Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation
Advanced Intelligent Discovery, EarlyView.We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
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Temperature-pressure characteristics of CO<sub>2</sub> phase-transition blasting and the failure mechanism of fracturing tubes. [PDF]
Sci RepChen Z, Yuan Y, Li B, Qin Z, Li H.
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Advanced Intelligent Discovery, EarlyView.Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez +4 more
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Microwave-assisted N,S doped carbon quantum dots as fluorescent nanoprobes for vibegron determination: face-centered design optimization, validation, and green chemistry assessment. [PDF]
RSC AdvAl Shmrany H +4 more
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Advanced Intelligent Discovery, EarlyView.This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
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BEST-CSP Benchmark Study of Polymorphs I and II of Sulfamerazine and the Perils of Polytype Polymorphs. [PDF]
Cryst Growth DesWood WP +40 more
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Advanced Intelligent Discovery, EarlyView.Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi +2 more
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Consequences of Medium‐Pore Zeolite Constraints for Alkene Cracking—The Case of n‐Pentene
Angewandte Chemie, EarlyView.Alkene cracking in medium‐pore zeolites is governed by a balance of enthalpic and entropic effects. Intrinsic barriers are quantified and shown to be lower than for alkanes due to stabilization of carbenium‐ion‐like transition states. Confinement and extra‐framework aluminum modulate reactivity by tuning transition‐state energetics, providing a ...
Ruixue Zhao +4 more
wiley +2 more sources
DFT-Based Design and Characterization of Organic Chromophores Based on Symmetric Thio-Bridge Quinoxaline Push-Pull (STQ-PP) for Solar Cells. [PDF]
MoleculesRivera E +4 more
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Data‐Driven Review and Machine Learning Prediction of Diamond Vacancy Center Synthesis
Advanced Intelligent Systems, EarlyView.A machine learning framework is applied to photoluminescence spectra to extract linewidths and uncover how NV, SiV, GeV, and SnV centers evolve with growth and processing conditions. Unified normalization and k‐fold validation reveal cross‐method trends and enable rapid prediction of defect size and fabrication parameters, offering a data‐driven route ...
Zhi Jiang +3 more
wiley +1 more source