5 citations · 10 across the 5 of their papers we have counts for
5 papers
Score-based denoising for atomic structure identification
Tim Hsu, Babak Sadigh, Nicolas Bertin +4
We propose an effective method for removing thermal vibrations that complicate the task of analyzing complex dynamics in atomistic simulation of condensed matter. Our method iterat…
Quantifying Free-volume Topology in Atomistic Structures Through a Combination of Voxelization and Graph Theory
James Chapman, Nir Goldman
We introduce a new computational methodology for the identification and characterization of free volume within/around atomistic configurations. This scheme employs a three-stage wo…
Efficient, Interpretable Graph Neural Network Representation for Angle-dependent Properties and its Application to Optical Spectroscopy
Tim Hsu, Tuan Anh Pham, Nathan Keilbart +6
Graph neural networks are attractive for learning properties of atomic structures thanks to their intuitive graph encoding of atoms and bonds. However, conventional encoding does n…
Machine learning force fields: Construction, validation, and outlook
Venkatesh Botu, Rohit Batra, James Chapman +1
Force fields developed with machine learning methods in tandem with quantum mechanics are beginning to find merit, given their (i) low cost, (ii) accuracy, and (iii) versatility. R…
A study of adatom ripening on an Al (111) surface with machine learning force fields
Venkatesh Botu, James Chapman, Rampi Ramprasad
Surface phenomena are increasingly becoming important in exploring nanoscale materials growth and characterization. Consequently, the need for atomistic based simulations is increa…