Materials discovery and design using machine learning
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Yue Liu | Tianlu Zhao | Wangwei Ju | S. Shi
[1] A. Choudhary,et al. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science , 2016 .
[2] Ankit Agrawal,et al. Context Aware Machine Learning Approaches for Modeling Elastic Localization in Three-Dimensional Composite Microstructures , 2017, Integrating Materials and Manufacturing Innovation.
[3] Mingpu Wang,et al. Hybrid genetic algorithms and support vector regression in forecasting atmospheric corrosion of metallic materials , 2008 .
[4] Yoram Reich,et al. Machine learning of material behaviour knowledge from empirical data , 1995 .
[5] Concha Bielza,et al. Machine Learning in Bioinformatics , 2008, Encyclopedia of Database Systems.
[6] Walter Kob,et al. A genetic algorithm for the atomistic design and global optimisation of substitutionally disordered materials , 2008, 0809.1613.
[7] Xi Chen,et al. A neural network approach to prediction of glass transition temperature of polymers , 2008, Int. J. Intell. Syst..
[8] Frédéric Clerc,et al. Virtual screening of materials using neuro-genetic approach : Concepts and implementation , 2009 .
[9] Ana Okariz,et al. Use of decision tree models based on evolutionary algorithms for the morphological classification of reinforcing nano-particle aggregates , 2014 .
[10] Klaus-Robert Müller,et al. Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies. , 2013, Journal of chemical theory and computation.
[11] Maysam F. Abbod,et al. Physically based and neuro-fuzzy hybrid modelling of thermomechanical processing of aluminium alloys , 2002 .
[12] Mark S. Drew,et al. Improved machine learning for image category recognition by local color constancy , 2010, 2010 IEEE International Conference on Image Processing.
[13] I. Foster,et al. The Materials Data Facility: Data Services to Advance Materials Science Research , 2016, JOM.
[14] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[15] D. T. Lee,et al. State-of-Charge Estimation for Electric Scooters by Using Learning Mechanisms , 2007, IEEE Transactions on Vehicular Technology.
[16] Ta-Peng Chang,et al. Estimation of exposed temperature for fire-damaged concrete using support vector machine , 2009 .
[17] Thomas E. Potok,et al. A bridge for accelerating materials by design , 2015 .
[18] H. V. Jagadish,et al. The Materials Commons: A Collaboration Platform and Information Repository for the Global Materials Community , 2016 .
[19] Ichiro Terasaki,et al. Design and discovery of materials guided by theory and computation , 2015 .
[20] J. Hutchinson. Determination of the glass transition temperature , 2009 .
[21] P. Luksch,et al. New developments in the Inorganic Crystal Structure Database (ICSD): accessibility in support of materials research and design. , 2002, Acta crystallographica. Section B, Structural science.
[22] Kevin P. Murphy,et al. Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.
[23] Jun‐Fang Pei,et al. Modeling and Predicting the Glass Transition Temperature of Polymethacrylates Based on Quantum Chemical Descriptors by Using Hybrid PSO‐SVR , 2013 .
[24] Aneesur Rahman,et al. Correlations in the Motion of Atoms in Liquid Argon , 1964 .
[25] Mohamed Othman,et al. A Naïve-Bayes classifier for damage detection in engineering materials , 2007 .
[26] Maysam F. Abbod,et al. Hybrid modelling of aluminium–magnesium alloys during thermomechanical processing in terms of physically-based, neuro-fuzzy and finite element models , 2003 .
[27] Peter Norvig,et al. Artificial Intelligence: A Modern Approach , 1995 .
[28] Anubhav Jain,et al. Data mined ionic substitutions for the discovery of new compounds. , 2011, Inorganic chemistry.
[29] Long-Qing Chen. Phase-Field Models for Microstructure Evolution , 2002 .
[30] Tom K Woo,et al. Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture. , 2014, The journal of physical chemistry letters.
[31] Göran Lindbergh,et al. A support vector machine-based state-of-health estimation method for lithium-ion batteries under electric vehicle operation , 2014 .
[32] João Aires-de-Sousa,et al. Exploration of quantitative structure–property relationships (QSPR) for the design of new guanidinium ionic liquids , 2008 .
[33] Nicholas Zabaras,et al. Classification and reconstruction of three-dimensional microstructures using support vector machines , 2005 .
