Results 31 to 40 of about 8,521,646 (245)

The role of the atom-atomic interactions depth on the metallic nanofilms structure evolution [PDF]

E3S Web of Conferences
Stochastic methods of modeling slow–moving processes controlled by diffusion make it possible to analyze the order-disorder phase transitions. The features of the kinetics of these transformations can also be identified due to these methods.
Andrukhova Olga, Andrukhova Tatiana, Lomskikh Natalia, Mudretsova Liudmila   +3 more
doaj   +1 more source

Principal component analysis enables the design of deep learning potential precisely capturing LLZO phase transitions

npj Computational Materials
The development of accurate and efficient interatomic potentials using machine learning has emerged as an important approach in materials simulations and discovery.
Yiwei You, Dexin Zhang, Fulun Wu, Xinrui Cao, Yang Sun, Zi-Zhong Zhu, Shunqing Wu   +6 more
doaj   +1 more source

Massively parallel fitting of Gaussian approximation potentials

Machine Learning: Science and Technology, 2023
We present a data-parallel software package for fitting Gaussian approximation potentials (GAPs) on multiple nodes using the ScaLAPACK library with MPI and OpenMP.
Sascha Klawohn, James R Kermode, Albert P Bartók   +2 more
doaj   +1 more source

The reconciliation and validation of a combined interatomic potential for the description of Xe in γU-Mo

Frontiers in Nuclear Engineering, 2023
A U-Mo alloy has been selected as the fuel design for the conversion of high-performance research reactors in the United States. Efforts are ongoing to describe the fuel evolution as a function of time, for a variety of different reactor conditions.
Benjamin Beeler, Benjamin Beeler, Yongfeng Zhang, Yongfeng Zhang   +3 more
doaj   +1 more source

The Screening Length of Interatomic Potential in Atomic Collisions [PDF]

, 1998
In computer studies on the interaction of charged particle with solids, many authors treat the nuclear collision by the Thomas-Fermi screened Coulomb potential. For better agreement with experiment, the screening length is modified sometimes.
YAMAMURA, Yasunori, KAWAMURA, Takaichi, TAKEUCHI, W.   +2 more
core  

Ontology‐Aligned Structuring and Reuse of Multimodal Materials Data and Workflows Toward Automatic Reproduction

Advanced Engineering Materials, EarlyView.
Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari, Abril Azocar Guzman, Sarath Menon, Stefan Sandfeld, Tilmann Hickel, Markus Stricker   +5 more
wiley   +1 more source

Interatomic Potential to Predict the Favored Glass-Formation Compositions and Local Atomic Arrangements of Ternary Al-Ni-Ti Metallic Glasses

Crystals, 2022
An empirical potential under the formalism of second-moment approximation of tight-binding potential is constructed for an Al-Ni-Ti ternary system and proven reliable in reproducing the physical properties of pure elements and their various compounds ...
Qilin Yang, Jiahao Li, Wensheng Lai, Jianbo Liu, Baixin Liu   +4 more
doaj   +1 more source

Entropy‐Driven Design of Low‐Melting‐Point Alloys via Compositionally Complex Strategy

Advanced Engineering Materials, EarlyView.
Conventional low‐melting‐point alloys (LMPAs) are limited by a narrow compositional space and inherent property trade‐offs. This review presents an entropy‐driven design strategy that overcomes these limitations, ushering in a new class of low‐melting‐point compositionally complex alloys (LMCCAs).
Yinghui Shang, Jianxiang Zhao, Zhaoqi Chen, Hao Gong, Yinsen Yan, Junlei Tao, Yong Yang   +6 more
wiley   +1 more source

Leveraging Symbolic Artificial Intelligence and Fuzzy Logic for Materials Science: A Review of Methods, Challenges, and Applications to Scarce and Imperfect Experimental Data

Advanced Engineering Materials, EarlyView.
This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani, Mohamed‐Ali Belloum, Laurence Boudet, Valentin Fouillard, Edwin Friedmann, Guillaume Lacharme, Ali Mahmoud, Jean‐Philippe Poli   +7 more
wiley   +1 more source

Using graph neural network and symbolic regression to model disordered systems

Scientific Reports
The key to modeling disordered systems lies in accurately simulating atomic trajectories, typically achieved through molecular dynamic (MD) simulation. The accuracy of MD simulations depends on the precision of the interatomic potential function, which ...
Ruoxia Chen, Mathieu Bauchy, Wei Wang, Yizhou Sun, Xiaojie Tao, Jaime Marian   +5 more
doaj   +1 more source