Kang, Sungwoo Cai, Runlong Yang, Dong Sik Ham, Dong Jin Kulmala, Markku Seinfeld, John H. Jiang, Jingkun Lee, Hyun Chul 2025-07-29T02:00:20Z 2025-07-29T02:00:20Z 2025-07-28 2026-01 2366-9608 https://pubs.kist.re.kr/handle/201004/152856 Machine-learning potentials (MLPs), trained to predict energies from quantum chemical calculations, are widely employed to conduct large-scale MD simulations. However, MLPs are mostly trained on computationally inexpensive local/semilocal functionals, as generating training datasets using higher-accuracy theories, such as hybrid functionals, is challenging due to their high computational cost. Here, an active transfer learning scheme is developed to efficiently sample configurations for hybrid functional calculations. The proposed method is evaluated on atmospheric secondary aerosol formation reactions: clustering of sulfuric acid (SA), and dimethylamine (DMA), and oxidation of toluene. The accuracy of the trained MLP is shown to be comparable to that of the quantum chemical calculations, with errors within a few meV per atom. The molecular dynamics simulations are executed stably on a nanosecond scale, resulting in the formation of nanometer-size clusters. Thus, this study paves the way for establishing a general protocol to enable high-level atomistic simulations for a wide range of chemical systems. English WILEY-V C H VERLAG GMBH Efficient Configuration Sampling for Hybrid Functional DFT Calculations to Train Machine-Learning Potentials: Application to Atmospheric Chemistry Article 10.1002/smtd.202500547 1 Small Methods, v.10, no.2 Small Methods 10 2 N scie 001522400900001 2-s2.0-105009852236 Chemistry, Physical Nanoscience & Nanotechnology Materials Science, Multidisciplinary Chemistry Science & Technology - Other Topics Materials Science Article SECONDARY ORGANIC AEROSOL SULFURIC-ACID OXIDATION MECHANISM AMMONIA TOLUENE hybrid functional machine-learning potential molecular dynamics secondary aerosol formation atmospheric chemistry