| CONDENSED MATTER: STRUCTURAL, MECHANICAL, AND THERMAL PROPERTIES |
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Machine learning-assisted optimization of MTO basis sets |
| Zhiqiang Li(李志强) and Lei Wang(王蕾)† |
| Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing 211189, China |
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Abstract First-principles calculations based on density functional theory (DFT) have had a significant impact on chemistry, physics, and materials science, enabling in-depth exploration of the structural and electronic properties of a wide variety of materials. Among different implementations of DFT, the plane-wave method is widely used for periodic systems because of its high accuracy. However, this method typically requires a large number of basis functions for large systems, leading to high computational costs. Localized basis sets, such as the muffin-tin orbital (MTO) method, have been introduced to provide a more efficient description of electronic structure with a reduced basis set, albeit at the cost of reduced computational accuracy. In this work, we propose an optimization strategy using machine-learning techniques to automate MTO basis-set parameters, thereby improving the accuracy and efficiency of MTO-based calculations. Default MTO parameter settings primarily focus on lattice structure and give less consideration to element-specific differences. In contrast, our optimized parameters incorporate both structural and elemental information. Based on these converged parameters, we successfully recovered missing bands for CrTe$_2$. For the other three materials - Si, GaAs, and CrI$_3$ - we achieved band improvements of up to 2 eV. Furthermore, the generalization of the machine-learned method is validated by perturbation, strain, and elemental substitution, resulting in improved band structures. Additionally, lattice-constant optimization for GaAs using the converged parameters yields closer agreement with experiment.
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Received: 07 May 2025
Revised: 19 August 2025
Accepted manuscript online: 23 September 2025
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PACS:
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63.20.dk
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(First-principles theory)
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89.20.Ff
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(Computer science and technology)
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| Fund: This project was supported by the National Key Research and Development Program of China (Grant Nos. 2023YFA1406600 and 2021YFA1202200). |
Corresponding Authors:
Lei Wang
E-mail: wanglei.icer@seu.edu.cn
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Cite this article:
Zhiqiang Li(李志强) and Lei Wang(王蕾) Machine learning-assisted optimization of MTO basis sets 2026 Chin. Phys. B 35 016301
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