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Machine Learning Force Fields (MLFFs) are a promising alternative to expensive ab initio quantum mechanical molecular simulations.
Spectral Graph Theory
Chung, F · 1996
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A semi-empirical effective medium theory for metals and alloys
Jacobsen, K., Stoltze, P., and Nørskov, J · 1996
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Generalized neural-network representation of high-dimensional potential-energy surfaces
Behler, J. and Parrinello, M · 2007
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Covariate shift adaptation by importance weighted cross validation
Sugiyama, M., Krauledat, M., and Müller, K.-R · 2007
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A study of graph spectra for comparing graphs and trees
Wilson, R. C. and Zhu, P · 2008
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High-dimensional neural-network potentials for multicomponent systems: Applications to zinc oxide
Artrith, N., Morawietz, T., and Behler, J · 2011
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Spectral distances of graphs
Jovanović, I. and Stanić, Z · 2011
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Big data meets quantum chemistry approximations: The Δ \Delta -machine learning approach
Ramakrishnan, R., Dral, P. O., Rupp, M., and von Lilienfeld, O. A · 2015
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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
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Neural message passing for quantum chemistry
Gilmer, J., Schoenholz, S. S., Riley, P. F., Vinyals, O., and Dahl, G. E · 2017
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The atomic simulation environment—a python library for working with atoms
Hjorth Larsen, A., Jørgen Mortensen, J., Blomqvist, J., Castelli, I. E., Christensen, R., Dułak, M., Friis, J., Groves, M. N., Hammer, B., Hargus, C., Hermes, E. D., Jennings, P. C., Bjerre Jensen, P., Kermode, J., Kitchin, J. R., Leonhard Kolsbjerg, E., Kubal, J., Kaasbjerg, K., Lysgaard, S., Bergmann Maronsson, J., Maxson, T., Olsen, T., Pastewka, L., Peterson, A., Rostgaard, C., Schiøtz, J., Schütt, O., Strange, M., Thygesen, K. S., Vegge, T., Vilhelmsen, L., Walter, M., Zeng, Z., and Jacobsen, K. W · 2017
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Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, K., Kindermans, P.-J., Sauceda Felix, H. E., Chmiela, S., Tkatchenko, A., and Müller, K.-R · 2017
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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., and E, W · 2018
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Gfn2-xtb—an accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions
Bannwarth, C., Ehlert, S., and Grimme, S · 2019
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sgdml: Constructing accurate and data efficient molecular force fields using machine learning
Chmiela, S., Sauceda, H. E., Poltavsky, I., Müller, K.-R., and Tkatchenko, A · 2019
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Atomic cluster expansion for accurate and transferable interatomic potentials
Drautz, R · 2019
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Implicit bias of gradient descent on linear convolutional networks, 2019
Gunasekar, S., Lee, J., Soudry, D., and Srebro, N · 2019
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Sequence-to-sequence domain adaptation network for robust text image recognition
Zhang, Y., Nie, S., Liu, W., Xu, X., Zhang, D., and Shen, H. T · 2019
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Ood-maml: Meta-learning for few-shot out-of-distribution detection and classification
Jeong, T. and Kim, H · 2020
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Test-time training with self-supervision for generalization under distribution shifts
Sun, Y., Wang, X., Liu, Z., Miller, J., Efros, A. A., and Hardt, M · 2020
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Measuring robustness to natural distribution shifts in image classification
Taori, R., Dave, A., Shankar, V., Carlini, N., Recht, B., and Schmidt, L · 2020
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Open catalyst 2020 (oc20) dataset and community challenges
Chanussot, L., Das, A., Goyal, S., Lavril, T., Shuaibi, M., Riviere, M., Tran, K., Heras-Domingo, J., Ho, C., Hu, W., Palizhati, A., Sriram, A., Wood, B., Yoon, J., Parikh, D., Zitnick, C. L., and Ulissi, Z · 2021
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Gemnet: Universal directional graph neural networks for molecules
Gasteiger, J., Becker, F., and Günnemann, S · 2021
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Domain adaptive ensemble learning
Zhou, K., Yang, Y., Qiao, Y., and Xiang, T · 2021
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MACE: Higher order equivariant message passing neural networks for fast and accurate force fields
Batatia, I., Kovacs, D. P., Simm, G., Ortner, C., and Csányi, G · 2022
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Test-time training with masked autoencoders
Gandelsman, Y., Sun, Y., Chen, X., and Efros, A. A · 2022
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Gemnet-oc: Developing graph neural networks for large and diverse molecular simulation datasets
Egraffbench: evaluation of equivariant graph neural network force fields for atomistic simulations
Bihani, V., Mannan, S., Pratiush, U., Du, T., Chen, Z., Miret, S., Micoulaut, M., Smedskjaer, M. M., Ranu, S., and Krishnan, N. M. A · 2024
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Deng, B., Choi, Y., Zhong, P., Riebesell, J., Anand, S., Li, Z., Jun, K., Persson, K. A., and Ceder, G · 2024
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Nutmeg and spice: Models and data for biomolecular machine learning, 2024
Eastman, P., Pritchard, B. P., Chodera, J. D., and Markland, T. E · 2024
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Synthetic pre-training for neural-network interatomic potentials
Gardner, J. L. A., Baker, K. T., and Deringer, V. L · 2024
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Test-time training on nearest neighbors for large language models
Hardt, M. and Sun, Y · 2024
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Gasteiger, J., Shuaibi, M., Sriram, A., Günnemann, S., Ulissi, Z., Zitnick, C. L., and Das, A · 2022
Cited alongside, same era.
