基于改进机器学习模型的容错量子化学计算
Fault-tolerant Quantum Chemical Calculations with Improved Machine-Learning Models
AI总结：
本文提出改进机器学习负载预测模型并结合梯度编码计算，为分布式量子化学计算引入容错能力，在REM-TDDFT激发态计算中提升了负载均衡和集群利用率。
摘要：
计算资源的便捷高效使用对于科学计算至关重要。继我们近期关于机器学习辅助调度优化的工作之后[J. Comput. Chem. 2023, 44, 1174]，我们进一步提出：1）改进的机器学习模型，以更好地预测计算负载，从而有望实现更精细的负载均衡计算；2）编码计算的思想，即梯度编码的集成，以便在分布式计算过程中引入容错能力；3）将这些方法与重正化激子模型结合含时密度泛函理论（REM-TDDFT）一起应用于激发态计算。示例性基准计算包括P38蛋白以及具有一个或多个可激发中心的溶剂模型。结果表明，改进的机器学习辅助编码计算能够进一步改善负载均衡和集群利用率，并且主要得益于容错能力的提升，可面向基态和激发态的自动化量子化学计算。
英文摘要：
Easy and effective usage of computational resources is crucial for scientific calculations. Following our recent work of machine-learning (ML) assisted scheduling optimization [Ref: J. Comput. Chem. 2023, 44, 1174], we further propose 1) the improve ML models for the better predictions of computational loads, and as such, more elaborate load-balancing calculations can be expected; 2) the idea of coded computation, i.e. the integration of gradient coding, in order to introduce fault tolerance during the distributed calculations; and 3) their applications together with re-normalized exciton model with time-dependent density functional theory (REM-TDDFT) for calculating the excited states. Illustrated benchmark calculations include P38 protein, and solvent model with one or several excitable centers. The results show that the improved ML-assisted coded calculations can further improve the load-balancing and cluster utilization, and owing primarily profit in fault tolerance that aiming at the automated quantum chemical calculations for both ground and excited states.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
