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RESEARCH PAPER · Materials Horizons · 2021

Toward autonomous design and synthesis of novel inorganic materials

Nathan J. Szymanski, Yan Zeng, Haoyan Huo, Christopher J. Bartel, Haegyeom Kim, Gerbrand Ceder

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Toward autonomous design and synthesis of novel inorganic materialsOriginalProcess intensification 4.0: A new approach for attaining new, sustainable and circular processes enabled by machine learning76XERUS: An open-source tool for quick XRD phase identification and refinement automation15Flexible automation accelerates materials discovery98Data-Centric Architecture for Self-Driving Laboratories with Autonomous Discovery of New Nanomaterials23Towards Predictive Synthesis of Inorganic Materials Using Network Science5Progress in Materials Data Availability and Application: A review1Constructing trifunctional MoTe 2 /As van der Waals heterostructures for versatile energy applications29Machine learning for design principles for single atom catalysts towards electrochemical reactions116Prospects and challenges for autonomous catalyst discovery viewed from an experimental perspective46Self-learning entropic population annealing for interpretable materials design8ULSA: unified language of synthesis actions for the representation of inorganic synthesis protocols28Why Invariance Becomes a Liability: Failure Modes of Rotationally Invariant GNNs for Anisotropic Elastic Constants0Toward autonomous materials research: Recent progress and future challenges61Active meta-learning for predicting and selecting perovskite crystallization experiments24Autonomous Nanocrystal Doping by Self‐Driving Fluidic Micro‐Processors20Recent advances and applications of deep learning methods in materials science1161Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification and Property Prediction55Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature83Recent progress in generative adversarial networks applied to inversely designing inorganic materials: A brief review50Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models7Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models5Machine-Learning Rationalization and Prediction of Solid-State Synthesis Conditions79Human- and machine-centred designs of molecules and materials for sustainability and decarbonization137Predicting hydrogen storage capacity of V–Ti–Cr–Fe alloy via ensemble machine learning67Emerging Heterogeneous Supports for Efficient Electrocatalysis37Intensification of catalytic reactors: A synergic effort of Multiscale Modeling, Machine Learning and Additive Manufacturing40Efficient autonomous material search method combining ab initio calculations, autoencoder, and multi-objective Bayesian optimization16Recommender system for discovery of inorganic compounds11A Framework for the Optimal Selection of High-Throughput Data Collection Workflows by Autonomous Experimentation Systems6Design of Dual Purpose Fe‐metallogel for Magnetic Refrigeration and Fabrication of Schottky Barrier Diode15Machine-learning for designing nanoarchitectured materials by dealloying13Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118High-entropy mechanism to boost ionic conductivity558Research Acceleration in Self‐Driving Labs: Technological Roadmap toward Accelerated Materials and Molecular Discovery51Machine learning-inspired battery material innovation49Materials synthesizability and stability prediction using a semi-supervised teacher-student dual neural network22The synthesis and combined electrical–magnetic and toxic dye sequestration properties of a Cr( iii )-metallogel4Real-Time Diagnostics of 2D Crystal Transformations by Pulsed Laser Deposition: Controlled Synthesis of Janus WSSe Monolayers and Alloys76Relationships between synthesis conditions and TiN coating properties discovered from the data driven approach16Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification85Autonomous experimental systems in materials science22Autonomous decision making for solid-state synthesis of inorganic materials1Computer-assisted discovery and rational synthesis of ternary oxides3Advanced Research Directions on AI for Science, Energy, and Security: Report on Summer 2022 Workshops24Proteolysis-targeting chimeras in biotherapeutics: Current trends and future applications16The promise and pitfalls of AI for molecular and materials synthesis22Bayesian optimization of metastable nickel formation during the spontaneous crystallization under extreme conditions3A Deep Learning Approach to Powder X‐Ray Diffraction Pattern Analysis: Addressing Generalizability and Perturbation Issues Simultaneously29Validating neural networks for spectroscopic classification on a universal synthetic dataset34Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature65Autonomous sputter synthesis of thin film nitrides with composition controlled by Bayesian optimization of optical plasma emission26Development of high‐throughput wet‐chemical synthesis techniques for material research38Machine learning based analysis of metal support co-sintering process for solid oxide fuel cells19Curated materials data of hybrid perovskites: approaches and potential usage3Improving efficiency of autonomous material search via transfer learning from nontarget properties4Data driven design of compositionally complex energy materials13Assessing Thermodynamic Selectivity of Solid-State Reactions for