Fault-tolerant quantum chemical calculations with improved machine-learning models,Journal of Computational Chemistry

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Fault-tolerant quantum chemical calculations with improved machine-learning models
Journal of Computational Chemistry ( IF 3.4 ) Pub Date : 2024-07-29 , DOI: 10.1002/jcc.27459
Kai Yuan _{1,

2} , Shuai Zhou _{3,

4} , Ning Li ₅ , Tianyan Li ₂ , Bowen Ding ₆ , Danhuai Guo ₁ , Yingjin Ma ₂

Affiliation

Easy and effective usage of computational resources is crucial for scientific calculations. Following our recent work of machine-learning (ML) assisted scheduling optimization [J. Comput. Chem. 2023, 44, 1174], we further propose (1) the improved 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, that is, 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, owing primarily profit in fault tolerance that aims at the automated quantum chemical calculations for both ground and excited states.

中文翻译：

使用改进的机器学习模型进行容错量子化学计算

简单有效地使用计算资源对于科学计算至关重要。根据我们最近的机器学习（ML）辅助调度优化工作 [J. Comput. Chem.2023， 44， 1174]，我们进一步提出（1）改进的 ML 模型可以更好地预测计算负载，因此，可以预期更精细的负载均衡计算;（2）编码计算的思想，即梯度编码的整合，以便在分布式计算过程中引入容错;（3）它们与具有时间依赖密度泛函理论（REM-TDDFT）的重归一化激子模型一起用于计算激发态。图示基准计算包括 P38 蛋白和具有一个或多个可激发中心的溶剂模型。结果表明，改进的 ML 辅助编码计算可以进一步提高负载均衡和集群利用率，这主要归因于容错性，旨在实现基态和激发态的自动量子化学计算。

更新日期：2024-07-29

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