A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials

[1]  Alexander Tropsha,et al.  Materials Informatics , 2019, J. Chem. Inf. Model..

[2]  Ryan O'Hayre,et al.  Predicting Density Functional Theory Total Energies and Enthalpies of Formation of Metal—Nonmetal Compounds by Linear Regression , 2016 .

[3]  A. Choudhary,et al.  Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science , 2016 .

[4]  Sean Paradiso,et al.  Perspective: Materials informatics across the product lifecycle: Selection, manufacturing, and certification , 2016 .

[5]  Alan R. Bishop,et al.  Perspective: Codesign for materials science: An optimal learning approach , 2016 .

[6]  G. Pilania,et al.  Machine learning bandgaps of double perovskites , 2016, Scientific Reports.

[7]  Bryce Meredig,et al.  Data mining our way to the next generation of thermoelectrics , 2016 .

[8]  Muratahan Aykol,et al.  The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies , 2015 .

[9]  Edward O. Pyzer-Knapp,et al.  A Bayesian Approach to Calibrating High-Throughput Virtual Screening Results and Application to Organic Photovoltaic Materials , 2015, 1510.00388.

[10]  Sergei V. Kalinin,et al.  Big-deep-smart data in imaging for guiding materials design. , 2015, Nature materials.

[11]  Krishna Rajan,et al.  Materials Informatics: The Materials ``Gene'' and Big Data , 2015 .

[12]  Surya R. Kalidindi,et al.  Materials Data Science: Current Status and Future Outlook , 2015 .

[13]  Yasser B. Ruiz-Blanco,et al.  ProtDCal: A program to compute general-purpose-numerical descriptors for sequences and 3D-structures of proteins , 2015, BMC Bioinformatics.

[14]  Felix A Faber,et al.  Crystal structure representations for machine learning models of formation energies , 2015, 1503.07406.

[15]  Liping Yu,et al.  Prediction and accelerated laboratory discovery of previously unknown 18-electron ABX compounds. , 2014, Nature chemistry.

[16]  Rahul Malik,et al.  Spinel compounds as multivalent battery cathodes: A systematic evaluation based on ab initio calculations , 2014 .

[17]  J. Vybíral,et al.  Big data of materials science: critical role of the descriptor. , 2014, Physical review letters.

[18]  Jan Schroers,et al.  Combinatorial development of bulk metallic glasses. , 2014, Nature materials.

[19]  Atsuto Seko,et al.  Sparse representation for a potential energy surface , 2014, 1403.7995.

[20]  Alok Choudhary,et al.  Combinatorial screening for new materials in unconstrained composition space with machine learning , 2014 .

[21]  Christopher M Wolverton,et al.  Dissolving the Periodic Table in Cubic Zirconia: Data Mining to Discover Chemical Trends , 2014 .

[22]  Somnath Datta,et al.  Informatics-aided bandgap engineering for solar materials , 2014 .

[23]  Lusann Yang,et al.  Data-mined similarity function between material compositions , 2013 .

[24]  Atsuto Seko,et al.  Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single- and binary-component solids , 2013, 1310.1546.

[25]  Sanguthevar Rajasekaran,et al.  Accelerating materials property predictions using machine learning , 2013, Scientific Reports.

[26]  Muratahan Aykol,et al.  Materials Design and Discovery with High-Throughput Density Functional Theory: The Open Quantum Materials Database (OQMD) , 2013 .

[27]  Kristin A. Persson,et al.  Commentary: The Materials Project: A materials genome approach to accelerating materials innovation , 2013 .

[28]  Kristof T. Schütt,et al.  How to represent crystal structures for machine learning: Towards fast prediction of electronic properties , 2013, 1307.1266.

[29]  Marco Buongiorno Nardelli,et al.  The high-throughput highway to computational materials design. , 2013, Nature materials.

[30]  Christopher M Wolverton,et al.  High‐Throughput Computational Screening of New Li‐Ion Battery Anode Materials , 2013 .

[31]  Krishna Rajan,et al.  “Property Phase Diagrams” for Compound Semiconductors through Data Mining , 2013, Materials.

[32]  Wei Luo,et al.  Information-Theoretic Approach for the Discovery of Design Rules for Crystal Chemistry , 2012, J. Chem. Inf. Model..

[33]  Stefano Curtarolo,et al.  A search model for topological insulators with high-throughput robustness descriptors. , 2012, Nature materials.

