1
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Tian Z, Zhang S, Chern GW. Machine learning for structure-property mapping of Ising models: Scalability and limitations. Phys Rev E 2023; 108:065304. [PMID: 38243546 DOI: 10.1103/physreve.108.065304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 11/27/2023] [Indexed: 01/21/2024]
Abstract
We present a scalable machine learning (ML) framework for predicting intensive properties and particularly classifying phases of Ising models. Scalability and transferability are central to the unprecedented computational efficiency of ML methods. In general, linear-scaling computation can be achieved through the divide-and-conquer approach, and the locality of physical properties is key to partitioning the system into subdomains that can be solved separately. Based on the locality assumption, ML model is developed for the prediction of intensive properties of a finite-size block. Predictions of large-scale systems can then be obtained by averaging results of the ML model from randomly sampled blocks of the system. We show that the applicability of this approach depends on whether the block-size of the ML model is greater than the characteristic length scale of the system. In particular, in the case of phase identification across a critical point, the accuracy of the ML prediction is limited by the diverging correlation length. We obtain an intriguing scaling relation between the prediction accuracy and the ratio of ML block size over the spin-spin correlation length. Implications for practical applications are also discussed. While the two-dimensional Ising model is used to demonstrate the proposed approach, the ML framework can be generalized to other many-body or condensed-matter systems.
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Affiliation(s)
- Zhongzheng Tian
- Department of Physics, University of Virginia, Charlottesville, Virginia 22904, USA
| | - Sheng Zhang
- Department of Physics, University of Virginia, Charlottesville, Virginia 22904, USA
| | - Gia-Wei Chern
- Department of Physics, University of Virginia, Charlottesville, Virginia 22904, USA
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2
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Kim EC, Chun DJ, Park CB, Sung BJ. Machine learning predicts the glass transition of two-dimensional colloids besides medium-range crystalline order. Phys Rev E 2023; 108:044602. [PMID: 37978710 DOI: 10.1103/physreve.108.044602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Accepted: 08/10/2023] [Indexed: 11/19/2023]
Abstract
We employ only the positions of colloidal particles and construct machine learning (ML) models to test the presence of structural order in glass transition for two kinds of two-dimensional (2D) colloids: 2D polydisperse colloids (PC) with medium-range crystalline order (MRCO) and 2D binary colloids (BC) without MRCO. ML models predict the glass transition of 2D colloids successfully without any information on MRCO. Even certain ML models trained with BC predict the glass transition of PC successfully, thus suggesting that universal structural characteristics would exist besides MRCO.
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Affiliation(s)
- Eun Cheol Kim
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Dong Jae Chun
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Chung Bin Park
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
| | - Bong June Sung
- Department of Chemistry, Sogang University, Seoul 04107, Republic of Korea
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3
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Ryu JY, Elala E, Rhee JKK. Quantum Graph Neural Network Models for Materials Search. MATERIALS (BASEL, SWITZERLAND) 2023; 16:4300. [PMID: 37374486 DOI: 10.3390/ma16124300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2023] [Revised: 06/03/2023] [Accepted: 06/05/2023] [Indexed: 06/29/2023]
Abstract
Inspired by classical graph neural networks, we discuss a novel quantum graph neural network (QGNN) model to predict the chemical and physical properties of molecules and materials. QGNNs were investigated to predict the energy gap between the highest occupied and lowest unoccupied molecular orbitals of small organic molecules. The models utilize the equivariantly diagonalizable unitary quantum graph circuit (EDU-QGC) framework to allow discrete link features and minimize quantum circuit embedding. The results show QGNNs can achieve lower test loss compared to classical models if a similar number of trainable variables are used, and converge faster in training. This paper also provides a review of classical graph neural network models for materials research and various QGNNs.
