From the 2 of 7 linked papers with an AI index.
7 papers
Singular geometry and eigenframe topology in local rank-2 tensor observables
I. C. J. Yap, B. Doerschel, S. Q. Jin +6
The paper investigates how symmetric second‑rank tensors, exemplified by the electric‑field‑gradient (EFG) tensor, develop singularities and a topological return‑parity when eigenv…
PAC Studio Machine Learning: Human-in-the-Loop Analysis of TDPAC Spectra
Thien Thanh Dang, Doru Constantin Lupascu, Juliana Heiniger-Schell
The paper presents PAC Studio ML, a Python desktop environment that combines physics‑based forward modeling with machine‑learning tools to assist researchers in analyzing time‑diff…
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…
Materials Discovery in Combinatorial and High-throughput Synthesis and Processing: A New Frontier for SPM
Boris N. Slautin, Yongtao Liu, Kamyar Barakati +13
For over three decades, scanning probe microscopy (SPM) has been a key method for exploring material structures and functionalities at nanometer and often atomic scales in ambient,…