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RESEARCH PAPER · Materials Today · 2021

Accelerating materials discovery with Bayesian optimization and graph deep learning

Yunxing Zuo, Mingde Qin, Chi Chen, Weike Ye, Xiangguo Li, Jian Shu Luo, Shyue Ping Ong

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Accelerating materials discovery with Bayesian optimization and graph deep learningOriginalDiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions4AtomSets as a hierarchical transfer learning framework for small and large materials datasets76DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions3TCSP: a Template based crystal structure prediction algorithm and web server for materials discovery2DiSCoVeR: a materials discovery screening tool for high performance, unique chemical compositions27Dispersion relation prediction and structure inverse design of elastic metamaterials via deep learning95Effectiveness of Developing Self-Discovery and Exploration (SDE) Integrated Low Level Organism Taxonomy Module to Improve Collaboration Skill0Review of computational approaches to predict the thermodynamic stability of inorganic solids196A review of the recent progress in battery informatics178Graph-based machine learning beyond stable materials and relaxed crystal structures10Recent advances and applications of deep learning methods in materials science1161MaterialsAtlas.org: a materials informatics web app platform for materials discovery and survey of state-of-the-art70TCSP: a Template-Based Crystal Structure Prediction Algorithm for Materials Discovery32Machine Learning Guided Prediction of Superhard Materials Based on Compositional Features5High‐Throughput Screening of Bicationic Redox Materials for Chemical Looping Ammonia Synthesis25Rapid discovery of stable materials by coordinate-free coarse graining62Facilitating ab initio configurational sampling of multicomponent solids using an on-lattice neural network model and active learning12Enhanced singlet oxygen production under nanoconfinement using silica nanocomposites towards improving the photooxygenation’s conversion10Emerging Ultraviolet Persistent Luminescent Materials57The most compact search space is not always the most efficient: A case study on maximizing solid rocket fuel packing fraction via constrained Bayesian optimization6Enabling high-throughput electrical conductivity optimization of doped conjugated polymers using explainable machine learning0Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118Direction-Based Graph Representation to Accelerate Stable Catalyst Discovery8Computer Alloy Design of Ti Modified Al-Si-Mg-Sr Casting Alloys for Achieving Simultaneous Enhancement in Strength and Ductility24A chemically inspired convolutional neural network using electronic structure representation4Accessing complex reconstructed material structures with hybrid global optimization accelerated via on-the-fly machine learning13Bayesian optimisation for efficient material discovery: a mini review49Strain data augmentation enables machine learning of inorganic crystal geometry optimization1Exploring the Conformers of an Organic Molecule on a Metal Cluster with Bayesian Optimization10Compactness Matters: Improving Bayesian Optimization Efficiency of Materials Formulations through Invariant Search Spaces12Rational design of high-entropy ceramics based on machine learning – A critical review52Physics guided deep learning for generative design of crystal materials with symmetry constraints111Machine‐Learning‐Assisted Determination of the Global Zero‐Temperature Phase Diagram of Materials109Prediction of mechanical properties of high-entropy ceramics by deep learning with compositional descriptors19Representations of Materials for Machine Learning132Computer-assisted discovery and rational synthesis of ternary oxides3Active learning for prediction of tensile properties for material extrusion additive manufacturing36Exploring Metal Nanocluster Catalysts for Ammonia Synthesis Using Informatics Methods: A Concerted Effort of Bayesian Optimization, Swarm Intelligence, and First-Principles Computation10GMP-Featurizer: A parallelized Python package for efficiently computing the Gaussian Multipole features of atomic systems2A structure translation model for crystal compounds9Optimization of SVM Classification Accuracy with Bayesian Optimization Utilizing Data Augmentation1Gradient-based adaptive sampling framework and application in the laser-driven ion acceleration2Graph deep learning accelerated efficient crystal structure search and feature extraction35Discovery of 2D Materials using Transformer Network‐Based Generative Design16A Study on the Material Properties and Process Conditions Reasoning using Regression and Classification of Machine Learning0Evolutionary Machine Learning in Science and Engineering0Bayesian parameter