2019年

Accelerated Data-Driven Accurate Positioning of the Band Edges of Mxenes

Analyzing machine learning models to accelerate generation of fundamental materials insights

Bandgap prediction by deep learning in configurationally hybridized graphene and boron nitride

Creating Machine Learning-Driven Material Recipes Based on Crystal Structure

Deep learning analysis of defect and phase evolution during electron beam-induced transformations in WS2

First-principles-based prediction of yield strength in the RhIrPdPtNiCu high-entropy alloy

hcp → ω phase transition mechanisms in shocked zirconium: A machine learning based atomic simulation study

Kinetic energy densities based on the fourth order gradient expansion: performance in different classes of materials and improvement via machine learning

Machine Learning-Aided Structure Determination for TiCl4–Capped MgCl2Nanoplate of Heterogeneous Ziegler–Natta Catalyst

Machine Learning Constrained with Dimensional Analysis and Scaling Laws: Simple, Transferable, and Interpretable Models of Materials from Small Datasets

Machine-learning guided discovery of a new thermoelectric material

Machine learning of optical properties of materials – predicting spectra from images and images from spectra

Manifold learning of four-dimensional scanning transmission electron microscopy

Performance analysis of perovskite solar cells in 2013–2018 using machinelearning tools

Phase diagram of a disordered higher-order topological insulator: A machine learning study

Predicting the Thermodynamic Stability of Solids Combining Density Functional Theory and Machine Learning

Predicting Young's Modulus of Glasses with Sparse Datasets using Machine Learning

Prediction of Synthesis of 2D Metal Carbides and Nitrides (MXenes) and Their Precursors with Positive and Unlabeled Machine Learning

Probabilistic Assessment of Glass Forming Ability Rules for Metallic Glasses Aided by Automated Analysis of Phase Diagrams

Simulating the NaK Eutectic Alloy with Monte Carlo and Machine Learning

Solving the electronic structure problem with machine learning

Study on the factors affecting solid solubility in binary alloys: An exploration by Machine Learning

Thermodynamic Stability Landscape of Halide Double Perovskites via High‐Throughput Computing and Machine Learning

2018年

Accelerated Discovery of Large Electrostrains in BaTiO3Based Piezoelectrics Using Active Learning

A high-throughput data analysis and materials discovery tool for strongly correlated materials

A machine learning approach for engineering bulk metallic glass alloys

A strategy to apply machine learning to small datasets in materials science

A thermodynamic potential for barium zirconate titanate solid solutions

Accelerated Discovery of Large Electrostrains in BaTiO3‐Based Piezoelectrics Using Active Learning

Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning

Active learning for accelerated design of layered materials

Advanced Steel Microstructural Classifcation by Deep Learning Methods

Combined machine learning and CALPHAD approach for discovering processing-structure relationships in soft magnetic alloys

Compositional optimization of hard-magnetic phases with machine-learning models

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

Data-driven approach for the prediction and interpretation of core-electron loss spectroscopy

Deep neural networks for accurate predictions of crystal stability

Developing an interatomic potential for martensitic phase transformations in zirconium by machine learning

Discovering chemical site occupancy- modulus correlations in Ni based intermetallics via statistical learning methods

Efficient search of compositional space for hybrid organic–inorganic perovskites via Bayesian optimization

Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning

Extracting Knowledge from Data through Catalysis Informatics

How To Optimize Materials and Devices via Design of Experiments and Machine Learning Demonstration Using Organic Photovoltaics

Machine learning–enabled identification of material phase transitions based on experimental data Exploring collective dynamics in ferroelectric relaxors

Machine Learning for Atomic Scale Chemical and Morphological Assessment

Machine learning for molecular and materials science

Machine learning for phase selection in multi-principal element alloys

Machine-learning guided discovery of a high-performance spin-driven thermoelectric material

Machine learning hydrogen adsorption on nanoclusters through structural descriptors

Machine learning modeling of superconducting critical temperature

Machine-learning the configurational energy of multicomponent crystalline solids

Mapping mesoscopic phase evolution during E-beam induced transformations via deep learning of atomically resolved images

Multi-objective Optimization for Materials Discovery via Adaptive Design

Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry

Predicting colloidal crystals from shapes via inverse design and machine learning

Predicting specific surface areas of layered double hydroxides based on support vector regression integrated with a residual bootstrapping method

Predictions of new ABO3 perovskite compounds by combining machine learning and density functional theory

Predictive modeling of dynamic fracture growth in brittle materials with machine learning

Rationalizing Perovskite Data for Machine Learning and Materials Design

Reconstructing phase diagrams from local measurements via Gaussian processes: mapping the temperature-composition space to confidence

Stability Trend of Tilted Perovskites

Two-way design of alloys for advanced ultra supercritical plants based on machine learning

Unexpected Event Prediction in Wire Electrical Discharge Machining Using Deep Learning Techniques

Using machine learning and a data-driven approach to identify the small fatigue crack driving force in polycrystalline materials

2017年

An informatics approach to transformation temperatures of NiTi-based shape memory alloys

Change in the primary solidification phase from fcc to bcc -based B2 in high entropy or complex concentrated alloys

Data driven modeling of plastic deformation

Designing high entropy alloys employing thermodynamics and Gaussian process statistical analysis

Effect of Electronic and Magnetic Valences on Phase Transition and Magnetic Properties in Co-Ni-Al-RE (RE = Gd, Dy and Er) Alloys.