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Affiliation(s)
- Ju-Young Ryu
- School of Electrical Engineering & ITRC of Quantum Computing for AI, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
- Qunova Computing, Incorporated, 193 Munji-ro, Yuseong-gu, Daejeon 34051, Republic of Korea
| | - Eyuel Elala
- School of Electrical Engineering & ITRC of Quantum Computing for AI, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
- Qunova Computing, Incorporated, 193 Munji-ro, Yuseong-gu, Daejeon 34051, Republic of Korea
| | - June-Koo Kevin Rhee
- School of Electrical Engineering & ITRC of Quantum Computing for AI, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Republic of Korea
- Qunova Computing, Incorporated, 193 Munji-ro, Yuseong-gu, Daejeon 34051, Republic of Korea
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4
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Verde AR, Alarcón LM, Appignanesi GA. Correlations between defect propensity and dynamical heterogeneities in supercooled water. J Chem Phys 2023; 158:114502. [PMID: 36948825 DOI: 10.1063/5.0139118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
A salient feature of supercooled liquids consists in the dramatic dynamical slowdown they undergo as temperature decreases while no significant structural change is evident. These systems also present dynamical heterogeneities (DH): certain molecules, spatially arranged in clusters, relax various orders of magnitude faster than the others. However, again, no static quantity (such as structural or energetic measures) shows strong direct correlations with such fast-moving molecules. In turn, the dynamic propensity approach, an indirect measure that quantifies the tendency of the molecules to move in a given structural configuration, has revealed that dynamical constraints, indeed, originate from the initial structure. Nevertheless, this approach is not able to elicit which structural quantity is, in fact, responsible for such a behavior. In an effort to remove dynamics from its definition in favor of a static quantity, an energy-based propensity has also been developed for supercooled water, but it could only find positive correlations between the lowest-energy and the least-mobile molecules, while no correlations could be found for those more relevant mobile molecules involved in the DH clusters responsible for the system's structural relaxation. Thus, in this work, we shall define a defect propensity measure based on a recently introduced structural index that accurately characterizes water structural defects. We shall show that this defect propensity measure provides positive correlations with dynamic propensity, being also able to account for the fast-moving molecules responsible for the structural relaxation. Moreover, time dependent correlations will show that defect propensity represents an appropriate early-time predictor of the long-time dynamical heterogeneity.
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Affiliation(s)
- Alejandro R Verde
- INQUISUR, Departamento de Química, Universidad Nacional del Sur (UNS)-CONICET, Avenida Alem 1253, 8000 Bahía Blanca, Argentina
| | - Laureano M Alarcón
- INQUISUR, Departamento de Química, Universidad Nacional del Sur (UNS)-CONICET, Avenida Alem 1253, 8000 Bahía Blanca, Argentina
| | - Gustavo A Appignanesi
- INQUISUR, Departamento de Química, Universidad Nacional del Sur (UNS)-CONICET, Avenida Alem 1253, 8000 Bahía Blanca, Argentina
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5
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Duarte JC, da Rocha RD, Borges I. Which molecular properties determine the impact sensitivity of an explosive? A machine learning quantitative investigation of nitroaromatic explosives. Phys Chem Chem Phys 2023; 25:6877-6890. [PMID: 36799468 DOI: 10.1039/d2cp05339j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
Abstract
We decomposed density functional theory charge densities of 53 nitroaromatic molecules into atom-centered electric multipoles using the distributed multipole analysis that provides a detailed picture of the molecular electronic structure. Three electric multipoles, (the charge of the nitro groups), (the total dipole, i.e., polarization, of the nitro groups), (the total electron delocalization of the C ring atoms), and the number of explosophore groups (#NO2) were selected as features for a comprehensive machine learning (ML) investigation. The target property was the impact sensitivity h50 (cm) values quantified by drop-weight measurements, with a large h50 (e.g., 150 cm) indicating that an explosive is insensitive and vice versa. After a preliminary screening of 42 ML algorithms, four were selected based on the lowest root mean square errors: Extra Trees, Random Forests, Gradient Boosting, and AdaBoost. Compared to experimental data, the predicted h50 values of molecules having very different sensitivities for the four algorithms have differences in the range 19-28%. The most important properties for predicting h50 are the electron delocalization in the ring atoms and the polarization of the nitro groups with averaged weights of 39% and 35%, followed by the charge (16%) and number (10%) of nitro groups. A significant result is how the contribution of these properties to h50 depends on their actual sensitivities: for the most sensitive explosives (h50 up to ∼50 cm), the four properties contribute to reducing h50, and for intermediate ones (∼50 cm ≲ h50 ≲ 100 cm) #NO2 and contribute to increasing it and the other two properties to reducing it. For highly insensitive explosives (h50 ≳ 200 cm), all four properties essentially contribute to increasing it. These results furnish a consistent molecular basis of the sensitivities of known explosives that also can be used for developing safer new ones.
