2 citations · 5 across the 7 of their papers we have counts for
8 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…
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…
Grand canonical generative diffusion model for crystalline phases and grain boundaries
Bo Lei, Enze Chen, Hyuna Kwon +5
The diffusion model has emerged as a powerful tool for generating atomic structures for materials science. This work calls attention to the deficiency of current particle-based dif…
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…
Spectroscopy-Guided Discovery of Three-Dimensional Structures of Disordered Materials with Diffusion Models
Hyuna Kwon, Tim Hsu, Wenyu Sun +8
The ability to rapidly develop materials with desired properties has a transformative impact on a broad range of emerging technologies. In this work, we introduce a new framework b…
Accelerate Microstructure Evolution Simulation Using Graph Neural Networks with Adaptive Spatiotemporal Resolution
Shaoxun Fan, Andrew L. Hitt, Ming Tang +2
Surrogate models driven by sizeable datasets and scientific machine-learning methods have emerged as an attractive microstructure simulation tool with the potential to deliver pred…
Score dynamics: scaling molecular dynamics with picoseconds timestep via conditional diffusion model
Tim Hsu, Babak Sadigh, Vasily Bulatov +1
We propose score dynamics (SD), a general framework for learning accelerated evolution operators with large timesteps from molecular-dynamics simulations. SD is centered around sco…