Recent advances in machine learning towards multiscale soft materials design

Jackson, Nicholas E.; Webb, Michael A.; de Pablo, Juan J.

doi:10.1016/j.coche.2019.03.005

Title: Recent advances in machine learning towards multiscale soft materials design

Journal Article · Sun Apr 28 00:00:00 EDT 2019 · Current Opinion in Chemical Engineering

DOI:https://doi.org/10.1016/j.coche.2019.03.005· OSTI ID:1532508

Jackson, Nicholas E. ^[1]; Webb, Michael A. ^[2]; de Pablo, Juan J. ^[1]

Univ. of Chicago, Chicago, IL (United States); Argonne National Lab. (ANL), Lemont, IL (United States)
Univ. of Chicago, Chicago, IL (United States)

The multiscale design of soft materials requires an ensemble of computational techniques spanning quantum-chemistry to molecular dynamics to continuum modeling. The recent emergence of machine-learning (ML) and modern optimization algorithms has accelerated material property prediction, as well as stimulated the development of hybrid ML/molecular modeling methodologies capable of providing physical insights unobtainable from purely physics-based modeling and intuition. Such hybrid techniques also have important ramifications for the ML-enhanced interpretation of results from simulations and experiments alike. Leveraging ML techniques for the design of chemical or morphological structures based on a target property or functionality represents an exciting goal for the general area of soft materials, including polymers, liquid crystals, colloids, or biomolecules, to name a few representative classes of systems. Furthermore, we provide a perspective on recent work using ML techniques of relevance for the multiscale design of soft materials and outline potential future directions of interest to the soft materials community.

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Research Organization:: Argonne National Lab. (ANL), Argonne, IL (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division. Midwest Integrated Center for Computational Materials (MICCoM)

Grant/Contract Number:: AC02-06CH11357

OSTI ID:: 1532508

Journal Information:: Current Opinion in Chemical Engineering, Vol. 23, Issue C; ISSN 2211-3398

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 73 works

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References (58)

