Structural Embedding Methods for Machine Learning Models Accelerate Research on Stacked 2D Materials-Reference-Cited by-同舟云学术

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Structural Embedding Methods for Machine Learning Models Accelerate Research on Stacked 2D Materials

Published:2024-09-04 Issue: Volume: Page:
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Chen Xinyu¹,Ru Guoliang¹,Qi Weihong¹²^ORCID

Affiliation:

1. State Key Laboratory of Solidification Processing and Center of Advanced Lubrication and Seal Materials, Northwestern Polytechnical University, Xi’an 710072, China

2. Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing, Yantai 265503, China

Funder

National Natural Science Foundation of China

Science and Technology Innovation as a Whole Plan Projects of Shaanxi Province

Open Project of Basic Research of Shandong Laboratory of Yantai Advanced Materials and Green Manufacturing

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.4c02953

Reference24 articles.

1. Robust superconductivity in magic-angle multilayer graphene family

2. First-principles calculations combined with friction models to predict the moiré pattern effect on the interlayer friction of two-dimensional materials

3. Precise control of the interlayer twist angle in large scale MoS2 homostructures

4. Recent progress of the Computational 2D Materials Database (C2DB)

5. Two-dimensional materials from high-throughput computational exfoliation of experimentally known compounds

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