帕迪:一种化学系统和空间的进化优化算法
Armen G Beck1, Sanjay Iyer1, Jonathan Fine1
1Department of Chemistry, Purdue University 720 Clinic Drive West Lafayette IN 47907 USA gchopra@purdue.edu.
Digital discovery
|May 9, 2025
概括
新的Paddy软件包基于一种生物启发的算法,有效地优化化学系统. 它在各种基准上表现出强的表现,避免了在自动化实验中获得更好的全球解决方案的本地最小值.
科学领域:
- 计算化学是一种计算化学.
- 化学过程优化优化 化学过程优化
- 算法开发的发展算法.
背景情况:
- 化学系统和过程需要高效的优化方法.
- 现有的算法往往需要大量的实验,并且可以在局部最小值上收.
- 化学系统的复杂性需要先进的实验设计算法.
研究的目的:
- 介绍 Paddy 软件包及其基础的 Paddy 字段算法.
- 将Paddy与化学和数学任务的各种优化方法进行基准测试.
- 评估Paddy优化目标的能力,取样参数空间,并避免局部最佳.
主要方法:
- 开发 Paddy 软件包,利用生物启发的 Paddy 字段算法.
- 与帕森估计器树 (Hyperopt),贝叶斯优化 (Ax框架) 和EvoTorch算法进行对比.
- 在数学任务 (双模分布,函数插值) 和化学任务 (ANN超参数优化,分子生成,实验规划) 上测试性能.
主要成果:
- 在所有经过测试的优化基准中,Paddy表现出强大而多样化的性能.
- 与其他算法相比,Paddy保持了强的表现,结果各不相同.
- 该算法有效地避免了早期的收,并绕过了局部最佳情况,寻找全球解决方案.
结论:
- 帕迪为化学问题解决提供了一个简单,多功能,强大和开源的工具包.
- 该软件非常适合自动化实验,优先考虑探索性采样.
- 帕迪对早期收的抵抗有助于确定复杂化学系统的最佳解决方案.
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