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Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., Aspuru-Guzik, A. Automatic chemical design using a data-driven continuous representation of molecules, ACS central science , 4:268–276 (2018)
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Chen, R. T., Rubanova, Y., Bettencourt, J., Duvenaud, D. K., Neural ordinary differential equations, Advances in neural information processing systems , 31 (2018)
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Noé, F., Olsson, S., Köhler, J. and Wu, H., Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning, Science , 365:eaaw1147 (2019)
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Albergo, M. S., Kanwar, G., Shanahan, P. E. Flow-based generative models for Markov chain Monte Carlo in lattice field theory, Physical Review D , 100:034515 (2019)
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Nicoli, K. A., Nakajima, S., Strodthoff, N., Samek, W., Müller, K.-R., Kessel, P. Asymptotically unbiased estimation of physical observables with neural samplers, Physical Review E , 101:023304 (2020)
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Wirnsberger, P., Ballard, A. J., Papamakarios, G., Abercrombie, S., Racaniére, S., Pritzel, A., Jimenez Rezende, D., Blundell, C. Targeted free energy estimation via learned mappings, The Journal of Chemical Physics , 153 (2020)
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Wu, H., Köhler, J., Noé, F. Stochastic normalizing flows, Advances in Neural Information Processing Systems , 33:5933–5944 (2020)
2020
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Köhler, J., Klein, L., Noé, F., Equivariant flows: exact likelihood generative learning for symmetric densities, International conference on machine learning (2020) pp 5361–5370
2020
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Huang, B. and von Lilienfeld, O. A., Ab initio machine learning in chemical compound space, Chemical Reviews , 121:10001 (2021)
2021
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Behler, J. Four Generations of High-Dimensional Neural Network Potentials, Chemical Reviews , 121:10037–10072 (2021)
2021
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Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., Müller, K.-R. Machine Learning Force Fields, Chemical Reviews , 121:10142–10186 (2021)
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Caselle, M., Cellini, E., Nada, A., Panero, M. Stochastic normalizing flows as non-equilibrium transformations, Journal of High Energy Physics , 1–31 (2022)
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2022
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Wirnsberger, P., Papamakarios, G., Ibarz, B., Racanière, S., Ballard, A. J., Pritzel, A., Blundell, C. Normalizing flows for atomic solids, Machine Learning: Science and Technology , 3:025009 (2022)
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2021
Cited alongside, same era.
Qiu, Y. et al. Development and Benchmarking of Open Force Field v1.0.0—the Parsley Small-Molecule Force Field, Journal of Chemical Theory and Computation , 17:6262–6280 (2021)
2021
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Nicoli, K. A., Anders, C. J., Funcke, L., Hartung, T., Jansen, K., Kessel, P., Nakajima, S., Stornati, P. Estimation of thermodynamic observables in lattice field theories with deep generative models, Physical review letters , 126:032001 (2021)
2021
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Sbailò, L., Dibak, M., Noé, F. Neural mode jump monte carlo, The Journal of Chemical Physics , 154 (2021)
2021
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Köhler, J., Krämer, A., Noé, F. Smooth normalizing flows, Advances in Neural Information Processing Systems , 34:2796–2809 (2021)
2021
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Gabrié, M., Rotskoff, G. M., Vanden-Eijnden, E. Adaptive Monte Carlo augmented with normalizing flows, Proceedings of the National Academy of Sciences , 119:e2109420119 (2022)
2022
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Ahmad, R., Cai, W. Free energy calculation of crystalline solids using normalizing flows, Modelling and Simulation in Materials Science and Engineering , 30:065007 (2022)
2022
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2022
Cited alongside, same era.
Weinreich, J., Lemm, D., von Rudorff, G. F., von Lilienfeld, O. A. Ab initio machine learning of phase space averages, The Journal of Chemical Physics , 157:024303 (2022)
2022
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Käser, S., Vazquez-Salazar, L. I., Meuwly, M., Töpfer, K. Neural network potentials for chemistry: concepts, applications and prospects, Digital Discovery , 2:28–58 (2023)
2023
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Singha, A., Chakrabarti, D., Arora, V. Conditional normalizing flow for Markov chain Monte Carlo sampling in the critical region of lattice field theory, Physical Review D , 107:014512 (2023)
2023
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Falkner, S., Coretti, A., Romano, S., Geissler, P. L., Dellago, C. Conditioning Boltzmann generators for rare event sampling, Machine Learning: Science and Technology , 4:035050 (2023)
2023
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Klein, L., Krämer, A., Noé, F. Equivariant flow matching, arXiv preprint, arXiv:2306.15030 (2023)
2023
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2023
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2023
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Wirnsberger, P., Ibarz, B., Papamakarios, G. Estimating Gibbs free energies via isobaric-isothermal flows, Machine Learning: Science and Technology , 4:035039 (2023)
2023
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