1 code implementation • 29 Feb 2024 • Pranav Shetty, Aishat Adeboye, Sonakshi Gupta, Chao Zhang, Rampi Ramprasad
We present a natural language processing pipeline that was used to extract polymer solar cell property data from the literature and simulate various active learning strategies.
1 code implementation • 20 Feb 2024 • Yinghao Li, Rampi Ramprasad, Chao Zhang
It breaks the generation into a two-step pipeline: initially, LLMs generate answers in natural language as intermediate responses.
1 code implementation • 13 Nov 2023 • Jerry Junyang Cheung, Yuchen Zhuang, Yinghao Li, Pranav Shetty, Wantian Zhao, Sanjeev Grampurohit, Rampi Ramprasad, Chao Zhang
Scientific information extraction (SciIE), which aims to automatically extract information from scientific literature, is becoming more important than ever.
no code implementations • 1 Sep 2023 • Rui Feng, Huan Tran, Aubrey Toland, Binghong Chen, Qi Zhu, Rampi Ramprasad, Chao Zhang
Machine learning (ML) forcefields have been developed to achieve both the accuracy of ab initio methods and the efficiency of empirical force fields.
2 code implementations • 29 Sep 2022 • Christopher Kuenneth, Rampi Ramprasad
polyBERT is a chemical linguist that treats the chemical structure of polymers as a chemical language.
1 code implementation • 27 Sep 2022 • Pranav Shetty, Arunkumar Chitteth Rajan, Christopher Kuenneth, Sonkakshi Gupta, Lakshmi Prerana Panchumarti, Lauren Holm, Chao Zhang, Rampi Ramprasad
The ever-increasing number of materials science articles makes it hard to infer chemistry-structure-property relations from published literature.
1 code implementation • 22 Mar 2022 • Christopher Kuenneth, Jessica Lalonde, Babetta L. Marrone, Carl N. Iverson, Rampi Ramprasad, Ghanshyam Pilania
The developed multitask polymer property predictors are made available as a part of the Polymer Genome project at https://PolymerGenome. org.
1 code implementation • 25 Mar 2021 • Christopher Kuenneth, William Schertzer, Rampi Ramprasad
Polymer informatics tools have been recently gaining ground to efficiently and effectively develop, design, and discover new polymers that meet specific application needs.
no code implementations • 18 Mar 2021 • James Fox, Bo Zhao, Sivasankaran Rajamanickam, Rampi Ramprasad, Le Song
Learning 3D representations that generalize well to arbitrarily oriented inputs is a challenge of practical importance in applications varying from computer vision to physics and chemistry.
no code implementations • 1 Jan 2021 • Binghong Chen, Chengtao Li, Hanjun Dai, Rampi Ramprasad, Le Song
We demonstrate that our method is able to propose high-quality polymerization plans for a dataset of 52 real-world polymers, of which a significant portion successfully recovers the currently-in-used polymerization processes in the real world.
no code implementations • 4 Nov 2020 • Rohit Batra, Hanjun Dai, Tran Doan Huan, Lihua Chen, Chiho Kim, Will R. Gutekunst, Le Song, Rampi Ramprasad
The design/discovery of new materials is highly non-trivial owing to the near-infinite possibilities of material candidates, and multiple required property/performance objectives.
no code implementations • 1 Nov 2020 • Lihua Chen, Ghanshyam Pilania, Rohit Batra, Tran Doan Huan, Chiho Kim, Christopher Kuenneth, Rampi Ramprasad
Artificial intelligence (AI) based approaches are beginning to impact several domains of human life, science and technology.
no code implementations • 28 Oct 2020 • Christopher Künneth, Arunkumar Chitteth Rajan, Huan Tran, Lihua Chen, Chiho Kim, Rampi Ramprasad
Compared to conventional single-task learning models (that are trained on individual property datasets independently), the multi-task approach is accurate, efficient, scalable, and amenable to transfer learning as more data on the same or different properties become available.