no code implementations • 18 Mar 2024 • V Hewes, Adam Aurisano, Giuseppe Cerati, Jim Kowalkowski, Claire Lee, Wei-keng Liao, Daniel Grzenda, Kaushal Gumpula, Xiaohe Zhang
This article describes NuGraph2, a Graph Neural Network (GNN) for low-level reconstruction of simulated neutrino interactions in a LArTPC detector.
1 code implementation • 8 Nov 2022 • Dipendra Jha, K. V. L. V. Narayanachari, Ruifeng Zhang, Justin Liao, Denis T. Keane, Wei-keng Liao, Alok Choudhary, Yip-Wah Chung, Michael Bedzyk, Ankit Agrawal
Despite the huge advancement in knowledge discovery and data mining techniques, the X-ray diffraction (XRD) analysis process has mostly remained untouched and still involves manual investigation, comparison, and verification.
no code implementations • 10 Mar 2021 • Jeremy Hewes, Adam Aurisano, Giuseppe Cerati, Jim Kowalkowski, Claire Lee, Wei-keng Liao, Alexandra Day, Angrit Agrawal, Maria Spiropulu, Jean-Roch Vlimant, Lindsey Gray, Thomas Klijnsma, Paolo Calafiura, Sean Conlon, Steve Farrell, Xiangyang Ju, Daniel Murnane
This paper presents a graph neural network (GNN) technique for low-level reconstruction of neutrino interactions in a Liquid Argon Time Projection Chamber (LArTPC).
Object Reconstruction High Energy Physics - Experiment
1 code implementation • 26 Jan 2021 • Zijiang Yang, Dipendra Jha, Arindam Paul, Wei-keng Liao, Alok Choudhary, Ankit Agrawal
Microstructural materials design is one of the most important applications of inverse modeling in materials science.
no code implementations • 28 Jul 2019 • Arindam Paul, Mojtaba Mozaffar, Zijiang Yang, Wei-keng Liao, Alok Choudhary, Jian Cao, Ankit Agrawal
As the process for creating an intricate part for an expensive metal such as Titanium is prohibitive with respect to cost, computational models are used to simulate the behavior of AM processes before the experimental run.
2 code implementations • 7 Jul 2019 • Dipendra Jha, Logan Ward, Zijiang Yang, Christopher Wolverton, Ian Foster, Wei-keng Liao, Alok Choudhary, Ankit Agrawal
We use the problem of learning properties of inorganic materials from numerical attributes derived from material composition and/or crystal structure to compare IRNet's performance against that of other machine learning techniques.
1 code implementation • 7 Mar 2019 • Arindam Paul, Dipendra Jha, Reda Al-Bahrani, Wei-keng Liao, Alok Choudhary, Ankit Agrawal
In this work, we present an ensemble deep neural network architecture, called SINet, which harnesses both the SMILES and InChI molecular representations to predict HOMO values and leverage the potential of transfer learning from a sizeable DFT-computed dataset- Harvard CEP to build more robust predictive models for relatively smaller HOPV datasets.
3 code implementations • 14 Nov 2018 • Arindam Paul, Dipendra Jha, Reda Al-Bahrani, Wei-keng Liao, Alok Choudhary, Ankit Agrawal
SMILES is a linear representation of chemical structures which encodes the connection table, and the stereochemistry of a molecule as a line of text with a grammar structure denoting atoms, bonds, rings and chains, and this information can be used to predict chemical properties.