范围:一个强大的适应性自然灵感的潜在能源表面的全球探索者
Difan Zhang1, Małgorzata Z Makoś1, Roger Rousseau1
1Chemical Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37830, USA.
The Journal of chemical physics
|October 16, 2025
概括
本研究介绍了RANGE,这是一个混合算法,将人工蜂群 (ABC) 和遗传算法 (GA) 结合起来,用于计算化学中的高效全球优化. RANGE通过使用自然灵感的算法增强了对稳定的化学结构的搜索.
科学领域:
- 计算化学计算化学
- 材料科学 材料科学 材料科学
- 生物信息学是一种生物信息学.
背景情况:
- 精确的化学结构表示和全球最小值的识别对于计算化学和材料科学至关重要.
- 超大规模计算的进步使得探索潜在能量表面的更复杂的方法成为可能.
- 蜂群智能算法,如人工蜂群 (ABC),在优化任务中表现有希望.
研究的目的:
- 开发一个混合的元启发框架,集成ABC和遗传算法 (GA) 以实现强大的全球优化.
- 创建一个可扩展的,基于Python的工具,名为RANGE (灵感来自强大的适应性自然的全球探索器),用于计算化学应用.
- 评估RANGE与独立的ABC和GA算法的性能.
主要方法:
- 开发了一个混合的元启发框架,将ABC的探索与GA的开发相结合.
- 在一个可扩展的,基于Python的工具中实现了框架,RANGE,与潜在能源评估器的接口.
- 评估了RANGE在分子和异质表面上的性能.
主要成果:
- 与ABC或GA单独算法相比,RANGE显示出更高的效率和稳定性.
- 混合方法有效地解决了具有挑战性的全球优化问题.
- 在各种化学系统中成功应用,包括分子集群和表面.
结论:
- RANGE 工具为计算化学和材料科学领域的全球优化提供了强大而通用的解决方案.
- 结合勘探和开发的混合元启发方法是非常有效的.
- 范围非常适合高性能计算环境,促进复杂的化学结构的发现.
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