29 citations · 29 across the 5 of their papers we have counts for
6 papers
MOSAIC: Addressing Space for Local, Steerable, and Scalable Atomistic Generation
Ishan Nadkarni, Sebastien Hamel, Flynn Walsh +1
Generative models are opening new possibilities in molecular discovery and materials science. Realizing this potential, however, requires both local control over atomic placement a…
Extracting Atomic Environments for Machine Learning Interatomic Potentials
Jared C. Stimac, Fei Zhou, Kyle Bushick +4
In order to appropriately capture large-scale material features and emergent phenomena via atomistic simulations, such as Molecular Dynamics (MD), the system scale can range up to…
Polarizable atomic multipoles for learning long-range electrostatics
Yoonjae Park, Dongjin Kim, Daniel S. King +5
Long-range electrostatics and polarization remain central obstacles to extending machine learning interatomic potentials (MLIPs) to ionic, polar, and interfacial systems. Here we i…
A probabilistic framework for crystal structure denoising, phase classification, and order parameters
Hyuna Kwon, Babak Sadigh, Sebastien Hamel +3
Atomistic simulations generate large volumes of noisy structural data, yet extracting phase labels and continuous order parameters (OPs) in a robust and general manner remains chal…
Ice phase classification made easy with score-based denoising
Hong Sun, Sebastien Hamel, Tim Hsu +3
Accurate identification of ice phases is essential for understanding various physicochemical phenomena. However, such classification for structures simulated with molecular dynamic…
Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
Daniel Schwalbe-Koda, Sebastien Hamel, Babak Sadigh +2
An accurate description of information is relevant for a range of problems in atomistic machine learning (ML), such as crafting training sets, performing uncertainty quantification…