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RESEARCH PAPER · Current Opinion in Solid State and Materials Science · 2016

Statistical inference and adaptive design for materials discovery

Turab Lookman, Prasanna V. Balachandran, Dezhen Xue, John Hogden, James P. Theiler

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Statistical inference and adaptive design for materials discoveryOriginalAn informatics approach to transformation temperatures of NiTi-based shape memory alloys272Machine Learning and Materials Informatics: Recent Applications and Prospects8Rational design of patchy colloids via landscape engineering49Machine learning and data science in soft materials engineering173Incorporating Prior Information from Engineering Design into Gaussian Process Regression : with applications to Aeronautical Engineering0Machine learning in materials informatics: recent applications and prospects1912Scoping the polymer genome: A roadmap for rational polymer dielectrics design and beyond202Importance of Feature Selection in Machine Learning and Adaptive Design for Materials31Accelerated Discovery of Large Electrostrains in BaTiO3‐Based Piezoelectrics Using Active Learning395Materials 4.0: Materials big data enabled materials discovery170Materials informatics167Multi-objective Optimization for Materials Discovery via Adaptive Design171Experimental search for high-temperature ferroelectric perovskites guided by two-step machine learning277Two-way design of alloys for advanced ultra supercritical plants based on machine learning50Rational Design of Polymer Dielectrics: An Application of Density Functional Theory and Machine Learning2Autonomous efficient experiment design for materials discovery with Bayesian model averaging79Ionic Liquid Design Using Molecular Simulation and Statistical Methods0Learning from Imperfections: Predicting Structure and Thermodynamics from Atomic Imaging of Fluctuations32Materials informatics: From the atomic-level to the continuum189Active learning in materials science with emphasis on adaptive sampling using uncertainties for targeted design828Rocketsled: a software library for optimizing high-throughput computational searches19Machine learning assisted design of high entropy alloys with desired property933Machine learning guided design of functional materials with targeted properties60Systems Approaches to Materials Design: Past, Present, and Future79Multifidelity Information Fusion with Machine Learning: A Case Study of Dopant Formation Energies in Hafnia77Heat Transfer Coefficient Prediction of a Porous Material by Implementing a Machine Learning Model on a CFD Data Set5General Atomic Neighborhood Fingerprint for Machine Learning-Based Methods50A finite-strain thermomechanical behavior model for iron-based shape memory alloys accounting for coupling between phase transformation and plastic slip21A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering104Accelerating small-angle scattering experiments with simulation-based machine learning10Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel492Accelerated Search for BaTiO3‐Based Ceramics with Large Energy Storage at Low Fields Using Machine Learning and Experimental Design100Data science-enabled molecular-to-systems engineering for sustainable water treatment32An Automated Technique to Analyze Micro Indentation Load-Displacement Curve1Bayesian Global Optimization applied to the design of shape-memory alloys6Bayesian Optimization for Adaptive Experimental Design: A Review520Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity123Uncertainty quantification in materials modeling18Ceramic Genomics: Total Bond Order Density7Bayesian optimization based on a unified figure of merit for accelerated materials screening: A case study of halide perovskites45Composition design of 7XXX aluminum alloys optimizing stress corrosion cracking resistance using machine learning26Machine learning in materials genome initiative: A review231Opportunities and Challenges for Machine Learning in Materials Science522Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials294Adaptive machine learning for efficient materials design31An adaptive design approach for defects distribution modeling in materials from first-principle calculations20Role of uncertainty estimation in accelerating materials development via active learning45A Bayesian machine learning approach for inverse prediction of high-performance concrete ingredients with targeted performance63Emerging materials intelligence ecosystems propelled by machine learning440Investigating Active Learning and Meta-Learning for Iterative Peptide Design34Machine Learning Assisted Composition Effective Design for Precipitation Strengthened Copper Alloys259Machine learning-guided design and development of metallic structural materials27Sparse quantum Gaussian processes to counter the curse of dimensionality4Prediction of the Electronic Work Function by Regression Algorithm in Machine Learning6Efficient sampling for decision making in materials discovery*10Predictive 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in Computational Materials Engineering: Methods and Deployment Contexts0Global‐Oriented Strategy for Searching Ultrastrength Martensitic Stainless Steels11Dissimilar welding and brazing of zirconium and its alloys: Methods, parameters, metallurgy and properties of joints38Efficient Exploration of Microstructure-Property Spaces via Active Learning17Comparison of conceptually different multi-objective Bayesian optimization methods