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article-downloader
article-downloader PublicUses publisher APIs to programmatically retrieve scientific journal articles for text mining.
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materials-synthesis-generative-models
materials-synthesis-generative-models PublicPublic release of data and code for materials synthesis generation
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materials-word-embeddings
materials-word-embeddings PublicWord2Vec model trained across 640k+ materials science journal articles
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table_extractor
table_extractor PublicExtracts tables into json format from HTML/XML files
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- phase-sentiment Public
olivettigroup/phase-sentiment’s past year of commit activity - NLP4SIB Public
Datasets and pre-trained models for Munjal, Mrigi, et al. "Scaling Sodium-ion Battery Development with NLP." AI for Accelerated Materials Design-NeurIPS 2023 Workshop. 2023.
olivettigroup/NLP4SIB’s past year of commit activity - deep-rl-inorganic Public
Code and data for the paper Deep Reinforcement Learning for Inverse Inorganic Materials Design by Karpovich et al.
olivettigroup/deep-rl-inorganic’s past year of commit activity - Cementitious-reactivity-prediction-and-discovery Public
This repository contains the code and data associated with the research paper: "Material discovery of secondary and natural cementitious precursors."
olivettigroup/Cementitious-reactivity-prediction-and-discovery’s past year of commit activity - interpretable-condition-prediction Public
Code and data for the paper Interpretable Machine Learning Enabled Inorganic Reaction Classification and Synthesis Condition Prediction by Karpovich et al.
olivettigroup/interpretable-condition-prediction’s past year of commit activity - article-downloader Public
Uses publisher APIs to programmatically retrieve scientific journal articles for text mining.
olivettigroup/article-downloader’s past year of commit activity - materials-synthesis-generative-models Public
Public release of data and code for materials synthesis generation
olivettigroup/materials-synthesis-generative-models’s past year of commit activity - cross-domain-exploration Public
This project presents an exploratory search system that enables scientists to discover research outside their familiar domains by selecting text from paper abstracts to retrieve diverse yet relevant studies. By exploring domain-specific clusters, users can gain deeper insights, fostering cross-domain inspiration and innovation.
olivettigroup/cross-domain-exploration’s past year of commit activity
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