[34] M. Prabhakar,et al. Analysis of workability behavior of Al–SiC P/M composites using backpropagation neural network model and statistical technique , 2009 .
[35] Pascal Mougin,et al. Prediction of Density and Viscosity of Biofuel Compounds Using Machine Learning Methods , 2012 .
[36] Kristof T. Schütt,et al. How to represent crystal structures for machine learning: Towards fast prediction of electronic properties , 2013, 1307.1266.
[37] Tae-Sun Choi,et al. Predicting lattice constant of complex cubic perovskites using computational intelligence , 2011 .
[38] S. Ong,et al. New opportunities for materials informatics: Resources and data mining techniques for uncovering hidden relationships , 2016 .
[39] Alok Choudhary,et al. A predictive machine learning approach for microstructure optimization and materials design , 2015, Scientific Reports.
[40] Haiying Wang,et al. New Method for Estimation Modeling of SOC of Battery , 2009, 2009 WRI World Congress on Software Engineering.
[41] P. V. Coveney,et al. Prediction of the functional properties of ceramic materials from composition using artificial neural networks , 2007 .
[42] K. Fujimura,et al. Accelerated Materials Design of Lithium Superionic Conductors Based on First‐Principles Calculations and Machine Learning Algorithms , 2013 .
[43] İlker Bekir Topçu,et al. Prediction of properties of waste AAC aggregate concrete using artificial neural network , 2007 .
[44] Anubhav Jain,et al. From the computer to the laboratory: materials discovery and design using first-principles calculations , 2012, Journal of Materials Science.
[45] Asifullah Khan,et al. Lattice constant prediction of cubic and monoclinic perovskites using neural networks and support vector regression , 2010 .
[46] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[47] Z. Guo,et al. Modelling beta transus temperature of titanium alloys using artificial neural network , 2005 .
[48] Cormac Toher,et al. Universal fragment descriptors for predicting properties of inorganic crystals , 2016, Nature Communications.
[49] P M Woodward,et al. Prediction of the crystal structures of perovskites using the software program SPuDS. , 2001, Acta crystallographica. Section B, Structural science.
[50] Chartchalerm Isarankura-Na-Ayudhya,et al. A practical overview of quantitative structure-activity relationship , 2009 .
[51] C. Camacho-Zuñiga,et al. A New Group Contribution Scheme To Estimate the Glass Transition Temperature for Polymers and Diluents , 2003 .
[52] F ROSENBLATT,et al. The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.
[53] Alok Choudhary,et al. A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials , 2016 .
[54] Christian W. Omlin,et al. Rule extraction from recurrent neural networks using a symbolic machine learning algorithm , 1999, ICONIP'99. ANZIIS'99 & ANNES'99 & ACNN'99. 6th International Conference on Neural Information Processing. Proceedings (Cat. No.99EX378).
[55] Alán Aspuru-Guzik,et al. The Harvard Clean Energy Project: Large-Scale Computational Screening and Design of Organic Photovoltaics on the World Community Grid , 2011 .
[56] G. Beran,et al. A new era for ab initio molecular crystal lattice energy prediction. , 2014, Angewandte Chemie.
[57] Christopher M Wolverton,et al. Atomistic calculations and materials informatics: A review , 2017 .
[58] O. Kisi,et al. Predicting the compressive strength of steel fiber added lightweight concrete using neural network , 2008 .
[59] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.
[60] A group contribution method for estimation of glass transition temperature ionic liquids , 2012 .
[61] C. Cai,et al. MODELING AND PREDICTING THE GLASS TRANSITION TEMPERATURE OF VINYL POLYMERS BY USING HYBRID PSO-SVR METHOD , 2013 .
[62] Abdul Majid,et al. Lattice constant prediction of orthorhombic ABO3 perovskites using support vector machines , 2007 .
[63] Kristin A. Persson,et al. Predicting crystal structures with data mining of quantum calculations. , 2003, Physical review letters.
[64] J. Vybíral,et al. Big data of materials science: critical role of the descriptor. , 2014, Physical review letters.
[65] David L. McDowell,et al. Vision for Data and Informatics in the Future Materials Innovation Ecosystem , 2016, JOM.
[66] Klaus-Robert Müller,et al. Finding Density Functionals with Machine Learning , 2011, Physical review letters.
[67] K. Binder,et al. The Monte Carlo Method in Condensed Matter Physics , 1992 .
