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RESEARCH PAPER · npj Computational Materials · 2024

Deep reinforcement learning for inverse inorganic materials design

Christopher J. Karpovich, Elton Pan, Elsa Olivetti

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Deep reinforcement learning for inverse inorganic materials designOriginalAlgorithmic Consensus vs Scientific Consensus in Materials Prediction0Representation Drift in Iterative Materials Learning Systems0An AI-accelerated pathway for reproducible and stable halide perovskites17Artificial Intelligence and Generative Models for Materials Discovery: A Review14Colloidal systems as experimental platforms for physics-informed machine learning2Defining "Novel Material" in Generative Models: A Boundary between Chemical Validity and Synthesizability0Runaway Discovery Trajectories: Intervention Thresholds in Self-Reinforcing Materials AI Systems0Advances and Challenges in Perovskite Oxide Design for High‐Performance Zinc–Air Batteries: Integrating Experimental Strategies and Machine Learning34Artificial intelligence-empowered functional design of semi-transparent optoelectronic and photonic devices via deep Q-learning14Reinforcement Learning Approach to Advance Non‐Fullerene Acceptor‐Based Semi‐Transparent Organic Solar Cell7Experimentally validated inverse design of FeNiCrCoCu MPEAs and unlocking key insights with explainable AI11Artificial Intelligence‐Driven Approaches in Semiconductor Research44Artificial Intelligence for Materials Discovery, Development, and Optimization171How Machine Learning Has Driven the Development of Rechargeable Ion Batteries21Acceleration of crystal structure relaxation with deep reinforcement learning5Reinforcement learning with formation energy feedback for material diffusion models1Guidance on machine learning algorithm selection for materials science and engineering applications0From Data‐Driven to Intelligence‐Driven: Innovating Research Paradigms of Perovskite Solar Cells4AI-driven design of powder-based nanomaterials for smart textiles: from data intelligence to system integration27AI-based accurate and efficient flash point prediction for structurally diverse hydrocarbons via Bayesian-optimized deep neural networks0Toward AI ecosystems for electrolyte and interface engineering in solid-state batteries23Building Catalyst Exploration Highways by Integrating High‐Throughput and Machine Learning Technologies11Inverse Design Using Goal-Conditioned Reinforcement Learning for Organic Semiconductor Materials from Benzene and Thiophene-based Polycyclic Aromatic Compounds2Philosophy-informed machine learning0Data-driven materials discovery for inverted perovskite solar cells: machine learning in screening, design, and performance optimization0Inverse design of thermally active composite via policy-transferred reinforcement learning1Review on Recent Development of Artificial Intelligence and Machine Learning Approaches in Energy Applications0Toward self-driving laboratory 2.0 for chemistry and materials discovery15DiffSyn: a generative diffusion approach to materials synthesis planning8AIMATDESIGN: knowledge-augmented reinforcement learning for inverse materials design under data scarcity3AI-driven multi-property prediction and manufacturability-aware design for electronic materials and devices0Roadmaps for dendrite suppression in next generation lithium-ion batteries: Toward sustainable energy solutions3Machine Learning Toolkits and Frameworks for Materials Design7Modern machine learning in thin film device development0Improving Fidelity and Diversity in Chemical Language Transformers for Inverse Molecular Design2Spatiotemporal immunomodulation with programmable biomaterials to promote musculoskeletal tissue regeneration0CrysText: A Generative AI Approach for Text-Conditioned Crystal Structure Generation Using LLM9Revolutionizing layered double hydroxides for adsorption, catalysis and energy storage through artificial intelligence and machine learning: A critical review3Sustainable Materials Design With Multi‐Modal Artificial Intelligence1Artificial intelligence in additive Manufacturing: advances in smart materials, lattice optimization, and process intelligence17Polymer informatics: Integrating data‐driven strategies, advanced machine learning, and automated synthesis for next‐generation polymer design2A survey of agentic materials science and engineering: where are we and where are we going?0Band Gap Prediction of Two-Dimensional Materials Using a Gradient-Boosted Feature Selection Approach0Tunable Surface Chemistry of Molten Salt Derived MXenes for Data‐Driven Electrochemical Materials Discovery1Closed-Loop Machine Learning in Materials Discovery: From Predict-Validate-Update Paradigms to Autonomous Ecosystems0Autonomous laboratories for sustainable nanomaterials discovery0Artificial intelligence-driven inverse design of catalytic materials0Reinforcement Learning for Cathode Material Design Through Sequential Decision-Making Frameworks0Optimization of Pulsed Injection for Enhancing Supersonic Mixing via Deep Reinforcement Learning with Adaptive Noise Scaling0New Frontiers in Single-Atom Catalysis: Active-Site Dynamics and AI-Enabled Design1Leveraging Machine Learning for Accelerated Electrode–Electrolyte Interface Design in Rechargeable Li‐Based Batteries0Autonomous global-minimum search of Pd6Mo7 nanoclusters from deep reinforcement learning0High‐Entropy Perovskites for Oxygen Evolution Reaction: Progress, Challenges, and Prospects0A Gated Fusion-Enhanced Convolutional Neural Network–Graph Convolutional Network Model for Analyzing Key Microstructural Factors in Local Ductile Fracture Hotspots of Dual-Phase Steels0A Safe and Just Earth by Design0Graph-to-Graph Inverse Design of Surfactants under Multiobjective and Structural Constraints0Machine Learning Interatomic Potentials for Modeling Solid-State Batteries0Biodegradable electronic materials and devices0Toward automated discovery with generative models multimodal learning and closed loop workflows in inverse materials design0

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