1
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Zhao H, Deng HD, Cohen AE, Lim J, Li Y, Fraggedakis D, Jiang B, Storey BD, Chueh WC, Braatz RD, Bazant MZ. Learning heterogeneous reaction kinetics from X-ray videos pixel by pixel. Nature 2023; 621:289-294. [PMID: 37704764 PMCID: PMC10499602 DOI: 10.1038/s41586-023-06393-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 06/30/2023] [Indexed: 09/15/2023]
Abstract
Reaction rates at spatially heterogeneous, unstable interfaces are notoriously difficult to quantify, yet are essential in engineering many chemical systems, such as batteries1 and electrocatalysts2. Experimental characterizations of such materials by operando microscopy produce rich image datasets3-6, but data-driven methods to learn physics from these images are still lacking because of the complex coupling of reaction kinetics, surface chemistry and phase separation7. Here we show that heterogeneous reaction kinetics can be learned from in situ scanning transmission X-ray microscopy (STXM) images of carbon-coated lithium iron phosphate (LFP) nanoparticles. Combining a large dataset of STXM images with a thermodynamically consistent electrochemical phase-field model, partial differential equation (PDE)-constrained optimization and uncertainty quantification, we extract the free-energy landscape and reaction kinetics and verify their consistency with theoretical models. We also simultaneously learn the spatial heterogeneity of the reaction rate, which closely matches the carbon-coating thickness profiles obtained through Auger electron microscopy (AEM). Across 180,000 image pixels, the mean discrepancy with the learned model is remarkably small (<7%) and comparable with experimental noise. Our results open the possibility of learning nonequilibrium material properties beyond the reach of traditional experimental methods and offer a new non-destructive technique for characterizing and optimizing heterogeneous reactive surfaces.
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Affiliation(s)
- Hongbo Zhao
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Haitao Dean Deng
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA
| | - Alexander E Cohen
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Jongwoo Lim
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA
| | - Yiyang Li
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA
| | - Dimitrios Fraggedakis
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Benben Jiang
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - William C Chueh
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Menlo Park, CA, USA
| | - Richard D Braatz
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Martin Z Bazant
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA.
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2
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de Las Heras D, Zimmermann T, Sammüller F, Hermann S, Schmidt M. Perspective: How to overcome dynamical density functional theory. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2023; 35:271501. [PMID: 37023762 DOI: 10.1088/1361-648x/accb33] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 04/06/2023] [Indexed: 06/19/2023]
Abstract
We argue in favour of developing a comprehensive dynamical theory for rationalizing, predicting, designing, and machine learning nonequilibrium phenomena that occur in soft matter. To give guidance for navigating the theoretical and practical challenges that lie ahead, we discuss and exemplify the limitations of dynamical density functional theory (DDFT). Instead of the implied adiabatic sequence of equilibrium states that this approach provides as a makeshift for the true time evolution, we posit that the pending theoretical tasks lie in developing a systematic understanding of the dynamical functional relationships that govern the genuine nonequilibrium physics. While static density functional theory gives a comprehensive account of the equilibrium properties of many-body systems, we argue that power functional theory is the only present contender to shed similar insights into nonequilibrium dynamics, including the recognition and implementation of exact sum rules that result from the Noether theorem. As a demonstration of the power functional point of view, we consider an idealized steady sedimentation flow of the three-dimensional Lennard-Jones fluid and machine-learn the kinematic map from the mean motion to the internal force field. The trained model is capable of both predicting and designing the steady state dynamics universally for various target density modulations. This demonstrates the significant potential of using such techniques in nonequilibrium many-body physics and overcomes both the conceptual constraints of DDFT as well as the limited availability of its analytical functional approximations.
