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RESEARCH PAPER · npj Computational Materials · 2022

Explainable machine learning in materials science

Xiaoting Zhong, Brian J. Gallagher, Shusen Liu, Bhavya Kailkhura, Anna M. Hiszpanski, T. Yong-Jin Han

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Explainable machine learning in materials scienceOriginalAlgorithmic Screening Frontiers: How Model Priors Reshape Searchable Materials Space0Data Density as Discovery Bias: Uneven Sampling in Computational Materials Exploration0Latent Variable Leakage in Materials AI: A Conceptual Risk Framework0New trends in additive manufacturing of high-entropy alloys and alloy design by machine learning: from single-phase to multiphase systems24Prediction without Transferability: Domain Shift in Cross-Material AI Inference0Property Prediction vs Mechanistic Insight: A Conceptual Divide in Materials AI0Scientific Overconfidence in High-Performing Materials AI Systems0The Problem of Scientific Regret in AI-Driven Materials Selection0The Treatment of Absence and Null Results in Materials Machine Learning Literature: A Review Study0Topology without Physics: Structural Abstraction Limits in Graph-Based Materials Models0Uncertainty as Infrastructure: Governing Confidence in Data-Driven Materials Pipelines0A Conceptual Distinction between Generalization and Transfer in Materials Machine Learning0Algorithm for Density of Angle Distribution in Random Sections of Polyhedron0An Introduction to Machine Learning in Molecular Sciences0Computational and Data-Driven Materials Engineering: Multimodal Materials Datasets, Integration Frameworks, and Discovery Potential0Computational and data-driven modelling of solid polymer electrolytes21Explainable Machine Learning for Predicting the Mechanical Properties in Bainitic Steels0Explainable Machine Learning Models for Corn Yield Prediction Using Uav Multispectral Data0Exploration without Explanation: A Conceptual Critique of Black-Box Materials Discovery0Feature Engineering as Scientific Framing: Encoding Choices in Materials Informatics0Machine learning-guided investigation for a high-performance electrochromic device based on ammonium metatungstate-iron( ii ) chloride-heavy water electrochromic liquid11Materials AI as Policy Actor: A Conceptual Framework for Downstream Decision Impact0Perspective: Embedding Domain Knowledge Is Not Optional — A Position on Physics-Constrained Materials GNNs0Physics-Constrained Neural Networks for Predicting Phase Transformation Pathways in Multi-Component Alloys0Scientific Blind Spots in Materials AI—A Systematic Conceptual Mapping: A Review Study0Scientific Blind Spots Introduced by Feature Engineering in Materials Informatics0Self-Supervised Representation Learning of Microstructures from Electron Microscopy for Property Prediction0Shapley Values with Uncertain Value Functions4Simulation Priors in Machine Learning Materials Models: Hidden Physics Assumptions0The Curse of Optimality: When Perfect Optimization Undermines Scientific Understanding0The Interpretability–Complexity Paradox in Deep Materials Networks0What is missing in autonomous discovery: open challenges for the community44Discovering the Ultralow Thermal Conductive High-Entropy Pyrochlore Oxides Through the Hybrid Knowledge-Assisted Data-Driven Machine Learning1Machine-Guided Design of Oxidation-Resistant Superconductors for Quantum Information Applications0Monitoring Product Quantity, Purity and Potency of Biopharmaceuticals in Real-time by Predictive Chemometrics and Soft sensors5Explainable Artificial Intelligence Approach to Identify the Origin of Phonon‐Assisted Emission in WSe2 Monolayer19On the Opacity of Deep Neural Networks24CoSMoR: Decoding decision-making process along continuous composition pathways in machine learning models trained for material properties3Explainable machine learning for predicting the mechanical properties in bainitic steels16Artificial-intelligence-led revolution of construction materials: From molecules to Industry 4.0103Machine Learning Assisted Analysis, Prediction, and Fabrication of High‐Efficiency CZTSSe Thin Film Solar Cells40Discovering the ultralow thermal conductive A2B2O7-type high-entropy oxides through the hybrid knowledge-assisted data-driven machine learning43Materials characterisation and software tools as key enablers in Industry 5.0 and wider acceptance of new methods and products9Machine learning guided hydrothermal synthesis of thermochromic VO2 nanoparticles39Virtual sample generation in machine learning assisted materials design and discovery20Theoretical Insights into Neural Networks and Deep Learning: Advancing Understanding, Interpretability, and