.
J. Blumberger, “Recent advances in the theory and molecular simulation of biological electron transfer reactions,” Chem. Rev. 115
2015
Cited alongside, same era.
J. Glaser, T. D. Nguyen, J. A. Anderson, P. Lui, F. Spiga, J. A. Millan, D. C. Morse, and S. C. Glotzer, “Strong scaling of general-purpose molecular dynamics simulations on GPUs,” Comput. Phys. Commun. 192
2015
Cited alongside, same era.
2015
Cited alongside, same era.
G. A. Cisneros, K. T. Wikfeldt, L. Ojamäe, J. Lu, Y. Xu, H. Torabifard, A. P. Bartók, G. Csányi, V. Molinero, and F. Paesani, “Modeling Molecular Interactions in Water: From Pairwise to Many-Body Potential Energy Functions,” Chem. Rev. 116
2016
Cited alongside, same era.
A. Albaugh, H. A. Boateng, R. T. Bradshaw, O. N. Demerdash, J. Dziedzic, Y. Mao, D. T. Margul, J. Swails, Q. Zeng, D. A. Case, P. Eastman, L.-P. Wang, J. W. Essex, M. Head-Gordon, V. S. Pande, J. W. Ponder, Y. Shao, C.-K. Skylaris, I. T. Todorov, M. E. Tuckerman, and T. Head-Gordon, “Advanced Potential Energy Surfaces for Molecular Simulation,” J. Phys. Chem. B 120
2016
Cited alongside, same era.
E. Ustinova and V. Lempitsky, “Learning deep embeddings with histogram loss,” Adv. Neural Inf. Process. Syst. 29
2016
Cited alongside, same era.
2019
Later among the works it cites.
D. Rosenberger and N. F. van der Vegt, “Relative entropy indicates an ideal concentration for structure-based coarse graining of binary mixtures,” Phys. Rev. E 99
2019
Later among the works it cites.
2020
Later among the works it cites.
S. Schoenholz and E. D. Cubuk, “Jax md: a framework for differentiable physics,” Adv. Neural Inf. Process. Syst. 33
2020
Later among the works it cites.
H. Wang, F. H. Stillinger, and S. Torquato, “Sensitivity of pair statistics on pair potentials in many-body systems,” J. Chem. Phys. 153
2020
Later among the works it cites.
J. Fish, G. J. Wagner, and S. Keten, “Mesoscopic and multiscale modelling in materials,” Nat. Mater. 20
2021
Later among the works it cites.
P. C. T. Souza, R. Alessandri, J. Barnoud, S. Thallmair, I. Faustino, F. Grünewald, I. Patmanidis, H. Abdizadeh, B. M. H. Bruininks, T. A. Wassenaar, P. C. Kroon, J. Melcr, V. Nieto, V. Corradi, H. M. Khan, J. Domański, M. Javanainen, H. Martinez-Seara, N. Reuter, R. B. Best, I. Vattulainen, L. Monticelli, X. Periole, D. P. Tieleman, A. H. de Vries, and S. J. Marrink, “Martini 3: a general purpose force field for coarse-grained molecular dynamics,” Nat. Methods 18
2021
Later among the works it cites.
S. Dhamankar and M. A. Webb, “Chemically specific coarse-graining of polymers: Methods and prospects,” J. Polym. Sci. 59
2021
Later among the works it cites.
O. T. Unke, S. Chmiela, H. E. Sauceda, M. Gastegger, I. Poltavsky, K. T. Schutt, A. Tkatchenko, and K.-R. Muller, “Machine learning force fields,” Chem. Rev. 121
2021
Later among the works it cites.
M. F. Kasim and S. M. Vinko, “Learning the exchange-correlation functional from nature with fully differentiable density functional theory,” Phys. Rev. Lett. 127
2021
Later among the works it cites.
2021
Later among the works it cites.
S. Doerr, M. Majewski, A. Pérez, A. Kramer, C. Clementi, F. Noe, T. Giorgino, and G. De Fabritiis, “Torchmd: A deep learning framework for molecular simulations,” J. Chem. Theory Comput. 17
2021
Later among the works it cites.
S. Thaler and J. Zavadlav, “Learning neural network potentials from experimental data via differentiable trajectory reweighting,” Nat. Commun. 12
2021
Later among the works it cites.
J. G. Greener and D. T. Jones, “Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins,” PloS one 16
2021
Later among the works it cites.
2021
Later among the works it cites.
E. Pretti and M. S. Shell, “A microcanonical approach to temperature-transferable coarse-grained models using the relative entropy,” J. Chem. Phys. 155
2021
Later among the works it cites.
K. M. Kidder, R. J. Szukalo, and W. Noid, “Energetic and entropic considerations for coarse-graining,” Eur. Phys. J. B 94
2021
Later among the works it cites.
S. Stocker, J. Gasteiger, F. Becker, S. Günnemann, and J. Margraf, “How robust are modern graph neural network potentials in long and hot molecular dynamics simulations?” (2022), 10.26434/chemrxiv-2022-mc4gb
2022
Closest in time.
E. Kocer, T. W. Ko, and J. Behler, “Neural Network Potentials: A Concise Overview of Methods,” Annu. Rev. Phys. Chem. 73
2022
Closest in time.
S. L. Brunton and J. N. Kutz, Data-driven science and engineering: Machine learning, dynamical systems, and control (Cambridge University Press, 2022)
2022
Closest in time.
A. P. Thompson, H. M. Aktulga, R. Berger, D. S. Bolintineanu, W. M. Brown, P. S. Crozier, P. J. in’t Veld, A. Kohlmeyer, S. G. Moore, T. D. Nguyen, et al. , “Lammps-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” Computer Physics Communications 271
2022
Closest in time.