the Predictive Synthesis of Inorganic Materials41Autonomous and dynamic precursor selection for solid-state materials synthesis47Semiautomated Experiments to Accelerate the Design of Advanced Battery Materials: Combining Speed, Low Cost, and Adaptability20Accelerating the prediction of stable materials with machine learning104Role of multifidelity data in sequential active learning materials discovery campaigns: case study of electronic bandgap7Accelerated chemical science with AI86Neural structure fields with application to crystal structure autoencoders3Text Mining the Literature to Inform Experiments and Rationalize Impurity Phase Formation for BiFeO312How the AI-assisted discovery and synthesis of a ternary oxide highlights capability gaps in materials science21Mechanistic vs. Predictive Success: A Theory of What “Understanding” Means for AI in Materials Science0The rise of high-entropy battery materials176Challenges and Strategies for Synthesizing High Performance Micro and Nanoscale High Entropy Oxide Materials27Lead-Free Halide Perovskites for Photocatalysis via High-Throughput Exploration30Integrated analysis of X-ray diffraction patterns and pair distribution functions for machine-learned phase identification46Revealing the Potential and Challenges of High‐Entropy Layered Cathodes for Sodium‐Based Energy Storage174Navigating phase diagram complexity to guide robotic inorganic materials synthesis82Spatially resolved lock-in micro-thermography (SR-LIT): A tensor analysis-enhanced method for anisotropic thermal characterization14Deep learning of experimental electrochemistry for battery cathodes across diverse compositions49Exploring solvation structure and transport behavior for rational design of advanced electrolytes for next generation of lithium batteries15Autonomous Synthesis of Thin Film Materials with Pulsed Laser Deposition Enabled by In Situ Spectroscopy and Automation42Comparison of Predictive Modeling Concrete Compressive Strength with Machine Learning Approaches7Overview: Machine Learning for Segmentation and Classification of Complex Steel Microstructures35Data-driven auto-tuning strategy for RTO-MPC based on Bayesian optimization12Elevating energy storage: High-entropy materials take center stage9Bright Innovations: Review of Next-Generation Advances in Scintillator Engineering117Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies33Imbalanced spectral data analysis using data augmentation based on the generative adversarial network21Uncertainty-aware particle segmentation for electron microscopy at varied length scales21Review of the opportunities and limitations for powder-based high-throughput solid-state processing of advanced functional ceramics36A Perspective on Democratizing Mechanical Testing: Harnessing Artificial Intelligence to Advance Sustainable Material Adoption and Decentralized Manufacturing5From Design to Device: Challenges and Opportunities in Computational Discovery of p -Type Transparent Conductors18Self-Driving Laboratories for Chemistry and Materials Science674Autonomous search for materials with high Curie temperature using ab initio calculations and machine learning2Deep learning for symmetry classification using sparse 3D electron density data for inorganic compounds5Autonomous search for half-metallic materials with B 2 structure5Digital manufacturing of perovskite materials and solar cells12Crystal Structure Determination from Powder Diffraction Patterns with Generative Machine Learning25Identifying crystal structures beyond known prototypes from x-ray powder diffraction spectra7Toward Accelerating Discovery via Physics-Driven and Interactive Multifidelity Bayesian Optimization3Predicting and Accelerating Nanomaterial Synthesis Using Machine Learning Featurization32Analysis of the effect of the Fermi surface matching at Co–Fe and Cu interface on giant magnetoresistance effect using a combinatorial technique4Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility17The Changing Landscape of Materials Discovery0Deep reinforcement learning for inverse inorganic materials design59Autonomous laboratories in China: an embodied intelligence-driven platform to accelerate chemical discovery20Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry51Enabling technology for accelerated discovery of supramolecular materials2Harnessing machine learning to probe dielectrics in next generation telecommunication and automotive radar applications3High-Throughput Methods for Accelerated Catalyst Discovery3Balancing autonomy and expertise in autonomous synthesis laboratories5The Zintl pnictides Yb10CdSb9 and Yb14CdSb11: New candidate thermoelectric materials9From screens to scenes: A survey of embodied AI in healthcare51Multi-Objective Production Cost Control and Strategy Optimization Using Whale Optimization Algorithm0Autonomous materials search using machine learning and ab initio calculations for L1 0 -FePt-based quaternary alloys2Investigation of molybdate-tungstate based phosphors enriched with Dy3+ and Sm3+ ions: From synthesis to white LED applications13Ab initio structure solutions from nanocrystalline powder diffraction data via diffusion models27Applications of density functional theory and machine learning in nanomaterials: A review51Impact of halogenation and substituent position on supramolecular assemblies of mercury(II) complexes with halogenated imine ligands4Probabilistic phase labeling and lattice refinement for autonomous materials