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RESEARCH PAPER · Chemistry of Materials · 2023

Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition Prediction

Christopher J. Karpovich, Elton Pan, Zach Jensen, Elsa Olivetti

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Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition PredictionOriginalArtificial Intelligence for Surface‐Enhanced Raman Spectroscopy149Data-Driven Predetermination of Cu Oxidation State in Copper Nanoparticles: Application to the Synthesis by Laser Ablation in Liquid21Challenges and opportunities for AI in synthetic solid-state inorganic chemistry2ZeoSyn: A Comprehensive Zeolite Synthesis Dataset Enabling Machine-Learning Rationalization of Hydrothermal Parameters53Data-driven physics-informed descriptors of cation ordering in multicomponent perovskite oxides17Computationally Guided Synthesis of Battery Materials11Leveraging Triphenylphosphine-Containing Polymers to Explore Design Principles for Protein-Mimetic Catalysts9Dielectric Ceramics Database Automatically Constructed by Data Mining in the Literature3Computer‐Aided Synthesis Planning (CASP) and Machine Learning: Optimizing Chemical Reaction Conditions15A critical reflection on attempts to machine-learn materials synthesis insights from text-mined literature recipes16Machine learning-assisted screening of efficient ionic liquids for catalyzing CO2 cycloaddition reaction7Impact of data bias on machine learning for crystal compound synthesizability predictions16Matini-Net: Versatile Material Informatics Research Framework for Feature Engineering and Deep Neural Network Design1Deep reinforcement learning for inverse inorganic materials design59Artificial Intelligence and Generative Models for Materials Discovery: A Review14Learning Under Scarcity: A Conceptual Theory of Small-Data Regimes in Materials Artificial Intelligence0Predicting cobalt ion concentration in hydrometallurgy zinc process using data decomposition and machine learning9Solid-state synthesizability predictions using positive-unlabeled learning from human-curated literature data8How to accelerate the inorganic materials synthesis: from computational guidelines to data-driven method?9Foundation models for materials discovery – current state and future directions102Emerging Microelectronic Materials by Design: Navigating Combinatorial Design Space with Scarce and Dispersed Data6Machine learning for the generative discovery of K + -selective porous structures with aluminum sites0Artificial Intelligence for Materials Discovery, Development, and Optimization171High-Throughput Calculations and Machine Learning for Discovering Halide-Type Solid-State Electrolyte Materials12AI-driven advances in the design of RTP and TADF luminescent material6Data‐Driven Insights into Rare Earth Mineralization: Machine Learning Applications Using Functional Material Synthesis Data3Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials8Prediction of COF Synthesis Reaction Conditions Based on Data Mining and Machine Learning4Informatics for thermodynamics: Stability to synthesizability0Bridging theory and experiment in materials discovery: Machine-learning-assisted prediction of synthesizable structures3DiffSyn: a generative diffusion approach to materials synthesis planning8Roadmap for High‐Throughput Ceramic Materials Synthesis and Discovery for Batteries1OpenAI perovskite: A data-driven platform for exploration and performance analysis of two-dimensional perovskite solar cells0Toward a Paradigm Shift in Low‐Temperature SCR Catalyst Design: Defect Engineering and Data‐Driven Integration0A Large-Scale Nanocrystal Database with Aligned Synthesis and Properties, Enabling Generative Inverse Design0Towards high-performance halide electrolytes: Data-driven approaches for materials design and synthesis2Predicting Inorganic Material Synthesis Pathways Based on Language Models0AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization2Entropy-based materialistic and mechanistic insights for zinc ion battery applications0Structural stability in soft and hard spinel ferrites: A review0AI-Driven Sol–Gel Hybrid Materials for Transformative Food Systems: Process Optimization, Smart Sensing, and Functional Coatings0

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