Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Quantitative predictions of photo-emission dynamics in metal halide
  perovskites via machine learning   Metal halide perovskite (MHP) optoelectronics may become a viable alternative
to standard Si-based technologies, but the current lack of long-term stability
precludes their commercial adoption. Exposure to standard operational stressors
(light, temperature, bias, oxygen, and water) often instigate optical and
electronic dynamics, calling for a systematic investigation into MHP
photophysical processes and the development of quantitative models for their
prediction. We resolve the moisture-driven light emission dynamics for both
methylammonium lead tribromide and triiodide thin films as a function of
relative humidity (rH). With the humidity and photoluminescence time series, we
train recurrent neural networks and establish their ability to quantitatively
predict the path of future light emission with <11% error over 12 hours.
Together, our in situ rH-PL measurements and machine learning forecasting
models provide a framework for the rational design of future stable perovskite
devices and, thus, a faster transition towards commercial applications.

19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: patt-sol
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The categories of this paper are: funct-an
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The categories of this paper are: adap-org
6
The categories of this paper are: astro-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg