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RESEARCH PAPER · Scientific Reports · 2016

Adaptive Strategies for Materials Design using Uncertainties

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

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Adaptive Strategies for Materials Design using UncertaintiesOriginalStructure–Curie temperature relationships in BaTiO3 -based ferroelectric perovskites: Anomalous behavior of (Ba,Cd)TiO3 from DFT, statistical inference, and experiments51Finding New Perovskite Halides via Machine Learning204COMBO: An efficient Bayesian optimization library for materials science309Atomistic calculations and materials informatics: A review238High-throughput combinatorial study of the effect of M site alloying on the solid solution behavior of M2AlC MAX phases41A Statistical Learning Framework for Materials Science: Application to Elastic Moduli of k-nary Inorganic Polycrystalline Compounds242Statistical inference and adaptive design for materials discovery124Accelerated search for BaTiO 3 -based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning148Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn127Overview of Bayesian Optimization in Materials Science4An informatics approach to transformation temperatures of NiTi-based shape memory alloys272Optimal experimental design for materials discovery70Error estimation in high-throughput density functional theory calculation for material property: elastic constants of cubic binary alloy case7Data mining assisted materials design of layered double hydroxide with desired specific surface area24Selecting the selector: Comparison of update rules for discrete global optimization16Rapid prediction of molecule arrangements on metal surfaces via Bayesian optimization39Designing High Dielectric Permittivity Material in Barium Titanate70Recent advances in accelerated discovery through machine learning and statistical inference1New methods for prediction of elastic constants based on density functional theory combined with machine learning31On the stochastic phase stability of Ti2AlC-Cr2AlC23Rapid Bayesian optimisation for synthesis of short polymer fiber materials121MDTS: automatic complex materials design using Monte Carlo tree search67Diffusion in energy materials: Governing dynamics from atomistic modelling34Bayesian Optimization in Weakly Specified Search Space20Characterization of the Optical Properties of Turbid Media by Supervised Learning of Scattering Patterns21Dimensions, Bits, and Wows in Accelerating Materials Discovery2Importance of Feature Selection in Machine Learning and Adaptive Design for Materials31Machine Learning-Based Experimental Design in Materials Science17Accelerated Discovery of Large Electrostrains in BaTiO3‐Based Piezoelectrics Using Active Learning395Materials informatics167Multi-objective Optimization for Materials Discovery via Adaptive Design171Material data mining in Nianyi Chen's scientific family4Bayesian optimization for computationally extensive probability distributions28Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models24Structure prediction of boron-doped graphene by machine learning70Bayesian-Driven First-Principles Calculations for Accelerating Exploration of Fast Ion Conductors for Rechargeable Battery Application117Identifying interphase properties in polymer nanocomposites using adaptive optimization58New Bayesian-Optimization-Based Design of High-Strength 7xxx-Series Alloys from Recycled Aluminum34Filtering Bayesian optimization approach in weakly specified search space25Multi-Objective Bayesian Materials Discovery: Application on the Discovery of Precipitation Strengthened NiTi Shape Memory Alloys through Micromechanical Modeling0Multi-Information Source Fusion and Optimization to Realize ICME: Application to Dual-Phase Materials42Strategies for accelerating the adoption of materials informatics39Data-driven approach for the prediction and interpretation of core-electron loss spectroscopy51Multi-objective Bayesian materials discovery: Application on the discovery of precipitation strengthened NiTi shape memory alloys through micromechanical modeling125Accelerating Experimental Design by Incorporating Experimenter Hunches18Using Data Mining To Search for Perovskite Materials with Higher Specific Surface Area44Autonomous efficient experiment design for materials discovery with Bayesian model averaging79The Search for BaTiO3-Based Piezoelectrics With Large Piezoelectric Coefficient Using Machine Learning39Machine learning in materials design and discovery: Examples from the present and suggestions for the future198Bulk and surface DFT investigations of inorganic halide perovskites screened using machine learning and materials property databases104Efficient Use of Multiple Information Sources in Material Design32Extensive deep neural networks for transferring small scale learning to large scale systems57Selected Applications1Toward a Predictive Hierarchical Multiscale Modeling Approach for Energetic Materials17Materials 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 searches19Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models86Machine learning guided design of functional materials with targeted properties60Experiment Design Frameworks for Accelerated Discovery of Targeted Materials Across Scales30Monte Carlo tree search for materials design and discovery43Accelerated Discovery of Sustainable Building Materials8Bayesian optimization for conformer generation56Bayesian Optimization for Accelerating Hyper-Parameter Tuning163A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors110Recent advances and applications of machine learning in solid-state materials science2604A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering104Cognitive Chemistry: The Marriage of Machine Learning and Chemistry to Accelerate Materials Discovery1Understanding ML Driven HPC: Applications and Infrastructure12Accelerated Search for BaTiO3‐Based Ceramics with Large Energy Storage at Low Fields Using Machine Learning and Experimental Design100Understanding ML driven HPC: Applications and Infrastructure2Autonome Entdeckung in den chemischen Wissenschaften, Teil I: Fortschritt12Autonomous Discovery in the Chemical Sciences Part I: Progress316Data Infrastructure Elements in Support of Accelerated Materials Innovation: ELA, PyMKS, and MATIN21Virtual Issue on Machine-Learning Discoveries in Materials Science29Computational Strategies to Increase Efficiency of Gaussian-Process-Driven Autonomous Experiments4Machine learning approaches for ELNES/XANES37The Materials Research Platform: Defining the Requirements from User Stories31Phase prediction in high entropy alloys with a rational selection of materials descriptors and machine learning models461Bayesian Global Optimization applied to the design of shape-memory alloys6Benchmarking the acceleration of materials discovery by sequential learning151Data centric nanocomposites design via mixed-variable Bayesian optimization34Learning with Large