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies Hansen, Katja; Montavon, Grégoire; Biegler, Franziska Journal of Chemical Theory and Computation, Vol. 9, Issue 8 https://doi.org/10.1021/ct400195d	journal	July 2013
Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials Imbalzano, Giulio; Anelli, Andrea; Giofré, Daniele The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5024611	journal	June 2018
Machine Learning Predictions of Molecular Properties: Accurate Many-Body Potentials and Nonlocality in Chemical Space Hansen, Katja; Biegler, Franziska; Ramakrishnan, Raghunathan The Journal of Physical Chemistry Letters, Vol. 6, Issue 12 https://doi.org/10.1021/acs.jpclett.5b00831	journal	June 2015
On representing chemical environments Bartók, Albert P.; Kondor, Risi; Csányi, Gábor Physical Review B, Vol. 87, Issue 18 https://doi.org/10.1103/PhysRevB.87.184115	journal	May 2013
Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems Grisafi, Andrea; Wilkins, David M.; Csányi, Gábor Physical Review Letters, Vol. 120, Issue 3 https://doi.org/10.1103/PhysRevLett.120.036002	journal	January 2018
Machine learning for the structure–energy–property landscapes of molecular crystals Musil, Félix; De, Sandip; Yang, Jack Chemical Science, Vol. 9, Issue 5 https://doi.org/10.1039/C7SC04665K	journal	January 2018
Many-Body Descriptors for Predicting Molecular Properties with Machine Learning: Analysis of Pairwise and Three-Body Interactions in Molecules Pronobis, Wiktor; Tkatchenko, Alexandre; Müller, Klaus-Robert Journal of Chemical Theory and Computation, Vol. 14, Issue 6 https://doi.org/10.1021/acs.jctc.8b00110	journal	May 2018
Molecular graph convolutions: moving beyond fingerprints Kearnes, Steven; McCloskey, Kevin; Berndl, Marc Journal of Computer-Aided Molecular Design, Vol. 30, Issue 8 https://doi.org/10.1007/s10822-016-9938-8	journal	August 2016
MoleculeNet: a benchmark for molecular machine learning Wu, Zhenqin; Ramsundar, Bharath; Feinberg, Evan N. Chemical Science, Vol. 9, Issue 2 https://doi.org/10.1039/C7SC02664A	journal	January 2018
Quantum-chemical insights from deep tensor neural networks Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan Nature Communications, Vol. 8, Issue 1 https://doi.org/10.1038/ncomms13890	journal	January 2017
SchNet – A deep learning architecture for molecules and materials Schütt, K. T.; Sauceda, H. E.; Kindermans, P. -J. The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5019779	journal	June 2018
Prediction Errors of Molecular Machine Learning Models Lower than Hybrid DFT Error Faber, Felix A.; Hutchison, Luke; Huang, Bing Journal of Chemical Theory and Computation, Vol. 13, Issue 11 https://doi.org/10.1021/acs.jctc.7b00577	journal	October 2017
Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach Gómez-Bombarelli, Rafael; Aguilera-Iparraguirre, Jorge; Hirzel, Timothy D. Nature Materials, Vol. 15, Issue 10 https://doi.org/10.1038/nmat4717	journal	August 2016
Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules Gómez-Bombarelli, Rafael; Wei, Jennifer N.; Duvenaud, David ACS Central Science, Vol. 4, Issue 2 https://doi.org/10.1021/acscentsci.7b00572	journal	January 2018
Deep reinforcement learning for de novo drug design Popova, Mariya; Isayev, Olexandr; Tropsha, Alexander Science Advances, Vol. 4, Issue 7 https://doi.org/10.1126/sciadv.aap7885	journal	July 2018
Inverse molecular design using machine learning: Generative models for matter engineering Sanchez-Lengeling, Benjamin; Aspuru-Guzik, Alán Science, Vol. 361, Issue 6400 https://doi.org/10.1126/science.aat2663	journal	July 2018
Machine Learning Strategy for Accelerated Design of Polymer Dielectrics Mannodi-Kanakkithodi, Arun; Pilania, Ghanshyam; Huan, Tran Doan Scientific Reports, Vol. 6, Issue 1 https://doi.org/10.1038/srep20952	journal	February 2016
Polymer Genome: A Data-Powered Polymer Informatics Platform for Property Predictions Kim, Chiho; Chandrasekaran, Anand; Huan, Tran Doan The Journal of Physical Chemistry C, Vol. 122, Issue 31 https://doi.org/10.1021/acs.jpcc.8b02913	journal	July 2018
Polymer Informatics: Opportunities and Challenges Audus, Debra J.; de Pablo, Juan J. ACS Macro Letters, Vol. 6, Issue 10 https://doi.org/10.1021/acsmacrolett.7b00228	journal	September 2017
Blending Education and Polymer Science: Semiautomated Creation of a Thermodynamic Property Database Tchoua, Roselyne B.; Qin, Jian; Audus, Debra J. Journal of Chemical Education, Vol. 93, Issue 9 https://doi.org/10.1021/acs.jchemed.5b01032	journal	July 2016
