78 citations · 156 across the 9 of their papers we have counts for
4 papers · 1 filter
Reconstructing local environments from concise atomistic representations
Jigyasa Nigam, Tuong Phung, Ameya Daigavane +2
Symmetry-based representations of local atomic structure, such as the power spectrum or bispectrum, are routinely used to characterize the structural diversity of datasets and as i…
Learning Lattice Parameters from Powder X-Ray Diffraction Data Using Invariants
Elyssa Hofgard, Kyucheol Min, Nofit Segal +8
We present a machine learning (ML) method to determine unit cell parameters from powder X-Ray diffraction (XRD) data using a novel invariant lattice representation. In ML, the data…
Expanding Density-Correlation Machine Learning Representations for Anisotropic Coarse-Grained Particles
Arthur Y. Lin, Kevin K. Huguenin-Dumittan, Yong-Cheol Cho +2
Physics-based, atom-centered machine learning (ML) representations have been instrumental to the effective integration of ML within the atomistic simulation community. Many of thes…
Multi-scale approach for the prediction of atomic scale properties
Andrea Grisafi, Jigyasa Nigam, Michele Ceriotti
Electronic nearsightedness is one of the fundamental principles governing the behavior of condensed matter and supporting its description in terms of local entities such as chemica…