BookDOI
Information science for materials discovery and design
Turab Lookman,Francis J. Alexander,Krishna Rajan +2 more
- Vol. 225
TLDR
In this paper, data-driven discovery of physical, chemical, and pharmaceutical materials is discussed. But the authors focus on the development of new data mining techniques in the defense establishment.Abstract:
Introduction.- Data-Driven Discovery of Physical, Chemical, and Pharmaceutical Materials.- Cross-Validation and Inference in Bioinformatics/Cancer Genomics.- Applying MQSPRs - New Challenges and Opportunities.- Data Mining in Materials Science.- Data Science in the Defense Establishment.- Combining Heuristic and Physics-Based Methods for Predicting Nanocomposite Properties.- From Ferroelectrics to Fuel Cells: In Search of Descriptors for the Transport Properties of Complex Oxides.- Computationally Driven Targeting of Advanced Thermoelectric Materials.- The MGI, Materials Informatics, and NIST) Microstructure Informatics for Mining Structure-Property-Processing Linkages.- A Genomic Approach to Properties of MAX Phase Compounds.- Accelerating Discovery of Complex Formulations and Molecules.- Optimal Learning for Discovering Minimal Peptide Substrates.- Model-based Classification: Predictive and Optimal.read more
Citations
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Journal ArticleDOI
Machine learning and the physical sciences
Giuseppe Carleo,J. Ignacio Cirac,Kyle Cranmer,Laurent Daudet,Maria Schuld,Naftali Tishby,Leslie Vogt-Maranto,Lenka Zdeborová +7 more
TL;DR: This article reviews in a selective way the recent research on the interface between machine learning and the physical sciences, including conceptual developments in ML motivated by physical insights, applications of machine learning techniques to several domains in physics, and cross fertilization between the two fields.
Journal ArticleDOI
Machine learning in materials informatics: recent applications and prospects
Rampi Ramprasad,Rohit Batra,Ghanshyam Pilania,Ghanshyam Pilania,Arun Mannodi-Kanakkithodi,Chiho Kim +5 more
TL;DR: This article attempts to provide an overview of some of the recent successful data-driven “materials informatics” strategies undertaken in the last decade, with particular emphasis on the fingerprint or descriptor choices.
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A strategy to apply machine learning to small datasets in materials science
Ying Zhang,Chen Ling +1 more
TL;DR: In three case studies of predicting the band gap of binary semiconductors, lattice thermal conductivity, and elastic properties of zeolites, the integration of crude estimation effectively boosted the predictive capability of machine learning models to state-of-art levels, demonstrating the generality of the proposed strategy to construct accurate ML models using small materials dataset.
Journal ArticleDOI
Machine learning modeling of superconducting critical temperature
Valentin Stanev,Corey Oses,A. Gilad Kusne,A. Gilad Kusne,Efrain E. Rodriguez,Johnpierre Paglione,Stefano Curtarolo,Stefano Curtarolo,Ichiro Takeuchi +8 more
TL;DR: In this article, several machine learning schemes are developed to model the critical temperatures (Tc) of the 12,000+ known superconductors available via the SuperCon database.
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A Critical Review of Machine Learning of Energy Materials
TL;DR: In this article, the authors provide an in-depth, critical review of ML-guided design and discovery of energy materials, a field where a novel material with superior performance (e.g., higher energy density, higher energy conversion efficiency, etc.) can have a transformative impact on the urgent global problem of climate change.
References
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Vincent D. Blondel,Jean-Loup Guillaume,Jean-Loup Guillaume,Renaud Lambiotte,Renaud Lambiotte,Etienne Lefebvre +5 more
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