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RESEARCH PAPER · Journal of the American Chemical Society · 2022

Into the Unknown: How Computation Can Help Explore Uncharted Material Space

Austin M. Mroz, Víctor Posligua, Andrew Tarzia, Emma Helen Wolpert, Kim E. Jelfs

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Into the Unknown: How Computation Can Help Explore Uncharted Material SpaceOriginalData-driven pursuit of electrochemically stable 2D materials with basal plane activity toward oxygen electrocatalysis66Frontiers of molecular crystal structure prediction for pharmaceuticals and functional organic materials105Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review146Evolutionary-docking targeting bacterial FtsZ15Experimental Studies on TiO2 NT with Metal Dopants through Co-Precipitation, Sol–Gel, Hydrothermal Scheme and Corresponding Computational Molecular Evaluations36Generative Models as an Emerging Paradigm in the Chemical Sciences313New star-shaped ligands generated by evolutionary fitting the Omicron spike inner-cavity10Anticoagulant rodenticide novel candidates predicted by evolutionary docking0Identifying Underexplored and Untapped Regions in the Chemical Space of Transition Metal Complexes21Could Acinetobacter baumannii Lol-abaucin docking be improved?9Anticoagulant rodenticide novel candidates predicted by evolutionary docking10Predictive Synthesis of Copper Selenides Using a Multidimensional Phase Map Constructed with a Data-Driven Classifier32Machine learning-assisted selection of adsorption-based carbon dioxide capture materials39Advances in data‐assisted high‐throughput computations for material design112In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99Confining He Atoms in Diverse Ice-Phases: Examining the Stability of He Hydrate Crystals through DFT Approaches1Discrete and mixed-variable experimental design with surrogate-based approach8Navigation through high-dimensional chemical space: discovery of Ba 5 Y 13 [SiO 4 ] 8 O 8.5 and Ba 3 Y 2 [Si 2 O 7 ] 22Recent advances in elemental doping and simulation techniques: improving structural, photophysical and electronic properties of titanium dioxide99Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory86Artificial intelligence generates novel 3D printing formulations52Biomimetic Frustrated Lewis Pair Catalysts for Hydrogenation of CO to Methanol at Low Temperatures5Methods and applications of machine learning in computational design of optoelectronic semiconductors22Advances in hydrogen storage materials: harnessing innovative technology, from machine learning to computational chemistry, for energy storage solutions270Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning25Deductive Machine Learning Challenges and Opportunities in Chemical Applications5Structure, Cohesion Energetics, and Hydrogen Bonding Cooperativity in Fumaric Acid and Alkyl Fumarates: Insights from Experiment and Theory4The devil in the details: lessons from Li 6 PS 5 X for robust high-throughput workflows3Property Prediction of Bio‐Derived Block Copolymer Thermoplastic Elastomers Using Graph Kernel Methods1Property Prediction of Bio‐Derived Block Copolymer Thermoplastic Elastomers Using Graph Kernel Methods8Advanced computational techniques: Bridging metaheuristic optimization and deep learning for material design through image enhancement9AI-powered exploration of molecular vibrations, phonons, and spectroscopy19exaPD: A highly parallelizable workflow for multi-element phase diagram (PD) construction0Predicting pore-carrier solubility and size-exclusivity towards the rational design of type II porous liquid solutions10Utilizing symmetry operations for high-throughput ferroelectric materials discovery1Chemical space as a unifying theme for chemistry20Beyond symmetric self-assembly and effective molarity: unlocking functional enzyme mimics with robust organic cages7Data-Driven Search Algorithm for Discovery of Synthesizable Zeolitic Imidazolate Frameworks8Discovering Stable Amorphous Ceramics: From Computational Prediction to Thin‐Film Synthesis4Recent advances in the inverse design of silicon photonic devices and related platforms using deep generative models9Coordination and organometallic compounds of graphyne and graphdiyne: Synthesis, properties and applications3Generative AI for drug discovery and protein design: the next frontier in AI-driven molecular science76Unveiling Unusual Reactivity of SO2 and Unusual Type of S–X Long Bonds3Discovering Molecular Insights in Organic Optoelectronics with Knowledge-Informed Interpretable Deep Learning3Design of Tough 3D Printable Elastomers with Human‐in‐the‐Loop Reinforcement Learning1Design of Tough 3D Printable Elastomers with Human‐in‐the‐Loop Reinforcement Learning8Closed-loop optimization of tetris-inspired robotic morphology evolution1Transforming Precision Medicine through Generative AI: Advanced Architectures and Tailored Therapeutic Design for Patient‐Specific Drug Discovery14Chemical Space Exploration with Artificial “Mindless” Molecules9Aqueous-phase photocatalytic performance of defective carbon nitride: the discrepancy between density functional theory calculations predicting and practical results6AI Driven Discovery and Design of Smart Materials0Data-augmented response surface methodology-machine learning hybrid model for predicting polyvinyl butyral synthesis0Large language models for porous materials: from text mining to autonomous laboratory6Artificial Intelligence Empowered New Materials: Discovery, Synthesis, Prediction to Validation15Density functional theory insights into decorated, doped, and defective graphene as a model system for understanding hydrogen storage in carbon materials2Quantum Chemistry-Driven Molecular Inverse Design of Stable Isomers with Data-Free Reinforcement Learning1In-Line Molar Mass Determination in Semidilute Polymer Solutions0Recent Advances in Artificial Intelligence in Organic Electronic Research1Polymer informatics: Integrating data‐driven strategies, advanced machine learning, and automated synthesis for next‐generation polymer design2Unbiased Structure Prediction of Sophisticated Cage Structures0Unbiased Structure Prediction of Sophisticated Cage Structures0The quantum ensemble variational optimization algorithm: Applications to molecular inverse design0A Problem-Setup-Centric Framework for Mixed-Variable Chemical Formulation Optimization with Customized Constraints0Photovoltaics beyond Silicon: Interfacial Engineering, Sustainable Processing, and Data-Driven Design for High-Efficiency and Durable Solar Energy Systems0Machine learning as a tool for predicting and engineering the next generation of piezoelectric materials0

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