针对强相关材料的变压器神经网络量子态量子嵌入方法

IF 9.4 1区 材料科学 Q1 CHEMISTRY, PHYSICAL npj Computational Materials Pub Date : 2024-09-17 DOI:10.1038/s41524-024-01406-3
Huan Ma, Honghui Shang, Jinlong Yang
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引用次数: 0

摘要

神经网络量子态(NNQS)方法正迅速成为量子机制的有力工具。虽然利用神经网络量子态模拟简单分子取得了重大进展,但复杂固态材料的原子序数模拟仍然充满挑战。在这项工作中,我们采用周期性密度矩阵嵌入理论来扩展 NNQS 方法,以处理复杂的固态系统。我们的方法在保持高精度的同时显著减少了计算问题的大小。我们根据传统方法和扩展系统中的实验数据验证了我们方法的准确性和效率,并研究了过渡金属化合物中的磁有序性和电荷密度波态。我们的研究结果表明,量子嵌入与直观化学破碎的结合可以显著提高对现实材料的 NNQS 模拟。
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Quantum embedding method with transformer neural network quantum states for strongly correlated materials

The neural-network quantum states (NNQS) method is rapidly emerging as a powerful tool in quantum mechanisms. While significant advancements have been achieved in simulating simple molecules using NNQS, the ab initio simulation of complex solid-state materials remains challenging. Here in this work, we have adopted the periodic density matrix embedding theory to extend the NNQS method to deal with complex solid-state systems. Our approach notably reduces the computational problem size while maintaining high accuracy. We have validated the accuracy and efficiency of our method against traditional methodologies and experimental data in extended systems, and have investigated the magnetic ordering and charge density wave state in transition metal compounds. The findings from our research indicate that the integration of quantum embedding with intuitive chemical fragmentation can significantly enhance the NNQS simulation of realistic materials.

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来源期刊
npj Computational Materials
npj Computational Materials Mathematics-Modeling and Simulation
CiteScore
15.30
自引率
5.20%
发文量
229
审稿时长
6 weeks
期刊介绍: npj Computational Materials is a high-quality open access journal from Nature Research that publishes research papers applying computational approaches for the design of new materials and enhancing our understanding of existing ones. The journal also welcomes papers on new computational techniques and the refinement of current approaches that support these aims, as well as experimental papers that complement computational findings. Some key features of npj Computational Materials include a 2-year impact factor of 12.241 (2021), article downloads of 1,138,590 (2021), and a fast turnaround time of 11 days from submission to the first editorial decision. The journal is indexed in various databases and services, including Chemical Abstracts Service (ACS), Astrophysics Data System (ADS), Current Contents/Physical, Chemical and Earth Sciences, Journal Citation Reports/Science Edition, SCOPUS, EI Compendex, INSPEC, Google Scholar, SCImago, DOAJ, CNKI, and Science Citation Index Expanded (SCIE), among others.
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