11 papers
Predicting Physical and Physical-Chemical Properties of Molecular-Based Materials Using Computational Neural Networks
Andrei A. Gakh, Bobby G. Sumpter, Donald W. Noid
A computational scheme, which utilizes neural networks, was developed to predict properties of molecular-based materials from chemical structures. The method uses a set of simple a…
Physically Interpretable Interatomic Potentials via Symbolic Regression and Reinforcement Learning
Bilvin Varughese, Troy D. Loeffler, Suvo Banik +9
The development of next-generation molecular simulation models requires moving beyond pre-defined functional forms toward machine learning (ML) techniques that directly capture mul…
Cross-scale Modeling of Polymer Topology Impact on Extrudability through Molecular Dynamics and Computational Fluid Dynamics
Yawei Gao, Jan Michael Carrillo, Logan T. Kearney +7
Understanding how polymer topology influences melt extrudability is critical for advancing material design in extrusion-based additive manufacturing. In this work, we develop a bot…
Room-Temperature High-Purity Single Photon Emission from Carbon-Doped Boron Nitride Thin Films
Arka Chatterjee, Abhijit Biswas, Addis S. Fuhr +5
Hexagonal boron nitride (hBN) has emerged as an excellent host material for generating room temperature single photons exhibiting high brightness and spin-photon entanglement. Howe…
Deciphering the Scattering of Mechanically Driven Polymers using Deep Learning
Lijie Ding, Chi-Huan Tung, Bobby G. Sumpter +2
We present a deep learning approach for analyzing two-dimensional scattering data of semiflexible polymers under external forces. In our framework, scattering functions are compres…
Unlocking Hidden Information in Sparse Small-Angle Neutron Scattering Measurement
Chi-Huan Tung, Sidney Yip, Guan-Rong Huang +6
Small-angle neutron scattering (SANS) is a powerful technique for probing the nanoscale structure of materials. However, the fundamental limitations of neutron flux pose significan…