Fetching the paper…
Reading the bibliography…
Graph Neural Networks (GNNs) are promising surrogates for quantum mechanical calculations as they establish unprecedented low errors on collections of molecular dynamics (MD) trajectories.
Merck molecular force field. I. Basis, form, scope, parameterization, and performance of MMFF94
Halgren, T. A · 1996
Earlier work this paper cites.
Molecular dynamics machine: Special-purpose computer for molecular dynamics simulations
Narumi, T., Susukita, R., Ebisuzaki, T., McNiven, G., and Elmegreen, B · 1999
Earlier work this paper cites.
Molecular modelling: principles and applications
Leach, A. R · 2001
Earlier work this paper cites.
Pattern recognition and machine learning , volume 4
Bishop, C. M. and Nasrabadi, N. M · 2006
Earlier work this paper cites.
Gaussian processes for machine learning
Rasmussen, C. E. and Williams, C. K. I · 2009
Earlier work this paper cites.
Variational Learning of Inducing Variables in Sparse Gaussian Processes
Titsias, M. K · 2009
Earlier work this paper cites.
Molecular modeling basics
Jensen, J. H · 2010
Earlier work this paper cites.
Atom-centered symmetry functions for constructing high-dimensional neural network potentials
Behler, J · 2011
Earlier work this paper cites.
Bayesian classifier combination
Kim, H.-C. and Ghahramani, Z · 2012
Earlier work this paper cites.
Dynamic bayesian combination of multiple imperfect classifiers
Simpson, E., Roberts, S., Psorakis, I., and Smith, A · 2012
Earlier work this paper cites.
Finding Density Functionals with Machine Learning
Snyder, J. C., Rupp, M., Hansen, K., Müller, K.-R., and Burke, K · 2012
Earlier work this paper cites.
On representing chemical environments
Bartók, A. P., Kondor, R., and Csányi, G · 2013
Earlier work this paper cites.
Gaussian Processes for Big Data
Hensman, J., Fusi, N., and Lawrence, N. D · 2013
Earlier work this paper cites.
Scalable variational gaussian process classification, 2014
Hensman, J., Matthews, A., and Ghahramani, Z · 2014
Earlier work this paper cites.
Dropout: a simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
Earlier work this paper cites.
Weight uncertainty in neural networks
Blundell, C., Cornebise, J., Kavukcuoglu, K., and Wierstra, D · 2015
Earlier work this paper cites.
Probabilistic backpropagation for scalable learning of bayesian neural networks
Hernandez-Lobato, J. M. and Adams, R · 2015
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Gal, Y. and Ghahramani, Z · 2016
Earlier work this paper cites.
Natural-parameter networks: A class of probabilistic neural networks
Wang, H., SHI, X., and Yeung, D.-Y · 2016
Earlier work this paper cites.
Deep kernel learning, 2016
Wilson, A. G., Hu, Z., Salakhutdinov, R., and Xing, E. P · 2016
Earlier work this paper cites.
Machine learning of accurate energy-conserving molecular force fields
Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., and Müller, K.-R · 2017
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2017
Earlier work this paper cites.
Lightweight probabilistic deep networks
Gast, J. and Roth, S · 2018
Earlier work this paper cites.
Accurate uncertainties for deep learning using calibrated regression
Kuleshov, V., Fenner, N., and Ermon, S · 2018
Earlier work this paper cites.
A scalable laplace approximation for neural networks
Ritter, H., Botev, A., and Barber, D · 2018
Earlier work this paper cites.
SchNet – A deep learning architecture for molecules and materials
Schütt, K. T., Sauceda, H. E., Kindermans, P.-J., Tkatchenko, A., and Müller, K.-R · 2018
Earlier work this paper cites.
Tensor field networks: Rotation- and translation-equivariant neural networks for 3D point clouds
Thomas, N., Smidt, T., Kearnes, S., Yang, L., Li, L., Kohlhoff, K., and Riley, P · 2018
Earlier work this paper cites.
Uncertainty on asynchronous time event prediction
Biloš, M., Charpentier, B., and Günnemann, S · 2019
Cited alongside, same era.
Directional Message Passing for Molecular Graphs
Gasteiger, J., Groß, J., and Günnemann, S · 2019
Cited alongside, same era.
A simple baseline for bayesian uncertainty in deep learning
Maddox, W. J., Izmailov, P., Garipov, T., Vetrov, D. P., and Wilson, A. G · 2019
Cited alongside, same era.
Parting with illusions about deep active learning, 2019
Mittal, S., Tatarchenko, M., Özgün Çiçek, and Brox, T · 2019
Cited alongside, same era.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J., Lakshminarayanan, B., and Snoek, J · 2019
Cited alongside, same era.
Sampling-free epistemic uncertainty estimation using approximated variance propagation
Graph mixture density networks, 2021
Errica, F., Bacciu, D., and Micheli, A · 2021
Later among the works it cites.
A survey of uncertainty in deep neural networks
Gawlikowski, J., Tassi, C. R. N., Ali, M., Lee, J., Humt, M., Feng, J., Kruspe, A., Triebel, R., Jung, P., Roscher, R., et al · 2021
Later among the works it cites.
QM7-X, a comprehensive dataset of quantum-mechanical properties spanning the chemical space of small organic molecules
Hoja, J., Medrano Sandonas, L., Ernst, B. G., Vazquez-Mayagoitia, A., DiStasio Jr., R. A., and Tkatchenko, A · 2021
Later among the works it cites.
Deep gaussian processes: A survey
Jakkala, K · 2021
Later among the works it cites.