Perspective: Machine learning potentials for atomistic simulations Behler, Jörg The Journal of Chemical Physics, Vol. 145, Issue 17 https://doi.org/10.1063/1.4966192	journal	November 2016
First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems Behler, Jörg Angewandte Chemie International Edition, Vol. 56, Issue 42 https://doi.org/10.1002/anie.201703114	journal	August 2017
A universal strategy for the creation of machine learning-based atomistic force fields Huan, Tran Doan; Batra, Rohit; Chapman, James npj Computational Materials, Vol. 3, Issue 1 https://doi.org/10.1038/s41524-017-0042-y	journal	September 2017
Deep Potential Molecular Dynamics: A Scalable Model with the Accuracy of Quantum Mechanics Zhang, Linfeng; Han, Jiequn; Wang, Han Physical Review Letters, Vol. 120, Issue 14 https://doi.org/10.1103/PhysRevLett.120.143001	journal	April 2018
Comparison of permutationally invariant polynomials, neural networks, and Gaussian approximation potentials in representing water interactions through many-body expansions Nguyen, Thuong T.; Székely, Eszter; Imbalzano, Giulio The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5024577	journal	June 2018
Nuclear Quantum Effects in Sodium Hydroxide Solutions from Neural Network Molecular Dynamics Simulations Hellström, Matti; Ceriotti, Michele; Behler, Jörg The Journal of Physical Chemistry B, Vol. 122, Issue 44 https://doi.org/10.1021/acs.jpcb.8b06433	journal	October 2018
Machine learning of accurate energy-conserving molecular force fields Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel E. Science Advances, Vol. 3, Issue 5 https://doi.org/10.1126/sciadv.1603015	journal	May 2017
wACSF—Weighted atom-centered symmetry functions as descriptors in machine learning potentials Gastegger, M.; Schwiedrzik, L.; Bittermann, M. The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5019667	journal	June 2018
The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics Yao, Kun; Herr, John E.; Toth, David W. Chemical Science, Vol. 9, Issue 8 https://doi.org/10.1039/C7SC04934J	journal	January 2018
Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning Bereau, Tristan; DiStasio, Robert A.; Tkatchenko, Alexandre The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5009502	journal	June 2018
Less is more: Sampling chemical space with active learning Smith, Justin S.; Nebgen, Ben; Lubbers, Nicholas The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5023802	journal	June 2018
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost Smith, J. S.; Isayev, O.; Roitberg, A. E. Chemical Science, Vol. 8, Issue 4 https://doi.org/10.1039/C6SC05720A	journal	January 2017
Adaptive machine learning framework to accelerate ab initio molecular dynamics Botu, Venkatesh; Ramprasad, Rampi International Journal of Quantum Chemistry, Vol. 115, Issue 16 https://doi.org/10.1002/qua.24836	journal	December 2014
Metadynamics for training neural network model chemistries: A competitive assessment Herr, John E.; Yao, Kun; McIntyre, Ryker The Journal of Chemical Physics, Vol. 148, Issue 24 https://doi.org/10.1063/1.5020067	journal	June 2018
Towards exact molecular dynamics simulations with machine-learned force fields Chmiela, Stefan; Sauceda, Huziel E.; Müller, Klaus-Robert Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-06169-2	journal	September 2018
DeePCG: Constructing coarse-grained models via deep neural networks Zhang, Linfeng; Han, Jiequn; Wang, Han The Journal of Chemical Physics, Vol. 149, Issue 3 https://doi.org/10.1063/1.5027645	journal	July 2018
Neural Network Based Prediction of Conformational Free Energies - A New Route toward Coarse-Grained Simulation Models Lemke, Tobias; Peter, Christine Journal of Chemical Theory and Computation, Vol. 13, Issue 12 https://doi.org/10.1021/acs.jctc.7b00864	journal	November 2017
Automated Parametrization of the Coarse-Grained Martini Force Field for Small Organic Molecules Bereau, Tristan; Kremer, Kurt Journal of Chemical Theory and Computation, Vol. 11, Issue 6 https://doi.org/10.1021/acs.jctc.5b00056	journal	May 2015
Encoding and selecting coarse-grain mapping operators with hierarchical graphs Chakraborty, Maghesree; Xu, Chenliang; White, Andrew D. The Journal of Chemical Physics, Vol. 149, Issue 13 https://doi.org/10.1063/1.5040114	journal	October 2018
