Physical and Life Sciences Directorate, Lawrence Livermore National Laboratory, Livermore, California 94550, United States.
Department of Chemical Engineering, University of California, Davis, California 95616, United States.
J Phys Chem Lett. 2022 Apr 7;13(13):2934-2942. doi: 10.1021/acs.jpclett.2c00453. Epub 2022 Mar 28.
A great need exists for computationally efficient quantum simulation approaches that can achieve an accuracy similar to high-level theories at a fraction of the computational cost. In this regard, we have leveraged a machine-learned interaction potential based on Chebyshev polynomials to improve density functional tight binding (DFTB) models for organic materials. The benefit of our approach is two-fold: (1) many-body interactions can be corrected for in a systematic and rapidly tunable process, and (2) high-level quantum accuracy for a broad range of compounds can be achieved with ∼0.3% of data required for one advanced deep learning potential. Our model exhibits both transferability and extensibility through comparison to quantum chemical results for organic clusters, solid carbon phases, and molecular crystal phase stability rankings. Our efforts thus allow for high-throughput physical and chemical predictions with up to coupled-cluster accuracy for systems that are computationally intractable with standard approaches.
目前非常需要计算效率高的量子模拟方法，这种方法能够以较低的计算成本达到与高精度理论相当的准确度。在这方面，我们利用基于切比雪夫多项式的机器学习相互作用势改进了用于有机材料的密度泛函紧束缚（DFTB）模型。我们的方法有两个优点：（1）可以系统地、快速地调整多体相互作用的修正，（2）仅用约 0.3%的所需数据即可实现广泛化合物的高精度量子计算，而这一数据量是一个先进的深度学习势所需数据的 0.3%。我们的模型通过与有机团簇、固体碳相和分子晶体相稳定性排序的量子化学结果进行比较，表现出可转移性和可扩展性。因此，我们的工作可以实现高通量的物理和化学预测，对于用标准方法难以计算的系统，可以达到耦合簇精度。