Pushing the frontiers of density functionals by solving the fractional electron problem
Kirkpatrick, J., McMorrow, B., Turban, D. H. P., Gaunt, A. L., Spencer, J. S., Matthews, A. G. D. G., Obika, A., Thiry, L., Fortunato, M., Pfau, D., Castellanos, L. R., Petersen, S., Nelson, A. W. R., Kohli, P., Mori-Sánchez, P., Hassabis, D., and Cohen, A. J · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Postels, J., Ferroni, F., Coskun, H., Navab, N., and Tombari, F · 2019
Cited alongside, same era.
Feed-forward propagation in probabilistic neural networks with categorical and max layers
Shekhovtsov, A. and Flach, B · 2019
Cited alongside, same era.
Distribution calibration for regression
Song, H., Diethe, T., Kull, M., and Flach, P · 2019
Cited alongside, same era.
PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges
Unke, O. T. and Meuwly, M · 2019
Cited alongside, same era.
Deep evidential regression
Amini, A., Schwarting, W., Soleimany, A., and Rus, D · 2020
Cited alongside, same era.
Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts
Charpentier, B., Zügner, D., and Günnemann, S · 2020
Cited alongside, same era.
FCHL revisited: Faster and more accurate quantum machine learning
Christensen, A. S., Bratholm, L. A., Faber, F. A., and Anatole von Lilienfeld, O · 2020
Cited alongside, same era.
Later among the works it cites.
Köhler, J., Krämer, A., and Noé, F · 2021
Later among the works it cites.
Evaluating robustness of predictive uncertainty estimation: Are dirichlet-based models reliable ?
Kopetzki, A.-K., Charpentier, B., Zügner, D., Giri, S., and Günnemann, S · 2021
Later among the works it cites.
Spherical Message Passing for 3D Molecular Graphs
Liu, Y., Wang, L., Liu, M., Lin, Y., Zhang, X., Oztekin, B., and Ji, S · 2021
Later among the works it cites.
The Promises and Pitfalls of Deep Kernel Learning, July 2021
Ober, S. W., Rasmussen, C. E., and van der Wilk, M · 2021
Later among the works it cites.
UNiTE: Unitary N-body Tensor Equivariant Network with Applications to Quantum Chemistry
Qiao, Z., Christensen, A. S., Manby, F. R., Welborn, M., Anandkumar, A., and Miller III, T. F · 2021
Later among the works it cites.
A survey of deep active learning, 2021
Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Gupta, B. B., Chen, X., and Wang, X · 2021
Later among the works it cites.
Equivariant message passing for the prediction of tensorial properties and molecular spectra
Schütt, K., Unke, O., and Gastegger, M · 2021
Later among the works it cites.
Evidential deep learning for guided molecular property prediction and discovery
Soleimany, A. P., Amini, A., Goldman, S., Rus, D., Bhatia, S. N., and Coley, C. W · 2021
Later among the works it cites.
Graph posterior network: Bayesian predictive uncertainty for node classification
Stadler, M., Charpentier, B., Geisler, S., Zügner, D., and Günnemann, S · 2021
Later among the works it cites.
A survey on evidential deep learning for single-pass uncertainty estimation
Ulmer, D · 2021
Later among the works it cites.
SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
Unke, O. T., Chmiela, S., Gastegger, M., Schütt, K. T., Sauceda, H. E., and Müller, K.-R · 2021
Later among the works it cites.
On feature collapse and deep kernel learning for single forward pass uncertainty
van Amersfoort, J., Smith, L., Jesson, A., Key, O., and Gal, Y · 2021
Later among the works it cites.
The design space of e(3)-equivariant atom-centered interatomic potentials, 2022
Batatia, I., Batzner, S., Kovács, D. P., Musaelian, A., Simm, G. N. C., Drautz, R., Ortner, C., Kozinsky, B., and Csányi, G · 2022
Later among the works it cites.
E(3)-equivariant graph neural networks for data-efficient and accurate interatomic potentials
Batzner, S., Musaelian, A., Sun, L., Geiger, M., Mailoa, J. P., Kornbluth, M., Molinari, N., Smidt, T. E., and Kozinsky, B · 2022
Later among the works it cites.
Natural Posterior Network: Deep Bayesian Uncertainty for Exponential Family Distributions
Charpentier, B., Borchert, O., Zügner, D., Geisler, S., and Günnemann, S · 2022
Later among the works it cites.
Forces are not enough: Benchmark and critical evaluation for machine learning force fields with molecular simulations, 2022
Fu, X., Wu, Z., Wang, W., Xie, T., Keten, S., Gomez-Bombarelli, R., and Jaakkola, T · 2022
Later among the works it cites.
Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functions
Gao, N. and Günnemann, S · 2022
Later among the works it cites.
Out-Of-Distribution Generalization on Graphs: A Survey, December 2022
Li, H., Wang, X., Zhang, Z., and Zhu, W · 2022
Later among the works it cites.
How Robust are Modern Graph Neural Network Potentials in Long and Hot Molecular Dynamics Simulations?
Stocker, S., Gasteiger, J., Becker, F., Günnemann, S., and Margraf, J. T · 2022
Later among the works it cites.
Deep kernel learning for uncertainty estimation in multiple trajectory prediction networks
Strohbeck, J., Müller, J., Herrmann, M., and Buchholz, M · 2022
Later among the works it cites.
AdsorbML: Accelerating Adsorption Energy Calculations with Machine Learning, January 2023
Lan, J., Palizhati, A., Shuaibi, M., Wood, B. M., Wander, B., Das, A., Uyttendaele, M., Zitnick, C. L., and Ulissi, Z. W · 2023
Closest in time.