estimation for characterising mobile ion vacancies in perovskite solar cells14Data-Driven Score-Based Models for Generating Stable Structures with Adaptive Crystal Cells8Data-driven discovery of electrocatalysts for CO2 reduction using active motifs-based machine learning164PAL 2.0: a physics-driven bayesian optimization framework for material discovery21A Novel Simulation-Based Optimization Method for Autonomous Vehicle Path Tracking with Urban Driving Application3Scope of machine learning in materials research—A review229Generative adversarial networks and diffusion models in material discovery88Boundary Conditions for Trustworthy Uncertainty Quantification in Materials AI: A Framework for Decision-Ready Confidence0Explainable AI for Interpretable Structure–Property Maps in Polymer Blend Systems0How the AI-assisted discovery and synthesis of a ternary oxide highlights capability gaps in materials science21Uncertainty-Aware Surrogate Modeling of High-Throughput DFT Data for Rapid Materials Screening0Synchrotron radiation data-driven artificial intelligence approaches in materials discovery30Accelerating defect predictions in semiconductors using graph neural networks39An effective method for generating crystal structures based on the variational autoencoder and the diffusion model16Accelerated Bayesian Inference for Molecular Simulations using Local Gaussian Process Surrogate Models12Accelerated Data-Driven Discovery and Screening of Two-Dimensional Magnets Using Graph Neural Networks21An active machine learning approach for optimal design of magnesium alloys using Bayesian optimisation31Explainable machine learning to enable high-throughput electrical conductivity optimization and discovery of doped conjugated polymers18Chapter 4: Physically informed deep learning networks for simulating microstructure evolution of 3D polycrystals0Review on automated 2D material design10Revolutionizing inverse design of ionic liquids through the multi-property prediction of over 300,000 novel variants using ensemble deep learning43Bridging multimodal data and battery science with machine learning35Local-distortion-informed exceptional multicomponent transition-metal carbides uncovered by machine learning19Mechanical Design Parameter Optimization through Graph-Based Bayesian Optimization and Pseudo Labeling1A deep generative modeling architecture for designing lattice-constrained perovskite materials29High‐Throughput Computational Screening of Metal Sulfides for the Chemical Looping Elemental Decomposition of H 2 S5Scalable crystal structure relaxation using an iteration-free deep generative model with uncertainty quantification30Graph-text contrastive learning of inorganic crystal structure toward a foundation model of inorganic materials5Discovery of high-performance dielectric materials with machine-learning-guided search9Phase Prediction via Crystal Structure Similarity in the Periodic Number Representation3Prediction methods for phonon transport properties of inorganic crystals: from traditional approaches to artificial intelligence4Accelerating the Discovery of Binary Interconnect Materials: A Graph Neural Network and Diversity Ranking Workflow0Artificial Intelligence and Generative Models for Materials Discovery: A Review14Artificial intelligence-driven revolution in nanozyme design: from serendipity to rational engineering57Machine Learning Algorithms for Applications in Materials Science I2Machine Learning-Based Methods for Materials Inverse Design: A Review34Perspective: From Prediction to Design — A Position on Optimization-Centric Workflows in Materials ML0Reactivity descriptors for sulfur redox kinetics in lithium–sulfur batteries: from mechanistic insights to machine learning driven catalyst design73Uncertainty in the era of machine learning for atomistic modeling11Advanced Morphological and Material Engineering for High‐Performance Interfacial Iontronic Pressure Sensors76Materials laboratories of the future for alloys, amorphous, and composite materials8Machine learning-assisted development of TMDs-type gas-sensitive materials for dissolved gases in oil-immersed transformer oils24Bayesian optimization of entropy-stabilized metal fluoride conversion cathodes and their synthesis5Recent Advances in High-Entropy Oxides for Photocatalytic Applications57Evolving biomaterials design from trial and error to intelligent innovation29Accelerated material discovery of high-performance Mg alloys via active learning and high throughput multi-objective informed Bayesian optimization7Generative deep learning for predicting ultrahigh lattice thermal conductivity materials21Machine learning-assisted screening of transition metal-doped TMDs for binding energy and charge transfer prediction2Rapid Discovery of Graphene Nanoflakes with Desired Absorption Spectra Using DFT and Bayesian Optimization with