Efficient and accurate machine-learning interpolation of atomic energies in compositions with many species

Fundamental Band Gap and Alignment of Two-Dimensional Semiconductors Explored by Machine Learning

Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability

Machine learning in materials informatics recent applicationsand prospects

Machine-Learning Methods Enable Exhaustive Searches for Active Bimetallic Facets and Reveal Active Site Motifs for CO2 Reduction

Material descriptors for morphotropic phase boundary curvature in lead-free piezoelectrics

Materials discovery and design using machine learning

Mining Materials Design Rules from Data: The Example of Polymer Dielectrics

Including crystal structure attributes in machine learning models of formation energies via Voronoi tessellations

Optimal experimental design for materials discovery

Predicting Catalytic Activity of Nanoparticles by a DFT-Aided Machine-Learning Algorithm

Predicting the thermodynamic stability of solids combining density functional theory and machine learning

Machine Learning Approach for Prediction and Understanding of Glass-Forming Ability

Rational Design: A High-Throughput Computational Screening and Experimental Validation Methodology for Lead-Free and Emergent Hybrid Perovskites

Statistical inference and adaptive design for materials discovery

Supervised Machine-Learning-Based Determination of Three-Dimensional Structure of Metallic Nanoparticles

The use of artificial intelligence combiners for modeling steel pitting risk and corrosion rate

Thermodynamic Stability Trend of Cubic Perovskites

Universal fragment descriptors for predicting properties of inorganic crystals

2016年

A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds

Comparing molecules and solids across structural and alchemical space

Informatics derived materials databases for multifunctional properties

Machine learning bandgaps of double perovskites

Material synthesis and design from first principle calculations and machine learning

Modeling Off-Stoichiometry Materials with a High-Throughput Ab-Initio Approach

Predictive analytics for crystalline materials: bulk modulus

Spectral descriptors for bulk metallic glasses based on the thermodynamics of competing crystalline phases

Structural-disorder and its effect on mechanical properties in single-phase TaNbHfZr high-entropy alloy

The Materials Data Facility: Data Services to Advance Materials Science Research

2015年

A learning scheme to predict atomic forces and accelerate materials simulations 

A machine learning based meta-heuristic approach for constrained continuous optimization

A predictive machine learning approach for microstructure optimization and materials design

Accelerated materials property predictions and design using motif-based fingerprints

Adaptive machine learning framework to accelerate ab initio molecular dynamics

Big data of materials science: critical role of the descriptor

Evaluation of machine learning interpolation techniques for prediction of physical properties

Identifying structural flow defects in disordered solids using machine-learning methods

Materials Data Science Current Status and Future Outlook

Materials Informatics The Materials “Gene” and Big Data

Materials Prediction via Classification Learning

Mining for elastic constants of intermetallics from the charge density landscape

Prediction of primary water stress corrosion crack growth rates in Alloy 600 using artificial neural networks

Probabilistic machine learning and artificial intelligence

Structure classification and melting temperature prediction in octet AB solids via machine learning

2014年

Combinatorial screening for new materials in unconstrained composition

On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets

Origins of hole traps in hydrogenated nanocrystalline and amorphous silicon revealed through machine learning

Stability and structure prediction of cubic phase in as cast high entropy alloys

2013年

Accelerated Materials Design of Lithium Superionic Conductors Based on First‐Principles Calculations and Machine Learning Algorithms 

Accelerating materials property predictions using machine learning

Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies

Unravelling the materials genome: Symmetry relationships in alloy properties

2012年

Data-Driven Model for Estimation of Friction Coefficient Via Informatics Methods

Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

2011年

Data Mining Technique for Knowledge Discovery from Engineering Materials Data Sets

2010年

Finding Nature′s Missing Ternary Oxide Compounds Using Machine Learning and Density Functional Theory 

Modeling the environmental dependence of pit growth using neural network approaches

2009年

An approach for the aging process optimization of Al–Zn–Mg–Cu series alloys

Artificial neural network approach to predict the mechanical properties of Cu–Sn–Pb–Zn–Ni cast alloys

Materials informatics_ An emerging technology for materials development

2007年

Discovering key meteorological variables in atmospheric corrosion through an artificial neural network model

Multi-Fidelity Optimization via Surrogate Modelling

Rapid structural mapping of ternary metallic alloy systems using the combinatorial approach and cluster analysis

2006年

Predicting crystal structure by merging data mining with quantum mechanics

2003年

Predicting Crystal Structures with Data Mining of Quantum Calculations

2000年

Prediction of the Corrosion Rate of Steel in Seawater Using Neural Network Methods