Graph-Based Approach to Systematic Molecular Coarse-Graining Webb, Michael A.; Delannoy, Jean-Yves; de Pablo, Juan J. Journal of Chemical Theory and Computation, Vol. 15, Issue 2 https://doi.org/10.1021/acs.jctc.8b00920	journal	November 2018
Electronic structure at coarse-grained resolutions from supervised machine learning Jackson, Nicholas E.; Bowen, Alec S.; Antony, Lucas W. Science Advances, Vol. 5, Issue 3 https://doi.org/10.1126/sciadv.aav1190	journal	March 2019
SSAGES: Software Suite for Advanced General Ensemble Simulations Sidky, Hythem; Colón, Yamil J.; Helfferich, Julian The Journal of Chemical Physics, Vol. 148, Issue 4 https://doi.org/10.1063/1.5008853	journal	January 2018
Learning free energy landscapes using artificial neural networks Sidky, Hythem; Whitmer, Jonathan K. The Journal of Chemical Physics, Vol. 148, Issue 10 https://doi.org/10.1063/1.5018708	journal	March 2018
Adaptive enhanced sampling by force-biasing using neural networks Guo, Ashley Z.; Sevgen, Emre; Sidky, Hythem The Journal of Chemical Physics, Vol. 148, Issue 13 https://doi.org/10.1063/1.5020733	journal	April 2018
Reinforced dynamics for enhanced sampling in large atomic and molecular systems Zhang, Linfeng; Wang, Han; E., Weinan The Journal of Chemical Physics, Vol. 148, Issue 12 https://doi.org/10.1063/1.5019675	journal	March 2018
A structural approach to relaxation in glassy liquids Schoenholz, S. S.; Cubuk, E. D.; Sussman, D. M. Nature Physics, Vol. 12, Issue 5 https://doi.org/10.1038/nphys3644	journal	February 2016
Unsupervised machine learning for detection of phase transitions in off-lattice systems. I. Foundations Jadrich, R. B.; Lindquist, B. A.; Truskett, T. M. The Journal of Chemical Physics, Vol. 149, Issue 19 https://doi.org/10.1063/1.5049849	journal	November 2018
Unsupervised machine learning for detection of phase transitions in off-lattice systems. II. Applications Jadrich, R. B.; Lindquist, B. A.; Piñeros, W. D. The Journal of Chemical Physics, Vol. 149, Issue 19 https://doi.org/10.1063/1.5049850	journal	November 2018
Markov State Models: From an Art to a Science Husic, Brooke E.; Pande, Vijay S. Journal of the American Chemical Society, Vol. 140, Issue 7 https://doi.org/10.1021/jacs.7b12191	journal	February 2018
Automated design of collective variables using supervised machine learning Sultan, Mohammad M.; Pande, Vijay S. The Journal of Chemical Physics, Vol. 149, Issue 9 https://doi.org/10.1063/1.5029972	journal	September 2018
Extracting collective motions underlying nucleosome dynamics via nonlinear manifold learning Guo, Ashley Z.; Lequieu, Joshua; de Pablo, Juan J. The Journal of Chemical Physics, Vol. 150, Issue 5 https://doi.org/10.1063/1.5063851	journal	February 2019
Collective variable discovery and enhanced sampling using autoencoders: Innovations in network architecture and error function design Chen, Wei; Tan, Aik Rui; Ferguson, Andrew L. The Journal of Chemical Physics, Vol. 149, Issue 7 https://doi.org/10.1063/1.5023804	journal	August 2018
Reweighted autoencoded variational Bayes for enhanced sampling (RAVE) Ribeiro, João Marcelo Lamim; Bravo, Pablo; Wang, Yihang The Journal of Chemical Physics, Vol. 149, Issue 7 https://doi.org/10.1063/1.5025487	journal	August 2018
Computer simulation as a tool for the interpretation of total scattering data from glasses and liquids Soper, Alan K. Molecular Simulation, Vol. 38, Issue 14-15 https://doi.org/10.1080/08927022.2012.732222	journal	December 2012
Characterization of Protein Kinase a Free Energy Landscape by NMR-Restrained Metadynamics Wang, Yingjie; Camilloni, Carlo; Kim, Jonggul Biophysical Journal, Vol. 112, Issue 3 https://doi.org/10.1016/j.bpj.2016.11.310	journal	February 2017
Derivation of Multiple Covarying Material and Process Parameters Using Physics-Based Modeling of X-ray Data Khaira, Gurdaman; Doxastakis, Manolis; Bowen, Alec Macromolecules, Vol. 50, Issue 19 https://doi.org/10.1021/acs.macromol.7b00691	journal	September 2017
Evolutionary strategy for inverse charge measurements of dielectric particles Jiang, Xikai; Li, Jiyuan; Lee, Victor The Journal of Chemical Physics, Vol. 148, Issue 23 https://doi.org/10.1063/1.5027435	journal	June 2018
On the Use of Experimental Observations to Bias Simulated Ensembles Pitera, Jed W.; Chodera, John D. Journal of Chemical Theory and Computation, Vol. 8, Issue 10 https://doi.org/10.1021/ct300112v	journal	March 2012

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