189 citations · 310 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
Insights into the Hierarchical Structure of Spider Dragline Silk Fibers: Evidence for Fractal Clustering of -Sheet Nano-Crystallites
Qiushi Mou, Chris J. Benmore, Warner S. Weber +1
Spider dragline silk is one of the toughest materials known and understanding the hierarchical structure is a critical component in the efforts to connect structure to function. In…
Modelling the atomic structure of very high-density amorphous ice
J. K. Christie, M. Guthrie, C. A. Tulk +4
The structure of very high-density amorphous (VHDA) ice has been modelled by positionally disordering three crystalline phases, namely ice IV, VI and XII. These phases were chosen…