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Affiliation(s)
- Julio Cesar Duarte
- Departamento de Engenharia de Computação, Instituto Militar de Engenharia, Instituto Militar de Engenharia, Praça General Tibúrcio, 80, Urca, Rio de Janeiro (RJ), 22290-270, Brazil. .,Programa de Pós-Graduação em Engenharia de Defesa, Militar de Engenharia, Instituto Militar de Engenharia, Praça General Tibúrcio, 80, Urca, Rio de Janeiro (RJ), 22290-270, Brazil
| | - Romulo Dias da Rocha
- Programa de Pós-Graduação em Engenharia de Defesa, Militar de Engenharia, Instituto Militar de Engenharia, Praça General Tibúrcio, 80, Urca, Rio de Janeiro (RJ), 22290-270, Brazil
| | - Itamar Borges
- Departamento de Engenharia de Computação, Instituto Militar de Engenharia, Instituto Militar de Engenharia, Praça General Tibúrcio, 80, Urca, Rio de Janeiro (RJ), 22290-270, Brazil. .,Departamento de Química, Militar de Engenharia, Instituto Militar de Engenharia, Praça General Tibúrcio, 80, Urca, Rio de Janeiro (RJ), 22290-270, Brazil
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6
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Lansford JL, Barnes BC, Rice BM, Jensen KF. Building Chemical Property Models for Energetic Materials from Small Datasets Using a Transfer Learning Approach. J Chem Inf Model 2022; 62:5397-5410. [PMID: 36240441 DOI: 10.1021/acs.jcim.2c00841] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
For many experimentally measured chemical properties that cannot be directly computed from first-principles, the existing physics-based models do not extrapolate well to out-of-sample molecules, and experimental datasets themselves are too small for traditional machine learning (ML) approaches. To overcome these limitations, we apply a transfer learning approach, whereby we simultaneously train a multi-target regression model on a small number of molecules with experimentally measured values and a large number of molecules with related computed properties. We demonstrate this methodology on predicting the experimentally measured impact sensitivity of energetic crystals, finding that both characteristics of the computed dataset and model architecture are important to prediction accuracy of the small experimental dataset. Our directed-message passing neural network (D-MPNN) ML model using transfer learning outperforms direct-ML and physics-based models on a diverse test set, and the new methods described here are widely applicable to modeling many other structure-property relationships.
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Affiliation(s)
- Joshua L Lansford
- U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, United States.,Department of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States
| | - Brian C Barnes
- U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, United States
| | - Betsy M Rice
- U.S. Army Combat Capabilities Development Command (DEVCOM) Army Research Laboratory, Aberdeen Proving Ground, Maryland 21005, United States
| | - Klavs F Jensen
- Department of Chemical Engineering, MIT, Cambridge, Massachusetts 02139, United States
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7
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Reiser P, Neubert M, Eberhard A, Torresi L, Zhou C, Shao C, Metni H, van Hoesel C, Schopmans H, Sommer T, Friederich P. Graph neural networks for materials science and chemistry. COMMUNICATIONS MATERIALS 2022; 3:93. [PMID: 36468086 PMCID: PMC9702700 DOI: 10.1038/s43246-022-00315-6] [Citation(s) in RCA: 57] [Impact Index Per Article: 28.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Accepted: 11/07/2022] [Indexed: 05/14/2023]
Abstract
Machine learning plays an increasingly important role in many areas of chemistry and materials science, being used to predict materials properties, accelerate simulations, design new structures, and predict synthesis routes of new materials. Graph neural networks (GNNs) are one of the fastest growing classes of machine learning models. They are of particular relevance for chemistry and materials science, as they directly work on a graph or structural representation of molecules and materials and therefore have full access to all relevant information required to characterize materials. In this Review, we provide an overview of the basic principles of GNNs, widely used datasets, and state-of-the-art architectures, followed by a discussion of a wide range of recent applications of GNNs in chemistry and materials science, and concluding with a road-map for the further development and application of GNNs.
