Proposed PhD theme / Field of Research:
Artificial Intelligence in Computational Chemistry
As AI technologies continue to evolve, they become also more and more integrated within Computational Chemistry. In this PhD project artificial intelligence (AI) methods are introduced to improve the performance and accuracy in a broad range of numerical and theoretical applications, from the development of predictive molecular and chemical models to data mining and screening, as well as interpretation, and verification of modeling and simulation data with corresponding experimental findings.
The project starts by focusing on the development of AI models in Quantum Chemistry and molecular simulations. We will design algorithms that can predict chemical properties and behaviors, going beyond traditional computational methods in both speed and precision. This involves training AI models on large datasets to learn complex chemical interactions in the discovery of new materials and drugs as examples. The project involves data mining and screening AI tools are used to filter through extensive chemical databases, identifying patterns and relationships that human researchers cannot detect. This helps to uncover novel compounds and reaction pathways, reducing the time and cost compared to current traditional methods.
In more details the project goes into Machine Learning (ML) and Deep Learning algorithms for predictive modelling using various neural networks, regression models and decision trees to forecast molecular interactions, reaction outcomes, material properties, and in drug discovery to predict the efficacy and safety profiles of potential drug candidates. The project teaches basic chemoinformatics such as data mining, screening and pattern recognition using clustering algorithms, principal component analysis, and others.

Requirements: MSc or corresponding exam in Chemistry, Physics, Chemical Engineering, Biotechnology, Informatics, etc. Some programming skills are necessary but can be caught up during the project.