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Affiliation(s)
- Daniel de Las Heras
- Theoretische Physik II, Physikalisches Institut, Universität Bayreuth, D-95447 Bayreuth, Germany
| | - Toni Zimmermann
- Theoretische Physik II, Physikalisches Institut, Universität Bayreuth, D-95447 Bayreuth, Germany
| | - Florian Sammüller
- Theoretische Physik II, Physikalisches Institut, Universität Bayreuth, D-95447 Bayreuth, Germany
| | - Sophie Hermann
- Theoretische Physik II, Physikalisches Institut, Universität Bayreuth, D-95447 Bayreuth, Germany
| | - Matthias Schmidt
- Theoretische Physik II, Physikalisches Institut, Universität Bayreuth, D-95447 Bayreuth, Germany
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3
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Zhang KH, Jiang Y, Zhang LS. Inferring the Physics of Structural Evolution of Multicomponent Polymers via Machine-Learning-Accelerated Method. CHINESE JOURNAL OF POLYMER SCIENCE 2022. [DOI: 10.1007/s10118-023-2891-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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4
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Te Vrugt M, Wittkowski R. Perspective: New directions in dynamical density functional theory. JOURNAL OF PHYSICS. CONDENSED MATTER : AN INSTITUTE OF PHYSICS JOURNAL 2022; 35:041501. [PMID: 35917827 DOI: 10.1088/1361-648x/ac8633] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2022] [Accepted: 08/01/2022] [Indexed: 06/15/2023]
Abstract
Classical dynamical density functional theory (DDFT) has become one of the central modeling approaches in nonequilibrium soft matter physics. Recent years have seen the emergence of novel and interesting fields of application for DDFT. In particular, there has been a remarkable growth in the amount of work related to chemistry. Moreover, DDFT has stimulated research on other theories such as phase field crystal models and power functional theory. In this perspective, we summarize the latest developments in the field of DDFT and discuss a variety of possible directions for future research.
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Affiliation(s)
- Michael Te Vrugt
- Institut für Theoretische Physik, Center for Soft Nanoscience, Westfälische Wilhelms-Universität Münster, 48149 Münster, Germany
| | - Raphael Wittkowski
- Institut für Theoretische Physik, Center for Soft Nanoscience, Westfälische Wilhelms-Universität Münster, 48149 Münster, Germany
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5
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Yoshinaga N, Tokuda S. Bayesian modeling of pattern formation from one snapshot of pattern. Phys Rev E 2022; 106:065301. [PMID: 36671103 DOI: 10.1103/physreve.106.065301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 11/07/2022] [Indexed: 06/17/2023]
Abstract
Partial differential equations (PDEs) have been widely used to reproduce patterns in nature and to give insight into the mechanism underlying pattern formation. Although many PDE models have been proposed, they rely on the pre-request knowledge of physical laws and symmetries, and developing a model to reproduce a given desired pattern remains difficult. We propose a method, referred to as Bayesian modeling of PDEs (BM-PDEs), to estimate the best dynamical PDE for one snapshot of a objective pattern under the stationary state without ground truth. We apply BM-PDEs to nontrivial patterns, such as quasicrystals (QCs), a double gyroid, and Frank-Kasper structures. We also generate three-dimensional dodecagonal QCs from a PDE model. This is done by using the estimated parameters for the Frank-Kasper A15 structure, which closely approximates the local structures of QCs. Our method works for noisy patterns and the pattern synthesized without the ground-truth parameters, which are required for the application toward experimental data.
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Affiliation(s)
- Natsuhiko Yoshinaga
- WPI-Advanced Institute for Materials Research, Tohoku University, Sendai 980-8577, Japan
- MathAM-OIL, AIST, Sendai 980-8577, Japan
| | - Satoru Tokuda
- MathAM-OIL, AIST, Sendai 980-8577, Japan
- Research Institute for Information Technology, Kyushu University, Kasuga 816-8580, Japan
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6
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Tian H, Bazant MZ. Interfacial Resistive Switching by Multiphase Polarization in Ion-Intercalation Nanofilms. NANO LETTERS 2022; 22:5866-5873. [PMID: 35815943 DOI: 10.1021/acs.nanolett.2c01765] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Nonvolatile resistive-switching (RS) memories promise to revolutionize hardware architectures with in-memory computing. Recently, ion-interclation materials have attracted increasing attention as potential RS materials for their ion-modulated electronic conductivity. In this Letter, we propose RS by multiphase polarization (MP) of ion-intercalated thin films between ion-blocking electrodes, in which interfacial phase separation triggered by an applied voltage switches the electron-transfer resistance. We develop an electrochemical phase-field model for simulations of coupled ion-electron transport and ion-modulated electron-transfer rates and use it to analyze the MP switching current and time, resistance ratio, and current-voltage response. The model is able to reproduce the complex cyclic voltammograms of lithium titanate (LTO) memristors, which cannot be explained by existing models based on bulk dielectric breakdown. The theory predicts the achievable switching speeds for multiphase ion-intercalation materials and could be used to guide the design of high-performance MP-based RS memories.
