Results 111 to 120 of about 10,497 (241)

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

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, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Active‐Site Photothermal Energy Utilization in PET Depolymerization

Angewandte Chemie, EarlyView.
Photothermal catalysis enables efficient PET depolymerization, yet the role of active‐site temperature remains unclear. A catalytic activity reversal between Pd–CNG and PdPOM–CNG is observed under thermal and photothermal conditions. Efficient nanoscale photothermal energy delivery raises active‐site temperature, compensates for lower intrinsic ...
Xin Li, Qining Fan, Quanxiang Li, Qi Han, Yaojie Lei, Yiming Bu, Christoper Hurren, Jingliang Li   +7 more
wiley   +2 more sources

The Interoperability Challenge in DFT Workflows Across Implementations

Advanced Intelligent Discovery, EarlyView.
Interoperability and cross‐validation remain major challenges in the computational materials science. In this work, we introduce a common input/output standard that enables internal translation across multiple workflow managers—AiiDA, PerQueue, Pipeline Pilot, and SimStack—while producing results in a unified schema.
Simon K. Steensen, Tushar Singh Thakur, Manuel Dillenz, Johan M. Carlsson, Celso R. C. Rêgo, Eibar Flores, Hamidreza Hajiyani, Felix Hanke, Juan María García Lastra, Wolfgang Wenzel, Nicola Marzari, Tejs Vegge, Giovanni Pizzi, Ivano E. Castelli   +13 more
wiley   +1 more source

Materials Representation Learning Based on a Material–Motif Network and Heterogeneous Graphs

Advanced Intelligent Discovery, EarlyView.
Structure motifs in materials are used to construct a bipartite material–motif network that links each material to its constituent motifs and establishes connectivity among materials sharing common motifs. Network analysis reveals material clusters associated with different functional applications and supports motif‐guided screening of materials.
Anoj Aryal, Weiyi Gong, Huta Banjade, Qimin Yan   +3 more
wiley   +1 more source

S‐Scheme Charge Separation Enables Photocarrier‐Regulated Aqueous NH4+ Storage in g‐C3N4@WO3 Heterojunctions

Angewandte Chemie, EarlyView.
Light irradiation activates the g‐C3N4@WO3 S‐scheme heterojunction, where the built‐in electric field drives charge separation to generate high‐energy carriers, facilitating NH4+ adsorption and enhancing electron/ion transport for high‐capacity, stable aqueous NH4+ storage.
Yue Zhang, Yulong Jia, Fanfang Sun, Jichi Liu, Bing Tang, Hui Li, Wubin Du, Hongge Pan, Zi‐Hang Huang, Tianyi Ma   +9 more
wiley   +2 more sources

Functionalized TiO2 Catalysts: Revolutionary Candidate for Low‐Temperature MgH2 and Prospects for Intelligent Integration

Advanced Intelligent Discovery, EarlyView.
Recent advances in TiO2 modification strategies: structure regulation and composite engineering for improved activity and functionality. TiO2 has emerged as a pivotal catalyst for enhancing MgH2, a high‐capacity solid‐state hydrogen storage material, owing to its structural versatility.
Xiaopeng Chu, Jinghao Wang, Haojie Shi, Chuanxin Peng, Jiacheng Qi, Ge Gao, Guoquan Na, Haibo Yu, Kela Xiao, Gangqiang Wu, Zhendong Yao   +10 more
wiley   +1 more source

One‐Pot Synthesis of Aqueous PEDOT‐Based Conductive Inks via Sequential RAFT/Oxidative Polymerization

Angewandte Chemie, EarlyView.
We developed a fast, aqueous one‐pot synthesis for PEDOT:PSTFSI, combining sequential reversible addition–fragmentation chain‐transfer (RAFT) and oxidative polymerization. This method eliminates toxic solvents and purification steps, achieving full monomer conversion in under 3 h.
Fantine Negny, Melanie Bousquet, Benjamin Cabannes‐Boué, Pierre‐Yves Dugas, Lionel Hirsch, Thiago R. Guimaraes, Eric Cloutet   +6 more
wiley   +2 more sources

Crystal Structure Prediction of Inorganic Materials: A Benchmark and Modern Evaluation

Advanced Intelligent Discovery, EarlyView.
Predicting a crystal’s structure from composition alone is a long‐standing challenge in materials discovery. The CSP180 benchmark of 180 inorganic crystals evaluates thirteen crystal structure prediction algorithms requiring no density functional theory (DFT) against DFT‐based baselines across twelve metrics.
Lai Wei, Sadman Sadeed Omee, Rongzhi Dong, Nihang Fu, Yuqi Song, Edirisuriya M. Dilanga Siriwardane, Meiling Xu, Ming Hu, Chris Wolverton, Jianjun Hu   +9 more
wiley   +1 more source

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

Advanced Intelligent Systems, EarlyView.
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne, Haodong Yao, Liyuan Wu, Yihe Pang, Fazhi Qi, Lina Zhao   +5 more
wiley   +1 more source

A model predictive control-based energy management strategy for grid-connected nanogrids. [PDF]

Sci Rep
Selmy M, Abdulnabi A, Ali AA, Saied EM, Elsayed WT.   +4 more
europepmc   +1 more source