Accelerating AFIR reaction path search using Neural Network Potentials

• Project/Area Number: 23K13702
• Research Category: Grant-in-Aid for Early-Career Scientists
• Allocation Type: Multi-year Fund
• Review Section: Basic Section 32010:Fundamental physical chemistry-related
• Research Institution: Hokkaido University
• Principal Investigator: STAUB RUBEN・MORGAN・ADRIEN 北海道大学, 化学反応創成研究拠点, 特任助教 (20940274)
• Project Period (FY): 2023-04-01 – 2026-03-31
• Project Status: Granted (Fiscal Year 2023)
• Budget Amount: ¥4,550,000 (Direct Cost: ¥3,500,000、Indirect Cost: ¥1,050,000); Fiscal Year 2025: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000); Fiscal Year 2024: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000); Fiscal Year 2023: ¥2,730,000 (Direct Cost: ¥2,100,000、Indirect Cost: ¥630,000)
• Keywords: NNP / Reaction Design / MLFF / Reaction path network

Outline of Research at the Start

Innovative chemical reactions are essential tools for humanity's upcoming challenges. This project aims to combine the latest Machine Learning technology (Neural Network Potentials) with efficient reaction path search algorithms, to accelerate the design and discovery of novel chemical reactions.