Computational Materials Science X

https://pubs.acs.org/doi/pdf/10.1021/jp9052049

The Journal of Physical Chemistry Letters

Low-dimensional hybrid perovskites have demonstrated excellent performance as white-light emitters. The broadband white emission originates from self-trapped excitons (STEs). Since the mechanism of STEs formation in perovskites is still not clear, preparing new low-dimensional white perovskites relies mostly on screening lots of intercalated organic molecules rather than rational design. Here, we report an atom-substituting strategy to trigger STEs formation in layered perovskites. Halogen-substituted phenyl molecules are applied to synthesize perovskite crystals. The halogen-substituents will withdraw electrons from the branched chain (-R-NH3+) of the phenyl molecule. This will result in positive charge accumulation on -R-NH3+, and thus stronger Coulomb force of bond (-R-NH3+)-(PbBr42-), which facilitates excitons self-trapping. Our designed white perovskites exhibit photoluminescence quantum yield of 32%, color-rendering index of near 90 and chromaticity coordinates close to standard white-light. Our joint experiment-theory study provides insights into the STEs formation in perovskites and will benefit tailoring white perovskites with boosting performance.

/pdf/molecular-engineering-towards-efficientwhite-light-emitting-3wn14vsz13.pdf

Molecular engineering towards efficientwhite-light-emitting perovskite.

The development of advanced perovskite emitters has considerably improved the performance of perovskite light-emitting diodes (LEDs). However, the further development of perovskite LEDs requires ideal device electrical properties, which strongly depend on its interfaces. In perovskite LEDs with conventional p-i-n structures, hole injection is generally less efficient than electron injection, causing charge imbalance. Furthermore, the popular hole injection structure of NiOx/poly(9-vinylcarbazole) suffers from several issues, such as weak interfacial adhesion, high interfacial trap density and mismatched energy levels. In this work, we insert a self-assembled monolayer of [2-(9H-carbazol-9-yl)ethyl]phosphonic acid between the NiOx and poly(9-vinylcarbazole) layers to overcome these challenges at the organic/inorganic heterointerfaces by establishing a robust interface, passivating interfacial trap states and aligning the energy levels. We successfully demonstrate blue (emission at 493 nm) and green (emission at 515 nm) devices with external quantum efficiencies of 14.5% and 26.0%, respectively. More importantly, the self-assembled monolayer also gives rise to devices with much faster response speeds by reducing interfacial capacitance and resistance. Our results pave the way for developing more efficient and brighter perovskite LEDs with quick response, widening their potential application scope. 

Charge injection engineering at organic/inorganic heterointerfaces for high-efficiency and fast-response perovskite light-emitting diodes

The photovoltaic performance of perovskite solar cell is determined by multiple interrelated factors, such as perovskite compositions, electronic properties of each transport layer and fabrication parameters, which makes it rather challenging for optimization of device performances and discovery of underlying mechanisms. Here, we propose and realize a novel machine learning approach based on forward-reverse framework to establish the relationship between key parameters and photovoltaic performance in high-profile MASnxPb1-xI3 perovskite materials. The proposed method establishes the asymmetrically bowing relationship between band gap and Sn composition, which is precisely verified by our experiments. Based on the analysis of structural evolution and SHAP library, the rapid-change region and low-bandgap plateau region for small and large Sn composition are explained, respectively. By establishing the models for photovoltaic parameters of working photovoltaic devices, the deviation of short-circuit current and open-circuit voltage with band gap in defective-zone and low-bandgap-plateau regions from Shockley-Queisser theory is captured by our models, and the former is due to the deep-level traps formed by crystallographic distortion and the latter is due to the enhanced susceptibility by increased Sn4+ content. The more difficulty for hole extraction than electron is also concluded in the models and the prediction curve of power conversion efficiency is in a good agreement with Shockley-Queisser limit. With the help of search and optimization algorithms, an optimized Sn:Pb composition ratio near 0.6 is finally obtained for high-performance perovskite solar cells, then verified by our experiments. Our constructive method could also be applicable to other material optimization and efficient device development. 

/pdf/data-driven-design-of-high-performance-masnxpb1-xi3-3ojzgi1f.pdf

Data-driven design of high-performance MASnxPb1-xI3 perovskite materials by machine learning and experimental realization

Materials informatics has emerged as a promisingly new paradigm for accelerating materials discovery and design. It exploits the intelligent power of machine learning methods in massive materials data from experiments or simulations to seek for new materials, functionality, principles, etc. Developing specialized facility to generate, collect, manage, learn and mine large-scale materials data is crucial to materials informatics. We herein developed an artificial-intelligence-aided data-driven infrastructure named Jilin Artificial-intelligence aided Materials-design Integrated Package (JAMIP), which is an open-source Python framework to meet the research requirements of computational materials informatics. It is integrated by materials production factory, high-throughput first-principles calculations engine, automatic tasks submission and monitoring progress, data extraction, management and storage system, and artificial intelligence machine learning based data mining functions. We have integrated specific features such as inorganic crystal structure prototype database to facilitate high-throughput calculations and essential modules associated with machine learning studies of functional materials. We demonstrated how our developed code is useful in exploring materials informatics of optoelectronic semiconductors by taking halide perovskites as typical case. By obeying the principles of automation, extensibility, reliability and intelligence, the JAMIP code is a promisingly powerful tool contributing to the fast-growing field of computational materials informatics.

JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics

Materials informatics has emerged as a promisingly new paradigm for accelerating materials discovery and design. It exploits the intelligent power of machine learning methods in massive materials data from experiments or simulations to seek new materials, functionality, and principles, etc. Developing specialized facilities to generate, collect, manage, learn, and mine large-scale materials data is crucial to materials informatics. We herein developed an artificial-intelligence-aided data-driven infrastructure named Jilin Artificial-intelligence aided Materials-design Integrated Package (JAMIP), which is an open-source Python framework to meet the research requirements of computational materials informatics. It is integrated by materials production factory, high-throughput first-principles calculations engine, automatic tasks submission and monitoring progress, data extraction, management and storage system, and artificial intelligence machine learning based data mining functions. We have integrated specific features such as an inorganic crystal structure prototype database to facilitate high-throughput calculations and essential modules associated with machine learning studies of functional materials. We demonstrated how our developed code is useful in exploring materials informatics of optoelectronic semiconductors by taking halide perovskites as typical case. By obeying the principles of automation, extensibility, reliability, and intelligence, the JAMIP code is a promisingly powerful tool contributing to the fast-growing field of computational materials informatics.

pdf/jamip-an-artificial-intelligence-aided-data-driven-44xd7nbz8m.pdf

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JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics

JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics