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RESEARCH PAPER · Nature Reviews Materials · 2022

Human- and machine-centred designs of molecules and materials for sustainability and decarbonization

Jiayu Peng, Daniel Schwalbe‐Koda, Karthik Akkiraju, Tian Xie, Livia Giordano, Yang Yu, C. John Eom, Jaclyn R. Lunger, Daniel J. Zheng, Reshma R. Rao, Sokseiha Muy, Jeffrey C. Grossman, Karsten Reuter, Rafael Gómez‐Bombarelli, Yang Shao‐Horn

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Human- and machine-centred designs of molecules and materials for sustainability and decarbonizationOriginalComputational discovery of Metal–Organic Frameworks for sustainable energy systems: Open challenges9Nanoporous ices: an emerging class in the water/ice family18Design principles for transition metal nitride stability and ammonia generation in acid43A Theory of Justifiable Abstraction in Multi-Scale Materials AI0Experimentally validated machine learning predictions of ultralow thermal conductivity for SnSe materials22Machine learning-aided unraveling of the importance of structural features for the electrocatalytic oxygen evolution reaction on multimetal oxides based on their A-site metal configurations7Towards a comprehensive data infrastructure for redox-active organic molecules targeting non-aqueous redox flow batteries20Machine learning approach to understanding the ‘synergistic’ pseudocapacitive effects of heteroatom doped graphene27Bridging the complexity gap in computational heterogeneous catalysis with machine learning280Speciation of Nanocatalysts Using X-ray Absorption Spectroscopy Assisted by Machine Learning25Biomass carbon mining to develop nature-inspired materials for a circular economy25Synthesizable materials discovery via interpretable, physics-informed machine learning models0Representations of Materials for Machine Learning133Machine Learning Descriptors for Data‐Driven Catalysis Study154X-ray photoelectron spectroscopy meets electrochemistry: From UHV to operando conditions1Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use37Achieving Digital Catalysis: Strategies for Data Acquisition, Storage and Use6Faux-Data Injection Optimization for Accelerating Data-Driven Discovery of Materials1A fuzzy classification framework to identify equivalent atoms in complex materials and molecules5Intelligent Biomaterialomics: Molecular Design, Manufacturing, and Biomedical Applications51mkite: A distributed computing platform for high-throughput materials simulations6Theoretical tuning of local coordination environment of metal-nitrogen doped carbon catalysts for selective chlorine-evolution reaction10Closed‐Loop Multi‐Objective Optimization for Cu–Sb–S Photo‐Electrocatalytic Materials’ Discovery24Machine Learning‐Aided Prediction and Construction of a Descriptor for Polymer Properties: A Case Study on the Lower Critical Solution Temperature of Copolymerized N ‐Isopropylacrylamides8Discovery of Unconventional Proton‐Conducting Inorganic Solids via Defect‐Chemistry‐Trained, Interpretable Machine Learning31Recent Advances in Multifunctional Reticular Framework Nanoparticles: A Paradigm Shift in Materials Science Road to a Structured Future47Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction207VOLTA: a tool for battery screening bridging the gap between virtual electrode materials and practical applications0Morphing matter: from mechanical principles to robotic applications111Identifying the Quantitative Relationship Between the Molecular Structure and the Horizontal Transition Dipole Orientation of TADF Emitters13Autonomous, multiproperty-driven molecular discovery: From predictions to measurements and back172Controlling neural network extrapolation enables efficient and comprehensive sampling of coverage effects in catalysis1Decarbonisation: A Case Study of Malaysia3Untitled work0Advancing oxygen evolution electrocatalysis with human-machine intelligence2Data-driven discovery of carbonyl organic electrode molecules: machine learning and experiment15Data-driven discovery of electrode materials for protonic ceramic cells37Discovering Electrochemistry with an Electrochemistry‐Informed Neural Network (ECINN)17Discovering Electrochemistry with an Electrochemistry‐Informed Neural Network (ECINN)3Roadmap on Data-Centric Materials Science0The potential of plasma-derived hard carbon for sodium-ion batteries17Roadmap on Data-Centric Materials Science1Roadmap on Data-Centric Materials Science1Roadmap on Data-Centric Materials Science2Large language model enhanced