Self-supervised learning and prediction of microstructure evolution with convolutional recurrent neural networks
暂无分享,去创建一个
Yifan Cao | Babak Sadigh | Ming Tang | Kaiqi Yang | Youtian Zhang | Shaoxun Fan | Daniel Aberg | Fei Zhou | B. Sadigh | D. Åberg | F. Zhou | M. Tang | Shaoxun Fan | Yifan Cao | Kaiqi Yang | Youtian Zhang | Yifan Cao
[1] M. Harmer,et al. Materials informatics for the screening of multi-principal elements and high-entropy alloys , 2019, Nature Communications.
[2] Arnulf Jentzen,et al. Solving high-dimensional partial differential equations using deep learning , 2017, Proceedings of the National Academy of Sciences.
[3] J. E. Hilliard,et al. Free Energy of a Nonuniform System. I. Interfacial Free Energy and Free Energy of a Nonuniform System. III. Nucleation in a Two‐Component Incompressible Fluid , 2013 .
[4] Zhiliang Xu,et al. Neural Time-Dependent Partial Differential Equation , 2020, ArXiv.
[5] Chiho Kim,et al. Machine Learning Assisted Predictions of Intrinsic Dielectric Breakdown Strength of ABX3 Perovskites , 2016 .
[6] I. Steinbach,et al. Topological phase inversion after long-term thermal exposure of nickel-base superalloys: Experiment and phase-field simulation , 2017 .
[7] H. Schaeffer,et al. Learning partial differential equations via data discovery and sparse optimization , 2017, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.
[8] R. Batra,et al. Physically informed artificial neural networks for atomistic modeling of materials , 2018, Nature Communications.
[9] Erin Antono,et al. Building Data-driven Models with Microstructural Images: Generalization and Interpretability , 2017, ArXiv.
[10] Long-Qing Chen. Phase-Field Models for Microstructure Evolution , 2002 .
[11] Franz Aurenhammer,et al. Voronoi Diagrams , 2000, Handbook of Computational Geometry.
[12] P. Alam. ‘W’ , 2021, Composites Engineering.
[13] P. Alam. ‘T’ , 2021, Composites Engineering: An A–Z Guide.
[14] Bülent Yener,et al. Image driven machine learning methods for microstructure recognition , 2016 .
[15] Felix A Faber,et al. Machine Learning Energies of 2 Million Elpasolite (ABC_{2}D_{6}) Crystals. , 2015, Physical review letters.
[16] Stefano Curtarolo,et al. Finding Unprecedentedly Low-Thermal-Conductivity Half-Heusler Semiconductors via High-Throughput Materials Modeling , 2014, 1401.2439.
[17] Nian Liu,et al. Visualizing Battery Reactions and Processes by Using In Situ and In Operando Microscopies , 2018 .
[18] Jake Graser,et al. Machine Learning and Energy Minimization Approaches for Crystal Structure Predictions: A Review and New Horizons , 2018 .
[19] Elizabeth A. Holm,et al. A computer vision approach for automated analysis and classification of microstructural image data , 2015 .
[20] Christopher M Wolverton,et al. Multiscale modeling of θ′ precipitation in Al–Cu binary alloys , 2004 .
[21] Fei Meng,et al. Two-dimensional lithium diffusion behavior and probable hybrid phase transformation kinetics in olivine lithium iron phosphate , 2017, Nature Communications.
[22] Surya R. Kalidindi,et al. Structure–property linkages using a data science approach: Application to a non-metallic inclusion/steel composite system , 2015 .
[23] Xiaolin Li,et al. A Deep Adversarial Learning Methodology for Designing Microstructural Material Systems , 2018, Volume 2B: 44th Design Automation Conference.
[24] Markus Gusenbauer,et al. Magnetic microstructure machine learning analysis , 2018, Journal of Physics: Materials.
[25] J. Warren,et al. A general mechanism of polycrystalline growth , 2004, Nature materials.
[26] Zuhuang Chen,et al. Kinetic control of tunable multi-state switching in ferroelectric thin films , 2019, Nature Communications.
[27] Yitong Liu,et al. A joint deep learning model to recover information and reduce artifacts in missing-wedge sinograms for electron tomography and beyond , 2019, Scientific Reports.
[28] Martín Abadi,et al. TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.
[29] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[30] Sergey Levine,et al. Unsupervised Learning for Physical Interaction through Video Prediction , 2016, NIPS.
[31] Tim Mueller,et al. Identifying models of dielectric breakdown strength from high-throughput data via genetic programming , 2017, Scientific Reports.