Neural Network Kernel6AUGUR, a flexible and efficient optimization algorithm for identification of optimal adsorption sites2Predicting Lattice Thermal Conductivity in 2D Materials: Integrating Experiments, Theory, and Machine Learning4A framework to evaluate machine learning crystal stability predictions154Robust Lightweight Graph Neural Network Framework for Accelerating Crystal Structure Prediction2Universal machine learning interatomic potentials poised to supplant DFT in modeling general defects in metals and random alloys17Generative Design for Engineering Applications: A State-of-the-Art Review18Accelerating structure relaxation in chemically disordered materials with a chemistry-driven model3Exploring lightweight language models for materials informatics with AlchemBERT4Artificial Intelligence for Materials Discovery, Development, and Optimization170Enhancing 1D ionic conductivity in lithium manganese iron phosphate with low-energy optical phonons4Rapid design of processing parameters for Zero-Sample alloys via multi-acquisition-function Bayesian optimization5Predicting stress–strain constitutive relationships: a deep learning approach based on multi-head attention mechanism and long short-term memory networks1Prediction and characterization of chemically complex σ − phase intermetallics with graph neural network3Bayesian optimization for laser powder bed fusion of defect-free AA20246Integrated AI and DFT: A Revolutionary Computational Breakthrough for Microwave‐Absorbing Materials Design31Time-resolved classification of plasma crystals in a direct current discharge using an advanced graph neural network1Materials Discovery Using Uncertainty-Aware Constrained Bayesian Optimization With Representation Learning of High-Dimensional Inputs4Sparse multivariate regression with missing values in both predictors and responses0Artificial intelligence in the development of structural and functional materials: A transformative frontier and complementary enabler of the rational alloy design framework10DFT validated machine learning model for elastic property prediction and mechanical classification of ternary cubic alloys0Machine Learning Based Prediction of Biomaterial Hardness for Medical Devices0Accelerating Ceramic Materials Development via Machine Learning: A Critical Review0Artificial intelligence-driven nanomaterial design and applications: advances, challenges, and future directions0Cognitive AI beyond prediction: toward reasoning and discovery1Heteroscedastic Bayesian Optimization of Jetting Stability for Autonomous Electrohydrodynamic Inkjet Printing0Machine Learning–Driven Prediction and Multi-Objective Optimization of Strength and Electrical Conductivity in Cu Alloys0Machine-learning screening of cationic dopants in hydroxyapatite: Cross-domain predictions from large-scale databases validated by density functional theory0Toward dynamic crystal structure prediction: integrating thermodynamic and kinetic modeling0Machine Learning-Driven Discovery of Stable Vanadium Oxide Compositions for Aerospace Applications0Artificial Intelligence Nanocomposite Synergy for Next-Generation Fuel Desulfurization0Optimized photocatalytic degradation of agroindustrial effluents using Pt/TiO2–CeO2: Synergizing materials science and AI optimization1Machine learning-driven approaches in electrochemical CO2 reduction: Bridging the gap between data and sustainable catalysis6Microstructure‐Informed Analysis Framework for Lattice Structures: Guiding Topology‐Material Synergy in Titanium Alloys1End-to-End Molecular Crystal Structure Prediction via Physics-Constrained Retrieval-Augmented GNN-VAE0Data-driven approaches for functional ceramics3Interpretation of Crystal Energy Landscapes with Kolmogorov–Arnold Networks1Dual‐Atom Catalysts for Synergistic Adsorption and Adjustment of Reaction Kinetics of Polysulfides: Design Strategies, Characterizations and Mechanisms1CA-SMART: An active learning framework for accelerating materials discovery under resource constraints1A survey of agentic materials science and engineering: where are we and where are we going?0Data-Driven Design of Single-Phase High-Entropy Ultra-High-Temperature Ceramics0Development of an automated approach for designing material functions via precise control of lattice defects0Machine learning probes potential mechanisms of adsorption behavior: accelerating the screening of promising sensor materials for sniffing early lung cancer biomarkers1Uma abordagem com autoencoders variacionais condicionais para geração de materiais NLO com bandgap alvo0Artificial intelligence for carbon materials in electrochemical energy storage: Methods, applications, and perspectives0Artificial intelligence-driven inverse design of catalytic materials0Machine learning for high-performance alloy design: A review0

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