Aleksander E. P. Durumeric

Freie Universität Berlin — Germany

Biochemistry, Genetics and Molecular Biology · Life Sciences

13h-index885citations30works4.32yr avg
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Research Topics

Protein Structure and Dynamics(15), Machine Learning in Materials Science(14), Block Copolymer Self-Assembly(9), Cellular Mechanics and Interactions(4), Material Dynamics and Properties(4)

Publications30 total

Learning data efficient coarse-grained molecular dynamics from forces and noise
Zenodo (CERN European Organization for Nuclear Research)·2026Open Access
Learning data efficient coarse-grained molecular dynamics from forces and noise
Zenodo (CERN European Organization for Nuclear Research)·2026Open Access
Operator forces for coarse-grained molecular dynamics
The Journal of Chemical Physics·2025· 3 citedOpen Access
Navigating protein landscapes with a machine-learned transferable coarse-grained model
Nature Chemistry·2025· 25 citedOpen Access
Operator Forces For Coarse-Grained Molecular Dynamics
arXiv (Cornell University)·2025Open Access
Machine Learning Driven Simulations of the SARS-CoV-2 Fitness Landscape from Deep Mutational Scanning Experiments
2024Open Access
Learning data efficient coarse-grained molecular dynamics from forces and noise
arXiv (Cornell University)·2024· 2 citedOpen Access
Navigating protein landscapes with a machine-learned transferable coarse-grained model
2023· 3 citedOpen Access
Navigating protein landscapes with a machine-learned transferable coarse-grained model
arXiv (Cornell University)·2023· 5 citedOpen Access
OpenMSCG: A Software Tool for Bottom-Up Coarse-Graining
The Journal of Physical Chemistry B·2023· 50 citedOpen Access
OpenMSCG: A Software Tool for Bottom-up Coarse-graining
2023· 3 citedOpen Access
Using classifiers to understand coarse-grained models and their fidelity with the underlying all-atom systems
The Journal of Chemical Physics·2023· 3 cited
Statistically Optimal Force Aggregation for Coarse-Graining Molecular Dynamics
The Journal of Physical Chemistry Letters·2023· 32 cited
Utilizing Machine Learning to Greatly Expand the Range and Accuracy of Bottom-Up Coarse-Grained Models through Virtual Particles
Journal of Chemical Theory and Computation·2023· 29 citedOpen Access
Machine learned coarse-grained protein force-fields: Are we there yet?
Current Opinion in Structural Biology·2023· 73 citedOpen Access
Utilizing Machine Learning to Greatly Expand the Range and Accuracy of Bottom-Up Coarse-Grained Models Through Virtual Particles
arXiv (Cornell University)·2022· 1 citedOpen Access
Bottom-up Coarse-Graining: Principles and Perspectives
Journal of Chemical Theory and Computation·2022· 260 citedOpen Access
Explaining classifiers to understand coarse-grained models
arXiv (Cornell University)·2021· 3 citedOpen Access
Improving the accuracy and convergence of drug permeation simulations via machine-learned collective variables
The Journal of Chemical Physics·2021· 13 cited
Compatible observable decompositions for coarse-grained representations of real molecular systems
The Journal of Chemical Physics·2019· 20 cited
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Frequent Co-authors

Gregory A. Voth(17), Yaoyi Chen(8), Frank Noé(8), Cecilia Clementi(7), Atharva Kelkar(6), Alexander J. Pak(5), Timothy D. Loose(5), Nicholas E. Charron(5), Clark Templeton(4), Félix Musil(4), Klara Bonneau(4), Patrick G. Sahrmann(4), Andreas Krämer(4), Harshwardhan H. Katkar(4), Jaehyeok Jin(3), Aldo S. Pasos-Trejo(3), Aram Davtyan(3), Glen M. Hocky(3), Anthony C. Schramm(3), Enrique M. De La Cruz(3)