Study of the Electronic Band Structure and Structural Stability of Al(CN)2 and Si(CN)2 by Density Functional Theory

Autor: Sok-I Tam, Pak-Kin Leong, Chi-Pui Tang, Weng-Hang Leong, Toshimori Sekine, Chi-Long Tang, Kuan-Vai Tam, Kin-Tak U

Publikováno v: Crystals, Vol 13, Iss 5, p 824 (2023)

By substituting the A site in P21/c-A(CN)2 and varying the lattice parameters a, b, c, and the unit-cell angles, along with using crystal graph convolutional neural networks to calculate their cohesive energy, the candidate compounds, Al(CN)2 and Si(

Externí odkaz: https://doaj.org/article/2531e35157514891ae24d20d4a297bff

Clarifying trust of materials property predictions using neural networks with distribution-specific uncertainty quantification

Autor: Cameron J Gruich, Varun Madhavan, Yixin Wang, Bryan R Goldsmith

Publikováno v: Machine Learning: Science and Technology, Vol 4, Iss 2, p 025019 (2023)

It is critical that machine learning (ML) model predictions be trustworthy for high-throughput catalyst discovery approaches. Uncertainty quantification (UQ) methods allow estimation of the trustworthiness of an ML model, but these methods have not b

Externí odkaz: https://doaj.org/article/599808aa6dab40799d3dc21db3042f1a

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Análise computacional de materiais utilizando métodos de machine learning

Autor: Carriço, Pedro José Monteiro Alves

Dissertação de Mestrado em Física apresentada à Faculdade de Ciências e Tecnologia The increase in the number of materials in public databases like Materials Project and the advent of faster GPUs and CPUs made Machine Learning a prominent subjec

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______1271::f239ab8e5dfeec2c38d2f8009f48a5c4
https://hdl.handle.net/10316/103031

Enhanced Simulation of Complicated MXene Materials with Graph Convolutional Neural Networks.

Autor: Tian Z; Ningbo Institute of Industrial Technology Chinese Academy of Sciences, Institute of New Energy Technology, 1219 Zhongguan west Road, 315201, Ningbo, CHINA., Chen X; University of California Los Angeles, Department of Physics and Astronomy, UNITED STATES OF AMERICA., Wan Z; Ningbo Institute of Materials Technology and Engineering CAS, Institute of New Energy Technology, CHINA., Lao S; Ningbo Institute of Materials Technology and Engineering CAS, Institute of New Energy Technology, CHINA.

Publikováno v: Chemphyschem : a European journal of chemical physics and physical chemistry [Chemphyschem] 2024 Dec 23, pp. e202400749. Date of Electronic Publication: 2024 Dec 23.