High-Throughput DFT Calculations for the Development of Machine Learning Force Fields

Institut

Professur für Multiscale Modeling of Fluid Materials

Typ

Bachelorarbeit / Semesterarbeit / Masterarbeit /

Inhalt

theoretisch /  

Beschreibung

Multiscale modelling is essential for understanding complex phenomena in fields ranging from life

biochemistry to materials engineering. A promising research area in this area is the development of

machine learning potentials (MLPs), particularly those based on Graph Neural Networks (GNNs),

which have emerged as a powerful tool for bridging the gap between quantum-mechanical accuracy

and classical molecular dynamics efficiency.

This thesis focuses on developing high-throughput Density Functional Theory (DFT) datasets and

training GNN-based MLPs. These datasets will serve as the foundation for developing accurate and

transferable force fields that can be applied to large-scale molecular dynamics simulations. The

project will involve generating and curating DFT data, training GNN models, and evaluating their

performance in predicting interatomic forces and energies.

Voraussetzungen

We are looking for a highly motivated student with the following qualifications:

● Machine Learning Practice: Experience with training and evaluating machine learning

models, particularly neural networks.

● Programming Experience: Proficiency in Python

● Quantum Chemistry Knowledge: Understanding of DFT and quantum chemistry

calculations is advantageous.

● Motivation and Curiosity: A strong interest in machine learning, computational chemistry,

and materials modelling.

Möglicher Beginn

sofort

Kontakt

Michał Sanocki
Tel.: 783880014
m.sanockitum.de

Ausschreibung