AI for Science时代下的电池平台化智能研发

doi:10.19799/j.cnki.2095-4239.2024.0698

摘要/Abstract

摘要：

在AI for Science时代，电池设计自动化智能研发(battery design automation，BDA)平台通过整合先进的人工智能技术，为电池研发领域带来了革命性进展。BDA平台覆盖了文献调研、实验设计、合成制备、表征测试和分析优化这五个电池研发的关键环节，利用机器学习、多尺度建模、预训练模型等先进算法，结合软件工程开发用户交互友好的工具，加速从理论设计到实验验证的整个电池研发周期。通过自动化的实验设计、合成制备、表征测试和性能优化，BDA平台不仅提升了研发效率，还提高了电池设计的精确度和可靠性，推动了电池技术向更高能量密度、更长循环寿命和更低成本的方向发展。

关键词: AI for Science, 电池, 智能研发, 机器学习, BDA, 多尺度

Abstract:

In the era of artificial intelligence (AI) in science, the battery design automation (BDA) intelligent R&D platform has revolutionized battery R&D by integrating advanced AI technologies. The BDA platform covers five key aspects of battery R&D: Read, Design, Make, Test, and Analysis. It uses advanced algorithms, such as machine learning, multi-scale modeling, and pre-training models, combined with software engineering to develop user-friendly tools for accelerating the complete battery R&D cycle from theoretical design to experimental validation. Through synthesis and preparation, characterization testing, performance optimization, and automated experimental design, the BDA platform enhances R&D efficiency and improves the accuracy and reliability of battery design, which results in battery technology with higher energy density, longer cycle life, and lower costs.

Key words: artificial intelligence for science, battery, intelligent R&D, machine learning, battery design automation, multi-scale

中图分类号:

TP 181

谢莹莹, 邓斌, 张与之, 王晓旭, 张林峰. AI for Science时代下的电池平台化智能研发[J]. 储能科学与技术, 2024, 13(9): 3182-3197.

Yingying XIE, Bin DENG, Yuzhi ZHANG, Xiaoxu WANG, Linfeng ZHANG. Intelligent R&D of battery design automation in the era of artificial intelligence[J]. Energy Storage Science and Technology, 2024, 13(9): 3182-3197.

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任务		指标	Uni-SMART	GPT-4	GPT-3.5	Gemini
对表格数据进行评估	电解液数据提取	价值召回	0.451	0.420	0.437	0.443
对表格数据进行评估	电解质表格问答	价值召回	0.320	0.274	0.359	0.175

对分子结构式数据进行评估	文档中的分子	准确率	0.889	0.022	0.489	0.500

对化学反应式数据进行评估	化学反应问答	准确率	0.400	0.200	0.000	0.133

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