催化剂和材料的人工智能驱动设计用于环开放聚合,使用特定领域的语言
Nathaniel H Park1, Matteo Manica2, Jannis Born2,3
1IBM Research-Almaden, 650 Harry Rd., San Jose, CA, 95120, USA. npark@us.ibm.com.
Nature communications
|June 21, 2023
概括
一种新的化学标记语言 (CMDL) 允许在聚合物科学中用于机器学习 (ML) 的灵活数据表示. 这种方法加速了使用历史数据进行实验验证的预测模型的开发.
科学领域:
- 聚合物科学 聚合物科学
- 材料 信息学 信息学
- 计算化学计算化学
背景情况:
- 机器学习 (ML) 和自动化实验可以加速聚合物科学研究.
- 目前的数据模型缺乏适用于多种聚合物实验和数据类型的灵活性,阻碍了ML集成.
- 在聚合物科学中利用ML开发的历史数据是具有挑战性的,因为数据表示的限制.
研究的目的:
- 引入一种灵活和可扩展的特定领域语言,即化学标记语言 (CMDL),用于表示各种聚合物科学数据.
- 展示CMDL如何促进使用历史实验数据来训练ML模型.
- 展示CMDL在生成分子设计和实验验证中的应用.
主要方法:
- 开发化学标记语言 (CMDL) 用于数据表示.
- 微调回归变压器 (RT) 模型使用CMDL.编码的历史数据.
- 来自环开聚合的ML产生的催化剂和聚合物的实验验证.
主要成果:
- CMDL为各种实验类型和聚合物结构提供了灵活,可扩展和一致的数据表示.
- 通过CMDL,可以无集成历史数据,为生成任务微调ML模型.
- 该CMDL调整模型成功生成并通过实验验证了催化剂和聚合物,并保留了关键的功能组.
结论:
- CMDL提供了一个多功能解决方案来表示复杂的聚合物科学数据,克服了刚性模型的局限性.
- 这种方法可以加速将历史数据转化为预测性和生成性ML模型.
- CMDL促进了实验可行的产品的生产,促进了聚合物研发.
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