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RESEARCH PAPER · Journal of Applied Physics · 2020

Feature engineering of material structure for AI-based materials knowledge systems

Surya R. Kalidindi

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Feature engineering of material structure for AI-based materials knowledge systemsOriginalMachine learning based surrogate modeling approach for mapping crystal deformation in three dimensions66Exploration of optimal microstructure and mechanical properties in continuous microstructure space using a variational autoencoder99Strategic control of atomic-scale defects for tuning properties in metals14Machine learning for materials design and discovery128Computational discovery of energy materials in the era of big data and machine learning: A critical review60Machine learning for alloys698Entropy of Alloy Phases: A Macroscopic Perspective2Data-Driven Methods for Accelerating Polymer Design123Accelerated design for elastocaloric performance in NiTi-based alloys through machine learning17Digital Twins for Materials90Advanced Machine Learning Methods for Learning from Sparse Data in High-Dimensional Spaces: A Perspective on Uses in the Upstream of Development of Novel Energy Technologies21Recent progress on the mesoscale modeling of architected thin-films via phase-field formulations of physical vapor deposition6Autonomous materials discovery and manufacturing (AMDM): A review and perspectives18Automated Analysis of Continuum Fields from Atomistic Simulations Using Statistical Machine Learning3Deep blue AI: A new bridge from data to knowledge for the ocean science27Charge-density based convolutional neural networks for stacking fault energy prediction in concentrated alloys9Graph neural networks for efficient learning of mechanical properties of polycrystals65The role of AI for developing digital twins in healthcare: The case of cancer care113Machine learning in computational chemistry: interplay between (non)linearity, basis sets, and dimensionality24Digital advancements in smart materials design and multifunctional coating manufacturing46Data-driven design of electrocatalysts: principle, progress, and perspective46Interpretable models for extrapolation in scientific machine learning98Inverse Stochastic Microstructure Design25Voxelized Representations of Atomic Systems for Machine Learning Applications2Theoretical design of optoelectronic semiconductors2Accelerating the design of compositionally complex materials via physics-informed artificial intelligence110Artificial Intelligence and Knowledge Management: Impacts, Benefits, and Implementation138Characterization of porous membranes using artificial neural networks25Mechanical energy metamaterials in interstellar travel74An artificial neural network approach on crystal plasticity for material modelling in macroscopic simulations10Estimation for Microstructure Formation Factor in TRIP-aided Multi-phase Steel by Using Primary Component Analysis of Data on the Elemental Distribution1Voxelized atomic structure framework for materials design and discovery7Unleashing the Power of Artificial Intelligence in Materials Design179Graph neural network for predicting the effective properties of polycrystalline materials: A comprehensive analysis36An analytical solution for multiple inclusions subject to a general applied thermal field5Transfer-Learning and Texture Features for Recognition of the Conditions of Construction Materials with Small Data Sets35Mechanical metamaterials and beyond733Advances of machine learning in materials science: Ideas and techniques105A Conceptual Framework for Trustworthy AI Decisions Across the Materials Design Lifecycle1Process-structure-property models for metal additive manufacturing using AI/ML approaches1What Counts as Knowledge in AI-Driven Materials Science? A Philosophical and Practical Reframing0Fusion and classification algorithm of octacalcium phosphate production based on XRD and FTIR data6Virtual development of competitive products made of customized ceramic composites2Calibration of thermal spray microstructure simulations using Bayesian optimization6Signal acquisition of brain–computer interfaces: A medical-engineering crossover perspective review67A guide to discovering next-generation semiconductor materials using atomistic simulations and machine learning14Comprehensive review of AI and ML tools for earthquake damage assessment and retrofitting strategies19Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media12Navigating high-dimensional process-structure–property relations in nanocrystalline Pt-Au alloys with machine learning12Integration of AI-Based Nano Synergy in Bayesian Uncertainty Quantification for Advanced Engineering Design0Applications of machine learning in high-entropy alloys: a review of recent advances in design, discovery, and characterization41Emerging trends in advanced biomimetic composite materials inspired by biological structures and functions in nature4Harnessing the leading edge: machine learning ventures in chemistry and materials science3Solving real-life wear problems: Streamlined lab tests for diverse industrial applications. 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