Former Research associate
| Stay period: | Jan. 1, 2013 - Sept. 13, 2026 |
| Now: | Researcher at Center for Advanced Systems Understanding (CASUS) |
| Link to external profile: | https://lmedranos.github.io/ |
| Group: | computational materials science and theoretical nanophysics |
Leonardo Medrano Sandonas is a research associate at the Center for Advanced Systems Understanding (CASUS) and Helmholtz-Zentrum Dresden-Rossendorf (HZDR) in Germany. He works on the research and development of quantum-inspired AI methods for biomolecular modeling and drug discovery. During his previous work experiences, he developed several computational frameworks combining machine learning methods with quantum/statistical mechanics to study inorganic and organic materials. 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. He serves as a referee for numerous scientific journals and has also organized workshops and conferences in the past years.
Towards sustainable exploration of chemical spaces with machine learning
Toward the
Rational Design of Molecular Field-Coupled
Nanocomputing Candidates
Exploring Conformational
Transitions of Adenine RNA
Dimer via Machine Learning Potentials
Towards the design of artificial sensing materials via quantum-informed explainable AI
Advancing density functional tight-binding method for large organic molecules through equivariant neural networks
Optimization Strategies for Equivariant Many-Body ∆
TB
Potentials in Molecular Simulations
On-the-fly machine learning of interatomic potentials for elastic property modeling in Al–Mg–Zr solid solutions
Are nonequilibrium effects relevant for chiral molecule discrimination?
MORE-Q, a dataset for molecular olfactorial receptor engineering by quantum mechanics
Former Research associate
| Stay period: | Jan. 1, 2013 - Sept. 13, 2026 |
| Now: | Researcher at Center for Advanced Systems Understanding (CASUS) |
| Link to external profile: | https://lmedranos.github.io/ |
| Group: | computational materials science and theoretical nanophysics |
Leonardo Medrano Sandonas is a research associate at the Center for Advanced Systems Understanding (CASUS) and Helmholtz-Zentrum Dresden-Rossendorf (HZDR) in Germany. He works on the research and development of quantum-inspired AI methods for biomolecular modeling and drug discovery. During his previous work experiences, he developed several computational frameworks combining machine learning methods with quantum/statistical mechanics to study inorganic and organic materials. 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. He serves as a referee for numerous scientific journals and has also organized workshops and conferences in the past years.
Towards sustainable exploration of chemical spaces with machine learning
Toward the
Rational Design of Molecular Field-Coupled
Nanocomputing Candidates
Exploring Conformational
Transitions of Adenine RNA
Dimer via Machine Learning Potentials
Towards the design of artificial sensing materials via quantum-informed explainable AI
Advancing density functional tight-binding method for large organic molecules through equivariant neural networks
Optimization Strategies for Equivariant Many-Body ∆
TB
Potentials in Molecular Simulations
On-the-fly machine learning of interatomic potentials for elastic property modeling in Al–Mg–Zr solid solutions
Are nonequilibrium effects relevant for chiral molecule discrimination?
MORE-Q, a dataset for molecular olfactorial receptor engineering by quantum mechanics
