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RESEARCH PAPER · ACS Nano · 2021

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

Seungbum Hong, Chi Hao Liow, Jong Min Yuk, Hye Ryung Byon, Yongsoo Yang, EunAe Cho, Jiwon Yeom, Gun Park, Hyeonmuk Kang, Seung-Gu Kim, Yoonsu Shim, Moony Na, Chaehwa Jeong, Gyuseong Hwang, Hongjun Kim, Hoon Kim, Seongmun Eom, Seongwoo Cho, Hosun Jun, Yongju Lee, Arthur Baucour, Kihoon Bang, Myungjoon Kim, Seokjung Yun, Jeongjae Ryu, Youngjoon Han, Albina Jetybayeva, Pyuck‐Pa Choi, Joshua Agar, Sergei V. Kalinin, Peter W. Voorhees, P. B. Littlewood, Hyuck Mo Lee

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Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and IntegrationOriginalCelebrating 50 Years of KAIST: Collective Intelligence and Innovation for Confronting Contemporary Issues1Materials design for resilience in the biointegration of electronics6Machine learning for polymeric materials: an introduction95Accelerating colloidal quantum dot innovation with algorithms and automation30Mechanics-based design of lithium-ion batteries: a perspective19Unraveling the State of Charge-Dependent Electronic and Ionic Structure–Property Relationships in NCM622 Cells by Multiscale Characterization38The materials tetrahedron has a “digital twin”34Recent advance on machine learning of MXenes for energy storage and conversion45Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy90Machine learning assisted synthesis of lithium-ion batteries cathode materials102Integrating atomistic simulations and machine learning to design multi-principal element alloys with superior elastic modulus20Modeling and simulation of microstructure in metallic systems based on multi-physics approaches32Autonomous materials discovery and manufacturing (AMDM): A review and perspectives18Multimodal Orbital Angular Momentum Data Model Based on Mechanically Reconfigurable Arrays and Neural Networks0A Generative Approach to Materials Discovery, Design, and Optimization121Synthesis of One-Dimensional Titanium Oxide Nanowires for Polyvinylidene Fluoride Membrane Optimization5Computational Assessment of Xanthones from African Medicinal Plants as Aldose Reductase Inhibitors12Theory+AI/ML for microscopy and spectroscopy: Challenges and opportunities20Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118Quantum circuit learning as a potential algorithm to predict experimental chemical properties7Representation Compression and Scientific Loss in Materials AI0Temporal Generalization in Materials AI — What We Know About Model Aging and Drift0Unveiling Hidden Zeolitic Imidazolate Frameworks Guided by Intuition‐Based Geometrical Factors15Critical review on recently developed lithium and non-lithium anode-based solid-state lithium-ion batteries45TEXplorer.org: Thermoelectric material properties data platform for experimental and first-principles calculation results16Data-driven-aided strategies in battery lifecycle management: Prediction, monitoring, and optimization87Data Processing and Machine Learning0Future Vision0Transforming Nanomaterial Synthesis with Flow Chemistry7High-Throughput in Situ Characterization of Polymer Crystallization under an Intense Flow, High Pressure, and Cooling Gradient during Injection Molding33AI for Nanomaterials Development in Clean Energy and Carbon Capture, Utilization and Storage (CCUS)122Inverse design and AI/Deep generative networks in food design: A comprehensive review59Photoelectrochemical Engineering for Light‐Assisted Rechargeable Metal Batteries: Mechanism, Development, and Future14The Evolutionary Characteristics and Interaction of Interdisciplinarity and Scientific Collaboration under the Convergence Paradigm: Analysis in the Field of Materials Genome Engineering8Machine Learning for Heavy Metal Removal from Water: Recent Advances and Challenges48Perspective and prospects of in situ transmission/scanning transmission electron microscopy9Machine learning-based strain-load prediction of high-performance 3D woven fabrics5Unraveling Structure–Property Relationship in Lithium-Ion Cathodes Through Transmission Electron Microscopy Via Deep Learning0Unsupervised learning and pattern recognition in alloy design15Scientists’ disciplinary characteristics and collaboration behaviour under the convergence paradigm: A multilevel network perspective11Machine Learning-Assisted Exploration and Identification of Aqueous Dispersants in the Vast Diversity of Organic Chemicals8Cutting Edge High‐Throughput Synthesis and Characterization Techniques in Combinatorial Materials Science19Advancing Sustainability: Biodegradable Electronics and New Materials through AI and Machine Learning3Deep learning of experimental electrochemistry for battery cathodes across diverse compositions49Data‐Driven Design of NASICON‐Type Electrodes Using Graph‐Based Neural Networks9Optoelectronic Response to the Fluor Ion Bond on 4-(4,4,5,5-Tetramethyl-1,3,2-dioxoborolan-2-yl)benzaldehyde1Merits and Demerits of Machine Learning of Ferroelectric, Flexoelectric, and Electrolytic Properties of Ceramic Materials10Maximum a posteriori estimation for high-throughput peak fitting in X-ray photoelectron spectroscopy4Supramolecular chemistry in cyclodextrin inclusion complexes: The formation rules of terpenes/β-cyclodextrin inclusion complexes51Bridging Mechanical and Electrical Analyses in AFM: Advances, Techniques, and Applications5Trends in adolescent and adult vaccination in pharmacy and medical settings in the United States, 2018–2024: a database study12Optimizing CO 2 electroreduction: theoretical insights for enhancing efficiency across elementary steps65Roadmap on metal-halide perovskite semiconductors and devices17395Machine learning innovations in biomedical materials from drug discovery to personalized medicine0Knowledge Distillation for Molecular Property Prediction: A Scalability Analysis7Multiscale Materials Imaging and Spectroscopy for Battery Materials12A machine learning approach to reveal microstructures contributing to dislocation clusters in multicrystalline silicon1Advances in relaxation and memory effects of magnetic nanoparticles for biomedical applications17Field-Effect Crystal Engineering in Proton–π-Electron Correlated Systems1Subsampling factorization machine annealing1High‐Content Analysis of Energy Materials Using Facile and Versatile Cross‐Sectional Preparation0Machine learning in nanoscience and beyond: Workflows, data processing, XAI and ITAP metrics, language-based models5Materials Informatics: Emergence to Autonomous Discovery in the Age of AI15Efficient quantum algorithm for the design of complex materials: quantum circuit learning0Machine Learning in Materials Science: Data-Driven Discovery and Functional Applications1

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  2. Materials design for resilience in the biointegration of electronics

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  3. Machine learning for polymeric materials: an introduction

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  4. Accelerating colloidal quantum dot innovation with algorithms and automation

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  5. Mechanics-based design of lithium-ion batteries: a perspective

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  6. Unraveling the State of Charge-Dependent Electronic and Ionic Structure–Property Relationships in NCM622 Cells by Multiscale Characterization

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  8. Recent advance on machine learning of MXenes for energy storage and conversion

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  9. Automated Image Analysis for Single-Atom Detection in Catalytic Materials by Transmission Electron Microscopy

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  13. Autonomous materials discovery and manufacturing (AMDM): A review and perspectives

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  16. Synthesis of One-Dimensional Titanium Oxide Nanowires for Polyvinylidene Fluoride Membrane Optimization

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  17. Computational Assessment of Xanthones from African Medicinal Plants as Aldose Reductase Inhibitors

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  19. Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

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  20. Quantum circuit learning as a potential algorithm to predict experimental chemical properties

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  21. Representation Compression and Scientific Loss in Materials AI

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