终身机器学习潜力 终身机器学习潜力
Marco Eckhoff1, Markus Reiher1
1ETH Zürich, Departement Chemie und Angewandte Biowissenschaften, 8093 Zürich, Switzerland.
Journal of chemical theory and computation
|June 8, 2023
概括
这项研究介绍了终身机器学习潜力 (lMLPs),这些潜力不断适应新数据,而不会忘记. 包含元素的以原子为中心的对称函数 (eeACSFs) 能够为化学模拟提供更广泛的应用.
科学领域:
- 计算化学的计算化学
- 材料科学 材料科学 材料科学
- 机器学习 机器学习
背景情况:
- 机器学习潜力 (MLP) 提供高精度,低计算成本.
- 目前的MLPs需要系统特定的培训,并与各种化学元素作斗争.
- 在新数据上对MLP进行再培训是计算密集的,并可能导致知识丢失.
研究的目的:
- 开发一个不断适应的机器学习潜力 (lMLP).
- 提高MLP处理各种化学元素和系统的能力.
- 为了实现MLP在新数据流上的自主,即时培训.
主要方法:
- 引入包含元素的以原子为中心的对称函数 (eeACSFs),结合结构和元素信息.
- 使用不确定性量化开发终身机器学习潜力 (lMLP).
- 应用持续学习策略,包括CoRe优化器,用于自主培训.
主要成果:
- eeACSFs实际上代表了MLP中的各种化学元素.
- 在lmlp显示持续的适应和保持准确性在新的数据.
- 持续学习策略有助于飞行培训和更广泛的应用.
结论:
- 使用eACSF开发的lmLP克服了传统MLP的局限性.
- 终身学习和持续策略使复杂的化学系统具有强大而可适应的ML潜力.
- 这种方法为更高效和多功能计算模拟铺平了道路.
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