Publications (9)
Polymer Informatics Beyond Homopolymers
Shivank S. Shukla, Christopher Kuenneth, Rampi Ramprasad
Polymers are diverse and versatile materials that have met a wide range of material application demands. They come in several flavors and architectures, e.g., homopolymers, copolym…
polyBERT: A chemical language model to enable fully machine-driven ultrafast polymer informatics
Christopher Kuenneth, Rampi Ramprasad
Polymers are a vital part of everyday life. Their chemical universe is so large that it presents unprecedented opportunities as well as significant challenges to identify suitable…
Superconductor discovery in the emerging paradigm of Materials Informatics
Huan Tran, Hieu-Chi Dam, Christopher Kuenneth +2
The last two decades have witnessed a tremendous number of computational predictions of hydride-based (phonon-mediated) superconductors, mostly at extremely high pressures, i.e., h…
Bioplastic Design using Multitask Deep Neural Networks
Christopher Kuenneth, Jessica Lalonde, Babetta L. Marrone +3
Non-degradable plastic waste stays for decades on land and in water, jeopardizing our environment; yet our modern lifestyle and current technologies are impossible to sustain witho…
Polymer Informatics: Current Status and Critical Next Steps
Lihua Chen, Ghanshyam Pilania, Rohit Batra +4
Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology. Polymer informatics is one such domain where AI and mac…
AI-Driven Discovery of High Performance Polymer Electrodes for Next-Generation Batteries
Subhash V. S. Ganti, Lukas Woelfel, Christopher Kuenneth
The use of transition group metals in electric batteries requires extensive usage of critical elements like lithium, cobalt and nickel, which poses significant environmental challe…
It's All Connected: Topology-Aware Structural Graph Encoding Improves Performance on Polymer Prediction
H. Ibrahim Erdogan, Punith Raviswamy, Nikita Agrawal +5
Graph Neural Networks (GNNs) have achieved strong results in molecular property prediction, but polymers present distinct challenges: labeled datasets are scarce and small (typical…
Polymer informatics at-scale with multitask graph neural networks
Rishi Gurnani, Christopher Kuenneth, Aubrey Toland +1
Artificial intelligence-based methods are becoming increasingly effective at screening libraries of polymers down to a selection that is manageable for experimental inquiry. The va…
A general-purpose material property data extraction pipeline from large polymer corpora using Natural Language Processing
Pranav Shetty, Arunkumar Chitteth Rajan, Christopher Kuenneth +5
The ever-increasing number of materials science articles makes it hard to infer chemistry-structure-property relations from published literature. We used natural language processin…