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Affiliation(s)
- Huanhuan Tian
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Martin Z Bazant
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
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7
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Rassolov G, Tociu L, Fodor E, Vaikuntanathan S. From predicting to learning dissipation from pair correlations of active liquids. J Chem Phys 2022; 157:054901. [DOI: 10.1063/5.0097863] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Active systems, which are driven out of equilibrium by local non-conservative forces, can adopt unique behaviors and configurations. Towards designing such materials, an important challenge is to precisely connect the static structure of active systems to the dissipation of energy induced by the local driving. Here, we use tools from liquid-state theories and machine learning to take on these challenges. We first demonstrate analytically for an isotropic active matter system that dissipation and pair correlations are closely related when driving forces behave like an active temperature. We then extend a nonequilibrium mean-field framework for predicting these pair correlations which, unlike most existing approaches, is applicable even for strongly interacting particles and far from equilibrium, to predict dissipation in these systems. Based on this theory, we reveal analytically a robust relation between dissipation and structure which holds even as the system approaches a nonequilibrium phase transition. Finally, we construct a neural network which maps static configurations of particles to their dissipation rate without any prior knowledge of the underlying dynamics. Our results open novel perspectives on the interplay between dissipation and organization out-of-equilibrium.
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Affiliation(s)
| | - Laura Tociu
- The University of Chicago, United States of America
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8
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Deng HD, Zhao H, Jin N, Hughes L, Savitzky BH, Ophus C, Fraggedakis D, Borbély A, Yu YS, Lomeli EG, Yan R, Liu J, Shapiro DA, Cai W, Bazant MZ, Minor AM, Chueh WC. Correlative image learning of chemo-mechanics in phase-transforming solids. NATURE MATERIALS 2022; 21:547-554. [PMID: 35177785 DOI: 10.1038/s41563-021-01191-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 12/16/2021] [Indexed: 06/14/2023]
Abstract
Constitutive laws underlie most physical processes in nature. However, learning such equations in heterogeneous solids (for example, due to phase separation) is challenging. One such relationship is between composition and eigenstrain, which governs the chemo-mechanical expansion in solids. Here we developed a generalizable, physically constrained image-learning framework to algorithmically learn the chemo-mechanical constitutive law at the nanoscale from correlative four-dimensional scanning transmission electron microscopy and X-ray spectro-ptychography images. We demonstrated this approach on LiXFePO4, a technologically relevant battery positive electrode material. We uncovered the functional form of the composition-eigenstrain relation in this two-phase binary solid across the entire composition range (0 ≤ X ≤ 1), including inside the thermodynamically unstable miscibility gap. The learned relation directly validates Vegard's law of linear response at the nanoscale. Our physics-constrained data-driven approach directly visualizes the residual strain field (by removing the compositional and coherency strain), which is otherwise impossible to quantify. Heterogeneities in the residual strain arise from misfit dislocations and were independently verified by X-ray diffraction line profile analysis. Our work provides the means to simultaneously quantify chemical expansion, coherency strain and dislocations in battery electrodes, which has implications on rate capabilities and lifetime. Broadly, this work also highlights the potential of integrating correlative microscopy and image learning for extracting material properties and physics.
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Affiliation(s)
- Haitao D Deng
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - Hongbo Zhao
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Norman Jin
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA
| | - Lauren Hughes
- National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Benjamin H Savitzky
- National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Colin Ophus
- National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Dimitrios Fraggedakis
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - András Borbély
- Centre SMS, Georges Friedel Laboratory (UMR 5307), Mines Saint-Etienne, Univ. Lyon, CNRS, Saint-Etienne, France
| | - Young-Sang Yu
- Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
- Department of Physics, Chungbuk National University, Cheongju, Republic of Korea
| | - Eder G Lomeli
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA
| | - Rui Yan
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - Jueyi Liu
- Institute for Computational and Mathematical Engineering, Stanford University, Stanford, CA, USA
| | - David A Shapiro
- Advanced Light Source, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Wei Cai
- Department of Mechanical Engineering, Stanford University, Stanford, CA, USA
| | - Martin Z Bazant
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Andrew M Minor
- National Center for Electron Microscopy, Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
- Department of Materials Science and Engineering, University of California, Berkeley, CA, USA.
| | - William C Chueh
- Department of Materials Science and Engineering, Stanford University, Stanford, CA, USA.
- Stanford Institute for Materials and Energy Sciences, SLAC National Accelerator Laboratory, Stanford University, Menlo Park, CA, USA.