Generalization11Sensors and chemometrics in downstream processing24Discrimination of Quartz Genesis Based on Explainable Machine Learning15Interpretable Fragment‐Based Molecule Design with Self‐Learning Entropic Population Annealing2Accurate, interpretable predictions of materials properties within transformer language models39Machine learning-assisted selection of adsorption-based carbon dioxide capture materials39Using limited neural networks to assess relative mechanistic influence on shock heating in granular solids8Improving Graph Convolutional Neural Networks for q-Consistent Summary-Explanations with Application to Credit Evaluation Systems0Algorithm for density of angle distribution in random sections of polyhedron2Insights into the Luminescence Quantum Yields of Cyclometalated Iridium(III) Complexes: A Density Functional Theory and Machine Learning Approach9CEEM: a Chemically Explainable Deep Learning Platform for Identifying Compounds with Low Effective Mass4Neural Network Models for Estimating the Impact of Physicochemical and Operational Parameters on the Specific Capacity of Activated-Carbon-Based Supercapacitors3An Effective Framework for Predicting Performance of Solid-Solution Copper Alloys Using a Feature Engineering Technique in Machine Learning3In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99Machine Learning-Aided High-Throughput First-Principles Calculations to Predict the Formation Energy of μ Phase12Revolutionizing drug formulation development: The increasing impact of machine learning128From prediction to design: Recent advances in machine learning for the study of 2D materials90Extracting the Synthetic Route of Pd-Based Catalysts in Methanol Steam Reforming from the Scientific Literature7ANN-based structure peciliaties evaluation of polymer composite reinforced with unidirectional carbon fiber8Process-structure multi-objective inverse optimisation for additive manufacturing of lattice structures using a physics-enhanced data-driven method39Review of Machine Learning for Social and Behavioral Research (Methodology in the Social Sciences)0A perspective on data-driven screening and discovery of polymer membranes for gas separation, from the molecular structure to the industrial performance11Advances of machine learning in materials science: Ideas and techniques105Explainable AI for Material Property Prediction Based on Energy Cloud: A Shapley-Driven Approach38Sensitivity Evaluation of Enveloped and Non-enveloped Viruses to Ethanol Using Machine Learning: A Systematic Review15Automated Machine Learning to Develop Predictive Models of Metabolic Syndrome in Patients with Periodontal Disease11Intelligent design and synthesis of energy catalytic materials25pudu: A Python library for agnostic feature selection and explainability of Machine Learning spectroscopic problems2An interpretable deep learning strategy for effective thermal conductivity prediction of porous materials55Towards physics-informed explainable machine learning and causal models for materials research34A Conceptual Blueprint for “Digital Materials Twins” without Simulation: Definitions, Boundaries, and Use-Cases0A Conceptual Framework for Trustworthy AI Decisions Across the Materials Design Lifecycle1A prediction model for CO 2 /CO adsorption performance on binary alloys based on machine learning11Algorithmic Consensus vs Scientific Consensus in Materials Prediction0Closed-World Training in an Open Materials Universe0Conceptual Models of Trust in Materials AI — 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concentrators: Current and future applications in smart cities4Machine Learning in Malware Analysis: Current Trends and Future Directions6Mechanistic vs. Predictive Success: A Theory of What “Understanding” Means for AI in Materials Science0Recent Advances in Machine Learning-Accelerated Materials Discovery — From Descriptors to Autonomous Experiments0The Literature on Scientific Explanation in AI-Driven Materials Science — Concepts and Criteria: A Review Study0Transfer Learning Across Materials Classes: Conceptual Boundaries of Reusability0What Counts as Knowledge in AI-Driven Materials Science? A Philosophical and Practical Reframing0Battery safety: Machine learning-based prognostics211Machine learning assisted discovery of high-efficiency self-healing epoxy coating for corrosion protection41AI methods in materials design, discovery and manufacturing: A review142Lanthanide-Doped ZnO Nanoparticles: Unraveling Their Role in Cytotoxicity, Antioxidant Capacity, and Nanotoxicology31Cationic perturbation strategy to solve the information drought in material explainable machine learning9Learning from machine