Results 121 to 130 of about 19,335,962 (161)

Exploring the Effects of Ionic Defects on the Stability of CsPbI3 with a Deep Learning Potential.

ChemPhysChem, 2022
Inorganic metal halide perovskites, such as CsPbI3 , have recently drawn extensive attention due to their excellent optical properties and high photoelectric efficiencies.
Weijie Yang, Jiajia Li, Xuelu Chen, Yajun Feng, Chongchong Wu, I. Gates, Zhengyang Gao, Xunlei Ding, Jianxi Yao, Hao Li   +9 more
semanticscholar   +1 more source

Temperature Dependent Thermal and Elastic Properties of High Entropy (Ti0.2Zr0.2Hf0.2Nb0.2Ta0.2)B2: Molecular Dynamics Simulation by Deep Learning Potential

, 2021
High entropy diborides are new categories of ultra-high temperature ceramics, which are believed promising candidates for applications in hypersonic vehicles.
Fu‐Zhi Dai, Yinjie Sun, Bo Wen, Huimin Xiang, Yanchun Zhou   +4 more
semanticscholar   +1 more source

Molecular dynamics simulations of lithium superionic conductor Li10GeP2S12 using a machine learning potential

, 2021
The molecular dynamics simulations have been performed for tetragonal Li10GeP2S12 (LGPS) to investigate the Li-ion conductivity and underlying diffusion mechanism.
K. Miwa, R. Asahi
semanticscholar   +1 more source

Theoretical prediction on thermal and mechanical properties of high entropy (Zr0.2Hf0.2Ti0.2Nb0.2Ta0.2)C by deep learning potential

, 2020
High entropy materials (HEMs, e.g. high entropy alloys, high entropy ceramics) have gained increasing interests due to the possibility that they can provide challenge properties unattainable by traditional materials.
Fu‐Zhi Dai, Bo Wen, Yinjie Sun, Huimin Xiang, Yanchun Zhou   +4 more
semanticscholar   +1 more source

Combining Machine Learning Potential and Structure Prediction for Accelerated Materials Design and Discovery.

Journal of Physical Chemistry Letters, 2020
Theoretical structure prediction method via quantum mechanical atomistic simulations such as density functional theory (DFT), solely based on chemical composition, already becomes a routine tool to determine the structures of physical and chemical ...
Qunchao Tong, P. Gao, Hanyu Liu, Yu Xie, Jian Lv, Yanchao Wang, Jijun Zhao   +6 more
semanticscholar   +1 more source

Insight into Interfacial Heat Transfer of β-Ga2O3/Diamond Heterostructures via the Machine Learning Potential.

ACS Applied Materials and Interfaces
β-Ga2O3 is an ultrawide-band gap semiconductor with excellent potential for high-power and ultraviolet optoelectronic device applications. Low thermal conductivity is one of the major obstacles to enable the full performance of β-Ga2O3-based devices.
Zhanpeng Sun, Dongliang Zhang, Zijun Qi, Qijun Wang, Xiang Sun, Kang Liang, Fang Dong, Yuanlai Zhao, Diwei Zou, Lijie Li, Gai Wu, Wei Shen, Sheng Liu   +12 more
semanticscholar   +1 more source

Predicting Mechanical and Thermal Properties of High‐Entropy Ceramics via Transferable Machine‐Learning‐Potential‐Based Molecular Dynamics

Advanced Functional Materials
The mechanical and thermal performance of high‐entropy ceramics are critical to their use in extreme conditions. However, the vast composition space of high‐entropy ceramics significantly hinders their development with desired mechanical and thermal ...
Yiwen Liu, Hong Meng, Zijie Zhu, Hulei Yu, Z. Lei, Yanhui Chu   +5 more
semanticscholar   +1 more source

Psychological constructions of learning potential and a systemic approach to the development of excellence

High Ability Studies, 2020
In this article, we introduce a systemic framework for the development of learning potential to professional excellence inspired by the Actiotope model of giftedness.
J. Mudrak, K. Zábrodská, K. Machovcová   +2 more
semanticscholar   +1 more source

A Deep-Learning Potential for Crystalline and Amorphous Li–Si Alloys

, 2020
This work investigates the ability of the deep-learning potential (DP) to describe structural, dynamic and energetic properties of crystalline and amorphous Li-Si alloys.
Nan Xu, Yao Shi, Yi He, Qing Shao
semanticscholar   +1 more source

Grain boundary strengthening in ZrB2 by segregation of W: Atomistic simulations with deep learning potential

, 2020
Interaction between grain boundaries and impurities usually leads to significant altering of material properties. Understanding the composition-structure-property relationship of grain boundaries is a key avenue for tailoring and designing high ...
Fu‐Zhi Dai, Bo Wen, Huimin Xiang, Yanchun Zhou   +3 more
semanticscholar   +1 more source