Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning
By Shuaihua Lu, Qionghua Zhou, Y. Ouyang, Yilv Guo, Qiang Li, Jinlan Wang. Nature Communications, 2018
622 citations recorded by the source corpus.
Rapidly discovering functional materials remains an open challenge because the traditional trial-and-error methods are usually inefficient especially when thousands of candidates are treated. Here, we develop a target-driven method to predict undiscovered hybrid organic-inorganic perovskites (HOIPs) for photovoltaics. This strategy, combining machine learning techniques and density functional theory calculations, aims to quickly screen the HOIPs based on bandgap and solve the problems of toxicity and poor environmental stability in HOIPs. Successfully, six orthorhombic lead-free HOIPs with proper bandgap for solar cells and room temperature thermal stability are screened out from 5158 unexplored HOIPs and two of them stand out with direct bandgaps in the visible region and excellent environmental stability. Essentially, a close structure-property relationship mapping the HOIPs bandgap is established. Our method can achieve high accuracy in a flash and be applicable to a broad class of functional material design. Conventional trial-error method is inefficient in discovering new functional materials in vast chemical and structural space. Here Lu et al. use machine learning techniques to screen out the most promising lead-free organic-inorganic perovskites with proper bandgap and stability from thousands of them in a flash.
Subfields: Electrical and Electronic Engineering, Materials Chemistry, Polymers and Plastics
View Paper: https://www.semanticscholar.org/paper/e04a31bd8213aea7a11e1b16e90e114d5d28a7a8
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Citations in the corpus
- Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel (2019, methodology): “[47], the performance of hybrid organicinorganic perovskites (HOIPs) was calculated by combining a ML model with the density functional theory.”