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RESEARCH PAPER · 中国科学通报：英文版 · 2023

Auto-MatRegressor: liberating machine learning alchemists

Yue Liu, Shuangyan Wang, Zhengwei Yang, Maxim Avdeev, Siqi Shi

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Auto-MatRegressor: liberating machine learning alchemistsOriginalScope of machine learning in materials research—A review230Development and application of automatic identification methods based on deep learning for oxide scale structures of iron and steel materials7A generalized model for estimating adsorption energies of single atoms on doped carbon materials5Conformal active learning-aided screening of ligand-protected Cu-nanoclusters for CO 2 reduction reactions4Rational electrolyte design for Li-metal batteries operated under extreme conditions: a combined DFT, COSMO-RS, and machine learning study22Stability prediction of gold nanoclusters with different ligands and doped metals: deep learning and experimental tests35Parameter-Free and Electron Counting Satisfied Material Representation for Machine Learning Potential Energy and Force Fields4Deep neural network-based lifetime diagnosis algorithm with electrical capacitor accelerated life test10Progress in the prognosis of battery degradation and estimation of battery states26MLMD: a programming-free AI platform to predict and design materials63Exploring the Ambient-Temperature Degradation Reactions of PET through Two-Step Machine Learning and High-Throughput Experimentation11Exploring the Phase Stability of Li2Mn1–xTMxO3 (TM = Ni, Co, Cr, Ru) Cathode Materials in Lithium-Ion Batteries via the Cluster Expansion Method2Transformer enables ion transport behavior evolution and conductivity regulation for solid electrolyte12Knowledge-reused transfer learning for molecular and materials science28Applying Machine Learning to Design Delicate Amorphous Micro-Nano Materials for Rechargeable Batteries20Compositional design of compounds with elements not in training data using supervised learning1A data-driven method to construct prediction model of solar stills7Prediction of long-term creep modulus of thermoplastics using brief tests and interpretable machine learning5Modeling the properties of terminal blend crumb rubber modified bitumen with crosslinking additives3Symbolic transform optimized convolutional neural network model for high-performance prediction and analysis of MXenes hydrogen evolution reaction catalysts15Artificial intelligence driven design of cathode materials for sodium-ion batteries using graph deep learning method41Lithium Localization by Anions in Argyrodite Solid Electrolytes from Machine‐Learning‐based Simulations19Electro‐Chemo‐Mechanical Domain to Enable Less Hysteretic Fast‐Charging6Use of deep learning and data augmentation by physics-based modelling for crack characterisation from multimodal ultrasonic TFM images6Applications of machine learning in high-entropy alloys: a review of recent advances in design, discovery, and characterization41Revisiting the in-plane and in-channel diffusion of lithium ions in a solid-state electrolyte at room temperature through neural network-assisted molecular dynamics simulations7Automated Machine Learning of Interfacial Interaction Descriptors and Energies in Metal-Catalyzed N 2 and CO 2 Reduction Reactions14Utilizing Machine Learning to Advance Battery Materials Design: Challenges and Prospects51State‐of‐the‐Art Machine Learning Technology for Sustainable Lithium Battery Cathode Design: A Perspective13A general framework to govern machine learning oriented materials data quality42What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials2Harnessing Machine Learning for the Design of Surface Coatings: Challenges and Opportunities13Two-Phase Deep Learning for Long-Term Lithium-Ion Battery Remaining Useful Life Prediction Considering Different Charging Periods0Descriptors construction and application in catalytic site design26A Database of Composition‐Processing‐Performance Parameters for over 400 Inorganic‐Polymer Composite Solid‐State Electrolytes10How Machine Learning Has Driven the Development of Rechargeable Ion Batteries21Machine learning-driven materials discovery: Unlocking next-generation functional materials – A review50A comprehensive benchmark of active learning strategies with AutoML for small-sample regression in materials science12AI-driven next-generation lithium-ion battery design automation (BDA) software5Multifunctional composite coatings for energy transportation, storage, conversion and utilization: Progress, challenge, and perspectives0Generalized capacity estimation for retired lithium-ion batteries: Unsupervised electrochemical impedance spectroscopy dimensionality reduction under random retirement conditions6Advanced Machine Learning Interatomic Potential for Accelerated Atomistic Simulations of Lithiation Dynamics in Large-Scale Si@C Core–Shell Anodes1An integrated high-throughput screening framework for perovskite light-emitting diode passivators based on machine learning0The paradigm shift of intelligent material design and development for fire safety: progress, challenges, and machine learning2Artificial intelligence-assisted non-metallic inclusion particle analysis in advanced steels using machine learning: A review6Coupling Data‐Driven and Reinforcement Learning for Material Development and Device Management in Batteries4Density Functional Theory–Machine Learning-Assisted Insightful Identification of Next-Generation High-Voltage Organic Dual-Ion Batteries3Automated Pipeline for High-Throughput Screening of 2D Electrode Materials for Alkali Metal-Ion Batteries1Adaptive Reduction of Multicollinearity for Machine Learning Prediction of CO Adsorption Energies on Single Atoms0Data driven intelligent research and development of gel electrolyte: Methods, challenges and cutting-edge trends1Graph representation learning for predicting creep rupture life in Nickel-Based single crystal superalloys0LiClustCondAI: A clustering-based ensemble regression framework for ionic conductivity predictions in solid-state electrolytes0The role and strategies of data-driven machine learning in design optimization and performance evaluation of electrochemical energy storage materials0AI-enabled cross-scale modeling and parameter transfer for lithium-ion batteries1Application of Machine Learning in the Study of Creep Properties of Alloys0Machine learning-driven prediction and validation for ionic conductivity of hydrogel electrolytes1SIMON: Intelligent System for Automatic Selection of Machine Learning Algorithms in Social Sciences0Intelligent Computing for Future Layered Cathodes in Sodium‐Ion Batteries0Machine learning for cracking the molecular code of room-temperature organic phosphorescent materials: From structure-property relationship to performance prediction0Revealing interaction mechanisms of substitutional and interstitial solutes in metals through machine learning0Uncertainty in Process Mining: Survey and Conceptual Framework0Artificial intelligence-assisted investigation of ion transport mechanisms and material screening for solid-state electrolytes0A document-level information extraction pipeline for high-entropy layered cathode materials in sodium-ion batteries0

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