[34]  Marco Buongiorno Nardelli,et al.  AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations , 2012 .

[35]  Anubhav Jain,et al.  Carbonophosphates: A New Family of Cathode Materials for Li-Ion Batteries Identified Computationally , 2012 .

[36]  K. Müller,et al.  Fast and accurate modeling of molecular atomization energies with machine learning. , 2011, Physical review letters.

[37]  Anubhav Jain,et al.  A high-throughput infrastructure for density functional theory calculations , 2011 .

[38]  Anubhav Jain,et al.  Data mined ionic substitutions for the discovery of new compounds. , 2011, Inorganic chemistry.

[39]  Davis E. King,et al.  Dlib-ml: A Machine Learning Toolkit , 2009, J. Mach. Learn. Res..

[40]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.

[41]  R. Kondor,et al.  Gaussian approximation potentials: the accuracy of quantum mechanics, without the electrons. , 2009, Physical review letters.

[42]  H. K. D. H. Bhadeshia,et al.  Performance of neural networks in materials science , 2009 .

[43]  Jian Xu,et al.  Formation of Bulk Metallic Glasses and Their Composites , 2007 .

[44]  H. K. D. H. Bhadeshia,et al.  δ TRIP steel , 2007 .

[45]  Juan José Rodríguez Diez,et al.  Rotation Forest: A New Classifier Ensemble Method , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[46]  Gerbrand Ceder,et al.  Predicting crystal structure by merging data mining with quantum mechanics , 2006, Nature materials.

[47]  Phillip B. Messersmith,et al.  Bioinspired antifouling polymers , 2005 .

[48]  Shuichi Iwata,et al.  Data-Driven Atomic Environment Prediction for Binaries Using the Mendeleev Number. Part 1. Composition AB. , 2004 .

[49]  Weihua Wang,et al.  Bulk metallic glasses , 2004 .

[50]  Shuichi Iwata,et al.  Data-driven atomic environment prediction for binaries using the Mendeleev number: Part 1. Composition AB , 2004 .

[51]  Kristin A. Persson,et al.  Predicting crystal structures with data mining of quantum calculations. , 2003, Physical review letters.

[52]  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.

[53]  Tao Zhang,et al.  Formation and High Mechanical Strength of Bulk Glassy Alloys in Zr-Al-Co-Cu System , 2002 .

[54]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[55]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[56]  Xiaoqun Wu,et al.  Artificial neural network aided design of catalyst for propane ammoxidation , 1997 .

[57]  D. W. Noid,et al.  On the Design, Analysis, and Characterization of Materials Using Computational Neural Networks , 1996 .

[58]  Anubhav Jain,et al.  Cumulative Author Index , 1995, Powder Diffraction.

[59]  N. Ashcroft,et al.  Vegard's law. , 1991, Physical review. A, Atomic, molecular, and optical physics.

[60]  H. Queisser,et al.  Detailed Balance Limit of Efficiency of p‐n Junction Solar Cells , 1961 .

[61]  Geoffroy Hautier,et al.  Data mining approaches to high-throughput crystal structure and compound prediction. , 2014, Topics in current chemistry.

[62]  Mark C. Lonergan,et al.  Solution phase n-doping of C60and PCBM using tetrabutylammonium fluoride , 2014 .

[63]  Alán Aspuru-Guzik,et al.  Prediction and Calculation of Crystal Structures: Methods and Applications , 2014 .

[64]  Manuela Pavan,et al.  DRAGON SOFTWARE: AN EASY APPROACH TO MOLECULAR DESCRIPTOR CALCULATIONS , 2006 .

[65]  Roberto Todeschini,et al.  Handbook of Molecular Descriptors , 2002 .

[66]  A. Inoue.  Stabilization of metallic supercooled liquid and bulk amorphous alloys , 2000 .

[67]  A. Tsai,et al.  Nonequilibrium phase diagrams of ternary amorphous alloys , 1997 .

[68]  W. W. Wright,et al.  Materials science and engineering. An introduction 2nd Edition W. D. Callister, Jr John Wiley & Sons, New York, 1991. pp. xxi + 791, price E53.00. ISBN 0‐471‐50488‐2 , 1993 .

[69]  William D. Callister,et al.  Materials Science and Engineering: An Introduction , 1985 .