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RESEARCH PAPER · Advanced Materials · 2022

Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials Libraries

Maxim A Ziatdinov, Yongtao Liu, Anna N. Morozovska, Eugene А. Eliseev, Xiaohang Zhang, Ichiro Takeuchi, Sergei V. Kalinin

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Hypothesis Learning in Automated Experiment: Application to Combinatorial Materials LibrariesOriginalBayesian optimization in continuous spaces via virtual process embeddings11MODEL, GUESS, CHECK: Wordle as a primer on active learning for materials research15Bayesian optimization with experimental failure for high-throughput materials growth53Recommender system for discovery of inorganic compounds11Machine Learning for Optical Scanning Probe Nanoscopy42Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118Machine Intelligence-Centered System for Automated Characterization of Functional Materials and Interfaces15GFlowNets for AI-driven scientific discovery22Orchestrating nimble experiments across interconnected labs37Toward self-organizing low-dimensional organic–inorganic hybrid perovskites: Machine learning-driven co-navigation of chemical and compositional spaces11Autonomous scanning probe microscopy with hypothesis learning: Exploring the physics of domain switching in ferroelectric materials33Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification85Learning the right channel in multimodal imaging: automated experiment in piezoresponse force microscopy24Combinatorial synthesis for AI-driven materials discovery114Crack Growth Rate Model Derived from Domain Knowledge-Guided Symbolic Regression31Autonomous atomic Hamiltonian construction and active sampling of X-ray absorption spectroscopy by adversarial Bayesian optimization14Stoichiometric growth of SrTiO3 films via Bayesian optimization with adaptive prior mean9A Combinatorial Approach for the Solution Deposition of Thin Films5Empowering scientists with data-driven automated experimentation9BAM line —A real-life sample materials research beamline30Machine Learning-driven Autonomous Microscopy for Materials and Physics Discovery5Physics-Augmented Machine Learning for Automated and Autonomous Experiments in Microscopy0Quantitative Description of Metal Center Organization and Interactions in Single‐Atom Catalysts39Explainability and human intervention in autonomous scanning probe microscopy17Discovery of structure–property relations for molecules via hypothesis-driven active learning over the chemical space9Data-Driven Predetermination of Cu Oxidation State in Copper Nanoparticles: Application to the Synthesis by Laser Ablation in Liquid21Towards physics-informed explainable machine learning and causal models for materials research34Co-orchestration of multiple instruments to uncover structure–property relationships in combinatorial libraries11Human-in-the-Loop: The Future of Machine Learning in Automated Electron Microscopy27Machine learning approaches for screening of materials in flexible electronic devices0Physics-driven discovery and bandgap engineering of hybrid perovskites12The future of self-driving laboratories: from human in the loop interactive AI to gamification102Deep kernel methods learn better: from cards to process optimization8Interpretable Machine Learning Method for Modelling Fatigue Short Crack Growth Behaviour19Designing workflows for materials characterization22Structural mode coupling in perovskite oxides using hypothesis-driven active learning3AEcroscopy: A Software–Hardware Framework Empowering Microscopy Toward Automated and Autonomous Experimentation33High‐throughput combinatorial approach expedites the synthesis of a lead‐free relaxor ferroelectric system10How scanning probe microscopy can be supported by artificial intelligence and quantum computing?10Automating Experiments with Scanning Probe Microscopy3Human-in-the-Loop Automated Experiment Simulated with Scanning Tunneling Microscopy0Realizing Smart STEM via Machine Learning on Remote High Performance Computer2Insights into Prismatic Loop Formation in Irradiated Fe–Cr Alloys from Hypothesis-Driven Active Learning and Causal Analysis3Self-Driving Laboratories for Chemistry and Materials Science674Computational and Machine Learning Methods for CO 2 Capture Using Metal–Organic Frameworks74Integration of scanning probe microscope with high-performance computing: Fixed-policy and reward-driven workflows implementation9Evolution of Ferroelectric Properties in SmxBi1–xFeO3 via Automated Piezoresponse Force Microscopy across combinatorial spread libraries13Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization3Integrated top-down process and voxel-based microstructure modeling for Ti-6Al-4V in laser wire direct energy deposition process4Measurements with noise: Bayesian optimization for co-optimizing noise and property discovery in automated experiments8SANE: strategic autonomous non-smooth exploration for multiple optima discovery in multi-modal and non-differentiable black-box functions4Reinforcement Learning-Assisted Ferroelectric Domain Wall Design Using a Machine Learning Phase-Field Surrogate5Artificial intelligence for advanced functional materials: exploring current and future directions12Science acceleration and accessibility with self-driving labs83Integrating Expert Knowledge in Autonomous Microscopy to Accelerate Discovery0Machine-learning-driven QSPR models for energetic molecules: A review on safety and energetic properties prediction13Machine‐learning‐assisted photonic device development: a multiscale approach from theory to characterization19Real-time feedback control microscopy for automation of optogenetic targeting9Materials discovery in combinatorial and high-throughput synthesis and processing: A new Frontier for SPM9Active deep kernel learning of molecular properties from structural embeddings1PhaseXplorer Creates High-Dimensional Phase Diagrams with Closed-Loop Active Learning5Microstructure-guided glass ceramics design via physics transfer1Integrated AI Framework for Room-Temperature Atom Manipulation in Scanning Probe Microscopy6Pareto-Optimal Experimentation: Human-Guided Multi-Objective Bayesian Optimization in Scanning Probe Microscopy4AI-Driven Optimization of Polymer Structure0Bringing ML to the real world: Rewards are all we need1Smart microscopy: current implementations and a roadmap for interoperability12Interpretable artificial intelligence for materials science: A comprehensive review of methods, applications, and outlook0Self-Driving Scanning Probe Microscopy: From Acceleration to Discovery and Manipulation3Bayesian Conavigation of a Computational Physical Model and Atomic Force Microscopy Experiment to Autonomously Survey a Combinatorial Materials Library0Automated scanning probe microscopy of combinatorial ferroelectric libraries: Gaussian-process-guided exploration and noise-aware experiment planning0Predicting Nepheline Formation in Nuclear Waste Glasses Using Machine Learning With Uncertainty Quantification0

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  17. Stoichiometric growth of SrTiO3 films via Bayesian optimization with adaptive prior mean

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  18. A Combinatorial Approach for the Solution Deposition of Thin Films

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  20. BAM line —A real-life sample materials research beamline

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