29 citations · 34 across the 8 of their papers we have counts for
8 papers
Efficient method for calculation of low-temperature phase boundaries
Lucas Svensson, Babak Sadigh, Christine Wu +1
Understanding phase stability and phase transformations is central to predicting material behavior under varying thermodynamic conditions. One of the earliest and most influential…
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…
Monte Carlo Simulations of Crystal Defects in Open Ensembles
Flynn Walsh, Babak Sadigh, Joseph T. McKeown +1
Zero- and two-dimensional crystal defects form in open statistical ensembles, such as the grand canonical, that are usually inaccessible with conventional simulation techniques. Th…
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…
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…
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…