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9
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Gandhi P, Ciocanel MV, Niklas K, Dawes AT. Identification of approximate symmetries in biological development. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200273. [PMID: 34743597 PMCID: PMC8580469 DOI: 10.1098/rsta.2020.0273] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/11/2021] [Indexed: 05/04/2023]
Abstract
Virtually all forms of life, from single-cell eukaryotes to complex, highly differentiated multicellular organisms, exhibit a property referred to as symmetry. However, precise measures of symmetry are often difficult to formulate and apply in a meaningful way to biological systems, where symmetries and asymmetries can be dynamic and transient, or be visually apparent but not reliably quantifiable using standard measures from mathematics and physics. Here, we present and illustrate a novel measure that draws on concepts from information theory to quantify the degree of symmetry, enabling the identification of approximate symmetries that may be present in a pattern or a biological image. We apply the measure to rotation, reflection and translation symmetries in patterns produced by a Turing model, as well as natural objects (algae, flowers and leaves). This method of symmetry quantification is unbiased and rigorous, and requires minimal manual processing compared to alternative measures. The proposed method is therefore a useful tool for comparison and identification of symmetries in biological systems, with potential future applications to symmetries that arise during development, as observed in vivo or as produced by mathematical models. This article is part of the theme issue 'Recent progress and open frontiers in Turing's theory of morphogenesis'.
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Affiliation(s)
- Punit Gandhi
- Department of Mathematics and Applied Mathematics, Virginia Commonwealth University, Richmond, VA, USA
| | | | - Karl Niklas
- School of Integrative Plant Biology, Cornell University, Ithaca, NY, USA
| | - Adriana T. Dawes
- Department of Mathematics and Department of Molecular Genetics, The Ohio State University, Columbus, OH, USA
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10
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Lombardo T, Duquesnoy M, El-Bouysidy H, Årén F, Gallo-Bueno A, Jørgensen PB, Bhowmik A, Demortière A, Ayerbe E, Alcaide F, Reynaud M, Carrasco J, Grimaud A, Zhang C, Vegge T, Johansson P, Franco AA. Artificial Intelligence Applied to Battery Research: Hype or Reality? Chem Rev 2021; 122:10899-10969. [PMID: 34529918 PMCID: PMC9227745 DOI: 10.1021/acs.chemrev.1c00108] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
![]()
This is a critical
review of artificial intelligence/machine learning
(AI/ML) methods applied to battery research. It aims at providing
a comprehensive, authoritative, and critical, yet easily understandable,
review of general interest to the battery community. It addresses
the concepts, approaches, tools, outcomes, and challenges of using
AI/ML as an accelerator for the design and optimization of the next
generation of batteries—a current hot topic. It intends to
create both accessibility of these tools to the chemistry and electrochemical
energy sciences communities and completeness in terms of the different
battery R&D aspects covered.
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Affiliation(s)
- Teo Lombardo
- Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Réseau sur le Stockage Electrochimique de l'Energie (RS2E), FR CNRS 3459, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France
| | - Marc Duquesnoy
- Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Réseau sur le Stockage Electrochimique de l'Energie (RS2E), FR CNRS 3459, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France
| | - Hassna El-Bouysidy
- Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Department of Physics, Chalmers University of Technology, SE-41296 Göteborg, Sweden
| | - Fabian Årén
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Department of Physics, Chalmers University of Technology, SE-41296 Göteborg, Sweden
| | - Alfonso Gallo-Bueno
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Centre for Cooperative Research on Alternative Energies (CIC energiGUNE), Basque Research and Technology Alliance (BRTA), Alava Technology Park, Albert Einstein 48, 01510 Vitoria-Gasteiz, Spain
| | - Peter Bjørn Jørgensen
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, Building 301, 2800 Kgs. Lyngby, Denmark
| | - Arghya Bhowmik
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, Building 301, 2800 Kgs. Lyngby, Denmark
| | - Arnaud Demortière
- Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Réseau sur le Stockage Electrochimique de l'Energie (RS2E), FR CNRS 3459, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France
| | - Elixabete Ayerbe
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,CIDETEC, Basque Research and Technology Alliance (BRTA), Po. Miramón 196, 20014 Donostia-San Sebastián, Spain
| | - Francisco Alcaide
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,CIDETEC, Basque Research and Technology Alliance (BRTA), Po. Miramón 196, 20014 Donostia-San Sebastián, Spain
| | - Marine Reynaud
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Centre for Cooperative Research on Alternative Energies (CIC energiGUNE), Basque Research and Technology Alliance (BRTA), Alava Technology Park, Albert Einstein 48, 01510 Vitoria-Gasteiz, Spain
| | - Javier Carrasco
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Centre for Cooperative Research on Alternative Energies (CIC energiGUNE), Basque Research and Technology Alliance (BRTA), Alava Technology Park, Albert Einstein 48, 01510 Vitoria-Gasteiz, Spain
| | - Alexis Grimaud
- Réseau sur le Stockage Electrochimique de l'Energie (RS2E), FR CNRS 3459, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,UMR CNRS 8260 "Chimie du Solide et Energie", Collège de France, 11 Place Marcelin Berthelot, 75231 Paris Cedex 05, France Sorbonne Universités - UPMC Univ Paris 06, 4 Place Jussieu, F-75005 Paris, France
| | - Chao Zhang
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Department of Chemistry - Ångström Laboratory, Box 538, 75121 Uppsala, Sweden
| | - Tejs Vegge
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Department of Energy Conversion and Storage, Technical University of Denmark, Anker Engelunds Vej, Building 301, 2800 Kgs. Lyngby, Denmark
| | - Patrik Johansson
- ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Department of Physics, Chalmers University of Technology, SE-41296 Göteborg, Sweden
| | - Alejandro A Franco
- Laboratoire de Réactivité et Chimie des Solides (LRCS), UMR CNRS 7314, Université de Picardie Jules Verne, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Réseau sur le Stockage Electrochimique de l'Energie (RS2E), FR CNRS 3459, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,ALISTORE-European Research Institute, FR CNRS 3104, Hub de l'Energie, 15, rue Baudelocque, 80039 Amiens Cedex, France.,Institut Universitaire de France, 103 Boulevard Saint Michel, 75005 Paris, France
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11
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Wong GCL, Antani JD, Lele PP, Chen J, Nan B, Kühn MJ, Persat A, Bru JL, Høyland-Kroghsbo NM, Siryaporn A, Conrad JC, Carrara F, Yawata Y, Stocker R, V Brun Y, Whitfield GB, Lee CK, de Anda J, Schmidt WC, Golestanian R, O'Toole GA, Floyd KA, Yildiz FH, Yang S, Jin F, Toyofuku M, Eberl L, Nomura N, Zacharoff LA, El-Naggar MY, Yalcin SE, Malvankar NS, Rojas-Andrade MD, Hochbaum AI, Yan J, Stone HA, Wingreen NS, Bassler BL, Wu Y, Xu H, Drescher K, Dunkel J. Roadmap on emerging concepts in the physical biology of bacterial biofilms: from surface sensing to community formation. Phys Biol 2021; 18. [PMID: 33462162 PMCID: PMC8506656 DOI: 10.1088/1478-3975/abdc0e] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 01/14/2021] [Indexed: 11/29/2022]
Abstract
Bacterial biofilms are communities of bacteria that exist as aggregates that can adhere to surfaces or be free-standing. This complex, social mode of cellular organization is fundamental to the physiology of microbes and often exhibits surprising behavior. Bacterial biofilms are more than the sum of their parts: single-cell behavior has a complex relation to collective community behavior, in a manner perhaps cognate to the complex relation between atomic physics and condensed matter physics. Biofilm microbiology is a relatively young field by biology standards, but it has already attracted intense attention from physicists. Sometimes, this attention takes the form of seeing biofilms as inspiration for new physics. In this roadmap, we highlight the work of those who have taken the opposite strategy: we highlight the work of physicists and physical scientists who use physics to engage fundamental concepts in bacterial biofilm microbiology, including adhesion, sensing, motility, signaling, memory, energy flow, community formation and cooperativity. These contributions are juxtaposed with microbiologists who have made recent important discoveries on bacterial biofilms using state-of-the-art physical methods. The contributions to this roadmap exemplify how well physics and biology can be combined to achieve a new synthesis, rather than just a division of labor.
