Results 151 to 160 of about 17,342 (289)

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Prevalence of Halogens in 2025 FDA-Approved Drugs: Highlight on Suzetrigine, the First Nonopioid Painkiller in Decades. [PDF]

J Med Chem
Ali S, Zhang C, Zhou J.
europepmc   +1 more source

Mechanisms of Alkali Ionic Transport in Amorphous Oxyhalides Solid State Conductors

Advanced Energy Materials, EarlyView.
Large‐scale machine learning‐based molecular dynamics simulations are used to investigate isovalent amorphous oxyhalides, revealing a remarkable chemically independent ionic conductivity. A rigorous analysis of alkali residence times across different metal–anion environments identifies divalent anions as key diffusion bottlenecks.
Luca Binci, KyuJung Jun, Bowen Deng, Gerbrand Ceder   +3 more
wiley   +1 more source

Rethinking the Nature and Extent of Inductive Effects in Organic Compounds. [PDF]

J Chem Educ
Elliott MC, Johnson EC, Gregory KP, Hughes CE.   +3 more
europepmc   +1 more source

A Kinetic–Energetic Bottleneck of Charge‐Transfer Injection Governs Energy Loss in Organic Solar Cells

Advanced Energy Materials, EarlyView.
Kinetic–energetic projection of time‐resolved photoluminescence reveals that charge‐transfer injection acts as a universal bottleneck in organic solar cells. A physics‐constrained Bayesian framework identifies an emergent effective CT injection rate governing the trade‐off between charge generation and nonradiative energy loss.
Rong Wang, Tobias Osterrieder, Atul Shukla, Christos L. Chochos, Vasilis G. Gregoriou, Yakun He, Hua Tang, Julian Matthias Haffner‐Schirmer, Karen Forberich, Ning Li, Thomas Heumueller, Frédéric Laquai, Jens Hauch, Safa Shoaee, Dieter Neher, Larry Lüer, Christoph J. Brabec   +16 more
wiley   +1 more source

A review of metallurgical processing and value-added utilization strategies for zinc oxide. [PDF]

Sci Prog
Zhang Y, Ma Z, Gu X, Jia S, An X, Niu M, Zha X, He R, Deng R.   +8 more
europepmc   +1 more source

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox

Advanced Intelligent Discovery, EarlyView.
A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley   +1 more source

Formation of Gallium Monofluoride in the Coordination Sphere of Nickel

Angewandte Chemie, EarlyView.
The elusive gas‐phase species gallium monofluoride forms selectively in the coordination sphere of a nickel(II) centre as a product of C(sp3)–F bond activation, with a weakly coordinating anion as the fluorine source. The herein reported gallium monofluoride ligand acts as a very strong σ‐donor ligand at nickel and serves as a fluorine donor towards ...
Johannes Stephan, Samia Kahlal, Jean‐Yves Saillard, Marvin Mühlau, Lisa Gremmel, Christian Gemel, Roland A. Fischer   +6 more
wiley   +2 more sources

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

Advanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi, Yu Kumagai, Arata Takamatsu, Fumiyasu Oba   +3 more
wiley   +1 more source

A New Metal‐Ester Bonding Motif for the Synthesis of Hybrid Molecular Catalysts on Metal Oxide Supports Leads to Tunable Reactivity

Angewandte Chemie, EarlyView.
This work reports a new metal–ester bonding strategy for anchoring molecular catalysts to metal oxide supports, achieving exceptionally high surface loadings. Catalyst reactivity is predictably tuned by the support's properties, enabling control over electronic behavior and reaction pathways, thus opening new possibilities for designing highly ...
Joseph J. Kuchta III, Sarah M. Moody, Alexia M. Bradbury, D. M. S. C. Dissanayake, Laura C. Maybach, Santosh K. Balijepalli, John R. Regalbuto, Aaron K. Vannucci   +7 more
wiley   +2 more sources