BatteryDataExtractor: battery-aware text-mining software embedded with BERT models-Reference-Cited by-同舟云学术

BatteryDataExtractor: battery-aware text-mining software embedded with BERT models

Published:2022 Issue:39 Volume:13 Page:11487-11495
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Huang Shu¹^ORCID,Cole Jacqueline M.¹²^ORCID

Affiliation:

1. Cavendish Laboratory, Department of Physics, University of Cambridge, J. J. Thomson Avenue, Cambridge CB3 0HE, UK

2. ISIS Neutron and Muon Source, Rutherford Appleton Laboratory, Harwell Science and Innovation Campus, Didcot, Oxfordshire, OX11 0QX, UK

Abstract

BatteryDataExtractor is the first property-specific text-mining tool for auto-generating databases of materials and their property, device, and associated characteristics. The software has been constructed by embedding the BatteryBERT model.

Funder

Royal Academy of Engineering

Science and Technology Facilities Council

Office of Science

Christ's College, University of Cambridge

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2022/SC/D2SC04322J

Reference61 articles.

1. Machine learning for heterogeneous catalyst design and discovery

2. Automated in Silico Design of Homogeneous Catalysts

3. Design‐to‐Device Approach Affords Panchromatic Co‐Sensitized Solar Cells

4. A Design-to-Device Pipeline for Data-Driven Materials Discovery

5. Data mining new energy materials from structure databases

Cited by 12 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Versatile Deep Learning Pipeline for Transferable Chemical Data Extraction;Journal of Chemical Information and Modeling;2024-07-15

2. Creation of a structured solar cell material dataset and performance prediction using large language models;Patterns;2024-05

3. How Beneficial Is Pretraining on a Narrow Domain-Specific Corpus for Information Extraction about Photocatalytic Water Splitting?;Journal of Chemical Information and Modeling;2024-03-28

4. Accelerating materials language processing with large language models;Communications Materials;2024-02-15

5. Capacitive tendency concept alongside supervised machine-learning toward classifying electrochemical behavior of battery and pseudocapacitor materials;Nature Communications;2024-02-07