Evaluation of Principal Features for Predicting Bulk and Shear Modulus of Inorganic Solids with Machine Learning

[1]  Alankar Alankar,et al.  Machine learning elastic constants of multi-component alloys , 2021 .

[2]  E. Choi,et al.  Searching for Mechanically Superior Solid-State Electrolytes in Li-Ion Batteries via Data-Driven Approaches. , 2021, ACS applied materials & interfaces.

[3]  G. Rignanese,et al.  Materials property prediction for limited datasets enabled by feature selection and joint learning with MODNet , 2021, npj Computational Materials.

[4]  G. Rignanese,et al.  Robust model benchmarking and bias-imbalance in data-driven materials science: a case study on MODNet , 2021, Journal of physics. Condensed matter : an Institute of Physics journal.

[5]  S. Sharma,et al.  Performance analysis of regression algorithms and feature selection techniques to predict PM2.5 in smart cities , 2021 .

[6]  Zhuo Wang,et al.  Characterization and identification of lysine crotonylation sites based on machine learning method on both plant and mammalian , 2020, Scientific Reports.

[7]  Seid Koric,et al.  Deep learning for topology optimization of 2D metamaterials , 2020 .

[8]  François-Xavier Coudert,et al.  Machine learning approaches for the prediction of materials properties , 2020, APL Materials.

[9]  Anubhav Jain,et al.  Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm , 2020, npj Computational Materials.

[10]  M. Rizwan,et al.  Investigation of structural, electronic, magnetic and mechanical properties of a new series of equiatomic quaternary Heusler alloys CoYCrZ (Z = Si, Ge, Ga, Al): A DFT study , 2020, Journal of Alloys and Compounds.

[11]  Lixia Li,et al.  Mechanical properties of siderite and hematite from DFT calculation , 2020 .

[12]  Jun Ni,et al.  Machine learning for hierarchical prediction of elastic properties in Fe-Cr-Al system , 2019, Computational Materials Science.

[13]  Kyle Chard,et al.  Matminer: An open source toolkit for materials data mining , 2018, Computational Materials Science.

[14]  M. Allendorf,et al.  Mechanical Properties in Metal–Organic Frameworks: Emerging Opportunities and Challenges for Device Functionality and Technological Applications , 2018, Advanced materials.

[15]  Feng Lin,et al.  Machine Learning Directed Search for Ultraincompressible, Superhard Materials. , 2018, Journal of the American Chemical Society.

[16]  Wei Li,et al.  Predicting the thermodynamic stability of perovskite oxides using machine learning models , 2018, Computational Materials Science.

[17]  J. Grossman,et al.  Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes , 2018, ACS central science.

[18]  Jeffrey C Grossman,et al.  Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties. , 2017, Physical review letters.

[19]  David B. Brough,et al.  Materials Knowledge Systems in Python—a Data Science Framework for Accelerated Development of Hierarchical Materials , 2017, Integrating Materials and Manufacturing Innovation.

[20]  Logan T. Ward,et al.  A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials , 2016, 1606.09551.

[21]  Kewei Cheng,et al.  Feature Selection , 2016, ACM Comput. Surv..

[22]  Zhen Zhang,et al.  A Feature Selection Method for Prediction Essential Protein , 2015 .

[23]  Rampi Ramprasad,et al.  Learning scheme to predict atomic forces and accelerate materials simulations , 2015, 1505.02701.

[24]  George K. Karagiannidis,et al.  Efficient Machine Learning for Big Data: A Review , 2015, Big Data Res..

[25]  Gokmen Zararsiz,et al.  Drug/nondrug classification using Support Vector Machines with various feature selection strategies , 2014, Comput. Methods Programs Biomed..

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

[27]  Anubhav Jain,et al.  Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis , 2012 .

[28]  Dianzhong Li,et al.  Extra-electron induced covalent strengthening and generalization of intrinsic ductile-to-brittle criterion , 2012, Scientific Reports.

[29]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[30]  Hao Lu,et al.  FIRST-PRINCIPLES CALCULATIONS OF MECHANICAL PROPERTIES OF TIC AND TIN , 2009 .

[31]  S. Chevrot,et al.  Decomposition of the elastic tensor and geophysical applications , 2004 .

[32]  T. Pavlenko.  On feature selection, curse-of-dimensionality and error probability in discriminant analysis , 2003 .

[33]  Daphne Koller,et al.  Toward Optimal Feature Selection , 1996, ICML.

[34]  Josef Kittler,et al.  Feature selection based on the approximation of class densities by finite mixtures of special type , 1995, Pattern Recognit..

[35]  S. Pugh.  XCII. Relations between the elastic moduli and the plastic properties of polycrystalline pure metals , 1954 .

[36]  R. Hill.  The Elastic Behaviour of a Crystalline Aggregate , 1952 .

[37]  H. Kodamana,et al.  Scalable Gaussian processes for predicting the optical, physical, thermal, and mechanical properties of inorganic glasses with large datasets , 2021, Materials Advances.

[38]  Ferat Sahin,et al.  A survey on feature selection methods , 2014, Comput. Electr. Eng..