Fetching the paper…
Reading the bibliography…
Machine learning interatomic potentials (MLIPs) enables molecular dynamics (MD) simulations with ab initio accuracy and has been applied to various fields of physical science.
Comparison of simple potential functions for simulating liquid water
Jorgensen, W. L., Chandrasekhar, J., Madura, J. D., Impey, R. W. & Klein, M. L · 1983
Earlier work this paper cites.
Unified approach for molecular dynamics and density-functional theory
Car, R. & Parrinello, M · 1985
Earlier work this paper cites.
Molecular dynamics simulations in biology
Karplus, M. & Petsko, G. A · 1990
Earlier work this paper cites.
Uff, a full periodic table force field for molecular mechanics and molecular dynamics simulations
Rappé, A. K., Casewit, C. J., Colwell, K., Goddard III, W. A. & Skiff, W. M · 1992
Earlier work this paper cites.
Projector augmented-wave method
Blöchl, P. E · 1994
Earlier work this paper cites.
Generalized gradient approximation made simple
Perdew, J. P., Burke, K. & Ernzerhof, M · 1996
Earlier work this paper cites.
Evaluation and reparametrization of the opls-aa force field for proteins via comparison with accurate quantum chemical calculations on peptides
Kaminski, G. A., Friesner, R. A., Tirado-Rives, J. & Jorgensen, W. L · 2001
Earlier work this paper cites.
Extracting and composing robust features with denoising autoencoders , 1096–1103 (2008)
Vincent, P., Larochelle, H., Bengio, Y. & Manzagol, P.-A · 2008
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M. & Von Lilienfeld, O. A · 2014
Earlier work this paper cites.
Molecular dynamics simulations: advances and applications
Hospital, A., Goñi, J. R., Orozco, M. & Gelpí, J. L · 2015
Earlier work this paper cites.
The reaxff reactive force-field: development, applications and future directions
Senftle, T. P. et al · 2016
Earlier work this paper cites.
Neural message passing for quantum chemistry , 1263–1272 (PMLR, 2017)
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O. & Dahl, G. E · 2017
Earlier work this paper cites.
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K. et al · 2017
Earlier work this paper cites.
Ani-1: an extensible neural network potential with dft accuracy at force field computational cost
Smith, J. S., Isayev, O. & Roitberg, A. E · 2017
Earlier work this paper cites.
Machine learning of accurate energy-conserving molecular force fields
Chmiela, S. et al · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Loshchilov, I. & Hutter, F · 2017
Earlier work this paper cites.
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A · 2018
Cited alongside, same era.
Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds
Thomas, N. et al · 2018
Cited alongside, same era.
Veličković, P. et al · 2018
Cited alongside, same era.
Umap: Uniform manifold approximation and projection
McInnes, L., Healy, J., Saul, N. & Großberger, L · 2018
Cited alongside, same era.
Directional message passing for molecular graphs (2019)
Gasteiger, J., Groß, J. & Günnemann, S · 2019
Cited alongside, same era.
Applying classical, ab initio, and machine-learning molecular dynamics simulations to the liquid electrolyte for rechargeable batteries
Yao, N., Chen, X., Fu, Z.-H. & Zhang, Q · 2022
Later among the works it cites.
Spherical message passing for 3d molecular graphs (2022)
Liu, Y. et al · 2022
Later among the works it cites.
E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Batzner, S. et al · 2022
Later among the works it cites.
Molecular contrastive learning of representations via graph neural networks
Wang, Y., Wang, J., Cao, Z. & Barati Farimani, A · 2022
Later among the works it cites.
Dpa-1: Pretraining of attention-based deep potential model for molecular simulation
Zhang, D. et al · 2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Hu, W. et al · 2019
Cited alongside, same era.
Machine learning for molecular simulation
Noé, F., Tkatchenko, A., Müller, K.-R. & Clementi, C · 2020
Cited alongside, same era.
Contrastive multi-view representation learning on graphs , 4116–4126 (PMLR, 2020)
Hassani, K. & Khasahmadi, A. H · 2020
Cited alongside, same era.
Gcc: Graph contrastive coding for graph neural network pretraining , 1150–1160 (2020)
Qiu, J. et al · 2020
Cited alongside, same era.
Machine learning force fields
Unke, O. T. et al · 2021
Cited alongside, same era.
E (n) equivariant graph neural networks , 9323–9332 (PMLR, 2021)
Satorras, V. G., Hoogeboom, E. & Welling, M · 2021
Cited alongside, same era.
Equivariant message passing for the prediction of tensorial properties and molecular spectra , 9377–9388 (PMLR, 2021)
Schütt, K., Unke, O. & Gastegger, M · 2021
Cited alongside, same era.
3d infomax improves gnns for molecular property prediction , 20479–20502 (PMLR, 2022)
Stärk, H. et al · 2022
Later among the works it cites.
Masked autoencoders are scalable vision learners , 16000–16009 (2022)
He, K. et al · 2022
Later among the works it cites.
Graphmae: Self-supervised masked graph autoencoders , 594–604 (2022)
Hou, Z. et al · 2022
Later among the works it cites.
Modeling chemical reactions in alkali carbonate–hydroxide electrolytes with deep learning potentials
Mondal, A., Kussainova, D., Yue, S. & Panagiotopoulos, A. Z · 2022
Later among the works it cites.
Lammps-a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales
Thompson, A. P. et al · 2022
Later among the works it cites.
Uni-mol: A universal 3d molecular representation learning framework (2023)
Zhou, G. et al · 2023
Closest in time.
Denoise pretraining on non-equilibrium molecules for accurate and transferable neural potentials
Wang, Y., Xu, C., Li, Z. & Farimani, A. B · 2023
Closest in time.
Forces are not enough: Benchmark and critical evaluation for machine learning force fields with molecular simulations
Fu, X. et al · 2023
Closest in time.
Machine learning interatomic potentials and long-range physics
Anstine, D. M. & Isayev, O · 2023
Closest in time.
Synthetic pretraining for neural-network interatomic potentials
Gardner, J. L., Baker, K. T. & Deringer, V. L · 2024
Closest in time.