research9Catalytic Nanozyme: Unlocking new horizons in articular cartilage degeneration treatment6AI-Robot-Driven Autonomous Synthesis of Functional Inorganic Thin Films0Searching for materials for next-generation on-chip interconnects4AI-driven new materials development and data standardization4Accelerated data-driven materials science with the Materials Project300Advancing chemical engineering technology with artificial intelligence21Artificial intelligence in materials science and engineering: Current landscape, key challenges, and future trajectories33Self-driving laboratories with artificial intelligence: An overview of process systems engineering perspective14Agentic deep graph reasoning yields self-organizing knowledge networks14Autonomous Materials Synthesis Laboratories: Integrating Artificial Intelligence with Advanced Robotics for Accelerated Discovery4Caracterización de Transiciones Espaciotemporales en Sistemas Químicos Mediante Herramientas Fractales y Dinámicas0AI‐Driven Advances in Sustainable Materials for Green Energy: From Innovation to Lifecycle Management31Machine learning-driven materials discovery: Unlocking next-generation functional materials – A review50Leveraging unlabeled SEM datasets with self-supervised learning for enhanced particle segmentation7Lightning risk assessment of offshore wind farms considering tropical cyclone impacts based on an improved gradient boosting algorithm2Optimization of Annealing Time for PS/PMMA Electrosprayed Surfaces Using Bayesian Optimization0Accelerated discovery of multinary chalcogenide quantum dots: combining aqueous chemistry with high-throughput experimentation and machine learning3Networking autonomous material exploration systems through transfer learning6Toward efficient dielectric material discovery: a comparative evaluation of Transformer and AutoML-based inverse learning1Hierarchical attention graph learning with LLM enhancement for molecular solubility prediction3Accelerating materials discovery in heterogeneous composition-property design spaces via collaborative Bayesian optimization4Autonomous Nanoparticle Synthesis Guided by In Situ Multiscale Structural Characterization6Can we automate scientific reasoning in closed-loop experiments using large language models?4Combinatorial to autonomous experimentation0Coordination compound connected to another structure: a case study0Large language models for porous materials: from text mining to autonomous laboratory6Navigating the path to autonomy: real-world lessons from an air-free self-driving laboratory1Recent advances in high-entropy metal sulfides, selenides, and phosphides for use in lithium/sodium-ion batteries2Benchtop Autonomous Electrochemical Characterization System for Combinatorial Thin-Film Solid Oxide Electrodes2Heavy Halide Nanomaterials as Next-Generation Hosts for Lanthanide Upconversion10Tailored oxygen vacancy engineering in hierarchical ceria-based materials: A pathway to high-efficiency solar hydrogen generation0Deep learning in chemical engineering: Strengths, weaknesses, opportunities, and threats1The concept of maximum pixel size in measurements and quantitative decision making in autonomous scanning speed for goal achievement0Designing Thermomechanically Stable Nanocrystalline Alloys for Low Friction and Wear: A Perspective0High-Throughput Methods in Materials Science (Part I): A Review of Chemical and Physical Methods and Automated Sample Logistics0

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  5. Towards Predictive Synthesis of Inorganic Materials Using Network Science

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  12. Why Invariance Becomes a Liability: Failure Modes of Rotationally Invariant GNNs for Anisotropic Elastic Constants

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  17. Powder X‐Ray Diffraction Pattern Is All You Need for Machine‐Learning‐Based Symmetry Identification and Property Prediction

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  18. Dataset of solution-based inorganic materials synthesis procedures extracted from the scientific literature

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  40. Adaptively driven X-ray diffraction guided by machine learning for autonomous phase identification

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  42. Autonomous decision making for solid-state synthesis of inorganic materials

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  45. Proteolysis-targeting chimeras in biotherapeutics: Current trends and future applications

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  46. The promise and pitfalls of AI for molecular and materials synthesis

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  47. Bayesian optimization of metastable nickel formation during the spontaneous crystallization under extreme conditions

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  48. A Deep Learning Approach to Powder X‐Ray Diffraction Pattern Analysis: Addressing Generalizability and Perturbation Issues Simultaneously

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  49. Validating neural networks for spectroscopic classification on a universal synthetic dataset

    npj Computational Materials · 2023-06-05

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  50. Precursor recommendation for inorganic synthesis by machine learning materials similarity from scientific literature

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