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RESEARCH PAPER · Journal of the American Chemical Society · 2023

In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

Joshua Schrier, Alexander J. Norquist, Tonio Buonassisi, Jakoah Brgoch

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In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials ScienceOriginalMulti-fidelity Bayesian optimization of covalent organic frameworks for xenon/krypton separations41Predicting Poisson's Ratio: A Study of Semi-supervised Anomaly Detection and Supervised Approaches1Predicting Poisson's Ratio: A Study of Semi-supervised Anomaly Detection and Supervised Approaches1Accelerated chemical science with AI86Predicting Poisson’s Ratio: A Study of Semisupervised Anomaly Detection and Supervised Approaches10Alloying Effects on the Transport Properties of Refractory High-Entropy Alloys36Tourists’ Virtual Experiences to Create Sustainability: A Conceptual Model and Propositions0Novelty Detection in the Design of Synthesis of Energy Storage Materials: a Case Study of Garnet-Structured Solid Electrolytes1Ultrasound and sonochemistry enhance education outcomes: From fundamentals and applied research to entrepreneurial potential24Expanding the Applicability Domain of Machine Learning Model for Advancements in Electrochemical Material Discovery7Exploring chemistry and additive manufacturing design spaces: a perspective on computationally-guided design of printable alloys26Revisiting El-Sayed Synthesis: Bayesian Optimization for Revealing New Insights during the Growth of Gold Nanorods12Physics-Guided Dual Self-Supervised Learning for Structure-Based Material Property Prediction13Novelty detection in the design of synthesis of garnet-structured solid electrolytes6Oxide ceramics of A2M3O12 family with negative and close-to-zero thermal expansion coefficients: Machine learning-based modeling of functional characteristics12Extrapolative Machine Learning for Accurate Efficiency Prediction in Non‐Fullerene Ternary Organic Solar Cells: Leveraging Computable Molecular Descriptors in High‐Throughput Virtual Screening10Discovery of Ternary Antimonides A–Al–Sb (A = Rb or Cs) with Desired Structural Motifs Guided by Machine Learning5A Vision for the Future of Materials Innovation and How to Fast-Track It with Services2Learning Molecular Mixture Property Using Chemistry-Aware Graph Neural Network19Toward soft robotic inspection for aircraft: An overview and perspective6Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study66Large language models in electronic laboratory notebooks: Transforming materials science research workflows11Active learning streamlines development of high performance catalysts for higher alcohol synthesis74Large Language Models for Inorganic Synthesis Predictions73Collaborative neural networks-accelerated prediction of transition state energy barriers for CO catalytic oxidation9Embedding human knowledge in material screening pipeline as filters to identify novel synthesizable inorganic materials4Dynamical Electron Correlation and the Chemical Bond. IV. Covalent Bonds in A2 Molecules (A = N–As and F–Br)1Catalysis in the digital age: Unlocking the power of data with machine learning75Multi-objective, multi-constraint high-throughput design, synthesis, and characterization of tungsten-containing refractory multi-principal element alloys44Quantitatively determining melting properties for energetic compounds via knowledge-infused molecular graphs and interpretable deep learning4Deeper insights into flame retardancy of polymers by interpretable, quantifiable, yet accurate machine-learning model17Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case1Can Deep Learning Search for Exceptional Chiroptical Properties? The Halogenated [6]Helicene Case14Long-term research and design strategies for fusion energy materials9A brief overview of deep generative models and how they can be used to discover new electrode materials8A review of large language models and autonomous agents in chemistry269Archerfish: a retrofitted 3D printer for high-throughput combinatorial experimentation via continuous printing10Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials3Coherent collections of rules describing exceptional materials identified with a multi-objective optimization of subgroups2Density-aware active learning for materials discovery: a case study on functionalized nanoporous materials3Digital features of chemical elements extracted from local geometries in crystal structures2Establishing baselines for generative discovery of inorganic crystals10Innovative Design and Applications of AIE Photosensitizers0Introduction to Machine Learning for Predictive Modeling of Organic Materials0Navigating materials design spaces with efficient Bayesian optimization: a case study in functionalized nanoporous materials0Probing out-of-distribution generalization in machine learning for materials80Machine Learning for Prediction and Synthesis of Anion Exchange Membranes11Efficient