计算机科学
互操作性
任务(项目管理)
推论
数据科学
领域(数学)
领域特定语言
钥匙(锁)
人工智能
软件工程
万维网
系统工程
工程类
数学
计算机安全
纯数学
作者
Ting Xie,Yuqing Wan,Wei Huang,Yufei Zhou,Yixuan Liu,Qingyuan Linghu,Shaozhou Wang,Chunyu Kit,Clara Grazian,Bram Hoex
出处
期刊:Cornell University - arXiv
日期:2023-04-05
被引量:1
标识
DOI:10.48550/arxiv.2304.02213
摘要
The amount of data has growing significance in exploring cutting-edge materials and a number of datasets have been generated either by hand or automated approaches. However, the materials science field struggles to effectively utilize the abundance of data, especially in applied disciplines where materials are evaluated based on device performance rather than their properties. This article presents a new natural language processing (NLP) task called structured information inference (SII) to address the complexities of information extraction at the device level in materials science. We accomplished this task by tuning GPT-3 on an existing perovskite solar cell FAIR (Findable, Accessible, Interoperable, Reusable) dataset with 91.8% F1-score and extended the dataset with data published since its release. The produced data is formatted and normalized, enabling its direct utilization as input in subsequent data analysis. This feature empowers materials scientists to develop models by selecting high-quality review articles within their domain. Additionally, we designed experiments to predict the electrical performance of solar cells and design materials or devices with targeted parameters using large language models (LLMs). Our results demonstrate comparable performance to traditional machine learning methods without feature selection, highlighting the potential of LLMs to acquire scientific knowledge and design new materials akin to materials scientists.
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