5 papers
Fine-Tuning Small Language Models for Reliable VASP INCAR Generation
Xinyue Zhang, Jixiang Li, Bin Shao +3
Language models can prepare VASP INCAR files from natural-language requests, but so far only large proprietary cloud models come close to handling the tightly coupled, physics-sens…
INCARBench: A Benchmark for Scientific Configuration in VASP INCAR by Large Language Models
Bin Shao, Jixiang Li, Xinyue Zhang +3
Large language models (LLMs) are increasingly being integrated into first-principles computational workflows, yet their ability to configure scientific calculations remains poorly…
Accelerating Locality-Driven Integration in Quantum Chemistry with Block-Structured Matrix Multiplication
Xinran Wei, Yan Pan, Fusong Ju +8
Locality-driven integration is a pervasive computational pattern in quantum chemistry, arising whenever spatially localized basis functions interact through numerical quadrature or…
Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity
Erpai Luo, Xinran Wei, Lin Huang +7
Hamiltonian matrix prediction is pivotal in computational chemistry, serving as the foundation for determining a wide range of molecular properties. While SE(3) equivariant graph n…
Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems
Yunyang Li, Zaishuo Xia, Lin Huang +8
Density Functional Theory (DFT) is a pivotal method within quantum chemistry and materials science, with its core involving the construction and solution of the Kohn-Sham Hamiltoni…