  The recent successes of emerging photovoltaics (PV) such as organic and
perovskite solar cells are largely driven by innovations in material science.
However, closing the gap to commercialization still requires significant
innovation to match contradicting requirements such as performance, longevity
and recyclability. The rate of innovation, as of today, is limited by a lack of
design principles linking chemical motifs to functional microscopic structures,
and by an incapacity to experimentally access microscopic structures from
investigating macroscopic device properties. In this work, we envision a layout
of a Digital Twin for PV materials aimed at removing both limitations. The
layout combines machine learning approaches, as performed in materials
acceleration platforms (MAPs), with mathematical models derived from the
underlying physics and digital twin concepts from the engineering world. This
layout will allow using high-throughput (HT) experimentation in MAPs to improve
the parametrization of quantum chemical and solid-state models. In turn, the
improved and generalized models can be used to obtain the crucial structural
parameters from HT data. HT experimentation will thus yield a detailed
understanding of generally valid structure-property relationships, enabling
inverse molecular design, that is, predicting the optimal chemical structure
and process conditions to build PV devices satisfying a multitude of
requirements at the same time. After motivating our proposed layout of the
digital twin with causal relationships in material science, we discuss the
current state of the enabling technologies, already being able to yield insight
from HT data today. We identify open challenges with respect to the multiscale
nature of PV materials and the needed volume and diversity of data, and mention
promising approaches to address these challenges.
