189 citations · 310 across the 10 of their papers we have counts for
10 papers
Neutron and X-ray Diffraction Reveal the Limits of Long-Range Machine Learning Potentials for Medium-Range Order in Silica Glass
Sai Harshit Balantrapu, Atul C. Thakur, Chris Benmore +1
Glassy silica is a foundational material in optics and electronics, yet accurately predicting its medium-range order (MRO) remains a major challenge for machine-learning interatomi…
Structural phase transitions between layered Indium Selenide for inte-grated photonic memory
Tiantian Li, Yong Wang, Wei Li +10
The primary mechanism of optical memristive devices relies on the phase transitions between amorphous-crystalline states. The slow or energy hungry amorphous-crystalline transition…
Deciphering diffuse scattering with machine learning and the equivariant foundation model: The case of molten FeO
Ganesh Sivaraman, Chris J. Benmore
Bridging the gap between diffuse x-ray or neutron scattering measurements and predicted structures derived from atom-atom pair potentials in disordered materials, has been a longst…
The First Direct Detection of Kirkwood Transitions in Concentrated Aqueous Electrolytes using Small Angle X-ray Scattering
Mohammadhasan Dinpajooh, Elisa Biasin, Christopher J. Mundy +6
Ion-ion correlations, screening, and equilibrium bulk structure in various concentrated electrolytes are investigated using synchrotron small angle X-ray scattering (SAXS), theory,…
An Experimentally Driven Automated Machine Learned lnter-Atomic Potential for a Refractory Oxide
Ganesh Sivaraman, Leighanne Gallington, Anand Narayanan Krishnamoorthy +4
Understanding the structure and properties of refractory oxides are critical for high temperature applications. In this work, a combined experimental and simulation approach uses a…
Machine Learning Inter-Atomic Potentials Generation Driven by Active Learning: A Case Study for Amorphous and Liquid Hafnium dioxide
Ganesh Sivaraman, Anand Narayanan Krishnamoorthy, Matthias Baur +5
We propose a novel active learning scheme for automatically sampling a minimum number of uncorrelated configurations for fitting the Gaussian Approximation Potential (GAP). Our act…