Search Results for author: Rampi Ramprasad

Found 13 papers, 7 papers with code

Accelerating materials discovery for polymer solar cells: Data-driven insights enabled by natural language processing

1 code implementation29 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.

Active Learning

A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction

1 code implementation20 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.

Language Modelling named-entity-recognition +4

PolyIE: A Dataset of Information Extraction from Polymer Material Scientific Literature

1 code implementation13 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.

Relation Extraction

PolyGET: Accelerating Polymer Simulations by Accurate and Generalizable Forcefield with Equivariant Transformer

no code implementations1 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.

Bioplastic Design using Multitask Deep Neural Networks

1 code implementation22 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.

Copolymer Informatics with Multi-Task Deep Neural Networks

1 code implementation25 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.

Meta-Learning Multi-Task Learning +1

Concentric Spherical GNN for 3D Representation Learning

no code implementations18 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.

3D Classification Representation Learning

PolyRetro: Few-shot Polymer Retrosynthesis via Domain Adaptation

no code implementations1 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.

Domain Adaptation Retrosynthesis

Polymers for Extreme Conditions Designed Using Syntax-Directed Variational Autoencoders

no code implementations4 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.

GPR Ingenuity

Polymer Informatics: Current Status and Critical Next Steps

no code implementations1 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.

Property Prediction

Polymer Informatics with Multi-Task Learning

no code implementations28 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.

Multi-Task Learning

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