Current and future machine learning approaches for modeling atmospheric cluster formation

TY - JOUR

T1 - Current and future machine learning approaches for modeling atmospheric cluster formation

AU - Kubečka, Jakub

AU - Knattrup, Yosef

AU - Engsvang, Morten

AU - Jensen, Andreas Buchgraitz

AU - Ayoubi, Daniel

AU - Wu, Haide

AU - Christiansen, Ove

AU - Elm, Jonas

PY - 2023/6

Y1 - 2023/6

N2 - The formation of strongly bound atmospheric molecular clusters is the first step towards forming new aerosol particles. Recent advances in the application of machine learning models open an enormous opportunity for complementing expensive quantum chemical calculations with efficient machine learning predictions. In this Perspective, we present how data-driven approaches can be applied to accelerate cluster configurational sampling, thereby greatly increasing the number of chemically relevant systems that can be covered.

AB - The formation of strongly bound atmospheric molecular clusters is the first step towards forming new aerosol particles. Recent advances in the application of machine learning models open an enormous opportunity for complementing expensive quantum chemical calculations with efficient machine learning predictions. In this Perspective, we present how data-driven approaches can be applied to accelerate cluster configurational sampling, thereby greatly increasing the number of chemically relevant systems that can be covered.

U2 - 10.1038/s43588-023-00435-0

DO - 10.1038/s43588-023-00435-0

M3 - Journal article

C2 - 38177415

AN - SCOPUS:85163584127

SN - 2662-8457

VL - 3

SP - 495

EP - 503

JO - Nature Computational Science

JF - Nature Computational Science

IS - 6

ER -