Research at TSLab focuses on the development and application of advanced computational methods to understand, predict, and design molecular and material systems. We integrate molecular modeling, quantum chemistry, molecular dynamics, machine learning, and high-performance computing to address complex scientific challenges in chemistry, biology, and materials science.
Our research is not limited to applying existing methods, but also includes the development of new computational workflows, AI-driven models, and simulation methodologies. We actively work on improving accuracy, efficiency, and scalability of computational approaches, particularly for systems that are difficult to study experimentally, such as metal complexes, novel materials, and large biomolecular assemblies.
Key research directions include structure–function relationships in molecular systems, reaction mechanisms, molecular interactions, material property prediction, and data-driven discovery using artificial intelligence. We also focus on the integration of quantum chemical methods with molecular simulations to bridge electronic-level understanding with macroscopic behavior.
Through the combination of scientific rigor and computational innovation, TSLab aims to accelerate discovery and provide deeper insights that support academic research, industrial development, and technological innovation.