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Affiliation(s)
- Gerard C L Wong
- Department of Bioengineering, University of California-Los Angeles, Los Angeles, California, CA 90095, United States of America.,Department of Chemistry and Biochemistry, University of California-Los Angeles, Los Angeles, California, CA 90095, United States of America.,California NanoSystems Institute, University of California-Los Angeles, Los Angeles, California, CA 90095, United States of America
| | - Jyot D Antani
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, United States of America
| | - Pushkar P Lele
- Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, United States of America
| | - Jing Chen
- Department of Biological Sciences, Virginia Polytechnic Institute and State University, Blacksburg, VA24061, United States of America
| | - Beiyan Nan
- Department of Biology, Texas A&M University, College Station, Texas, TX 77845, United States of America
| | - Marco J Kühn
- Institute of Bioengineering and Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Alexandre Persat
- Institute of Bioengineering and Global Health Institute, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Jean-Louis Bru
- Department of Molecular Biology & Biochemistry, University of California-Irvine, California, CA 92697, United States of America
| | | | - Albert Siryaporn
- Department of Molecular Biology & Biochemistry, University of California-Irvine, California, CA 92697, United States of America.,Department of Physics & Astronomy, University of California-Irvine, California, CA 92697, United States of America
| | - Jacinta C Conrad
- William A Brookshire Department of Chemical and Biomolecular Engineering, University of Houston, Houston, Texas, TX 77204, United States of America
| | - Francesco Carrara
- Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zurich, 8093 Zurich, Switzerland
| | - Yutaka Yawata
- Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8572, Japan.,Microbiology Research Center for Sustainability, University of Tsukuba, 305-8572 Tsukuba, Japan
| | - Roman Stocker
- Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zurich, 8093 Zurich, Switzerland
| | - Yves V Brun
- University of Montreal, Faculty of Medicine, Montreal, Quebec, H3C 3J7, Canada
| | - Gregory B Whitfield
- University of Montreal, Faculty of Medicine, Montreal, Quebec, H3C 3J7, Canada
| | - Calvin K Lee
- Department of Bioengineering, University of California-Los Angeles, Los Angeles, California, CA 90095, United States of America.,Department of Chemistry and Biochemistry, University of California-Los Angeles, Los Angeles, California, CA 90095, United States of America.,California NanoSystems Institute, University of California-Los Angeles, Los Angeles, California, CA 90095, United States of America
| | - Jaime de Anda
- Department of Bioengineering, University of California-Los Angeles, Los Angeles, California, CA 90095, United States of America.,Department of Chemistry and Biochemistry, University of California-Los Angeles, Los Angeles, California, CA 90095, United States of America.,California NanoSystems Institute, University of California-Los Angeles, Los Angeles, California, CA 90095, United States of America
| | - William C Schmidt
- Department of Bioengineering, University of California-Los Angeles, Los Angeles, California, CA 90095, United States of America.,Department of Chemistry and Biochemistry, University of California-Los Angeles, Los Angeles, California, CA 90095, United States of America.,California NanoSystems Institute, University of California-Los Angeles, Los Angeles, California, CA 90095, United States of America
| | - Ramin Golestanian
- Max Planck Institute for Dynamics and Self-Organization (MPIDS), D-37077 Göttingen, Germany.,Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford OX1 3PU, United Kingdom
| | - George A O'Toole
- Department of Microbiology and Immunology, Geisel School of Medicine at Dartmouth, Hanover, NH 03755, United States of America
| | - Kyle A Floyd
- Department of Microbiology and Environmental Toxicology, University of California-Santa Cruz, Santa Cruz, California, CA 95060, United States of America
| | - Fitnat H Yildiz
- Department of Microbiology and Environmental Toxicology, University of California-Santa Cruz, Santa Cruz, California, CA 95060, United States of America
| | - Shuai Yang
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
| | - Fan Jin
- CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, People's Republic of China
| | - Masanori Toyofuku
- Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8572, Japan.,Microbiology Research Center for Sustainability, University of Tsukuba, 305-8572 Tsukuba, Japan
| | - Leo Eberl
- Department of Plant and Microbial Biology, University of Zürich, 8008 Zürich, Switzerland
| | - Nobuhiko Nomura
- Faculty of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki 305-8572, Japan.,Microbiology Research Center for Sustainability, University of Tsukuba, 305-8572 Tsukuba, Japan
| | - Lori A Zacharoff
- Department of Physics and Astronomy, University of Southern California, Los Angeles, California, CA 90089, United States of America.,Department of Chemistry, University of Southern California, Los Angeles, California, CA 90089, United States of America
| | - Mohamed Y El-Naggar
- Department of Physics and Astronomy, University of Southern California, Los Angeles, California, CA 90089, United States of America.,Department of Chemistry, University of Southern California, Los Angeles, California, CA 90089, United States of America.,Department of Biological Sciences, University of Southern California, Los Angeles, California, CA 90089, United States of America
| | - Sibel Ebru Yalcin
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, CT 06516, United States of America.,Microbial Sciences Institute, Yale University, New Haven, Connecticut, CT 06516, United States of America
| | - Nikhil S Malvankar
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, Connecticut, CT 06516, United States of America.,Microbial Sciences Institute, Yale University, New Haven, Connecticut, CT 06516, United States of America
