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| 期刊介绍 |
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《npj 计算材料学》是在线出版、完全开放获取的国际学术期刊。发表结合计算模拟与设计的材料学一流的研究成果。本刊由中国科学院上海硅酸盐研究所与英国自然出版集团（Nature Publishing Group，NPG）以伙伴关系合作出版。
主编为陈龙庆博士，美国宾州大学材料科学与工程系、工程科学与力学系、数学系的杰出教授。共同主编为陈立东研究员，中国科学院上海硅酸盐研究所研究员高性能陶瓷与超微结构国家重点实验室主任。
办刊目的与报道范围
《npj 计算材料学》是在线出版、完全开放获取的国际学术期刊，... 【查看详细】 |
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Peculiar band geometry induced giant shift current in ferroelectric SnTe monolayer
Gan Jin & Lixin He
npj Computational Materials 10: 23 (2024); Published online: 29 January 2024
Editorial Summary
Giant Photocurrent Effect in Two-Dimensional Ferroelectric SnTe: driven by the Monopole Quantum Potential Fields Arising from Peculiar Band Structures.
The Bulk Photovoltaic Effect (BPVE) is a phenomenon in which light-induced electrical current or voltage is generated in non-centrosymmetric m... |
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Full-landscape selection rules of electrons and phonons and temperature-induced effects in 2D silicon and germanium allotropes
Le Shu, Yujie Xia, Ben Li, Lei Peng, Hezhu Shao, Zengxu Wang, Yan Ce, Heyuan Zhu & Hao Zhang
npj Computational Materials 10: 2 (2024); Published online: 02 January 2024
Editorial Summary
Electrons and phonons scattering: Importance of selection rules
The search for materials with a high thermoelectric figure of merit (zT) has attracted lots of attention for centur... |
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Efficient first-principles electronic transport approach to complex band structure materials: the case of n-type Mg3Sb2
Zhen Li, Patrizio Graziosi & Neophytos Neophytou
npj Computational Materials 10: 9 (2024); Published online: 06 Jan 2024
Editorial Summary
First-principles electronic transport approach: Efficiency, robustness, and flexibility
Transport parameters are crucial for novel material deployment in a variety of technological applications, including solar cells, solid-sta... |
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Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets
Vishu Gupta, Kamal Choudhary, Brian DeCost, Francesca Tavazza, Carelyn Campbell, Wei-keng Liao, Alok Choudhary & Ankit Agrawal
npj Computational Materials10: 1 (2024)
Editorial Summary
Structure-aware graph neural network: Enhanced prediction of material properties
Accurate materials property prediction using crystal structure occupies a pri... |
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Obtaining auxetic and isotropic metamaterials in counterintuitive design spaces: an automated optimization approach and experimental characterization
Timon Meier, Runxuan Li, Stefanos Mavrikos, Brian Blankenship, Zacharias Vangelatos, M. Erden Yildizdag & Costas P. Grigoropoulos
npj Computational Materials 10: 3 (2023)
Editorial Summary
Theoretical Design of Metamaterials with Unique Mechanical Properties
The design of mechanical materials with tailored properties has been subject of signif... |
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Atomistic fracture in bcc iron revealed by active learning of Gaussian approximation potential
Lei Zhang, Gábor Csányi, Erik van der Giessen & Francesco Maresca
npj Computational Materials 9: 217 (2023); Published online: 08 Dec 2023
Editorial Summary
Crack-tip deformation mechanism in bcc iron: dislocation emission VS. cleavage? Active learning interatomic potential!
The prediction of atomistic fracture mechanisms in body-centred cubic (bcc) iron is essential for understanding its... |
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An interpretable machine learning strategy for pursuing high piezoelectric coefficients in (K0.5Na0.5)NbO3-based ceramics
Bowen Ma, Xiao Wu, Chunlin Zhao, Cong Lin, Min Gao, Baisheng Sa & Zhimei Sun
npj Computational Materials 9: 229 (2023); Published online: 22 December 2023
Editorial Summary
Machine learning for interpretable KNN ceramic high piezoelectric coefficients: fast and good?
People have invested a lot of time and energy in making piezoelectric ceramics lead-free. Through cumbe... |
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Tunable ferroelectricity in oxygen-deficient perovskites with Grenier structure
Yongjin Shin & Giulia Galli
npj Computational Materials 9: 218 (2023).
Editorial Summary
Tunable ferroelectricity in Grenier perovskites
Ferroelectric materials have found many interesting applications in electronic and memory devices, and understanding and engineering their properties is a topic of great interest in condensed matter physics and materials science. Ferroelectricity can be realized in materi... |
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Towards understanding structure–property relations in materials with interpretable deep learning
Tien-Sinh Vu, Minh-Quyet Ha, Duong-Nguyen Nguyen, Viet-Cuong Nguyen, Yukihiro Abe, Truyen Tran, Huan Tran, Hiori Kino, Takashi Miyake, Koji Tsuda & Hieu-Chi Dam
npj Computational Materials9: 215 (2023)
Editorial Summary
Understanding structure–property relations in materials: Interpretable deep learning
A central challenge in the field of materials science involves the use of both exp... |
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Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles
Aik Rui Tan, Shingo Urata, Samuel Goldman, Johannes C. B. Dietschreit & Rafael Gómez-Bombarelli
npj Computational Materials 9: 225 (2023)
Editorial Summary
Neural network potentials: Who has the best performance of uncertainty quantification
Over the last decade, neural networks (NN) have increasingly been deployed to study complex materials systems. NN interatomic ... |
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