[68] Bofeng Zhang,et al. Extraction of if-then rules from trained neural network and its application to earthquake prediction , 2004 .
[69] Ruijuan Xiao,et al. Quantitative structure-property relationship study of cathode volume changes in lithium ion batteries using ab-initio and partial least squares analysis , 2017 .
[70] Paul Raccuglia,et al. Machine-learning-assisted materials discovery using failed experiments , 2016, Nature.
[71] Mohammad Farrokhi,et al. State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF , 2010, IEEE Transactions on Industrial Electronics.
[72] P. Cavaliere,et al. Flow curve prediction of an Al-MMC under hot working conditions using neural networks , 2007 .
[73] R. Edwin Raj,et al. Prediction of compressive properties of closed-cell aluminum foam using artificial neural network , 2008 .
[74] M. Rupp,et al. Fourier series of atomic radial distribution functions: A molecular fingerprint for machine learning models of quantum chemical properties , 2013, 1307.2918.
[75] Yuanfei Han,et al. Prediction of the mechanical properties of forged Ti–10V–2Fe–3Al titanium alloy using FNN , 2011 .
[76] Vladimir N. Vapnik,et al. The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.
[77] Carolyn L. Phillips,et al. Discovering crystals using shape matching and machine learning , 2013 .
[78] Abhijit Mukherjee,et al. Artificial neural networks for predicting the macromechanical behaviour of ceramic-matrix composites , 1996 .
[79] Alán Aspuru-Guzik,et al. Machine learning exciton dynamics , 2015, Chemical science.
[80] W. Chunmei,et al. Prediction of lattice constant in perovskites of GdFeO3 structure , 2003 .
[81] David A. Winkler,et al. Capturing the Crystal: Prediction of Enthalpy of Sublimation, Crystal Lattice Energy, and Melting Points of Organic Compounds , 2013, J. Chem. Inf. Model..
[82] Shaomin Wu,et al. A review on coarse warranty data and analysis , 2013, Reliab. Eng. Syst. Saf..
[83] Christian Fleischer,et al. Adaptive On-line State-of-available-power Prediction of Lithium-ion Batteries , 2013 .
[84] Sanguthevar Rajasekaran,et al. Accelerating materials property predictions using machine learning , 2013, Scientific Reports.
[85] Jay L. Devore,et al. Introduction to Statistics and Data Analysis , 2000 .
[86] B. Meredig,et al. Materials science with large-scale data and informatics: Unlocking new opportunities , 2016 .
[87] Kristin A. Persson,et al. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation , 2013 .
[88] Ryan P. Adams,et al. Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach. , 2016, Nature materials.
[89] Min Song,et al. An approach for the aging process optimization of Al–Zn–Mg–Cu series alloys , 2009 .
[90] B. Alder,et al. Studies in Molecular Dynamics. I. General Method , 1959 .
[91] Siqi Shi,et al. Multi-scale computation methods: Their applications in lithium-ion battery research and development , 2016 .
[92] Ekin D. Cubuk,et al. Holistic computational structure screening of more than 12 000 candidates for solid lithium-ion conductor materials , 2017 .
[93] Alok Choudhary,et al. Combinatorial screening for new materials in unconstrained composition space with machine learning , 2014 .
[94] W. Kohn,et al. Self-Consistent Equations Including Exchange and Correlation Effects , 1965 .
[95] Alessio Micheli,et al. Evaluation of hierarchical structured representations for QSPR studies of small molecules and polymers by recursive neural networks. , 2009, Journal of molecular graphics & modelling.
[96] Su Qiang,et al. Two semi-empirical approaches for the prediction of oxide ionic conductivities in ABO3 perovskites , 2009 .
[97] Fredrik Olsson,et al. A literature survey of active machine learning in the context of natural language processing , 2009 .
[98] Saban Eren,et al. Implementation and comparison of machine learning classifiers for information security risk analysis of a human resources department , 2010, 2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM).
[99] John Mylopoulos,et al. From object-oriented to goal-oriented requirements analysis , 1999, CACM.
[100] Anubhav Jain,et al. Finding Nature’s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory , 2010 .
[101] J. Maddox. Crystals from first principles , 1988, Nature.
[102] B. Yi,et al. PREDICTION OF THE GLASS TRANSITION TEMPERATURES FOR POLYMERS WITH ARTIFICIAL NEURAL NETWORK , 2008 .
