基于机器学习的预测亨利系数的长链基在一个维的岩:应用到水分异构化
Shrinjay Sharma1,2, Ping Yang3, Yachan Liu3
1Process and Energy Department, Faculty of Mechanical Engineering, Delft University of Technology, Leeghwaterstraat 39, 2628CB Delft, The Netherlands.
机器学习准确地预测了硫酸在热利石中的吸附,以实现可持续的航空燃料生产. 这加速了选择形状的催化剂的设计,降低了计算成本.
科学领域:
- 催化剂是一种催化剂.
- 材料科学 材料科学 材料科学
- 计算化学计算化学
背景情况:
- 在化物中选择形状吸附对于长链基的水分异构化至关重要.
- 生产可持续的航空燃料依赖于高效的催化过程.
- 预测基同位素在石中的吸附是计算要求很高的.
研究的目的:
- 开发一个机器学习 (ML) 框架来预测化石中异构体的亨利系数.
- 能够快速准确地预测吸附行为.
- 促进新型热带石催化剂的设计.
主要方法:
- 使用基于链条长度,分支模式和分子图形的描述符.
- 在各种热岩框架 (MTT,MTW,MRE,AFI) 中评估了多个ML模型 (随机森林,XGBoost,CatBoost,TabPFN,D-MPNN).
- 采用积极的学习和有针对性的过量采样,以提高模型性能和稳定性.
主要成果:
- TabPFN和D-MPNN模型显示出最高的预测准确性.
- 确定了微小结构变化显著影响吸附的活动悬崖.
- 积极学习提高了在选择信息异构体时的模型效率.
- ML预测的亨利系数结合热力学准确计算的反应平衡分布.
结论:
- 开发的ML框架为预测基吸附提供了传统模拟的计算效率高的替代方案.
- 这种以数据为导向的方法加速了选择形状的热酸盐催化剂的选和设计.
- 这些发现有助于推进可持续的航空燃料生产.
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