81 citations · 251 across the 12 of their papers we have counts for
14 papers
Singular geometry and eigenframe topology in local rank-2 tensor observables
I. C. J. Yap, B. Doerschel, S. Q. Jin +6
Many observables are symmetric second-rank tensors, reported through magnitude-ordered principal values and axes. This chart folds tensor space: the parameters develop cusps and ex…
PAC Studio Machine Learning: Human-in-the-Loop Analysis of TDPAC Spectra
Thien Thanh Dang, Juliana Heiniger-Schell, Doru Constantin Lupascu
Time-differential perturbed angular correlation (TDPAC or PAC) analysis is an ill-conditioned inverse problem in which site count, interaction type, correlated hyperfine parameters…
Dynamic Multiband Microscopy: A Universal Paradigm for Quantitative Nanoscale Metrology
Boris N. Slautin, Alwikh Rohi, Sanjay Mathur +4
Scanning Probe Microscopy (SPM) is the primary tool for exploring nanoscale functionality, yet standard single-frequency operation is fundamentally limited, because the dynamic tip…
Reward driven discovery of the optimal microstructure representations with invariant variational autoencoders
Boris N. Slautin, Kamyar Barakati, Hiroshi Funakubo +4
Microscopy techniques generate vast amounts of complex image data that in principle can be used to discover simpler, interpretable, and parsimonious forms to reveal the underlying…
Attention-Based Explainability for Structure-Property Relationships
Boris N. Slautin, Utkarsh Pratiush, Yongtao Liu +4
Machine learning methods are emerging as a universal paradigm for constructing correlative structure-property relationships in materials science based on multimodal characterizatio…
Integrating Predictive and Generative Capabilities by Latent Space Design via the DKL-VAE Model
Boris N. Slautin, Utkarsh Pratiush, Doru C. Lupascu +2
We introduce a Deep Kernel Learning Variational Autoencoder (VAE-DKL) framework that integrates the generative power of a Variational Autoencoder (VAE) with the predictive nature o…