feature extraction for morphologically complex self-assembled porous microstructures using computational homology and unsupervised machine learning0Is There a Simple Descriptor to Predict Laves Phases?3Machine Learning Assisted-Intelligent Lactic Acid Monitoring in Sweat Supported by a Perspiration-Driven Self-Powered Sensor42Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models28Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models1Automated Research Platform for Development of Triplet–Triplet Annihilation Photon Upconversion Systems16Recent strides in artificial intelligence for predicting thermoelectric properties and materials discovery9Machine‐Learning‐Enhanced Trial‐and‐Error for Efficient Optimization of Rubber Composites29AI-driven materials design: paradigm shift from small data to big data1A Helping Hand: A Survey About AI-Driven Experimental Design for Accelerating Scientific Research5Quantum Machine Learning in Drug Discovery: Applications in Academia and Pharmaceutical Industries49Assessing Uncertainty in Machine Learning for Polymer Property Prediction: A Benchmark Study22Feature Selection for Machine Learning‐Driven Accelerated Discovery and Optimization in Emerging Photovoltaics: A Review9Design of Tough 3D Printable Elastomers with Human‐in‐the‐Loop Reinforcement Learning1Design of Tough 3D Printable Elastomers with Human‐in‐the‐Loop Reinforcement Learning8Flow-driven data intensification to accelerate autonomous inorganic materials discovery25Lessons from the trenches on evaluating machine learning systems in materials science10Editor-in-the-loop: A perspective in the AI-driven science era033 Unresolved Questions in Nanoscience and Nanotechnology58Artificial intelligence for post-moore field-effect transistors: a review and perspective1Energy-GNoME: A living database of selected materials for energy applications5Machine learning-driven materials discovery: Unlocking next-generation functional materials – A review50Lost Data in Electron Microscopy2Synthesis-Aware Materials Redesign via Large Language Models12Emerging group-VA 2D materials: Antimonene and bismuthene for nanoelectronics and energy storage43Single Crystals of Vanadium Oxides as a Lens for Understanding Structural and Electronic Phase Transformations, Ion Transport, Chemo-Mechanical Coupling, and Electrothermal Neuronal Emulation13Accelerated discovery of CO2-to-C3-hydrocarbon electrocatalysts with human-in-the-loop13Main and advanced techniques for probing metal oxide–supported metal nanoparticles0Tailor-made adsorbent materials on machine learning instructions for efficient purification of heavy metal contaminants in wastewater1Decoding plasticity in van der Waals crystals via causality-driven interpretable machine learning1Accelerating catalytic advancements through the precision of high-throughput experiments & calculations4Autonomous microfluidic labs: progress and prospects7Combinatorial to autonomous experimentation0Describing materials via interpretable machine learning: the example of superconductivity0In situ X-ray-based spectroscopy techniques for the characterization of solid–gas interfaces in nanostructures: what is next?1Large language models as proxies for human experts in synthesis optimization0Generative Design for Alloys: Harnessing Generative Models for Faster Discovery6Multi-objective optimization of operation conditions by smart proxy model for geological CO2 storage design in an over-pressured aquifer in the Ulleung Basin0Continuous discovery of novel 2D materials via dual active learning-driven generative models3Do's and Don'ts When Visiting Circularly Polarized Luminescence9Data Distribution Matters: Enhancing model generalization in Data-Driven Materials Discovery by intentionally exploiting both positive and negative data1Molecular Foundation Models for Predicting Self-Assembly in Aqueous Mixtures2Unraveling the origin of circularly polarized luminescence by first-principles calculations2Machine Learning Optimization of Laser Ablation in Liquid for the Green and Low-Cost Synthesis of Clean Gold Nanoparticles7Precision orientation of hydrogen evolution performance of iron-based electrocatalysts empowered by machine learning: From electronic structure regulation to rational construction0Structure–Optical Property Relationships in AMM′Q 3 Chalcogenides0Enhancing Generalization in Synthesizability Prediction of Structurally Dissimilar Materials0Machine learning approaches for electrocatalyst design in water splitting: a review for green hydrogen production1Advances in automated microfluidic platforms with inline metrology and machine learning for the design, synthesis, and optimization of core–shell microstructures1Insights and opportunities from materials chemistry research at primarily undergraduate institutions in the U.S.0From Phosphor Ceramics to Intelligent Light‐Conversion Materials: High‐Flux Luminescence, Glassy Composite Interface, and Machine‐Learning‐Guided Design0

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