TY - JOUR
T1 - Machine Learning and Optoelectronic Materials Discovery
T2 - A Growing Synergy
AU - Mayr, Felix
AU - Harth, Milan
AU - Kouroudis, Ioannis
AU - Rinderle, Michael
AU - Gagliardi, Alessio
N1 - Publisher Copyright:
© 2022 The Authors. Published by American Chemical Society
PY - 2022/3/3
Y1 - 2022/3/3
N2 - Novel optoelectronic materials have the potential to revolutionize the ongoing green transition by both providing more efficient photovoltaic (PV) devices and lowering energy consumption of devices like LEDs and sensors. The lead candidate materials for these applications are both organic semiconductors and more recently perovskites. This Perspective illustrates how novel machine learning techniques can help explore these materials, from speeding up ab initio calculations toward experimental guidance. Furthermore, based on existing work, perspectives around machine-learned molecular dynamics potentials, physically informed neural networks, and generative methods are outlined.
AB - Novel optoelectronic materials have the potential to revolutionize the ongoing green transition by both providing more efficient photovoltaic (PV) devices and lowering energy consumption of devices like LEDs and sensors. The lead candidate materials for these applications are both organic semiconductors and more recently perovskites. This Perspective illustrates how novel machine learning techniques can help explore these materials, from speeding up ab initio calculations toward experimental guidance. Furthermore, based on existing work, perspectives around machine-learned molecular dynamics potentials, physically informed neural networks, and generative methods are outlined.
UR - http://www.scopus.com/inward/record.url?scp=85125679886&partnerID=8YFLogxK
U2 - 10.1021/acs.jpclett.1c04223
DO - 10.1021/acs.jpclett.1c04223
M3 - Article
C2 - 35188778
AN - SCOPUS:85125679886
SN - 1948-7185
VL - 13
SP - 1940
EP - 1951
JO - Journal of Physical Chemistry Letters
JF - Journal of Physical Chemistry Letters
IS - 8
ER -