no code implementations • CSRNLP (LREC) 2022 • Tapan Auti, Rajdeep Sarkar, Bernardo Stearns, Atul Kr. Ojha, Arindam Paul, Michaela Comerford, Jay Megaro, John Mariano, Vall Herard, John P. McCrae
Pharmaceutical text classification is an important area of research for commercial and research institutions working in the pharmaceutical domain.
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.
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.