| | - Mauricio D Rojas-Andrade
- Department of Materials Science and Engineering, University of California-Irvine, Irvine, California CA 92697, United States of America
| | - Allon I Hochbaum
- Department of Molecular Biology & Biochemistry, University of California-Irvine, California, CA 92697, United States of America.,Department of Materials Science and Engineering, University of California-Irvine, Irvine, California CA 92697, United States of America.,Department of Chemistry, University of California-Irvine, Irvine, California, CA 92697, United States of America.,Department of Chemical and Biomolecular Engineering, University of California-Irvine, Irvine, California, CA 92697, United States of America
| | - Jing Yan
- Department of Molecular, Cellular and Developmental Biology, Yale University, New Haven, Connecticut, CT 06511, United States of America
| | - Howard A Stone
- Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, New Jersey, NJ 08544, United States of America
| | - Ned S Wingreen
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, NJ 08544, United States of America.,Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, New Jersey, NJ 08544, United States of America
| | - Bonnie L Bassler
- Department of Molecular Biology, Princeton University, Princeton, New Jersey, NJ 08544, United States of America.,The Howard Hughes Medical Institute, Chevy Chase, Maryland MD 20815, United States of America
| | - Yilin Wu
- Department of Physics and Shenzhen Research Institute, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, People's Republic of China
| | - Haoran Xu
- Department of Physics and Shenzhen Research Institute, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, People's Republic of China
| | - Knut Drescher
- Max Planck Institute for Terrestrial Microbiology, 35043 Marburg, Germany.,Department of Physics, Philipps-Universität Marburg, 35043 Marburg, Germany
| | - Jörn Dunkel
- Department of Mathematics, Massachusetts Institute of Technology, Cambridge, Massachusetts, MA 02139-4307, United States of America
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12
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Inguva PK, Walker PJ, Yew HW, Zhu K, Haslam AJ, Matar OK. Continuum-scale modelling of polymer blends using the Cahn-Hilliard equation: transport and thermodynamics. SOFT MATTER 2021; 17:5645-5665. [PMID: 34095939 DOI: 10.1039/d1sm00272d] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The Cahn-Hilliard equation is commonly used to study multi-component soft systems such as polymer blends at continuum scales. We first systematically explore various features of the equation system, which give rise to a deep connection between transport and thermodynamics-specifically that the Gibbs free energy of mixing function is central to formulating a well-posed model. Accordingly, we explore how thermodynamic models from three broad classes of approach (lattice-based, activity-based and perturbation methods) can be incorporated within the Cahn-Hilliard equation and examine how they impact the numerical solution for two model polymer blends, noting that although the analysis presented here is focused on binary mixtures, it is readily extensible to multi-component mixtures. It is observed that, although the predicted liquid-liquid interfacial tension is quite strongly affected, the choice of thermodynamic model has little influence on the development of the morphology.
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Affiliation(s)
- Pavan K Inguva
- Department of Chemical Engineering, Massachusetts Institute of Technology, 25 Ames Street, Cambridge, MA 02142, USA and Department of Chemical Engineering, Imperial College London, SW7 2AZ, UK.
| | - Pierre J Walker
- Department of Chemical Engineering, Imperial College London, SW7 2AZ, UK.
| | - Hon Wa Yew
- Department of Chemical Engineering, Imperial College London, SW7 2AZ, UK.
| | - Kezheng Zhu
- Department of Chemical Engineering, Imperial College London, SW7 2AZ, UK.
| | - Andrew J Haslam
- Department of Chemical Engineering, Imperial College London, SW7 2AZ, UK.
| | - Omar K Matar
- Department of Chemical Engineering, Imperial College London, SW7 2AZ, UK.
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13
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Yin M, Zheng X, Humphrey JD, Em Karniadakis G. Non-invasive Inference of Thrombus Material Properties with Physics-Informed Neural Networks. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING 2021; 375:113603. [PMID: 33414569 PMCID: PMC7785048 DOI: 10.1016/j.cma.2020.113603] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
We employ physics-informed neural networks (PINNs) to infer properties of biological materials using synthetic data. In particular, we successfully apply PINNs on inferring permeability and viscoelastic modulus from thrombus deformation data, which can be described by the fourth-order Cahn-Hilliard and Navier-Stokes Equations. In PINNs, the partial differential equations are encoded into a loss function, where partial derivatives can be obtained through automatic differentiation (AD). In addition to tackling the challenge of calculating the fourth-order derivative in the Cahn-Hilliard equation with AD, we introduce an auxiliary network along with the main neural network to approximate the second-derivative of the energy potential term. Our model can simultaneously predict unknown material parameters and velocity, pressure, and deformation gradient fields by merely training with partial information among all data, i.e., phase field and pressure measurements, while remaining highly flexible in sampling within the spatio-temporal domain for data acquisition. We validate our model by numerical solutions from the spectral/hp element method (SEM) and demonstrate its robustness by training it with noisy measurements. Our results show that PINNs can infer the material properties from noisy synthetic data, and thus they have great potential for inferring these properties from experimental multi-modality and multi-fidelity data.
