New Tools to Accelerate DFT Calculations: Develop a "Physical Hessian Matrix" Preconditioner Based on Machine Learning Force Field

Publicly Offered Research

Project Area	Foundation of "Machine Learning Physics" --- Revolutionary Transformation of Fundamental Physics by A New Field Integrating Machine Learning and Physics
Project/Area Number	25H01508
Research Category	Grant-in-Aid for Transformative Research Areas (A)
Allocation Type	Single-year Grants
Review Section	Transformative Research Areas, Section (II)
Research Institution	Tohoku University
Principal Investigator	LI HAO 東北大学, 材料科学高等研究所, 教授 (50967198)
Project Period (FY)	2025-04-01 – 2027-03-31
Project Status	Granted (Fiscal Year 2025)
Budget Amount *help	¥2,600,000 (Direct Cost: ¥2,000,000、Indirect Cost: ¥600,000) Fiscal Year 2025: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
Keywords	Machine learning / Force field / DFT
Outline of Research at the Start	This proposed project aims to develop new machine learning tools speed up density functional theory in structural optimization.