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Computational methods for learning to sample from the Boltzmann distribution -- where the target distribution is known only up to an unnormalized energy function -- have advanced significantly recently.
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Reciprocal processes. A measure-theoretical point of view
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Yongxin Chen and Tryphon Georgiou · 2015
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Better informed distance geometry: using what we know to improve conformation generation
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Brownian motion, martingales, and stochastic calculus
Jean-François Le Gall · 2016
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OpenMM 7: Rapid development of high performance algorithms for molecular dynamics
Peter Eastman, Jason Swails, John D Chodera, Robert T McGibbon, Yutong Zhao, Kyle A Beauchamp, Lee-Ping Wang, Andrew C Simmonett, Matthew P Harrigan, Chaya D Stern, Rafal P. Wiewiora, Bernard R. Brooks, and Vijay S. Pande · 2017
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Paul CD Hawkins · 2017
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JAX: composable transformations of Python+NumPy programs, 2018
James Bradbury, Roy Frostig, Peter Hawkins, Matthew James Johnson, Chris Leary, Dougal Maclaurin, George Necula, Adam Paszke, Jake VanderPlas, Skye Wanderman-Milne, and Qiao Zhang · 2018
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Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David K Duvenaud · 2018
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Espen Bernton, Jeremy Heng, Arnaud Doucet, and Pierre E Jacob · 2019
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Boltzmann generators: Sampling equilibrium states of many-body systems with deep learning
Frank Noé, Simon Olsson, Jonas Köhler, and Hao Wu · 2019
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Path integral sampler: A stochastic control approach for sampling
Qinsheng Zhang and Yongxin Chen · 2022
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SPICE, a dataset of drug-like molecules and peptides for training machine learning potentials
Peter Eastman, Pavan Kumar Behara, David L Dotson, Raimondas Galvelis, John E Herr, Josh T Horton, Yuezhi Mao, John D Chodera, Benjamin P Pritchard, Yuanqing Wang, Gianni De Fabritiis, and Thomas E. Markland · 2023
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EigenFold: Generative protein structure prediction with diffusion models
Bowen Jing, Ezra Erives, Peter Pao-Huang, Gabriele Corso, Bonnie Berger, and Tommi S Jaakkola · 2023
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Timewarp: Transferable acceleration of molecular dynamics by learning time-coarsened dynamics
Leon Klein, Andrew Foong, Tor Fjelde, Bruno Mlodozeniec, Marc Brockschmidt, Sebastian Nowozin, Frank Noé, and Ryota Tomioka · 2023
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I 2 SB: Image-to-Image Schrödinger bridge
Guan-Horng Liu, Arash Vahdat, De-An Huang, Evangelos A Theodorou, Weili Nie, and Anima Anandkumar · 2023
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Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
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Flow annealed importance sampling bootstrap
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Denoising diffusion samplers
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Iterated denoising energy matching for sampling from Boltzmann densities
Tara Akhound-Sadegh, Jarrid Rector-Brooks, Avishek Joey Bose, Sarthak Mittal, Pablo Lemos, Cheng-Hao Liu, Marcin Sendera, Siamak Ravanbakhsh, Gauthier Gidel, Yoshua Bengio, Nikolay Malkin, and Alexander Tong · 2024
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Generalized Schrödinger bridge matching
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Particle denoising diffusion sampler
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CREST—A program for the exploration of low-energy molecular chemical space
Philipp Pracht, Stefan Grimme, Christoph Bannwarth, Fabian Bohle, Sebastian Ehlert, Gereon Feldmann, Johannes Gorges, Marcel Müller, Tim Neudecker, Christoph Plett, Sebastian Spicher, Pit Steinbach, Patryk A. Wesołowski, and Felix Zeller · 2024
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Transport meets variational inference: Controlled monte carlo diffusions
Francisco Vargas, Shreyas Padhy, Denis Blessing, and Nikolas Nüsken · 2024
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NETS: A non-equilibrium transport sampler
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