for material design problems28Accelerated materials design using batch Bayesian optimization: A case study for solving the inverse problem from materials microstructure to process specification45Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu-Ni-Co-Si-X alloy via Bayesian optimization machine learning44Machine learning-assisted design of biomedical high entropy alloys with low elastic modulus for orthopedic implants30Optimal Weighted Multiple-Testing Procedure for Clinical Trials2A Data-Driven Framework to Select a Cost-Efficient Subset of Parameters to Qualify Sourced Materials5Design of super-hard high-entropy ceramics coatings via machine learning62Now What Sequence? Pre-trained Ensembles for Bayesian Optimization of Protein Sequences14A Framework for the Optimal Selection of High-Throughput Data Collection Workflows by Autonomous Experimentation Systems6Machine learning overcomes human bias in the discovery of self-assembling peptides157Recent developments in the application of machine-learning towards accelerated predictive multiscale design and additive manufacturing133Polymer sequence design via molecular simulation-based active learning25Accelerated discovery of cost-effective Nd–Fe–B magnets through adaptive learning7Searching for medium entropy alloys with desired mechanical property by adaptive design combined with experiments4Accelerated design of lead-free high-performance piezoelectric ceramics with high accuracy via machine learning33Autonomous x-ray scattering26Three-step learning strategy for designing 15Cr ferritic steels with enhanced strength and plasticity at elevated temperature6Recent applications of machine learning in alloy design: A review158Hybrid Data‐Driven Discovery of High‐Performance Silver Selenide‐Based Thermoelectric Composites32Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations32Rapid discovery of promising materials via active learning with multi-objective optimization15Discrete and mixed-variable experimental design with surrogate-based approach8Advances in materials informatics: a review42Toward ultra-high strength high entropy alloys via feature engineering42Guided experimental design for static nonparametric modeling3Multi-objective design of Ni-B-Al master alloy by adaptive machine learning-driven aluminothermic reduction experiment3A study of the role of data and model uncertainty in active learning7Machine‐Learned Modeling for Accelerating Organic Solvent Design in Metal‐Ion Batteries2Active learning-enabled the discovery of ultra-high saturation magnetization soft magnetic alloys5Enhancing User Experience with AI-Generated CSS Through Machine Learning for Adaptive Web Design4Design of high-temperature NiTiCuHf shape memory alloys with minimum thermal hysteresis using Bayesian optimization15Machine learning-based prediction of the mechanical properties of β titanium shape memory alloys22Leveraging feature gradient for efficient acquisition function maximization in material composition design3Machine learning assisted development of Heusler alloys for high magnetic moment6Friction in Cylindrical Joints0Materials Engineering and Artificial Intelligence Innovations: A Comprehensive Review2A machine learning approach to mitigating hardness degradation in the heat-affected zone of AA7075-T6 alloy4Data selection strategies for minimizing measurement time in materials characterization1Machine learning approaches for intentional materials engineering5Rapid Discovery of Graphene Nanoflakes with Desired Absorption Spectra Using DFT and Bayesian Optimization with Neural Network Kernel6Unlocking the black box beyond Bayesian global optimization for materials design using reinforcement learning16Corrosion resistant performance prediction in high-entropy alloys: A framework for model, interpretation and multi-dimensional visualization11Characterization of Two Novel NiTiHf Shape Memory Alloys Designed by Machine Learning Utilizing Novel Experimental Techniques2Optimization of print parameters for batch and continuous manufacturing of three-dimensional (3D) printed dosage forms using artificial intelligence and machine learning3Accelerating materials discovery in heterogeneous composition-property design spaces via collaborative Bayesian optimization4Innovative technologies for succinic acid fermentation: A path to sustainable production1Interpretable machine learning-assisted discovery of BaTiO 3 -based ceramics with enhanced dielectric constant1A machine learning strategy for rapid design of preparation parameters in zero-sample complex alloy1Bayesian Optimization of Catalysis with In-Context Learning37Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness19Unveiling bootstrap uncertainty bias: understanding bagging efficiency over Gaussian processes in materials active learning0Compact and interpretable strength–ductility prediction of heat-treatable aluminum alloys using physics-retained explainable multi-target machine learning0Accelerated design of ultra-strong Ni–Al–Ti–Cr–V high-entropy alloys via interpretable CatBoost machine learning0

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  8. Importance of Feature Selection in Machine Learning and Adaptive Design for Materials

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  9. Accelerated Discovery of Large Electrostrains in BaTiO3‐Based Piezoelectrics Using Active Learning

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  10. Materials 4.0: Materials big data enabled materials discovery

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