Leonardo Medrano Sandonas

University of Luxembourg — LU

Materials Science · Physical Sciences

17h-index913citations85works8.62yr avg
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Research Topics

Machine Learning in Materials Science(35), Computational Drug Discovery Methods(20), Thermal properties of materials(17), Protein Structure and Dynamics(14), Advanced Thermoelectric Materials and Devices(14)

Publications85 total

Exploring Conformational Transitions of RNA Dimers via Machine Learning Potentials
2026Open Access
Advancing density functional tight-binding method for large organic molecules through equivariant neural networks
Physical Chemistry Chemical Physics·2026· 1 citedOpen Access
Assessing the performance of quantum-mechanical descriptors in physicochemical and biological property prediction
Digital Discovery·2026Open Access
On-the-fly machine learning of interatomic potentials for elastic property modeling in Al–Mg–Zr solid solutions
Acta Materialia·2025Open Access
Machine-Learned Electrostatic Potentials for Accurate Hydration Free Energy Calculations
arXiv (Cornell University)·2025Open Access
Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactions
Nature Communications·2025· 4 citedOpen Access
Assessing the performance of quantum-mechanical descriptors in physicochemical and biological property prediction
2025Open Access
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
Journal of the American Chemical Society·2025· 28 citedOpen Access
On-the-Fly Machine Learning of Interatomic Potentials for Elastic Property Modeling in Al-Mg-Zr Solid Solutions
arXiv (Cornell University)·2025Open Access
Are nonequilibrium effects relevant for chiral molecule discrimination?
The Journal of Chemical Physics·2025Open Access
Recent advances in machine learning and coarse-grained potentials for biomolecular simulations
Biophysical Journal·2025· 8 citedOpen Access
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
2025· 3 citedOpen Access
Advancing Density Functional Tight-Binding method for Large Organic Molecules through Equivariant Neural Networks
2025· 1 citedOpen Access
Are nonequilibrium effects relevant for chiral molecule discrimination?
arXiv (Cornell University)·2025Open Access
MORE-Q, a dataset for molecular olfactorial receptor engineering by quantum mechanics
Scientific Data·2025· 5 citedOpen Access
Recent Advances in Machine Learning and Coarse-Grained Potentials for Biomolecular Simulations and Their Applications
2025· 1 citedOpen Access
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
2025· 5 citedOpen Access
Extending quantum-mechanical benchmark accuracy to biological ligand-pocket interactions
2025· 2 citedOpen Access
Mapping the Extended Ground State Reactivity Landscape of a Photoswitchable Molecule at a Single Molecular Level
Journal of the American Chemical Society·2024· 9 citedOpen Access
Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields
2024· 17 citedOpen Access
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Frequent Co-authors

Rafael Gutiérrez(25), Gianaurelio Cuniberti(24), Alexandre Tkatchenko(22), Arezoo Dianat(13), Oliver T. Unke(6), Alessandro Pecchia(4), Martin Stöhr(4), Klaus‐Robert Müller(4), Adil Kabylda(4), J. Thorben Frank(4), Sergio Suarez Dou(4), Almaz Khabibrakhmanov(4), Stefan Chmiela(4), Shlomo Yitzchaik(4), Johannes Hoja(4), Brian G. Ernst(4), Álvaro Vázquez‐Mayagoitia(4), Robert A. DiStasio(4), Julian Cremer(3), Djork-Arné Clevert(3)