Results 51 to 60 of about 8,521,646 (245)

Functionalizing Micro‐to‐Mesoscopic Electrode Architectures for Regulating Electron Transfer Behaviors in Electrocatalysis

Advanced Functional Materials, EarlyView.
A systematic review is conducted to assess the influence of electrode architecture across micro‐ to mesoscopic length scales on electron‐transfer pathways in electrocatalysis. We discuss the structure‐activity relationships in electrocatalytic applications, including resource recovery and environmental remediation, and provide cost‐effective, efficient
Manshu Zhao, Jiaojiao Zhang, Shideng Yuan, Xinmeng Yu, Keming Wan, Xiaoguang Duan, Zhining Wang   +6 more
wiley   +1 more source

Impact of training and validation data on the performance of neural network potentials: A case study on carbon using the CA-9 dataset

Carbon Trends, 2021
The use of machine learning to accelerate computer simulations is on the rise. In atomistic simulations, the use of machine learning interatomic potentials (ML-IAPs) can significantly reduce computational costs while maintaining accuracy close to that of
Daniel Hedman, Tom Rothe, Gustav Johansson, Fredrik Sandin, J. Andreas Larsson, Yoshiyuki Miyamoto   +5 more
doaj   +1 more source

Anisotropic Phonon Dynamics and Directional Transport in Actinide Van der Waals Semiconductor Uranium Triselenide

Advanced Functional Materials, EarlyView.
USe3 is introduced as an actinide van der Waals semiconductor that mirrors the quasi‐one‐dimensional physics of transition‐metal trichalcogenides. Polarization‐resolved Raman spectroscopy on exfoliated flakes maps strong in‐plane phonon anisotropy and strain‐tunable mode shifts.
Aljoscha Söll, Valentino Jadriško, Sourav Dey, Nassima Benchtaber, Kalyan Sarkar, Borna Radatović, Jan Luxa, Fedor Lipilin, Kseniia Mosina, Vojtěch Kundrát, Jakub Zalesak, Jana Vejpravová, Martin Žáček, Szymon J. Zelewski, Nikolas Antonatos, Christoph Gadermaier, José J. Baldoví, Zdeněk Sofer   +17 more
wiley   +1 more source

Multi-fidelity learning for atomistic models via trainable data embeddings

Machine Learning: Science and Technology
We present an approach for end-to-end training of machine learning models for structure-property modeling on collections of datasets derived using different density functional theory functionals and basis sets. This approach overcomes the problem of data
Rick Oerder, Gerrit Schmieden, Jan Hamaekers   +2 more
doaj   +1 more source

Physics‐Grounded Materials Artificial Intelligence for Reliable Materials Discovery

Advanced Functional Materials, EarlyView.
Physics‐Grounded Materials AI (PhysMat AI) integrates physical priors, descriptors, constraints, verification, and data infrastructure into a unified full‐stack framework, enabling reliable, interpretable, and autonomous AI‐driven materials discovery.
Yuhang Wang, Qian Wang, Seong‐Hoon Jang, Hao Li   +3 more
wiley   +1 more source

Confinement in Metal‐Organic Frameworks as a Route to Harnessing Liquid Barocalorics in the Solid‐State

Advanced Functional Materials, EarlyView.
Encapsulation of solid–liquid barocalorics (BC) within MOFs harnesses their colossal BC performance whilst allowing active control of BC properties through BC‐MOF interactions. ABSTRACT Barocaloric (BC) effects at liquid–vapor transitions in hydrofluorocarbons drive most commercial technologies used for heating and cooling in the heating, ventilation ...
Ming Zeng, Frederic Rendell‐Bhatti, Eamonn T. Connolly, Yang Wang, Clàudia Pérez‐Junyent, Josep‐Lluís Tamarit, Ross S. Forgan, Pol Lloveras, David Boldrin   +8 more
wiley   +1 more source

Designing Vanadium‐Based Oxide Electrocatalysts for Water Splitting: Experimental and Mechanistic Insights with Machine Learning

Advanced Functional Materials, EarlyView.
This review establishes structure‐property‐mechanism relationships across six modification strategies for V‐based oxide water‐splitting electrocatalysts: lattice engineering, heteroatom doping, interface engineering, carbon‐based hybridization, morphology engineering, and surface reconstruction and pre‐catalyst design, where dissolution is reframed as ...
Youness El Issmaeli, Amina Lahrichi, Hoje Chun, Rafael Gómez‐Bombarelli, Bruno G. Pollet   +4 more
wiley   +1 more source

Hybrid Ferroelectric Tunnel Junctions with Intrinsic Nonlinearity and Self‐Rectification via Interfacial Reconstruction

Advanced Functional Materials, EarlyView.
Rapid thermal annealing reconstructs the BCFO/Nb:STO interface into an atomically thin reconstructed interfacial layer, which reshapes the tunneling barrier and converts a high‐TER ferroelectric tunnel junction into an intrinsically nonlinear, self‐rectifying device.
Hojin Lee, Joonbong Lee, Jinho Byun, Sang‐Hyeok Yang, A young Cho, Sangwoo Lee, Dae Haa Ryu, Hyunbin Chung, Hee Seo Yun, Moon Seop Choi, Yesul Choi, Sungkyun Park, Min‐Hyung Jung, Dongwon Lee, Yoon‐Uk Heo, Hu Young Jeong, Hongseok Kim, Shinhyeong Lee, Yunseok Kim, Young‐Min Kim, Jaekwang Lee, Taekjib Choi   +21 more
wiley   +1 more source

On machine learnability of local contributions to interatomic potentials from density functional theory calculations

Scientific Reports
Machine learning interatomic potentials, as a modern generation of classical force fields, take atomic environments as input and predict the corresponding atomic energies and forces.
Mahboobeh Babaei, Ali Sadeghi
doaj   +1 more source

Role of Structural Disorder on Phonon Transport and Thermal Conductivity in Li6PS5Br

Advanced Functional Materials, EarlyView.
Solid electrolytes are fast ion and slow heat conductors. Although both transport properties are governed by lattice dynamics and atomic structure, their interplay remains poorly understood. Here, we study this interrelation in anion‐ordered and disordered Li6PS5Br.
Lukas Ketter, Ryoma Sasaki, Kyra Strotmann, Wolfgang G. Zeier, Yoshitaka Tateyama   +4 more
wiley   +1 more source