Computational study demonstrates improved interaction energy predictions in molecular systems using low-cost calculations, suggesting accurate parameterization for particle dynamics.
In our previous report [H. Doi et al., JJAP 62 , 070901 (2023)], we presented a machine learning-based approach to reduce the overall cost of fragment molecular orbital (FMO) calculations from which the effective interaction parameters for dissipative particle dynamics are derived. The corresponding protocol has been used in several applications, but in some cases a loss of accuracy has been observed. To address this issue, the additional descriptor based on the low-cost FMO calculation has been introduced and successfully applied to the nitrobenzene-hexane system. Its improved applicability has also been tested for inter-residue interactions of Chignolin.
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Doi et al. (2025) studied this question.
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