Machine Learning Enhanced High‐Throughput Fabrication and Optimization of Quasi‐2D Ruddlesden–Popper Perovskite Solar Cells

Abstract

Organic–inorganic perovskite solar cells (PSCs) are promising candidates for next‐generation, inexpensive solar panels due to their commercially competitive cost and high power conversion efficiencies. However, PSCs suffer from poor stability. A new and vast subset of PSCs, quasi‐two‐dimensional Ruddlesden–Popper PSCs (quasi‐2D RP PSCs), has improved photostability and superior resilience to environmental conditions compared to three‐dimensional metal‐halide PSCs. To accelerate the search for new quasi‐2D RP PSCs, this work reports a combinatorial, machine learning (ML) enhanced high‐throughput perovskite film fabrication and optimization study. This work designs a bespoke experimental strategy and produces perovskite films with a range of different compositions using only spin‐coating free, reproducible robotic fabrication processes. The performance and characterization data of these solar cells are used to train a ML model that allow materials parameters to be optimized and direct the design of improved materials. The new, ML‐optimized, drop‐cast quasi‐2D RP perovskite films yield solar cells with power conversion efficiencies of up to 16.9%.

Halide Perovskites for Photoelectrochemical Water Splitting and CO2 Reduction: Challenges and Opportunities

“…Astonishing developments of computational tools and infrastructure, including efficient ML algorithms, have been combined with the increasing availability of scientific data in materials databases, data repositories, and online journals as well as other computational tools like DFT, and have created an attractive avenue for the discovery of new materials including MHPs. Various ML works on screening different materials (other than halide perovskites) for photocatalytic and PEC water splitting have already appeared in the literature in recent years , while only a few cases were involved MHPs. , On the other hand, a significant number of a ML work covering various aspects of MHPs in photovoltaics have already been published. ,,− Significant portion of the published work in this field aims to screen DFT generated data for the discovery of thermodynamically stable material with proper band gap; such an approach is usually called high-throughput computational screening. Databases such as International Crystal Structure Database (ICSD), Materials Project (MP), Open Quantum Materials Database (OQMD), Atomic-FLOW for materials discovery (AFLOW), and NOMAD together with high throughput workflow management programs like Firework, Atomate, and pymatgen have been used extensively in recent years.…”

Toward Self-Driven Autonomous Material and Device Acceleration Platforms (AMADAP) for Emerging Photovoltaics Technologies

“…Autonomous acceleration research platforms have been introduced to explore the realms of materials science research and development (R&D). , While the MGI expressed an acceleration of a factor of 2 and a reduction of costs by a factor of 2, some of the modern MAPs are proposing larger acceleration factors, ideally reducing the entire materials R&D cycle from typically 10 to 20 years to only 1 or 2 years. , Such acceleration factors become possible when automated laboratory setups are equipped with artificial intelligence (AI). , These setups build on recent scientific breakthroughs and the ability to program machines so that they can make independent and autonomous decisions to design and optimize materials and processes, moving away from the traditional Edisonian method of discovery. , Such breakthroughs hold the promise of a true digital twin of a technology, allowing science to predict optimized materials and processes according to the requirements of a target application. , The methodology of integrating automation and autonomous operation into emerging photovoltaics research, such as perovskite solar cell (PSC) and organic photovoltaic (OPV) materials, is drawing broad attention from the community. ,,− MAPs and DAPs offer tremendous opportunities for accelerating functional materials discovery and device optimization with respect to performance, environmental stability, and cost.…”

“… 21 , 22 The methodology of integrating automation and autonomous operation into emerging photovoltaics research, such as perovskite solar cell (PSC) and organic photovoltaic (OPV) materials, is drawing broad attention from the community. 3 , 4 , 23 − 27 MAPs and DAPs offer tremendous opportunities for accelerating functional materials discovery and device optimization with respect to performance, environmental stability, and cost.…”

Six metal cations in one double perovskite: exploring complexity of chloride elpasolites by high-throughput experimentation

“…Robot-assisted HTP approaches have already been successfully applied for the accelerated research and discovery of lead-based perovskite materials for PV applications, covering various aspects such as HTP discovery of new cations for A I and their combinations, 27–33 HTP studies of the formation of perovskite films and their PV activity, 33–38 as well as HTP evaluation of degradation stability of the lead perovskites. 37,39,40…”