[32] Tim Colonius,et al. FiniteNet: A Fully Convolutional LSTM Network Architecture for Time-Dependent Partial Differential Equations , 2020, ArXiv.
[33] Nikolas Provatas,et al. Phase-Field Methods in Materials Science and Engineering , 2010 .
[34] P. Alam. ‘S’ , 2021, Composites Engineering: An A–Z Guide.
[35] Yan Wang,et al. Data analytics using canonical correlation analysis and Monte Carlo simulation , 2017, npj Computational Materials.
[36] Kevin M. Ryan,et al. Crystal Structure Prediction via Deep Learning. , 2018, Journal of the American Chemical Society.
[37] Navdeep Jaitly,et al. Towards End-To-End Speech Recognition with Recurrent Neural Networks , 2014, ICML.
[38] Stefano Ermon,et al. High‐Voltage Charging‐Induced Strain, Heterogeneity, and Micro‐Cracks in Secondary Particles of a Nickel‐Rich Layered Cathode Material , 2019, Advanced Functional Materials.
[39] Steven L. Brunton,et al. Data-driven discovery of coordinates and governing equations , 2019, Proceedings of the National Academy of Sciences.
[40] R. Kobayashi. Modeling and numerical simulations of dendritic crystal growth , 1993 .
[41] J. Behler. Perspective: Machine learning potentials for atomistic simulations. , 2016, The Journal of chemical physics.
[42] Yoshua Bengio,et al. On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.
[43] Shyue Ping Ong,et al. Deep neural networks for accurate predictions of crystal stability , 2017, Nature Communications.
[44] Yuksel C. Yabansu,et al. Material structure-property linkages using three-dimensional convolutional neural networks , 2018 .
[45] Mario Fritz,et al. Advanced Steel Microstructural Classification by Deep Learning Methods , 2017, Scientific Reports.
[46] P. Alam,et al. R , 1823, The Herodotus Encyclopedia.
[47] Brian L. DeCost,et al. Exploring the microstructure manifold: Image texture representations applied to ultrahigh carbon steel microstructures , 2017, 1702.01117.
[48] Chiho Kim,et al. Machine learning in materials informatics: recent applications and prospects , 2017, npj Computational Materials.
[49] Paris Perdikaris,et al. Physics Informed Deep Learning (Part II): Data-driven Discovery of Nonlinear Partial Differential Equations , 2017, ArXiv.
[50] Weilin Huang,et al. V4D: 4D Convolutional Neural Networks for Video-level Representation Learning , 2020, ICLR.
[51] Paris Perdikaris,et al. Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations , 2017, ArXiv.
[52] W. Mullins. Two‐Dimensional Motion of Idealized Grain Boundaries , 1956 .
[53] M. Marques,et al. Recent advances and applications of machine learning in solid-state materials science , 2019, npj Computational Materials.
[54] R. Ramesh,et al. Observation of room-temperature polar skyrmions , 2019, Nature.
[55] Bart Blanpain,et al. Quantitative analysis of grain boundary properties in a generalized phase field model for grain growth in anisotropic systems , 2008 .
[56] Paul J. Werbos,et al. Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.
[57] Dit-Yan Yeung,et al. Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.
[58] Jeffrey C Grossman,et al. Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. , 2017, Physical review letters.
[59] S. Allen,et al. Ground state structures in ordered B.C.C. ternary alloys with second-neighbor interactions , 1992 .
[60] B. Yener,et al. Image-driven discriminative and generative machine learning algorithms for establishing microstructure-processing relationships , 2020, ArXiv.
[61] Philip S. Yu,et al. PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs , 2017, NIPS.
[62] Nitish Srivastava,et al. Unsupervised Learning of Video Representations using LSTMs , 2015, ICML.
[63] Chi Chen,et al. Random Forest Models for Accurate Identification of Coordination Environments from X-Ray Absorption Near-Edge Structure , 2019, Patterns.
[64] J. Cahn,et al. A correction to the ground state of FCC binary ordered alloys with first and second neighbor pairwise interactions , 1973 .
[65] Eero P. Simoncelli,et al. Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.
[66] Alok Choudhary,et al. A predictive machine learning approach for microstructure optimization and materials design , 2015, Scientific Reports.
[67] Steven L. Brunton,et al. Data-driven discovery of partial differential equations , 2016, Science Advances.
[68] Geoffrey E. Hinton,et al. Learning representations by back-propagating errors , 1986, Nature.
