Fetching the paper…
Reading the bibliography…
Deep learning has emerged as a promising paradigm to give access to highly accurate predictions of molecular and materials properties.
Neural network models of potential energy surfaces
Blank, T. B., Brown, S. D., Calhoun, A. W. & Doren, D. J · 1995
Earlier work this paper cites.
Speaker verification using adapted gaussian mixture models
Reynolds, D. A., Quatieri, T. F. & Dunn, R. B · 2000
Earlier work this paper cites.
Ensemble methods in machine learning
Dietterich, T. G · 2000
Earlier work this paper cites.
Language identification using gaussian mixture model tokenization
Torres-Carrasquillo, P. A., Reynolds, D. A. & Deller, J. R · 2002
Earlier work this paper cites.
Generalized neural-network representation of high-dimensional potential-energy surfaces
Behler, J. & Parrinello, M · 2007
Earlier work this paper cites.
A new image thresholding method based on gaussian mixture model
Huang, Z.-K. & Chau, K.-W · 2008
Earlier work this paper cites.
Optimal construction of a fast and accurate polarisable water potential based on multipole moments trained by machine learning
Handley, C. M., Hawe, G. I., Kell, D. B. & Popelier, P. L · 2009
Earlier work this paper cites.
Gaussian mixture models
Reynolds, D. A · 2009
Earlier work this paper cites.
Gaussian approximation potentials: The accuracy of quantum mechanics, without the electrons
Bartók, A. P., Payne, M. C., Kondor, R. & Csányi, G · 2010
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
Pedregosa, F. et al · 2011
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. & Ba, J · 2014
Earlier work this paper cites.
Spectral neighbor analysis method for automated generation of quantum-accurate interatomic potentials
Thompson, A. P., Swiler, L. P., Trott, C. R., Foiles, S. M. & Tucker, G. J · 2015
Earlier work this paper cites.
Constructing high-dimensional neural network potentials: a tutorial review
Behler, J · 2015
Earlier work this paper cites.
Moment tensor potentials: A class of systematically improvable interatomic potentials
Shapeev, A. V · 2016
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. & Ghahramani, Z · 2016
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.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A. & Blundell, C · 2017
Earlier work this paper cites.
Decoupled weight decay regularization
Loshchilov, I. & Hutter, F · 2017
Cited alongside, same era.
Schnet–a deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A. & Müller, K.-R · 2018
Cited alongside, same era.
Towards exact molecular dynamics simulations with machine-learned force fields
Chmiela, S., Sauceda, H. E., Müller, K.-R. & Tkatchenko, A · 2018
Cited alongside, same era.
Deep potential molecular dynamics: a scalable model with the accuracy of quantum mechanics
Zhang, L., Han, J., Wang, H., Car, R. & Weinan, E · 2018
Cited alongside, same era.
Physnet: A neural network for predicting energies, forces, dipole moments, and partial charges
Unke, O. T. & Meuwly, M · 2019
Cited alongside, same era.
Methods for comparing uncertainty quantifications for material property predictions
Tran, K. et al · 2020
Later among the works it cites.
Uncertainty quantification using neural networks for molecular property prediction
Hirschfeld, L., Swanson, K., Yang, K., Barzilay, R. & Coley, C. W · 2020
Later among the works it cites.
Uncertainty quantification in molecular simulations with dropout neural network potentials
Wen, M. & Tadmor, E. B · 2020
Later among the works it cites.
Bayesian force fields from active learning for simulation of inter-dimensional transformation of stanene
Xie, Y., Vandermause, J., Sun, L., Cepellotti, A. & Kozinsky, B · 2021
Later among the works it cites.
Active learning of reactive bayesian force fields: Application to heterogeneous catalysis dynamics of h/pt (2021)
Vandermause, J., Xie, Y., Lim, J. S., Owen, C. & Kozinsky, B · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Atomic cluster expansion for accurate and transferable interatomic potentials
Drautz, R · 2019
Cited alongside, same era.
A fast neural network approach for direct covariant forces prediction in complex multi-element extended systems
Mailoa, J. P. et al · 2019
Cited alongside, same era.
Cormorant: Covariant molecular neural networks
Anderson, B., Hy, T. S. & Kondor, R · 2019
Cited alongside, same era.
Active learning of uniformly accurate interatomic potentials for materials simulation
Zhang, L., Lin, D.-Y., Wang, H., Car, R. & Weinan, E · 2019
Cited alongside, same era.
A quantitative uncertainty metric controls error in neural network-driven chemical discovery
Janet, J. P., Duan, C., Yang, T., Nandy, A. & Kulik, H. J · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A. et al · 2019
Cited alongside, same era.
On the convergence of adam and beyond
Reddi, S. J., Kale, S. & Kumar, S · 2019
Cited alongside, same era.
Linear atomic cluster expansion force fields for organic molecules: beyond rmse
Kovács, D. P. et al · 2021
Later among the works it cites.
Unite: Unitary n-body tensor equivariant network with applications to quantum chemistry
Qiao, Z. et al · 2021
Later among the works it cites.
Gemnet: Universal directional graph neural networks for molecules
Klicpera, J., Becker, F. & Günnemann, S · 2021
Later among the works it cites.
Evidential deep learning for guided molecular property prediction and discovery
Soleimany, A. P. et al · 2021
Later among the works it cites.
e3nn/e3nn: 2021-12-15 (2021)
Geiger, M. et al · 2021
Later among the works it cites.
E (3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Batzner, S. et al · 2022
Closest in time.
Learning local equivariant representations for large-scale atomistic dynamics
Musaelian, A. et al · 2022
Closest in time.
Xie, Y. et al · 2022
Closest in time.
Johansson, A. et al · 2022
Closest in time.
Hu, Y., Musielewicz, J., Ulissi, Z. & Medford, A. J · 2022
Closest in time.
Quality of uncertainty estimates from neural network potential ensembles
Kahle, L. & Zipoli, F · 2022
Closest in time.