Behler, Jörg. 2016.
“Perspective: Machine Learning Potentials for Atomistic Simulations.” The Journal of Chemical Physics 145 (17): 170901.
Butler, Keith T., Daniel W. Davies, Hugh Cartwright, Olexandr Isayev, and Aron Walsh. 2018.
“Machine Learning for Molecular and Materials Science.” Nature 559 (7715): 547–55.
Chan, Henry, Mathew J. Cherukara, Badri Narayanan, Troy D. Loeffler, Chris Benmore, Stephen K. Gray, and Subramanian K. R. S. Sankaranarayanan. 2019.
“Machine Learning Coarse Grained Models for Water.” Nature Communications 10 (1): 379.
Cutajar, Kurt, Mark Pullin, Andreas Damianou, Neil Lawrence, and Javier González. 2019.
“Deep Gaussian Processes for Multi-Fidelity Modeling.” arXiv:1903.07320 [Cs, Stat], March.
Durumeric, Aleksander E. P., Nicholas E. Charron, Clark Templeton, Félix Musil, Klara Bonneau, Aldo S. Pasos-Trejo, Yaoyi Chen, Atharva Kelkar, Frank Noé, and Cecilia Clementi. 2023.
“Machine Learned Coarse-Grained Protein Force-Fields: Are We There yet?” Current Opinion in Structural Biology 79 (April): 102533.
Flack, Jessica C. 2017.
“Coarse-Graining as a Downward Causation Mechanism.” Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 375 (2109): 20160338.
Fu, Xiang, Tian Xie, Nathan J. Rebello, Bradley Olsen, and Tommi S. Jaakkola. 2022.
“Simulate Time-Integrated Coarse-Grained Molecular Dynamics with Geometric Machine Learning.” In.
Jeong, J., A. Moradzadeh, and N. R. Aluru. 2022.
“Extended DeepILST for Various Thermodynamic States and Applications in Coarse-Graining.” The Journal of Physical Chemistry A 126 (9): 1562–70.
Joshi, Soumil Y., and Sanket A. Deshmukh. 2021.
“A Review of Advancements in Coarse-Grained Molecular Dynamics Simulations.” Molecular Simulation 47 (10-11): 786–803.
Köhler, Jonas, Yaoyi Chen, Andreas Krämer, Cecilia Clementi, and Frank Noé. 2023.
“Flow-Matching: Efficient Coarse-Graining of Molecular Dynamics Without Forces.” Journal of Chemical Theory and Computation 19 (3): 942–52.
Kontolati, Katiana, Darius Alix-Williams, Nicholas M. Boffi, Michael L. Falk, Chris H. Rycroft, and Michael D. Shields. 2021.
“Manifold Learning for Coarse-Graining Atomistic Simulations: Application to Amorphous Solids.” Acta Materialia 215 (August): 117008.
Ma, Zhan, Shu Wang, Minhee Kim, Kaibo Liu, Chun-Long Chen, and Wenxiao Pan. 2021.
“Transfer Learning of Memory Kernels for Transferable Coarse-Graining of Polymer Dynamics.” Soft Matter 17 (24): 5864–77.
Mahmoud, Amr H., Matthew Masters, Soo Jung Lee, and Markus A. Lill. 2022.
“Accurate Sampling of Macromolecular Conformations Using Adaptive Deep Learning and Coarse-Grained Representation.” Journal of Chemical Information and Modeling 62 (7): 1602–17.
Mohan, Arvind T., Nicholas Lubbers, Misha Chertkov, and Daniel Livescu. 2023.
“Embedding Hard Physical Constraints in Neural Network Coarse-Graining of Three-Dimensional Turbulence.” Physical Review Fluids 8 (1): 014604.
Noid, William George. 2013. “Perspective: Coarse-Grained Models for Biomolecular Systems.” The Journal of Chemical Physics 139 (9): 09B201_1.
Perdikaris, Paris, Daniele Venturi, and George Em Karniadakis. 2016.
“Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data Sets.” SIAM Journal on Scientific Computing 38 (4): B521–38.
Perdikaris, P., D. Venturi, J. O. Royset, and G. E. Karniadakis. 2015.
“Multi-Fidelity Modelling via Recursive Co-Kriging and Gaussian–Markov Random Fields.” Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 471 (2179): 20150018.
Raissi, Maziar, and George Karniadakis. 2016.
“Deep Multi-Fidelity Gaussian Processes.” arXiv:1604.07484 [Cs, Stat], April.
Sarkar, Soumalya, and Michael Joly. 2019.
“Multi-FIdelity Learning with Heterogeneous Domains.” In
NeurIPS, 5.
Voth, Gregory A. 2008. Coarse-Graining of Condensed Phase and Biomolecular Systems. CRC press.
Wang, Jiang, Stefan Chmiela, Klaus-Robert Müller, Frank Noé, and Cecilia Clementi. 2020.
“Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach.” The Journal of Chemical Physics 152 (19): 194106.
Wang, Jiang, Simon Olsson, Christoph Wehmeyer, Adrià Pérez, Nicholas E. Charron, Gianni de Fabritiis, Frank Noé, and Cecilia Clementi. 2019.
“Machine Learning of Coarse-Grained Molecular Dynamics Force Fields.” ACS Central Science 5 (5): 755–67.
Wang, Wujie, and Rafael Gómez-Bombarelli. 2019.
“Coarse-Graining Auto-Encoders for Molecular Dynamics.” Npj Computational Materials 5 (1): 1–9.
White, Andrew D. 2021.
“Deep Learning for Molecules and Materials.” Living Journal of Computational Molecular Science 3 (1): 1499–99.
Zhang, Linfeng, Jiequn Han, Han Wang, Roberto Car, and Weinan E. 2018.
“DeePCG: Constructing Coarse-Grained Models via Deep Neural Networks.” The Journal of Chemical Physics 149 (3): 034101.
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