Unraveling the Photogenerated Electron Localization on the Defect-Free CH3NH3PbI3(001) Surfaces: Understanding and Implications from a First-Principles Study

Ding, Yunxuan; Yu-jie, Shen; Peng, Chao; Huang, Meilan; Hu, P.

doi:10.1021/acs.jpclett.0c02105

Cited by 6 publications

(5 citation statements)

References 60 publications

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“…Currently, the explicit and implicit solvation models are the widely applied methods to mimic the role of electrolytes. Typically, the explicit model involving solvent molecules and cations at the interface could provide complete and accurate descriptions of the effects of solvation, pH, cations, interfacial fields, and applied bias. − However, a considerable number of solvent molecules and large supercells are required to obtain the reasonable structure of the solvent in simulations. Thus, this method is computationally costly but often fails to adequately sample the phase space of the solvent under the desired thermodynamic conditions …”

Section: Methods For Modeling Enrrmentioning

confidence: 99%

Progress of Experimental and Computational Catalyst Design for Electrochemical Nitrogen Fixation

et al. 2022

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Large-scale ammonia synthesis via the electrochemical nitrogen reduction reaction (eNRR) under mild reaction conditions represents a green prospect for agriculture, industry, and energy. This bioinspired and carbon-free reaction has been proposed as an ideal alternative to the Haber–Bosch process. However, the yield and selectivity of the current eNRR have not met the requirements for industrialization. Mechanistic understanding and catalysts’ design are still long-term pursuits in this field, where theoretical simulations will have significant contributions. In this Review, we will start with the natural N2 fixation enzymes, the nitrogenases, followed by a summary of the key experimental eNRR performances on hundreds of recently reported catalysts, and we analyze the general trend and challenges before eNRR can significantly impact the ammonia industry. Then, we will systematically review the recent progress and contributions of computational studies in understanding the reaction mechanism and rational catalyst design for eNRR. The fundamental principles, reaction mechanisms, crucial theoretical criteria, modeling methods, and computationally predicted catalysts for eNRR are systematically summarized and discussed. Finally, we outline the current challenges and future opportunities for experimental and computational studies of electrochemical N2 reduction.

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Section: Methods For Modeling Enrrmentioning

confidence: 99%

Progress of Experimental and Computational Catalyst Design for Electrochemical Nitrogen Fixation

et al. 2022

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“…For the SnO 2 structure, SnO 2 (110) is the most stable surface in the presence of V O . Both the FAPbI 3 (001) and SnO 2 (110) surfaces possess different terminations. , FAPbI 3 (001) with FA termination and SnO 2 (110) with the most stable T3 termination were used in our calculations.…”

Section: Methodsmentioning

confidence: 99%

Multifunctional Histidine Cross-Linked Interface toward Efficient Planar Perovskite Solar Cells

Yu-jie

et al. 2022

ACS Appl. Mater. Interfaces

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Interface engineering mediated by a designed chemical agent is of paramount importance for developing high-performance perovskite solar cells (PSCs). It is especially critical for planar SnO2-based PSCs due to the presence of abundant surface defects on SnO2 and/or perovskite surfaces. Herein, a novel multifunctional agent histidine (abbreviated as His) capable of cross-linking SnO2 and perovskite is employed to modify the SnO2/perovskite interface. Density functional theory (DFT) calculations and experimental results demonstrate that the carboxylate oxygen of His can form a Sn–O bond to fill the oxygen vacancies on the surface of SnO2, while its positively charged imidazole ring can occupy the cationic vacancies and its −NH3 + group interacts with the I– ion on the perovskite lattice. This cross-linking contributes to the significantly decreased interfacial trap state density and nonradiative recombination loss. In addition, it facilitates electron extraction/transfer and also improves interfacial contact and the quality of perovskite film. Correspondingly, the His-modified device delivers a superior power conversion efficiency (PCE) of 22.91% (improved from 20.13%) and an excellent open-circuit voltage (V oc) of 1.17 V (improved from 1.11 V), along with significantly suppressed hysteresis. Furthermore, the unencapsulated device based on His modification shows much better humidity and thermal stability than the pristine one. The present work provides guidance for the design of innovative multifunctional interfacial material for highly efficient PSCs.

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“…If they are adequately trained, using MLPs can tremendously reduce the computation cost but maintain the ab initio accuracy at the same time. With the increasing interest in heterogeneous catalytic reactions at a finite temperature or with explicit solvents, ab initio molecular dynamics (AIMD) turns out to be the inevitable choice for free-energy calculations (FECs). − Thus, it is highly desirable to develop any protocol using MLPs in lieu of AIMD simulations to accelerate such time-consuming FECs. With respect to the MLP performance itself, two aspects, that is, the potential formalism and the training data set, must be thoroughly considered.…”

Section: Introductionmentioning

confidence: 99%

Accelerating Metadynamics-Based Free-Energy Calculations with Adaptive Machine Learning Potentials

Cao

2021

J. Chem. Theory Comput.

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There is an increasing demand for free-energy calculations using ab initio molecular dynamics these days. Metadynamics (MetaD) is frequently utilized to reconstruct the free-energy surface, but it is often computationally intractable for the first-principles calculations. Machine learning potentials (MLPs) have become popular alternatives. However, the training could be a long and arduous process before using them in practical applications. To accelerate MetaD use with MLPs for the freeenergy calculation in an easy manner, we propose the adaptive machine learning potential-accelerated metadynamics (AMLP-MetaD). In this method, the MLP in the form of a Gaussian approximation potential (GAP) can adapt itself based on its uncertainty estimation, which decides whether to accept the model prediction or recalculate it with a reference method (usually density functional theory) for further training during the MetaD simulation. We demonstrate that the free-energy landscape similar to the ab initio one can be obtained using AMLP-MetaD with a 10-time speedup. Moreover, the quality of the free-energy results can be deeply improved using Δ-MLP, which is the GAP-corrected density functional tight binding in our case. We exemplify this novel method with two model systems, CO adsorption on the Pt 13 cluster and the Pt(111) surface, which are of vital importance in heterogeneous catalysis. The successful application in these two tests highlights that our proposed method can be used in both cluster and periodic systems and for up to two collective variables.

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Unraveling the Photogenerated Electron Localization on the Defect-Free CH₃NH₃PbI₃(001) Surfaces: Understanding and Implications from a First-Principles Study

Cited by 6 publications

References 60 publications

Progress of Experimental and Computational Catalyst Design for Electrochemical Nitrogen Fixation

Progress of Experimental and Computational Catalyst Design for Electrochemical Nitrogen Fixation

Multifunctional Histidine Cross-Linked Interface toward Efficient Planar Perovskite Solar Cells

Accelerating Metadynamics-Based Free-Energy Calculations with Adaptive Machine Learning Potentials

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