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Affiliation(s)
- Patrick Reiser
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Marlen Neubert
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
| | - André Eberhard
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
| | - Luca Torresi
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
| | - Chen Zhou
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
| | - Chen Shao
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- Present Address: Institute for Applied Informatics and Formal Description Systems, Karlsruhe Institute of Technology, Kaiserstr. 89, 76133 Karlsruhe, Germany
| | - Houssam Metni
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- ECPM, Université de Strasbourg, 25 Rue Becquerel, 67087 Strasbourg, France
| | - Clint van Hoesel
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- Department of Applied Physics, Eindhoven University of Technology, Groene Loper 19, 5612 AP Eindhoven, The Netherlands
| | - Henrik Schopmans
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
| | - Timo Sommer
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- Institute for Theory of Condensed Matter, Karlsruhe Institute of Technology, Wolfgang-Gaede-Str. 1, 76131 Karlsruhe, Germany
- Present Address: School of Chemistry, Trinity College Dublin, College Green, Dublin 2, Ireland
| | - Pascal Friederich
- Institute of Theoretical Informatics, Karlsruhe Institute of Technology, Am Fasanengarten 5, 76131 Karlsruhe, Germany
- Institute of Nanotechnology, Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, 76344 Eggenstein-Leopoldshafen, Germany
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8
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van Mastrigt R, Dijkstra M, van Hecke M, Coulais C. Machine Learning of Implicit Combinatorial Rules in Mechanical Metamaterials. PHYSICAL REVIEW LETTERS 2022; 129:198003. [PMID: 36399748 DOI: 10.1103/physrevlett.129.198003] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
Combinatorial problems arising in puzzles, origami, and (meta)material design have rare sets of solutions, which define complex and sharply delineated boundaries in configuration space. These boundaries are difficult to capture with conventional statistical and numerical methods. Here we show that convolutional neural networks can learn to recognize these boundaries for combinatorial mechanical metamaterials, down to finest detail, despite using heavily undersampled training sets, and can successfully generalize. This suggests that the network infers the underlying combinatorial rules from the sparse training set, opening up new possibilities for complex design of (meta)materials.
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Affiliation(s)
- Ryan van Mastrigt
- Institute of Physics, Universiteit van Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
- AMOLF, Science Park 104, 1098 XG Amsterdam, The Netherlands
| | - Marjolein Dijkstra
- Soft Condensed Matter, Debye Institute for Nanomaterials Science, Department of Physics, Utrecht University, Princetonplein 5, 3584 CC Utrecht, The Netherlands
| | - Martin van Hecke
- AMOLF, Science Park 104, 1098 XG Amsterdam, The Netherlands
- Huygens-Kamerling Onnes Lab, Universiteit Leiden, Postbus 9504, 2300 RA Leiden, The Netherlands
| | - Corentin Coulais
- Institute of Physics, Universiteit van Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands
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9
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Font-Clos F, Zanchi M, Hiemer S, Bonfanti S, Guerra R, Zaiser M, Zapperi S. Predicting the failure of two-dimensional silica glasses. Nat Commun 2022; 13:2820. [PMID: 35595727 PMCID: PMC9122924 DOI: 10.1038/s41467-022-30530-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 04/29/2022] [Indexed: 11/09/2022] Open
Abstract
Being able to predict the failure of materials based on structural information is a fundamental issue with enormous practical and industrial relevance for the monitoring of devices and components. Thanks to recent advances in deep learning, accurate failure predictions are becoming possible even for strongly disordered solids, but the sheer number of parameters used in the process renders a physical interpretation of the results impossible. Here we address this issue and use machine learning methods to predict the failure of simulated two dimensional silica glasses from their initial undeformed structure. We then exploit Gradient-weighted Class Activation Mapping (Grad-CAM) to build attention maps associated with the predictions, and we demonstrate that these maps are amenable to physical interpretation in terms of topological defects and local potential energies. We show that our predictions can be transferred to samples with different shape or size than those used in training, as well as to experimental images. Our strategy illustrates how artificial neural networks trained with numerical simulation results can provide interpretable predictions of the behavior of experimentally measured structures. The sheer number of parameters in deep learning makes the physical interpretation of failure predictions in glasses challenging. Here the authors use Grad-CAM to reveal the role of topological defects and local potential energies in failure predictions.
