Results 151 to 160 of about 8,521,646 (245)

Comprehensive Evaluation of Oxidation Resistance for Tertiary Phosphines Using Universal Machine Learning Interatomic Potential-Aided Molecular Dynamics Simulation. [PDF]

JACS Au
Sato T, Asai Y, Koshimizu U, Hakozaki Y, Takei K, Mori K, Yasumura S, Ikeda T, Yayama Y.   +8 more
europepmc   +1 more source

Sn‐Driven Grain Boundary Elimination for Single‐Crystalline Ni‐Rich Cathodes with Superior High‐Voltage Stability in Lithium‐Ion Batteries

Advanced Science, EarlyView.
A low‐temperature synthesis method of single‐crystalline NCA was successfully achieved by Sn doping, employing the same synthetic conditions as its polycrystalline counterparts. This synthesis was conducted at a temperature that was 150 °C lower than that required for synthesizing single‐crystalline NCA without Sn doping.
Guomeng Xie, Delai Qian, Ruiwu Li, Terence Xiaoteng Liu, Shun Lu   +4 more
wiley   +1 more source

High-Pressure Inelastic Neutron Spectroscopy: Experimental Validation of Machine-Learned Interatomic Potential Energy Landscapes. [PDF]

J Phys Chem Lett
Armstrong J, Jackson A, Elena A.
europepmc   +1 more source

When Mechanics Rewrites Chemistry: Divergent Pathways of Hardening and Softening in High Strength Al‐Alloys

Advanced Science, EarlyView.
Mechanics reshapes chemistry: plastic flow actively governs two divergent, atomic‐scale mechanochemical pathways that tailor strain localization and ductility in high‐strength 7xxx Al alloys. Rather than relying on static structures, deformation dynamically reinforces the solution‐treated state via clustering, yet destabilizes the peak‐aged condition ...
Huan Zhao, Linze Li, Shaolou Wei, Jueyi Qi, Jiewen Xiao, Degang Xie, Xie Zhang, Gang Liu, Dierk Raabe, Jun Sun   +9 more
wiley   +1 more source

PET-MAD as a lightweight universal interatomic potential for advanced materials modeling. [PDF]

Nat Commun
Mazitov A, Bigi F, Kellner M, Pegolo P, Tisi D, Fraux G, Pozdnyakov S, Loche P, Ceriotti M.   +8 more
europepmc   +1 more source

Machine Learning Potential for Calcium Silicate Hydrates With Broad Compositional and Structural Diversity

Advanced Science, EarlyView.
Existing machine learning potentials remain limited in their ability to generalize across the broad chemical and structural variability of C–S–H. Here, a representative DFT‐labeled dataset is constructed through large‐scale model generation, redundancy‐controlled screening, first‐principles optimization, and targeted sampling of non‐equilibrium ...
Yunjian Li, Cheng Chen, Roland J.‐M. Pellenq, Zongjin Li, Jiaping Liu   +4 more
wiley   +1 more source

Fine-Tuning Unifies Foundational Machine-Learned Interatomic Potential Architectures at <i>ab initio</i> Accuracy. [PDF]

J Phys Chem Lett
Hänseroth J, Flötotto A, Qaisrani MN, Dreßler C.   +3 more
europepmc   +1 more source

Computational and Data‐Driven Strategies for Lignocellulosic Biomass Valorization: From Multiscale Modeling to Digitalized Biorefinery Design

Advanced Science, EarlyView.
This Review critically connects structural recalcitrance, molecular modeling, solvent and catalyst design, pyrolysis, machine learning, reactor simulation, and FAIR digital infrastructure. Emphasis is placed on experimentally validated information transfer across scales, uncertainty and applicability domains, and the recycle, durability, techno ...
Abdullahi Bello Umar, Jiaxian Zheng, Xiangfeng Lin, Hongwu Liao, Haoqian Xu, Yufei Huang, Zaharaddeen Nasiru Garba, Adamu Uzairu, Weiwei Zhao, Zhanhui Yuan   +9 more
wiley   +1 more source

Development of a Machine Learning Interatomic Potential for Zirconium and Its Verification in Molecular Dynamics. [PDF]

Nanomaterials (Basel)
Wan Y, Zhang X, Zhang L.
europepmc   +1 more source

WFS1 Deficiency Impairs PIAS4‐Associated SUMOylation and Increases Ubiquitin‐Mediated Spermatogenesis‐Related Protein Degradation Leading to Testicular Male Infertility

Advanced Science, EarlyView.
The findings establish a critical role for WFS1 in human male fertility. Mechanistically, WFS1 interacts with PIAS4 to promote the SUMOylation of key spermatogenesis‐associated proteins, which in turn competitively inhibits their K48‐linked ubiquitin‐mediated degradation during spermatogenesis.
Yunchuan Tian, Guicheng Zhao, Xiang Wang, Xinyao Tang, Gan Shen, Tiechao Ruan, Chuan Jiang, Shikun Zhao, Jincheng Zhang, Xinyue Cui, Liangchai Zhuo, Jun Ma, Yihong Yang, Tingting Lin, Ying Shen   +14 more
wiley   +1 more source