[103] P. Hohenberg,et al. Inhomogeneous Electron Gas , 1964 .
[104] Tim Mueller,et al. Machine Learning in Materials Science , 2016 .
[105] Ahmad Alzghoul,et al. Experimental and Computational Prediction of Glass Transition Temperature of Drugs , 2014, J. Chem. Inf. Model..
[106] Zhi-Hua Zhou,et al. Rule extraction: Using neural networks or for neural networks? , 2004, Journal of Computer Science and Technology.
[107] Corey Oses,et al. High-Throughput Computation of Thermal Conductivity of High-Temperature Solid Phases: The Case of Oxide and Fluoride Perovskites , 2016, 1606.03279.
[108] A. Karma,et al. Phase-Field Simulation of Solidification , 2002 .
[109] G. De’ath,et al. CLASSIFICATION AND REGRESSION TREES: A POWERFUL YET SIMPLE TECHNIQUE FOR ECOLOGICAL DATA ANALYSIS , 2000 .
[110] N. Castin,et al. Predicting vacancy migration energies in lattice-free environments using artificial neural networks , 2014 .
[111] Bryce Meredig,et al. Robust FCC solute diffusion predictions from ab-initio machine learning methods , 2017, 1705.08798.
[112] Stefano Curtarolo,et al. Data-Mining-Driven Quantum Mechanics for the Prediction of Structure , 2006 .
[113] James Theiler,et al. Materials Prediction via Classification Learning , 2015, Scientific Reports.
[114] Charles H. Ward. Materials Genome Initiative for Global Competitiveness , 2012 .
[115] Isabelle Guyon,et al. An Introduction to Variable and Feature Selection , 2003, J. Mach. Learn. Res..
[116] Xin-An Feng,et al. Material selection using an improved Genetic Algorithm for material design of components made of a multiphase material , 2008 .
[117] Lei Yang,et al. Studying the Explanatory Capacity of Artificial Neural Networks for Understanding Environmental Chemical Quantitative Structure-Activity Relationship Models , 2005, J. Chem. Inf. Model..
[118] Alán Aspuru-Guzik,et al. The Harvard Clean Energy Project. Large-scale computational screening and design of molecular motifs for organic photovoltaics on the World Community Grid , 2011 .
[119] S. T. Buckland,et al. An Introduction to the Bootstrap. , 1994 .
[120] Muratahan Aykol,et al. The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies , 2015 .
[121] Manh Cuong Nguyen,et al. On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets , 2014, Scientific Reports.
[122] G. B. Olson,et al. Designing a New Material World , 2000, Science.
[123] T. Sadowski,et al. Sensitivity analysis of crack propagation in pavement bituminous layered structures using a hybrid system integrating Artificial Neural Networks and Finite Element Method , 2014 .
[124] F. Allen. The Cambridge Structural Database: a quarter of a million crystal structures and rising. , 2002, Acta crystallographica. Section B, Structural science.
[125] Ankit Agrawal,et al. Machine learning approaches for elastic localization linkages in high-contrast composite materials , 2015, Integrating Materials and Manufacturing Innovation.
[126] Luc De Raedt,et al. Data Mining and Machine Learning Techniques for the Identification of Mutagenicity Inducing Substructures and Structure Activity Relationships of Noncongeneric Compounds , 2004, J. Chem. Inf. Model..
[127] W. M. Bolstad. Introduction to Bayesian Statistics , 2004 .
[128] Yiyu Cheng,et al. Machine learning techniques for the prediction of the peptide mobility in capillary zone electrophoresis. , 2007, Talanta: The International Journal of Pure and Applied Analytical Chemistry.
[129] Gerbrand Ceder,et al. Predicting crystal structure by merging data mining with quantum mechanics , 2006, Nature materials.
[130] I. Steinbach. Phase-field models in materials science , 2009 .
[131] Guo Jin,et al. Some regularities of melting points of AB-type intermetallic compounds , 1996 .
[132] Roy L. Johnston,et al. Genetic algorithms: A universal tool for solving computational tasks in Materials Science , 2009 .
[133] Tom M. Mitchell,et al. Machine Learning and Data Mining , 2012 .
[134] Francesco Ciucci,et al. Data mining of molecular dynamics data reveals Li diffusion characteristics in garnet Li7La3Zr2O12 , 2017, Scientific Reports.