A5046106872- Machine Learning in Materials Science
- Advanced Materials Characterization Techniques
- High Entropy Alloys Studies
- High-Temperature Coating Behaviors
- High Temperature Alloys and Creep
- Magnesium Alloys: Properties and Applications
- Additive Manufacturing Materials and Processes
- X-ray Diffraction in Crystallography
- Electronic and Structural Properties of Oxides
- Intermetallics and Advanced Alloy Properties
- Inorganic Chemistry and Materials
- Hydrogen embrittlement and corrosion behaviors in metals
- Hydrogen Storage and Materials
- Metallurgical and Alloy Processes
- Rare-earth and actinide compounds
- Magnetic and transport properties of perovskites and related materials
- High-pressure geophysics and materials
- Electron and X-Ray Spectroscopy Techniques
- Aluminum Alloys Composites Properties
- MXene and MAX Phase Materials
- Advanced Thermoelectric Materials and Devices
- Corrosion Behavior and Inhibition
- Advanced Condensed Matter Physics
- Glass properties and applications
- Thermal Expansion and Ionic Conductivity
Citrine Informatics (United States)
2020-2024
QuesTek (United States)
2015-2020
Pennsylvania State University
2007-2018
University of Geneva
2018
National Institute of Standards and Technology
2018
New York University Press
2018
MicroVision (United States)
2018
Oak Ridge National Laboratory
2017
Northwestern University
2012-2016
Northwest University
2016
Abstract The Open Quantum Materials Database (OQMD) is a high-throughput database currently consisting of nearly 300,000 density functional theory (DFT) total energy calculations compounds from the Inorganic Crystal Structure (ICSD) and decorations commonly occurring crystal structures. To maximise impact these data, entire being made available, without restrictions, at www.oqmd.org/download . In this paper, we outline structure contents database, then use it to evaluate accuracy therein by...
Typically, computational screens for new materials sharply constrain the compositional search space, structural or both, sake of tractability. To lift these constraints, we construct a machine learning model from database thousands density functional theory (DFT) calculations. The resulting can predict thermodynamic stability arbitrary compositions without any other input and with six orders magnitude less computer time than DFT. We use this to scan roughly 1.6 million candidate novel...
The use of hydrogen as fuel is a promising avenue to aid in the reduction greenhouse effect gases released atmosphere. In this work, we present high-throughput density functional theory (HT-DFT) study 5,329 cubic and distorted perovskite ABO3 compounds screen for thermodynamically favorable two-step thermochemical water splitting (TWS) materials. From data set more than 11,000 calculations, screened materials based on following: (a) thermodynamic stability (b) oxygen vacancy formation energy...
Abstract Structure, composition and surface properties dictate corrosion resistance in any given environment. The degrees of freedom alloy design are too numerous emerging materials such as high entropy alloys bulk metallic glasses for the use high-throughput methods or trial error. We review three domains knowledge that can be applied towards goal resistant (CRA) design: (a) aggregation gained through experience developing CRAs empirically, (b) data-driven approaches descriptive metrics...
The rapidly growing interest in machine learning (ML) for materials discovery has resulted a large body of published work. However, only small fraction these publications includes confirmation ML predictions, either via experiment or physics-based simulations. In this review, we first identify the core components common to informatics pipelines, such as training data, choice algorithm, and measurement model performance. Then discuss some prominent examples validated ML-driven across wide...
Abstract This data article presents a compilation of mechanical properties 630 multi-principal element alloys (MPEAs). Built upon recently published MPEA databases, this includes updated records from previous reviews (with minor error corrections) along with new articles that were since 2019. The extracted include reported composition, processing method, microstructure, density, hardness, yield strength, ultimate tensile strength (or maximum compression strength), elongation strain), and...
Corrosion is an electrochemical phenomenon. It can occur via different modes of attack, each having its own mechanisms, and therefore there are multiple metrics for evaluating corrosion resistance. In resistant alloys (CRAs), the rate localized exceed that uniform by orders magnitude. Therefore, instead rate, more complex parameters required to capture salient features phenomena. Here, we collect a database with emphasis on related corrosion. The six sections include data various metal...
On average, simple linear models perform equivalently to black box machine learning on extrapolation tasks.
Density functional theory (DFT) is widely used to predict materials properties, but the local density approximation (LDA) and generalized gradient (GGA) exchange-correlation functionals are known poorly energetics of reactions involving molecular species. In this paper, we obtain corrections for O${}_{2}$, H${}_{2}$, N${}_{2}$, F${}_{2}$, Cl${}_{2}$ molecules within Perdew-Burke-Enzerhof GGA, Perdew-Wang Perdew-Zunger LDA by comparing DFT-calculated formation energies oxides, hydrides,...
The regression model-based tool is developed for predicting the Seebeck coefficient of crystalline materials in temperature range from 300 K to 1000 K. accounts single crystal versus polycrystalline nature compound, production method, and properties constituent elements chemical formula. We introduce new descriptive features relevant prediction coefficient. To address off-stoichiometry materials, predictive trained on a mix stoichiometric nonstoichiometric materials. implemented into web...