  Perovskite photovoltaics (PV) have achieved rapid development in the past
decade in terms of power conversion efficiency of small-area lab-scale devices;
however, successful commercialization still requires further development of
low-cost, scalable, and high-throughput manufacturing techniques. One of the
critical challenges of developing a new fabrication technique is the
high-dimensional parameter space for optimization, but machine learning (ML)
can readily be used to accelerate perovskite PV scaling. Herein, we present an
ML-guided framework of sequential learning for manufacturing process
optimization. We apply our methodology to the Rapid Spray Plasma Processing
(RSPP) technique for perovskite thin films in ambient conditions. With a
limited experimental budget of screening 100 process conditions, we
demonstrated an efficiency improvement to 18.5% as the best-in-our-lab device
fabricated by RSPP, and we also experimentally found 10 unique process
conditions to produce the top-performing devices of more than 17% efficiency,
which is 5 times higher rate of success than the control experiments with
pseudo-random Latin hypercube sampling. Our model is enabled by three
innovations: (a) flexible knowledge transfer between experimental processes by
incorporating data from prior experimental data as a probabilistic constraint;
(b) incorporation of both subjective human observations and ML insights when
selecting next experiments; (c) adaptive strategy of locating the region of
interest using Bayesian optimization first, and then conducting local
exploration for high-efficiency devices. Furthermore, in virtual benchmarking,
our framework achieves faster improvements with limited experimental budgets
than traditional design-of-experiments methods (e.g., one-variable-at-a-time
sampling).