Databases0Learning with Small Databases0Machine-Learning-Assisted De Novo Design of Organic Molecules and Polymers: Opportunities and Challenges191Machine learning for parameter auto-tuning in molecular dynamics simulations: Efficient dynamics of ions near polarizable nanoparticles22Uncertainty Quantification and Propagation in Computational Materials Science and Simulation-Assisted Materials Design71Advances in Kriging-Based Autonomous X-Ray Scattering Experiments41A Critical Review of Machine Learning of Energy Materials605Computational functionality‐driven design of semiconductors for optoelectronic applications58Data-Based Methods for Materials Design and Discovery: Basic Ideas and General Methods10Bayesian optimization based on a unified figure of merit for accelerated materials screening: A case study of halide perovskites45Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables241Composition design of 7XXX aluminum alloys optimizing stress corrosion cracking resistance using machine learning26Toward Designing Highly Conductive Polymer Electrolytes by Machine Learning Assisted Coarse-Grained Molecular Dynamics112Discovery of superionic conductors by ensemble-scope descriptor31Adaptive design of a universal automotive ball joint separating device4Adaptive machine learning for efficient materials design31Role of uncertainty estimation in accelerating materials development via active learning45A New Era of Inorganic Materials Discovery Powered by Data Science5Machine Learning for Core-loss Spectrum0Adaptive Bayesian optimization for epitaxial growth of Si thin films under various constraints39Accelerated discovery of high-strength aluminum alloys by machine learning105Autonomous materials discovery driven by Gaussian process regression with inhomogeneous measurement noise and anisotropic kernels106Detecting stable adsorbates of (1S)-camphor on Cu(111) with Bayesian optimization26Machine learning based surrogate modeling approach for mapping crystal deformation in three dimensions66Featureless adaptive optimization accelerates functional electronic materials design39Navigating the Complex Compositional Landscape of High-Entropy Alloys2Accelerating high-throughput virtual screening through molecular pool-based active learning301Harnessing the Complex Compositional Space of High-Entropy Alloys9Machine Learning Assisted Composition Effective Design for Precipitation Strengthened Copper Alloys259Machine Learning Aided Design of Polymer with Targeted Band Gap Based on DFT Computation57Efficient estimation of material property curves and surfaces via active learning24Opportunities for Next-Generation Luminescent Materials through Artificial Intelligence85A physics-informed machine learning model for porosity analysis in laser powder bed fusion additive manufacturing132Determination of stable structure of a cluster using convolutional neural network and particle swarm optimization20Machine learning identified materials descriptors for ferroelectricity110Efficient sampling for decision making in materials discovery*10Latent variable Gaussian process models: A rank‐based analysis and an alternative approach4Probing impact of molecular structure on bulk modulus and impact sensitivity of energetic materials by machine learning methods25A new active learning approach for global optimization of atomic clusters23An Active Learning Approach for the Design of Doped LLZO Ceramic Garnets for Battery Applications39Bayesian optimization for goal-oriented multi-objective inverse material design36Bayesian Optimization in Materials Science: A Survey1Machine learning-based discovery of molecules, crystals, and composites: A perspective review16Accelerating materials discovery with Bayesian optimization and graph deep learning142Latent map Gaussian processes for mixed variable metamodeling40DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions0DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions4DiSCoVeR: a Materials Discovery Screening Tool for High Performance, Unique Chemical Compositions3Review of machine learning‐driven design of polymer‐based dielectrics50High‐Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research141Polymer Sequence Design via Active Learning1Accurate and explainable machine learning for the power factors of diamond-like thermoelectric materials17Data-Driven Methods for Accelerating Polymer Design122DiSCoVeR: a materials discovery screening tool for high performance, unique chemical compositions27Discovery Acceleration vs Epistemic Depth: Speed–Understanding Trade-Offs in Computational Design0Estimating the performance of a material in its service space via Bayesian active learning: a case study of the damping capacity of Mg alloys14NiTi shape memory alloys: properties16The Role of Surprise in AI-Driven Materials Discovery0Global‐Oriented Strategy for Searching Ultrastrength Martensitic Stainless Steels11Sample-efficient parameter exploration of the powder film drying process using experiment-based Bayesian optimization20Efficient Exploration of Microstructure-Property Spaces via Active Learning17Data Centric Design: A New Approach to Design of Microstructural Material Systems52Roadmap on Machine learning in electronic structure180Predicting Material Properties Using Machine Learning for Accelerated Materials Discovery0Macromolecular Modeling0Comparison of conceptually different multi-objective Bayesian optimization methods for material design problems28Active learning and molecular dynamics simulations to find high melting temperature alloys40Data‐Driven Materials Innovation and Applications186

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  2. Finding New Perovskite Halides via Machine Learning

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  5. High-throughput combinatorial study of the effect of M site alloying on the solid solution behavior of M2AlC MAX phases

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  8. Accelerated search for BaTiO 3 -based piezoelectrics with vertical morphotropic phase boundary using Bayesian learning

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  18. Recent advances in accelerated discovery through machine learning and statistical inference

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  40. Multi-Objective Bayesian Materials Discovery: Application on the Discovery of Precipitation Strengthened NiTi Shape Memory Alloys through Micromechanical Modeling

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  48. The Search for BaTiO3-Based Piezoelectrics With Large Piezoelectric Coefficient Using Machine Learning

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  49. Machine learning in materials design and discovery: Examples from the present and suggestions for the future

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  50. Bulk and surface DFT investigations of inorganic halide perovskites screened using machine learning and materials property databases

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