learning: the case of band-gap directness in semiconductors9Understanding the Performance of (Ni−Fe−Co−Ce)Ox‐Based Water Oxidation Catalysts via Explainable Artificial Intelligence Framework21Synthesis, properties, applications, 3D printing and machine learning of graphene quantum dots in polymer nanocomposites170Machine learning assisted material development for lithography-based additive manufacturing of porous alumina ceramics10Machine learning assisted prediction of the phonon cutoff frequency of ABO3 perovskite materials16Research on strength prediction of crack rock mass based on random forest algorithm9Towards interpretable machine learning for observational quantification of soil heavy metal concentrations under environmental constraints34Adaptable multi-objective optimization framework: application to metal additive manufacturing13Enhancing predictive modeling of photovoltaic materials’ solar power conversion efficiency using explainable AI10Harnessing the Power of Artificial Intelligence in Materials Science: An Overview14Machine Learning to Promote Efficient Screening of Low‐Contact Electrode for 2D Semiconductor Transistor Under Limited Data18Machine learning for the prediction and optimization of production of cellulose nanocrystals by sulfuric acid hydrolysis23Deep learning based on small sample dataset: prediction of dielectric properties of SrTiO 3 -type perovskite with doping modification8Optimizing MOF properties for seasonal heat storage: a machine learning approach0Interpreting and generalizing deep learning in physics-based problems with functional linear models13Incorporating background knowledge in symbolic regression using a computer algebra system3Machine-learning-enabled exploitation of gas-sensing descriptors: A case study of five pristine metal oxides25A framework for strategic discovery of credible neural network surrogate models under uncertainty14Smartphone based wearable sweat glucose sensing device correlated with machine learning for real-time diabetes screening68Machine Learning-Guided Performance Evaluation of an All-Liquid Electrochromic Device10Machine Learning Methods to Improve Crystallization through the Prediction of Solute–Solvent Interactions5Exploring the Core-shell Structure of BaTiO3-based Dielectric Ceramics Using Machine Learning Models and Interpretability Analysis3Beryllium-doped biphenylene network: A first-principles study6AI for dielectric capacitors16How Interpretable Machine Learning Can Benefit Process Understanding in the Geosciences182The glass transition of CuZr metallic glasses in the perspective of machine learning5Translating strain to stress: a single-layer Bi-LSTM approach to predicting stress-strain curves in alloys during hot deformation12Accelerating Polymer Discovery with Uncertainty-Guided PGCNN: Explainable AI for Predicting Properties and Mechanistic Insights25State-of-the-Art Review on Determining One-Dimensional Consolidation Parameters Based on Compression and Distribution of Pore Water Pressure: Coefficient of Consolidation (cv), End of Primary (EOP) Consolidation5Step Forward Cross Validation for Bioactivity Prediction: Out of Distribution Validation in Drug Discovery5Integrating digital chemistry within the broader chemistry community11Descriptor Design for Perovskite Material with Compatible Molecules via Language Model and First-Principles8Target Material Property‐Dependent Cluster Analysis of Inorganic Compounds4Accelerated discovery and formation mechanism of high-entropy carbide ceramics using machine learning based on low-cost descriptors18Active causal learning for decoding chemical complexities with targeted interventions9A review on machine learning-guided design of energy materials19Machine Learning-Guided Cycle Life Prediction for Electrochromic Devices Based on Deuterium and Water Mixing Solvent5Synergistic N-heterocyclic carbene and C2N integration for efficient and selective metal-free photocatalytic CO reduction to C2H5OH3ROBERT : Bridging the Gap Between Machine Learning and Chemistry49Data-driven analysis on perovskite solar cell devices10Machine learning to enhance sustainable plastics: A review21Insights into Na ion adsorption and diffusion in biphenylene as an anode material for sodium-ion batteries: A first-principles study14Unifying theory of electronic descriptors of metal surfaces upon perturbation8When Metal Nanoclusters Meet Smart Synthesis31Literature Review of Explainable Tabular Data Analysis17Towards neuromorphic computing on quantum many-body architectures : VO2 transition dynamics0A review of machine learning in additive manufacturing: design and process45

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