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Affiliation(s)
- Minglang Yin
- Center for Biomedical Engineering, Brown University, Providence, RI 02912
- School of Engineering, Brown University, Providence, RI 02912
| | - Xiaoning Zheng
- Division of Applied Mathematics, Brown University, Providence, RI 02912
| | - Jay D. Humphrey
- Department of Biomedical Engineering, Yale University, New Haven, CT 06520
| | - George Em Karniadakis
- Division of Applied Mathematics, Brown University, Providence, RI 02912
- Corresponding author:
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14
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Dornelas V, Colombo EH, López C, Hernández-García E, Anteneodo C. Landscape-induced spatial oscillations in population dynamics. Sci Rep 2021; 11:3470. [PMID: 33568726 PMCID: PMC7876042 DOI: 10.1038/s41598-021-82344-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Accepted: 01/07/2021] [Indexed: 01/30/2023] Open
Abstract
We study the effect that disturbances in the ecological landscape exert on the spatial distribution of a population that evolves according to the nonlocal FKPP equation. Using both numerical and analytical techniques, we characterize, as a function of the interaction kernel, the three types of stationary profiles that can develop near abrupt spatial variations in the environmental conditions vital for population growth: sustained oscillations, decaying oscillations and exponential relaxation towards a flat profile. Through the mapping between the features of the induced wrinkles and the shape of the interaction kernel, we discuss how heterogeneities can reveal information that would be hidden in a flat landscape.
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Affiliation(s)
- Vivian Dornelas
- Department of Physics, PUC-Rio, Rua Marquês de São Vicente, 225, Rio de Janeiro, 22451-900, Brazil
| | - Eduardo H Colombo
- IFISC (CSIC-UIB), Campus Universitat Illes Balears, 07122, Palma de Mallorca, Spain
- Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, 08544, USA
- Department of Ecology, Evolution, and Natural Resources, Rutgers University, New Brunswick, NJ, 08901, USA
| | - Cristóbal López
- IFISC (CSIC-UIB), Campus Universitat Illes Balears, 07122, Palma de Mallorca, Spain
| | | | - Celia Anteneodo
- Department of Physics, PUC-Rio, Rua Marquês de São Vicente, 225, Rio de Janeiro, 22451-900, Brazil.
- Institute of Science and Technology for Complex Systems, Rio de Janeiro, Brazil.
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15
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Xue T, Beatson A, Chiaramonte M, Roeder G, Ash JT, Menguc Y, Adriaenssens S, Adams RP, Mao S. A data-driven computational scheme for the nonlinear mechanical properties of cellular mechanical metamaterials under large deformation. SOFT MATTER 2020; 16:7524-7534. [PMID: 32700724 DOI: 10.1039/d0sm00488j] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Cellular mechanical metamaterials are a special class of materials whose mechanical properties are primarily determined by their geometry. However, capturing the nonlinear mechanical behavior of these materials, especially those with complex geometries and under large deformation, can be challenging due to inherent computational complexity. In this work, we propose a data-driven multiscale computational scheme as a possible route to resolve this challenge. We use a neural network to approximate the effective strain energy density as a function of cellular geometry and overall deformation. The network is constructed by "learning" from the data generated by finite element calculation of a set of representative volume elements at cellular scales. This effective strain energy density is then used to predict the mechanical responses of cellular materials at larger scales. Compared with direct finite element simulation, the proposed scheme can reduce the computational time up to two orders of magnitude. Potentially, this scheme can facilitate new optimization algorithms for designing cellular materials of highly specific mechanical properties.
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Affiliation(s)
- Tianju Xue
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA.
| | - Alex Beatson
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
| | | | - Geoffrey Roeder
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
| | - Jordan T Ash
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
| | | | - Sigrid Adriaenssens
- Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08544, USA.
| | - Ryan P Adams
- Department of Computer Science, Princeton University, Princeton, NJ 08544, USA
| | - Sheng Mao
- Department of Mechanics and Engineering Science, BIC-ESAT, College of Engineering, Peking University, Beijing 100871, People's Republic of China. and Department of Mechanical and Aerospace Engineering, Princeton University, Princeton, NJ 08544, USA
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