Transition1x – a dataset for building generalizable reactive machine learning potentials, 2022
Schreiner, M., Bhowmik, A., Vegge, T., Busk, J., and Winther, O · 2022
Cited alongside, same era.
Injecting domain knowledge from empirical interatomic potentials to neural networks for predicting material properties
Shui, Z., Karls, D. S., Wen, M., Nikiforov, I. A., Tadmor, E. B., and Karypis, G · 2022
Cited alongside, same era.
Ood-cv: A benchmark for robustness to out-of-distribution shifts of individual nuisances in natural images
Zhao, B., Yu, S., Ma, W., Yu, M., Mei, S., Wang, A., He, J., Yuille, A., and Kortylewski, A · 2022
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Accurate global machine learning force fields for molecules with hundreds of atoms
Chmiela, S., Vassilev-Galindo, V., Unke, O. T., Kabylda, A., Sauceda, H. E., Tkatchenko, A., and Müller, K.-R · 2023
Cited alongside, same era.
Materials project trajectory (mptrj) dataset, 2023
Deng, B · 2023
Cited alongside, same era.
Forces are not enough: Benchmark and critical evaluation for machine learning force fields with molecular simulations
Fu, X., Wu, Z., Wang, W., Xie, T., Keten, S., Gomez-Bombarelli, R., and Jaakkola, T. S · 2023
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Reducing training data needs with minimal multilevel machine learning (M3L)
Heinen, S., Khan, D., von Rudorff, G. F., Karandashev, K., Arismendi Arrieta, D. J., Price, A., Nandi, S., Bhowmik, A., Hermansson, K., and von Lilienfeld, A · 2024
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Data generation for machine learning interatomic potentials and beyond
Kulichenko, M., Nebgen, B., Lubbers, N., Smith, J. S., Barros, K., Allen, A. E. A., Habib, A., Shinkle, E., Fedik, N., Li, Y. W., Messerly, R. A., and Tretiak, S · 2024
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Equiformerv2: Improved equivariant transformer for scaling to higher-degree representations, 2024
Liao, Y.-L., Wood, B., Das, A., and Smidt, T · 2024
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100 years of the lennard-jones potential
Schwerdtfeger, P. and Wales, D. J · 2024
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Towards fast, specialized machine learning force fields: Distilling foundation models via energy hessians
Amin, I., Raja, S., and Krishnapriyan, A. S · 2025
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Learning smooth and expressive interatomic potentials for physical property prediction, 2025
Fu, X., Wood, B. M., Barroso-Luque, L., Levine, D. S., Gao, M., Dzamba, M., and Zitnick, C. L · 2025
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chemtrain: Learning deep potential models via automatic differentiation and statistical physics
Fuchs, P., Thaler, S., Röcken, S., and Zavadlav, J · 2025
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Probing out-of-distribution generalization in machine learning for materials
Li, K., Rubungo, A. N., Lei, X., Persaud, D., Choudhary, K., DeCost, B., Dieng, A. B., and Hattrick-Simpers, J · 2025
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Stability-aware training of machine learning force fields with differentiable boltzmann estimators
Raja, S., Amin, I., Pedregosa, F., and Krishnapriyan, A. S · 2025
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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 · 2041
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Quantum chemical accuracy from density functional approximations via machine learning
Bogojeski, M., Vogt-Maranto, L., Tuckerman, M. E., Müller, K.-R., and Burke, K · 2041
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Refining potential energy surface through dynamical properties via differentiable molecular simulation
Han, B. and Yu, K · 2041
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Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning
Jha, D., Choudhary, K., Tavazza, F., Liao, W.-k., Choudhary, A., Campbell, C., and Agrawal, A · 2041
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Spice, a dataset of drug-like molecules and peptides for training machine learning potentials
Eastman, P., Behara, P. K., Dotson, D. L., Galvelis, R., Herr, J. E., Horton, J. T., Mao, Y., Chodera, J. D., Pritchard, B. P., Wang, Y., De Fabritiis, G., and Markland, T. E · 2052
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The ani-1ccx and ani-1x data sets, coupled-cluster and density functional theory properties for molecules
Smith, J. S., Zubatyuk, R., Nebgen, B., Lubbers, N., Barros, K., Roitberg, A. E., Isayev, O., and Tretiak, S · 2052
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On-the-fly active learning of interpretable bayesian force fields for atomistic rare events
Vandermause, J., Torrisi, S. B., Batzner, S., Xie, Y., Sun, L., Kolpak, A. M., and Kozinsky, B · 2057
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