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Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning.

ABSTRACT: 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.

SUBMITTER: Lu S

PROVIDER: S-EPMC6109147 | biostudies-literature | 2018 Aug

REPOSITORIES: biostudies-literature

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Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning.

Lu Shuaihua S Zhou Qionghua Q Ouyang Yixin Y Guo Yilv Y Li Qiang Q Wang Jinlan J

Nature communications 20180824 1

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 toxicit ...[more]

PMID: 30143621

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Project description:Hybrid organic-inorganic perovskites (HOIPs) are crystals with the structural formula ABX3, where A, B, and X are organic and inorganic ions, respectively. While known for several decades, HOIPs have only in recent years emerged as extremely promising semiconducting materials for solar energy applications. In particular, power-conversion efficiencies of HOIP-based solar cells have improved at a record speed and, after only little more than 6 years of photovoltaics research, surpassed the 20% threshold, which is an outstanding result for a solution-processable material. It is thus of fundamental importance to reveal physical and chemical phenomena that contribute to, or limit, these impressive photovoltaic efficiencies. To understand charge-transport and light-absorption properties of semiconducting materials, one often invokes a lattice of ions displaced from their static positions only by harmonic vibrations. However, a preponderance of recent studies suggests that this picture is not sufficient for HOIPs, where a variety of structurally dynamic effects, beyond small harmonic vibrations, arises already at room temperature. In this Account, we focus on these effects. First, we review structure and bonding in HOIPs and relate them to the promising charge-transport and absorption properties of these materials, in terms of favorable electronic properties. We point out that HOIPs are much "softer" mechanically, compared to other efficient solar-cell materials, and that this can result in large ionic displacements at room temperature. We therefore focus next on dynamic structural effects in HOIPs, going beyond a static band-structure picture. Specifically, we discuss pertinent experimental and theoretical findings as to phase-transition behavior and molecular/octahedral rearrangements. We then discuss atomic diffusion phenomena in HOIPs, with an emphasis on the migration of intrinsic and extrinsic ionic species. From this combined perspective, HOIPs appear as highly dynamic materials, in which structural fluctuations and long-range ionic motion have an unusually strong impact on charge-transport and optical properties. We highlight the potential implications of these effects for several intriguing phenomenological observations, ranging from scattering mechanisms and lifetimes of charge carriers to light-induced structural effects and ionic conduction.

Dataset Information

Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning.

Publications

Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning.

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