Recycle-BERT: extracting knowledge about plastic waste recycling by natural language processing

A Kumar, BR Bakshi, M Ramteke… - ACS Sustainable …, 2023 - ACS Publications
Managing waste plastic is a serious global challenge since most of this waste is either
landfilled, incinerated, burned in the open, or littered. Each of these approaches has a large …

SUSIE: Pharmaceutical CMC ontology-based information extraction for drug development using machine learning

V Mann, S Viswanath, S Vaidyaraman… - Computers & Chemical …, 2023 - Elsevier
Automatically extracting information from unstructured text in pharmaceutical documents is
important for drug discovery and development. This information can be integrated with …

[HTML][HTML] Quo Vadis ChatGPT? From Large Language Models to Large Knowledge Models

V Venkatasubramanian, A Chakraborty - Computers & Chemical …, 2025 - Elsevier
The startling success of ChatGPT and other large language models (LLMs) using
transformer-based generative neural network architecture in applications such as natural …

eSFILES: Intelligent process flowsheet synthesis using process knowledge, symbolic AI, and machine learning

V Mann, M Sales-Cruz, R Gani… - Computers & Chemical …, 2024 - Elsevier
Process flowsheet synthesis, design, and simulation require integrated approaches that
combine domain knowledge and data-driven methods for fast, efficient, and reliable …

An artificial intelligence course for chemical engineers

M Wu, U Di Caprio, F Vermeire, P Hellinckx… - Education for Chemical …, 2023 - Elsevier
Artificial intelligence and machine learning are revolutionising fields of science and
engineering. In recent years, process engineering has widely benefited from this novel …

A scalable and integrated machine learning framework for molecular properties prediction

G Chen, Z Song, Z Qi, K Sundmacher - AIChE Journal, 2023 - Wiley Online Library
This work introduced a scalable and integrated machine learning (ML) framework to
facilitate important steps of building quantitative structure–property relationship (QSPR) …

AI-driven hypergraph network of organic chemistry: network statistics and applications in reaction classification

V Mann, V Venkatasubramanian - Reaction Chemistry & Engineering, 2023 - pubs.rsc.org
Rapid discovery of new reactions and molecules in recent years has been facilitated by the
advances in high throughput screening, accessibility to a highly complex chemical design …

Group contribution-based property modeling for chemical product design: A perspective in the AI era

V Mann, R Gani, V Venkatasubramanian - Fluid Phase Equilibria, 2023 - Elsevier
We provide a perspective of the challenges and opportunities for the group contribution
approach for property prediction modeling with respect to their use in the design of chemical …

Transformer-Based Representation of Organic Molecules for Potential Modeling of Physicochemical Properties

I Pérez-Correa, PD Giunta, FJ Mariño… - Journal of Chemical …, 2023 - ACS Publications
In this work, we study the use of three configurations of an autoencoder neural network to
process organic substances with the aim of generating meaningful molecular descriptors …

G‐MATT: Single‐step retrosynthesis prediction using molecular grammar tree transformer

K Zhang, V Mann, V Venkatasubramanian - AIChE Journal, 2024 - Wiley Online Library
Various template‐based and template‐free approaches have been proposed for single‐step
retrosynthesis prediction in recent years. While these approaches demonstrate strong …