CONFERENCE SCOPE:
Machine learning in nanotechnology
INSTITUTION:
Dresden University of Technology, Germany
TITLE:
Quantum-inspired AI strategies for Molecular Innovation
ABSTRACT:
The growing demand for sustainable solutions to technological and societal challenges has driven significant research efforts to integrate machine learning (ML) techniques into computational physics and chemistry. As ML becomes more prevalent in interdisciplinary research, the amount of comprehensive quantum-mechanical (QM) property data generated in recent years to train robust predictive models has significantly increased. Recently, we introduced high-fidelity property data at the level of non-empirical hybrid density-functional theory (DFT) with a many-body treatment of vdW dispersion interactions (i.e., PBE0+MBD) for both small [Sci. Data 8, 43, (2021)] and large [Sci. Data 11, 742, (2024)] drug-like molecules in equilibrium and non-equilibrium states. These datasets have proven instrumental for advancing QM-based ML interatomic potentials (e.g., SO3LR model [J. Am. Chem. Soc.147, 37 (2025)]) and for improving semi-empirical methods (e.g., EquiDTB model [chemRxiv, 10.26434/chemrxiv-2025-z3mhh]), thereby enabling accurate and efficient molecular simulations. Beyond these advances, the availability of QM structural and property data has also been key to developing novel molecular representations that enhance the accuracy and interpretability of ML models for predicting biological properties—such as toxicity and lipophilicity—of large drug-like molecules [chemRxiv, 10.26434/chemrxiv-2025-hj4dc]. In this presentation, I will discuss our recent developments in these areas.
BIO:
Leonardo Medrano Sandonas is a research associate at the Dresden University of Technology (TU Dresden) in Germany. He is currently working on combining machine learning methods with quantum/statistical mechanics to develop physics-inspired neural network potentials for the study of inorganic and organic materials. During his postdoc at the University of Luxembourg, he developed computational frameworks for investigating the dynamics of drug-protein systems as well as for data-driven molecular design. In 2018, Leonardo got his doctor degree in Mechanical Engineering at TU Dresden as an IMPRS fellow (International Max Planck Research School). Earlier, he got his bachelor and master’s degree in Physics at the National University of San Marcos in Lima-Peru. In addition to his theoretical investigations, Leonardo actively engages in multi-disciplinary projects with experimental/industrial collaborators to address current challenges in Physics and Chemistry (see Google Scholar page). He serves as a referee for numerous scientific journals and has also organized workshops and conferences in the past years.