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RESEARCH PAPER · The Journal of Physical Chemistry Letters · 2022

Materials Data toward Machine Learning: Advances and Challenges

Linggang Zhu, Jian Xin Zhou, Zhimei Sun

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Materials Data toward Machine Learning: Advances and ChallengesOriginalData-driven pitting evolution prediction for corrosion-resistant alloys by time-series analysis23Scientific Blind Spots Introduced by Feature Engineering in Materials Informatics0Computational insights into the rational design of organic electrode materials for metal ion batteries18Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis94Harnessing Deep Neural Networks to Analyze Multi‐Channel Anion Sensing Characteristics of a Ru(II)‐Pyrazolyl‐Bis(Benzimidazole) Complex3Small data machine learning in materials science866Unified roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and their Impact on Particle Size and Morphology2Advances in data‐assisted high‐throughput computations for material design112Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations32Ordering phenomena in ternary transition‐metal dichalcogenides: Critical role of lattice symmetry and vdW interaction16Data-Driven Predetermination of Cu Oxidation State in Copper Nanoparticles: Application to the Synthesis by Laser Ablation in Liquid21Advancing thermoelectric materials discovery through semi-supervised learning and high-throughput calculations23Depth analysis of battery performance based on a data-driven approach10Conceptual Treatments of Causality in Materials Informatics — From Correlation to Intervention: A Review Study0Recursive Elimination of “Outliers” to Get Benchmark Dataset11Uncovering the active sites of single atom-doped rutile oxides during methane activation by data-driven approach11Methods and applications of machine learning in computational design of optoelectronic semiconductors22Machine learning interatomic potential: Bridge the gap between small-scale models and realistic device-scale simulations147Unified Roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and Their Impact on Particle Size and Morphology48Exploring the mathematic equations behind the materials science data using interpretable symbolic regression37A materials terminology knowledge graph automatically constructed from text corpus21A physics‐informed neural network framework based on fatigue indicator parameters for very high cycle fatigue life prediction of an additively manufactured titanium alloy21Predicting glass transition temperature of polymers by combining molecular dynamics simulations and machine learning techniques11Machine Learning Approach to Investigate High Temperature Corrosion of Critical Infrastructure Materials8Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook31Machine learning prediction of materials properties from chemical composition: Status and prospects29Chalcogenide phase-change materials: unveiling new horizons with big data and machine learning10Machine Learning-Based Methods for Materials Inverse Design: A Review34Ternary materials discovery using human-in-the-loop generative machine learning3Research on multi-source microstructure image recognition of foam ceramics using convolutional network combine with frequency domain5Knowledge-guided large language model for material science16Accurate Neural Network Fine-Tuning Approach for Transferable Ab Initio Energy Prediction across Varying Molecular and Crystalline Scales8Machine learning for discrimination of phase‐change chalcogenide glasses25Machine Learning Assisted Design of High Thermal Conductivity and High Strength Mg Alloys8Predicting the Strength of Composites with Computer Vision Using Small Experimental Datasets6Accelerated discovery of near-zero ablation ultra-high temperature ceramics via GAN-enhanced directionally constrained active learning9Research progress of machine learning in the field of photocatalysis applications21Data-driven electrochemical behavior prediction for refractory high-entropy alloys by global and focused learning5Transfer Learning-Enhanced Prediction of Glass Transition Temperature in Bismaleimide-Based Polyimides1Artificial intelligence in materials science and engineering: Current landscape, key challenges, and future trajectories33Predicting efficient and stable inorganic photovoltaic materials using interpretable machine learning combined with DFT calculations based on band edge orbital engineering0Machine learning-driven design and optimization of electronic packaging: applications and future developments12Data-driven discovery of novel synthesizable MAX phase materials3Defect diagnostics in thin film photovoltaics: leveraging macroscopic J-V characteristics for microscopic insights via machine learning1A comprehensive benchmark of active learning strategies with AutoML for small-sample regression in materials science12Machine learning prediction and efficient screening method for thermally induced fluorescence enhancement of Er 3+ doped materials6TCSP 2.0: Template based crystal structure prediction with improved oxidation state prediction and chemistry heuristics3Competing sublattice short-range orders and gap state engineering in multicomponent transition-metal dichalcogenide3Adaptive AI decision interface for autonomous electronic material discovery10Predicting Materials Using Variational Autoencoders: A Systematic Literature Review8Extracting and reconstructing knowledge in materials science literature using large language models10Machine Learning-Assisted Polymer and Polymer Composite Design for Additive Manufacturing13Contrastive learning for data-efficient substrate deoxidation monitoring in edge-side adaptive molecular beam epitaxy systems0Data Distribution Matters: Enhancing model generalization in Data-Driven Materials Discovery by intentionally exploiting both positive and negative data1Machine Learning Prediction of Crystal Structure Stability toward the Design of High-Entropy Oxides2Unveiling Tribological Mechanisms of MoS2 Coatings on FeNiCr Alloys via Molecular Dynamics and Machine Learning1Data-driven design of electrocatalysts: Machine learning workflows, paradigm shifts, and applications4Quantifying and Interpreting Solvation Power of Cyclic Carbonate by Chemical Calculation and Machine Learning4Data-richness-driven machine learning for predictive design of high-performance g-C₃N₄ photocatalysts in NO removal0Machine learning modeling reveals unconventional nucleation mechanism in phase‐change material GeTe0A Dual Machine‐Learning Framework for Predicting Phase Stability and Mixing Enthalpy Difference in MSi 2 ‐Type High‐Entropy Refractory Metal Silicides0

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