Albert Musaelian

Harvard University — United States

Materials Science · Physical Sciences

14h-index2.6kcitations37works14.22yr avg
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Research Topics

Machine Learning in Materials Science(26), Protein Structure and Dynamics(8), nanoparticles nucleation surface interactions(6), Topic Modeling(5), Catalytic Processes in Materials Science(4)

Publications37 total

Multiscale Light-Matter Dynamics in Quantum Materials: From Electrons to Topological Superlattices
2025· 1 citedOpen Access
Multiscale light-matter dynamics in quantum materials: from electrons to topological superlattices
arXiv (Cornell University)·2025Open Access
Coupled reaction and diffusion governing interface evolution in solid-state batteries
ArXiv.org·2025· 1 citedOpen Access
Unified differentiable learning of electric response
Nature Communications·2025· 17 citedOpen Access
High-performance training and inference for deep equivariant interatomic potentials
arXiv (Cornell University)·2025· 7 citedOpen Access
Revealing the proton slingshot mechanism in solid acid electrolytes through machine learning molecular dynamics
arXiv (Cornell University)·2025· 1 citedOpen Access
The design space of E(3)-equivariant atom-centred interatomic potentials
Nature Machine Intelligence·2025· 118 citedOpen Access
Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining
2024· 4 citedOpen Access
Atomistic evolution of active sites in multi-component heterogeneous catalysts
arXiv (Cornell University)·2024· 6 citedOpen Access
Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
The Journal of Physical Chemistry Letters·2024· 26 cited
A Recipe for Charge Density Prediction
arXiv (Cornell University)·2024· 3 citedOpen Access
Thermodynamically Informed Multimodal Learning of High-Dimensional Free Energy Models in Molecular Coarse Graining
arXiv (Cornell University)·2024· 3 citedOpen Access
Complexity of many-body interactions in transition metals via machine-learned force fields from the TM23 data set
npj Computational Materials·2024· 34 citedOpen Access
Unified Differentiable Learning of Electric Response
arXiv (Cornell University)·2024· 4 citedOpen Access
Transferability and Accuracy of Ionic Liquid Simulations with Equivariant Machine Learning Interatomic Potentials
arXiv (Cornell University)·2024Open Access
Accurate Surface and Finite-Temperature Bulk Properties of Lithium Metal at Large Scales Using Machine Learning Interaction Potentials
ACS Omega·2024· 16 citedOpen Access
A Recipe for Charge Density Prediction
2024
Scaling the Leading Accuracy of Deep Equivariant Models to Biomolecular Simulations of Realistic Size
2023· 40 citedOpen Access
Learning Interatomic Potentials at Multiple Scales
arXiv (Cornell University)·2023· 1 citedOpen Access
Advancing molecular simulation with equivariant interatomic potentials
Nature Reviews Physics·2023· 28 cited
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Frequent Co-authors

Boris Kozinsky(36), Simon Batzner(16), Nicola Molinari(11), Cameron J. Owen(10), Anders Johansson(8), C. R. O'Connor(8), Lixin Sun(7), Jin Soo Lim(7), M. A. Van Spronsen(7), Jacob Florian(7), Kaining Duanmu(7), R. J. Madix(7), Philippe Sautet(7), C. M. Friend(7), Lixin Sun(7), Jonathan Vandermause(6), Tobias Egle(6), Yu Xie(5), Kyle Bystrom(5), Anders Johansson(5)