适应性修剪以提高强度和减少高斯过程中的计算开销 加速位搜索
Rohit Goswami1,2, Hannes Jónsson2
1Institute IMX and Lab-COSMO, École polytechnique fédérale de Lausanne (EPFL), Lausanne, Switzerland.
概括
高斯过程 (GP) 回归加速了在高维化学中的位点搜索. 新方法提高了效率和稳定性,在复杂的反应数据集中,计算时间减少了50%以上.
科学领域:
- 计算化学计算化学
- 化学物理 化学物理
- 机器学习在化学中的应用
背景情况:
- 高斯过程 (GP) 回归是加速化学中昂贵的计算计算的强大工具,例如点搜索.
- 传统的GP回归方法在超参数优化过程中面临着计算开销的挑战,以及潜在能量表面表现不佳的区域中的潜在故障.
- 有效地导航高维能量表面对于理解化学反应和分子性质至关重要.
研究的目的:
- 解决与GP回归相关的低效率和失败问题,以加速位点搜索.
- 开发一种更强大,更可扩展的基于GP的算法,用于探索高维的潜在能量表面.
- 为了降低与评估复杂化学系统中的能量和力量相关的计算成本.
主要方法:
- 实施了几何意识的最佳运输措施和使用Wasserstein-1距离进行最远点采样的积极修剪策略.
- 选择了几何多样化的配置,以管理GP更新的日益增加的成本.
- 引入了稳定性的一种换不变度量和对数障碍处罚来控制信号方差增长.
主要成果:
- 成功地将238个具有挑战性的化学反应配置的数据集的平均计算时间减少了50%以上.
- 证明了GP方法的增强稳定性和稳定性,即使在复杂的高维系统中.
- 在实际应用中验证了物理动机算法改进的有效性.
结论:
- 增强的GP回归算法提供了一个强大的和可扩展的解决方案,用于加速计算化学中的位点搜索.
- 这些改进大大降低了计算需求,使GP回归成为分析化学反应的更实用的工具.
- 开发的方法提供了一个可靠的策略,用于导航复杂的能源景观,其中能源和力量评估是昂贵的.
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