Leonardo Medrano
@lmedranos88.bsky.social
Computational physicist/chemist at Dresden University of Technology, Germany | Chemical Physics, Machine learning, NanoPhononics, Computational modeling, Materials Science 🇵🇪
As this field is rapidly evolving, we couldn't include all methods recently published.
🙏Thanks to @cecamevents.bsky.social for supporting the workshop “Leveraging #MachineLearning for Sampling #RareEvents in #BiomolecularSystems”, where the discussions that led to this review began.
🙏Thanks to @cecamevents.bsky.social for supporting the workshop “Leveraging #MachineLearning for Sampling #RareEvents in #BiomolecularSystems”, where the discussions that led to this review began.
July 7, 2025 at 8:08 PM
As this field is rapidly evolving, we couldn't include all methods recently published.
🙏Thanks to @cecamevents.bsky.social for supporting the workshop “Leveraging #MachineLearning for Sampling #RareEvents in #BiomolecularSystems”, where the discussions that led to this review began.
🙏Thanks to @cecamevents.bsky.social for supporting the workshop “Leveraging #MachineLearning for Sampling #RareEvents in #BiomolecularSystems”, where the discussions that led to this review began.
We believe this review will serve as a helpful starting point for researchers entering the field of “Machine Learning for Biomolecular Simulations”. Main topics:
-Atomistic ML potentials
-Coarse-grained potentials
-ML for #BiomolecularBackmapping
-Atomistic ML potentials
-Coarse-grained potentials
-ML for #BiomolecularBackmapping
July 7, 2025 at 8:08 PM
We believe this review will serve as a helpful starting point for researchers entering the field of “Machine Learning for Biomolecular Simulations”. Main topics:
-Atomistic ML potentials
-Coarse-grained potentials
-ML for #BiomolecularBackmapping
-Atomistic ML potentials
-Coarse-grained potentials
-ML for #BiomolecularBackmapping