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Affiliation(s)
- Francesc Font-Clos
- Center for Complexity and Biosystems, Department of Physics, University of Milan, via Celoria 16, 20133, Milan, Italy
| | - Marco Zanchi
- Center for Complexity and Biosystems, Department of Physics, University of Milan, via Celoria 16, 20133, Milan, Italy
| | - Stefan Hiemer
- Institute of Materials Simulation, Department of Materials Science Science and Engineering, Friedrich-Alexander-University Erlangen-Nuremberg, Dr.-Mack-Str. 77, 90762, Fürth, Germany
| | - Silvia Bonfanti
- Center for Complexity and Biosystems, Department of Physics, University of Milan, via Celoria 16, 20133, Milan, Italy.,CNR - Consiglio Nazionale delle Ricerche, Istituto di Chimica della Materia Condensata e di Tecnologie per l'Energia Via R. Cozzi 53, 20125, Milan, Italy
| | - Roberto Guerra
- Center for Complexity and Biosystems, Department of Physics, University of Milan, via Celoria 16, 20133, Milan, Italy
| | - Michael Zaiser
- Institute of Materials Simulation, Department of Materials Science Science and Engineering, Friedrich-Alexander-University Erlangen-Nuremberg, Dr.-Mack-Str. 77, 90762, Fürth, Germany
| | - Stefano Zapperi
- Center for Complexity and Biosystems, Department of Physics, University of Milan, via Celoria 16, 20133, Milan, Italy. .,Institute of Materials Simulation, Department of Materials Science Science and Engineering, Friedrich-Alexander-University Erlangen-Nuremberg, Dr.-Mack-Str. 77, 90762, Fürth, Germany. .,CNR - Consiglio Nazionale delle Ricerche, Istituto di Chimica della Materia Condensata e di Tecnologie per l'Energia Via R. Cozzi 53, 20125, Milan, Italy.
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10
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Mendels D, de Pablo JJ. Collective Variables for Free Energy Surface Tailoring: Understanding and Modifying Functionality in Systems Dominated by Rare Events. J Phys Chem Lett 2022; 13:2830-2837. [PMID: 35324208 DOI: 10.1021/acs.jpclett.2c00317] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
We introduce a method for elucidating and modifying the functionality of systems dominated by rare events that relies on the semiautomated tuning of their underlying free energy surface. The proposed approach seeks to construct collective variables (CVs) that encode the essential information regarding the rare events of the system of interest. The appropriate CVs are identified using harmonic linear discriminant analysis (HLDA), a machine-learning-based method that is trained solely on data collected from short ordinary simulations in the relevant metastable states of the system. Utilizing the interpretable form of the resulting CVs, the critical interaction potentials that determine the system's rare transitions are identified and purposely modified to tailor the free energy surface in a manner that alters functionality as desired. The applicability of the method is illustrated in the context of three different systems, thereby demonstrating that thermodynamic and kinetic properties can be tractably modified with little to no prior knowledge or intuition.
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Affiliation(s)
- Dan Mendels
- Pritzker School of Molecular Engineering, University of Chicago, South Ellise, Chicago, Illinois 60637, United States
| | - Juan J de Pablo
- Pritzker School of Molecular Engineering, University of Chicago, South Ellise, Chicago, Illinois 60637, United States
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11
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Hernandes VF, Marques MS, Bordin JR. Phase classification using neural networks: application to supercooled, polymorphic core-softened mixtures. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2021; 34:024002. [PMID: 34638114 DOI: 10.1088/1361-648x/ac2f0f] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Accepted: 10/12/2021] [Indexed: 06/13/2023]
Abstract
Characterization of phases of soft matter systems is a challenge faced in many physical chemical problems. For polymorphic fluids it is an even greater challenge. Specifically, glass forming fluids, as water, can have, besides solid polymorphism, more than one liquid and glassy phases, and even a liquid-liquid critical point. In this sense, we apply a neural network algorithm to analyze the phase behavior of a mixture of core-softened fluids that interact through the continuous-shouldered well (CSW) potential, which have liquid polymorphism and liquid-liquid critical points, similar to water. We also apply the neural network to mixtures of CSW fluids and core-softened alcohols models. We combine and expand methods based on bond-orientational order parameters to study mixtures, applied to mixtures of hardcore fluids and to supercooled water, to include longer range coordination shells. With this, the trained neural network was able to properly predict the crystalline solid phases, the fluid phases and the amorphous phase for the pure CSW and CSW-alcohols mixtures with high efficiency. More than this, information about the phase populations, obtained from the network approach, can help verify if the phase transition is continuous or discontinuous, and also to interpret how the metastable amorphous region spreads along the stable high density fluid phase. These findings help to understand the behavior of supercooled polymorphic fluids and extend the comprehension of how amphiphilic solutes affect the phases behavior.