corpus of CO2 reduction electrocatalysts and synthesis procedures22Uncovering Zeolitic Imidazolate Frameworks through Digitization of Chemical Insights1Promises and Perils of Big Data: Philosophical Constraints on Chemical Ontologies6Zero-dimensional nano-carbons: Synthesis, properties, and applications60Towards atom-level understanding of metal oxide catalysts for the oxygen evolution reaction with machine learning37Salience Interest Option: Temporal abstraction with salience interest functions5A substitutional quantum defect in WS2 discovered by high-throughput computational screening and fabricated by site-selective STM manipulation44Data-driven physics-informed descriptors of cation ordering in multicomponent perovskite oxides17Roadmap on data-centric materials science27Designing semiconductor materials and devices in the post-Moore era by tackling computational challenges with data-driven strategies33Systematic Exploration of a Multi-Promoter Catalyst Composition Space with Limited Experiments: Non-Oxidative Propane Dehydrogenation to Propylene16Rise of machine learning potentials in heterogeneous catalysis: Developments, applications, and prospects57Temporally extended successor feature neural episodic control3Active learning streamlines development of high performance catalysts for higher alcohol synthesis75Virtual node graph neural network for full phonon prediction25Accelerating electrocatalyst design for CO2 conversion through machine learning: Interpretable models and data-driven innovations21Engineering a Sustainable Future: Harnessing Automation, Robotics, and Artificial Intelligence with Self-Driving Laboratories42Designing membranes with specific binding sites for selective ion separations84Effective Screening Descriptors of Metal–Organic Framework-Supported Single-Atom Catalysts for Electrochemical CO 2 Reduction Reactions: A Computational Study48Catalysis in the digital age: Unlocking the power of data with machine learning75Digital manufacturing of perovskite materials and solar cells12Tailoring Surface Electronic Structure of Spinel Co3O4 Oxide via Fe and Cu Substitution for Enhanced Oxygen Evolution Reaction18Active learning accelerated exploration of single-atom local environments in multimetallic systems for oxygen electrocatalysis33Emerging computational and machine learning methodologies for proton-conducting oxides: materials discovery and fundamental understanding17Zero-Shot Discovery of High-Performance, Low-Cost Organic Battery Materials Using Machine Learning23AI-driven 3D bioprinting for regenerative medicine: From bench to bedside101Advanced systems for enhanced CO 2 electroreduction72Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models15Deciphering Oxidation-Dominated Abnormal Rheology to Design Performance-Stable Liquid-Metal-Based Nanofluids for Transmission Applications12Machine Learning-Aided Inverse Design and Discovery of Novel Polymeric Materials for Membrane Separation95ML-Accelerated Automatic Process Exploration Reveals Facile O-Induced Pd Step-Edge Restructuring on Catalytic Time Scales24A Data-Driven Platform for Automated Characterization of Polymer Electrolytes21A revolutionary paradigm in chemistry and materials science research: self-driving laboratories8AI-driven advances in metal–organic frameworks: from data to design and applications47Beyond activity: from machine learning screening to stability decoding on the study of self-supported metal phosphides in alkaline hydrogen evolution reaction4Machine learning driven design of spiropyran photoswitches1Predicting the excited-state properties of crystalline organic semiconductors using GW+BSE and machine learning10In‐Depth Discussion on Electrocatalytic Barrier with Electron Structure of High‐Entropy Alloy Predicted by Transfer Learning and Neural Networks7Role of the human-in-the-loop in emerging self-driving laboratories for heterogeneous catalysis48LUMI-lab: a Foundation Model-Driven Autonomous Platform Enabling Discovery of New Ionizable Lipid Designs for mRNA Delivery10Modeling the impact of structure and coverage on the reactivity of realistic heterogeneous catalysts39Electrocatalysts for Hydrogen Production – Catalyst Design Strategies Based on Crystal Structures of Multimetal Oxides11Interpretable physics-informed machine learning approaches to accelerate electrocatalyst development13Data-Driven Search Algorithm for Discovery of Synthesizable Zeolitic Imidazolate Frameworks8Artificial intelligence for advanced functional materials: exploring current and