PolyID:人工智能用于发现性能优化和可持续的聚合物
A Nolan Wilson1, Peter C St John1, Daniela H Marin1
1Renewable Resources and Enabling Sciences Center, National Renewable Energy Laboratory, 15013 Denver West Parkway, Golden, Colorado 80401, United States.
Macromolecules
|November 29, 2023
概括
科学家们开发了机器学习工具PolyID,以从可再生资源中发现高性能生物基聚合物. 这种人工智能加速了对可持续塑料的搜索,并确定了对化石来源材料的有希望的替代品.
科学领域:
- 材料科学 材料科学 材料科学
- 计算化学计算化学
- 可持续化学 可持续化学
背景情况:
- 向可持续经济的过渡需要用生物质和废物的聚合物取代化石塑料.
- 探索可再生原料的巨大潜力,以获得先进的材料性能,在实验上是具有挑战性的.
- 机器学习提供了一种强大的方法来导航复杂的材料设计空间.
研究的目的:
- 开发一种机器学习工具,PolyID,用于高效地发现具有增强性能的生物基聚合物.
- 减少对可再生原料的搜索空间,加快确定可持续聚合物候选物的速度.
- 为了使生物基聚合物的定量结构-特性关系 (QSPR) 分析.
主要方法:
- 开发PolyID,一个用于聚合物QSPR分析的多输出图形神经网络.
- 实施一种新的有效域方法,通过解决数据缺口来提高模型准确性.
- 对模型进行基准测试,并与现有数据和实验合成的聚合物进行比较.
- 预测超过140万个潜在的生物基聚合物的特性,这些聚合物来自可访问的小分子.
主要成果:
- 在测试数据上的玻璃过渡温度预测中,PolyID的平均绝对误差为19.8°C,在实验数据上的平均误差为26.4°C.
- 确定了五种聚乙烯二甲 (PET) 类似物,预计将改善热和传输性能.
- 实验验证证证实PET类型的玻璃过渡温度 (85112°C),超过PET的玻璃过渡温度,并与PolyID预测保持一致.
- 通过对生物基尼龙的债券重要性分析,证明了模型可解释性.
结论:
- PolyID有效地减少了可再生原料的设计空间,使高性能生物基聚合物的高效发现成为可能.
- 该工具提供了准确和可解释的预测,帮助研究人员在可持续材料的广景观中进行导航.
- PolyID促进了新型生物基聚合物的发现,具有卓越的热和传输特性,为更可持续的经济做出了贡献.
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