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Found 6 papers, 5 papers with code
Grand canonical generative diffusion model for crystalline phases and grain boundaries
The diffusion model has emerged as a powerful tool for generating atomic structures for materials science.
Model-free quantification of completeness, uncertainties, and outliers in atomistic machine learning using information theory
This method provides a general tool for data-driven atomistic modeling and combines efforts in ML, simulations, and physical explainability.
Score dynamics: scaling molecular dynamics with picoseconds timestep via conditional diffusion model
Our current SD implementation is about two orders of magnitude faster than the MD counterpart for the systems studied in this work.
Score-based denoising for atomic structure identification
We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter.
Semi-Automated Creation of Density Functional Tight Binding Models Through Leveraging Chebyshev Polynomial-based Force Fields
Our approach is easy to implement and can yield accurate DFTB models over a broad range of thermodynamic conditions, where physical and chemical properties can be difficult to interrogate directly and there is historically a significant reliance on theoretical approaches for interpretation and validation of experimental results.
Materials Science
Compressive sensing lattice dynamics. II. Efficient phonon calculations and long-range interactions
We apply the compressive sensing lattice dynamics (CSLD) method to calculate phonon dispersion for crystalline solids.
Materials Science
