Project Details
Projekt
Efficient Semiempirical Quantum Mechanical Method with Adaptive Learning
Applicant
Professor Dr. Stefan Grimme
Subject Area
Theoretical Chemistry: Electronic Structure, Dynamics, Simulation
Term
since 2022
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 497190956
The project focuses on advancing computational chemistry by an adaptive-learning-driven semi-empirical quantum mechanical (SQM) method tailored to improve efficiency and accuracy in general molecular modeling. This involves creating a new, lightweight tight-binding model, termed g0-xTB (adaptive g0-xTB), which will leverage machine learning to dynamically adjust its parameterization. The work involves three main tasks. The first is developing the g0-xTB base Hamiltonian and optimizing conventionally a global parameter set to ensure broad applicability and robust performance across diverse chemical spaces. This g0-xTB method can already be applied to various chemical problems where high speed and robustness in required. The second task is implementing adaptive learning for on-the-fly training, e.g., during MD runs. This will be achieved with automatic differentiation techniques like our previous dxtb to enable easily dynamic parameter adjustments. Finally, the new ag0-xTB method is applied in various chemical and methodological applications. These include conformer ensemble generation, reaction network exploration, and large-scale screening of catalysts and drug candidates. The project will make key outcomes available via open-source codes on GitHub, ensuring broad accessibility and reproducibility.
DFG Programme
Priority Programmes
