TY - JOUR
T1 - Toward a Holistic Understanding of the Formation and Growth of Atmospheric Molecular Clusters
T2 - A Quantum Machine Learning Perspective
AU - Elm, Jonas
N1 - Funding Information:
J.E. thanks the Independent Research Fund Denmark Grant No. 9064-00001B and the Swedish Research Council Formas Project No. 2018-01745-COBACCA for financial support. The author thanks Prof. H. Vehkamäki, University Helsinki, for insightful comments on the manuscript.
Publisher Copyright:
© 2021 American Chemical Society. All rights reserved.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021/2
Y1 - 2021/2
N2 - The formation of atmospheric molecular clusters is an important stage in forming new particles in the atmosphere. Despite being a highly focused research area, the exact chemical species involved in the initial steps in new particle formation remain elusive. In this Perspective the main challenges and recent progression in the field are outlined with a special emphasis on the chemical complexity of the puzzle and prospect of modeling larger clusters. In general, there is a high demand for accurate and more complete quantum chemical data sets that can be applied in cluster distribution dynamics models and coupled to atmospheric chemical transport models. A view on how the community could reach this goal by applying data-driven machine learning approaches for more efficient exploration of cluster configurations is presented. A path toward larger clusters and direct molecular dynamics simulations of cluster formation and growth using machine learning models is discussed.
AB - The formation of atmospheric molecular clusters is an important stage in forming new particles in the atmosphere. Despite being a highly focused research area, the exact chemical species involved in the initial steps in new particle formation remain elusive. In this Perspective the main challenges and recent progression in the field are outlined with a special emphasis on the chemical complexity of the puzzle and prospect of modeling larger clusters. In general, there is a high demand for accurate and more complete quantum chemical data sets that can be applied in cluster distribution dynamics models and coupled to atmospheric chemical transport models. A view on how the community could reach this goal by applying data-driven machine learning approaches for more efficient exploration of cluster configurations is presented. A path toward larger clusters and direct molecular dynamics simulations of cluster formation and growth using machine learning models is discussed.
UR - http://www.scopus.com/inward/record.url?scp=85100107784&partnerID=8YFLogxK
U2 - 10.1021/acs.jpca.0c09762
DO - 10.1021/acs.jpca.0c09762
M3 - Journal article
C2 - 33378191
AN - SCOPUS:85100107784
SN - 1089-5639
VL - 125
SP - 895
EP - 902
JO - The Journal of Physical Chemistry A
JF - The Journal of Physical Chemistry A
IS - 4
ER -