Search Results for author:
Found 5 papers, 5 papers with code
Information theory unifies atomistic machine learning, uncertainty quantification, and materials thermodynamics
An accurate description of information is relevant for a range of problems in atomistic modeling, such as sampling methods, detecting rare events, analyzing datasets, or performing uncertainty quantification (UQ) in machine learning (ML)-driven simulations.
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