[69] Hu Zhang,et al. Artificial neural network application to microstructure design of Nb-Si alloy to improve ultimate tensile strength , 2017 .
[70] Rampi Ramprasad,et al. Learning scheme to predict atomic forces and accelerate materials simulations , 2015, 1505.02701.
[71] P. Alam. ‘N’ , 2021, Composites Engineering: An A–Z Guide.
[72] Sergei V. Kalinin,et al. Materials informatics: From the atomic-level to the continuum , 2019, Acta Materialia.
[73] Yunbo Wang,et al. Eidetic 3D LSTM: A Model for Video Prediction and Beyond , 2019, ICLR.
[74] B. Blanpain,et al. An introduction to phase-field modeling of microstructure evolution , 2008 .
[75] Maxim Ziatdinov,et al. Learning surface molecular structures via machine vision , 2017, npj Computational Materials.
[76] Michele Parrinello,et al. Generalized neural-network representation of high-dimensional potential-energy surfaces. , 2007, Physical review letters.
[77] W. Carter,et al. Branching mechanisms in surfactant micellar growth. , 2012, The journal of physical chemistry. B.
[78] Cormac Toher,et al. Universal fragment descriptors for predicting properties of inorganic crystals , 2016, Nature Communications.
[79] T. Nissen‐Meyer,et al. Solving the wave equation with physics-informed deep learning , 2020, 2006.11894.
[80] Daniel Lewis,et al. An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction , 2019, Materials Characterization.
[81] Paris Perdikaris,et al. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , 2019, J. Comput. Phys..
[82] Turab Lookman,et al. Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning , 2018, Nature Communications.
[83] Amir Barati Farimani,et al. Deep Learning Phase Segregation , 2018, ArXiv.
[84] Elizabeth Kautz,et al. A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels , 2018, Computational Materials Science.
[85] R. Asahi,et al. Microstructure recognition using convolutional neural networks for prediction of ionic conductivity in ceramics , 2017 .
[86] Xiao‐Qing Yang,et al. Review of Recent Development of In Situ/Operando Characterization Techniques for Lithium Battery Research , 2019, Advanced materials.
[87] Martin Z. Bazant,et al. Origin and hysteresis of lithium compositional spatiodynamics within battery primary particles , 2016, Science.
[88] Masahiko Demura,et al. Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures , 2018, Scientific Reports.
[89] I. Steinbach. Phase-field models in materials science , 2009 .
[90] Robert W. Balluffi,et al. Kinetics Of Materials , 2005 .
[91] T. G. Woodcock,et al. Extracting local nucleation fields in permanent magnets using machine learning , 2019, npj Computational Materials.
[92] Rama Vasudevan,et al. Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations. , 2017, ACS nano.
[93] Peter W. Voorhees,et al. Optimizing convolutional neural networks to perform semantic segmentation on large materials imaging datasets: X-ray tomography and serial sectioning , 2020, Materials Characterization.
[94] Daniel W. Davies,et al. Machine learning for molecular and materials science , 2018, Nature.
[95] S. Brunton,et al. Discovering governing equations from data by sparse identification of nonlinear dynamical systems , 2015, Proceedings of the National Academy of Sciences.
[96] M. Tang,et al. Three-dimensional phase evolution and stress-induced non-uniform Li intercalation behavior in lithium iron phosphate , 2020 .
[97] Wei Chen,et al. Microstructural Materials Design Via Deep Adversarial Learning Methodology , 2018, Journal of Mechanical Design.
[98] Geoffrey E. Hinton,et al. Generating Text with Recurrent Neural Networks , 2011, ICML.
[99] Trevor Darrell,et al. Long-term recurrent convolutional networks for visual recognition and description , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[100] Jaideep Pathak,et al. Model-Free Prediction of Large Spatiotemporally Chaotic Systems from Data: A Reservoir Computing Approach. , 2018, Physical review letters.
[101] J. E. Hilliard,et al. Free Energy of a Nonuniform System. I. Interfacial Free Energy , 1958 .
[102] Surya R. Kalidindi,et al. Microstructure informatics using higher-order statistics and efficient data-mining protocols , 2011 .
[103] David T. Fullwood,et al. Microstructure Sensitive Design for Performance Optimization , 2012 .
[104] P. Voorhees,et al. Characterization of metals in four dimensions , 2020 .
[105] Philip S. Yu,et al. PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning , 2018, ICML.
[106] A. Karma,et al. Quantitative phase-field modeling of dendritic growth in two and three dimensions , 1996 .