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Affiliation(s)
- V F Hernandes
- Programa de Pós-Graduação em Física, Departamento de Física, Instituto de Física e Matemática, Universidade Federal de Pelotas, Caixa Postal 354, 96001-970, Pelotas-RS, Brazil
| | - M S Marques
- Centro das Ciências Exatas e das Tecnologias, Universidade Federal do Oeste da Bahia Rua Bertioga, 892, Morada Nobre, CEP 47810-059, Barreiras-BA, Brazil
| | - José Rafael Bordin
- Departamento de Física, Instituto de Física e Matemática, Universidade Federal de Pelotas, Caixa Postal 354, 96001-970, Pelotas-RS, Brazil
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12
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Dulaney AR, Brady JF. Machine learning for phase behavior in active matter systems. SOFT MATTER 2021; 17:6808-6816. [PMID: 34223598 DOI: 10.1039/d1sm00266j] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
We demonstrate that deep learning techniques can be used to predict motility-induced phase separation (MIPS) in suspensions of active Brownian particles (ABPs) by creating a notion of phase at the particle level. Using a fully connected network in conjunction with a graph neural network we use individual particle features to predict to which phase a particle belongs. From this, we are able to compute the fraction of dilute particles to determine if the system is in the homogeneous dilute, dense, or coexistence region. Our predictions are compared against the MIPS binodal computed from simulation. The strong agreement between the two suggests that machine learning provides an effective way to determine the phase behavior of ABPs and could prove useful for determining more complex phase diagrams.
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Affiliation(s)
- Austin R Dulaney
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
| | - John F Brady
- Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, CA 91125, USA.
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13
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Abstract
Machine learning is making a major impact in materials research. I review current progress across a selection of areas of ubiquitous soft matter. When applied to particle tracking, machine learning using convolution neural networks is providing impressive performance but there remain some significant problems to solve. Characterising ordered arrangements of particles is a huge challenge and machine learning has been deployed to create the description, perform the classification and tease out an interpretation using a wide array of techniques often with good success. In glass research, machine learning has proved decisive in quantifying very subtle correlations between the local structure around a site and the susceptibility towards a rearrangement event at that site. There are also beginning to be some impressive attempts to deploy machine learning in the design of composite soft materials. The discovery aspect of this new materials design meets the current interest in teaching algorithms to learn to extrapolate beyond the training data.
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Affiliation(s)
- Paul S Clegg
- School of Physics and Astronomy, University of Edinburgh, Edinburgh EH9 3FD, UK.
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14
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Lin T, Wang Z, Wang W, Sui Y. A neural network-based algorithm for high-throughput characterisation of viscoelastic properties of flowing microcapsules. SOFT MATTER 2021; 17:4027-4039. [PMID: 33480936 DOI: 10.1039/d0sm02121k] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Microcapsules, consisting of a liquid droplet enclosed by a viscoelastic membrane, have a wide range of biomedical and pharmaceutical applications and also serve as a popular mechanical model for biological cells. In this study, we develop a novel high throughput approach, by combining a machine learning method with a high-fidelity mechanistic capsule model, to accurately predict the membrane elasticity and viscosity of microcapsules from their dynamic deformation when flowing in a branched microchannel. The machine learning method consists of a deep convolutional neural network (DCNN) connected by a long short-term memory (LSTM) network. We demonstrate that with a superior prediction accuracy the present hybrid DCNN-LSTM network can still be faster than a conventional inverse method by five orders of magnitude, and can process thousands of capsules per second. We also show that the hybrid network has fewer restrictions compared with a simple DCNN.
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Affiliation(s)
- Tao Lin
- School of Engineering and Materials Science, Queen Mary University of London, London E1 4NS, UK.
| | - Zhen Wang
- Department of Mechanical Engineering, University College London, London WC1E 6BT, UK
| | - Wen Wang
- School of Engineering and Materials Science, Queen Mary University of London, London E1 4NS, UK.
| | - Yi Sui
- School of Engineering and Materials Science, Queen Mary University of London, London E1 4NS, UK.
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15
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Paret J, Jack RL, Coslovich D. Assessing the structural heterogeneity of supercooled liquids through community inference. J Chem Phys 2020; 152:144502. [DOI: 10.1063/5.0004732] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- Joris Paret
- Laboratoire Charles Coulomb (L2C), Université de Montpellier, CNRS, Montpellier, France
| | - Robert L. Jack
- Department of Chemistry, University of Cambridge, Lensfield Road, Cambridge CB2 1EW, United Kingdom
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Wilberforce Road, Cambridge CB3 0WA, United Kingdom
| | - Daniele Coslovich
- Laboratoire Charles Coulomb (L2C), Université de Montpellier, CNRS, Montpellier, France
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