future directions12Mechanistically Interpretable AI for Accelerated Energy Materials Design0PAH101: A GW+BSE Dataset of 101 Polycyclic Aromatic Hydrocarbon (PAH) Molecular Crystals4Empowering Generalist Material Intelligence with Large Language Models29Machine Learning Assisted Material Discovery: A Small Data Approach31Artificial Intelligence‐Driven Approaches in Semiconductor Research44Advancing our understanding of optoionic effects for the design of solar batteries: a theoretical perspective1Material intelligence by the convergence of artificial intelligence and robotic platforms16Advanced Nanomedicines for Treating Refractory Inflammation-Related Diseases33Toward data-driven predictive modeling of electrocatalyst stability and surface reconstruction24Development of Highly Active Catalysts for Low-Temperature CO 2 Hydrogenation to Methanol Using a Machine Learning Approach22A Multi-Scale Graph Neural Network for the Prediction of Multi-Component Gas Adsorption1Application of an Explainable Machine Learning to CO2 Methanation for Optimal Design Nickel-Based Catalysts12Prospects of AI in advancing green hydrogen production: From materials to applications9Autonomous Discovery of Polymer Electrolyte Formulations with Warm-Start Batch Bayesian Optimization14Polyelemental Nanoparticle Synthesis Enabled by Noncontact Metallothermic Reactions1Past, present, prospect: AI‐driven evolution of low‐dimensional material design for sustainable environmental solutions16Generative AI for navigating synthesizable chemical space38Uncovering battery electrochemical mechanisms by artificial intelligence22Accelerating multimetallic catalyst discovery with robotics and agentic AI8High-throughput DoE synthesis of chalcones and pyrazolines for fluorescent sensing3Accelerating OLED development with machine learning: advances and prospects4AI-Driven Design of Multifunctional Polyimides: Revolutionizing the Paradigm from Data to Synthesis0Enabling knowledge discovery from data0Machine learning-guided design principles for single-atom catalysts in persulfate-based advanced oxidation processes1The Role of Green Chemistry in Achieving Sustainability Goals2A practical inverse design approach for high-entropy catalysts using generative AI9Leveraging transfer learning for accurate estimation of ionic migration barriers in solids3Dynamic Polaronic Control of Metal Cluster Adaptability on Reducible Oxides0Role of artificial intelligence in the chemical industry transition to a sustainable, circular, and net-zero future0LUMI-lab: A foundation model-driven autonomous platform enabling discovery of ionizable lipid designs for mRNA delivery38A review on Janus biomaterials: Asymmetric design for advanced bone regeneration1Molecular machine learning in chemical process design5Materials Databases: Foundations of Modern Digital Materials12RETRACTED ARTICLE: Transfer-learning guided design of high-performance conjugated polymers for low-voltage electrochemical transistors0Autonomous closed-loop framework for reproducible perovskite solar cells22Strategies for Balancing Non-Covalent Interactions to Enhance the Performance of Polymer Electrolytes in Lithium Batteries2Unraveling promoter effects in ammonia cracking through multiscale simulations2Interpretable neural operators enable topology-optimized cooling for concentrating photovoltaics0Generalized machine-learning descriptor for identifying superionic materials0Data-Driven Insights into Ionic Conductivity in High-Dimensional Sodium Battery Electrolytes0Machine Learning-Assisted Development of High-Performance Ethanol Synthesis Catalysts via CO2 Hydrogenation0Literature-derived knowledge graphs powered by large language models for mechanism-informed membrane design0Machine learning approaches for electrocatalyst design in water splitting: a review for green hydrogen production1AI-ML innovations in membrane technology: current progress and future prospects3Artificial Intelligence Integration With Nanotechnology for Cancer Therapy0Data- and AI-Driven Catalysis Science: From Descriptors to Digital Catalysis for Next-Generation Catalysts0Machine learning-guided design of rare-earth single-atom catalysts for the oxygen reduction reaction0How SISSO-derived materials genes shape materials properties: An analytical sensitivity analysis0

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  49. Towards atom-level understanding of metal oxide catalysts for the oxygen evolution reaction with machine learning

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