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Revealing the Low Temperature Phase of FAPbI$_3$ using A Machine-Learned Potential
Authors:
Sangita Dutta,
Erik Fransson,
Tobias Hainer,
Benjamin M. Gallant,
Dominik J. Kubicki,
Paul Erhart,
Julia Wiktor
Abstract:
FAPbI$_3$ is a material of interest for its potential in solar cell applications, driven by its remarkable optoelectronic properties. However, the low-temperature phase of FAPbI$_3$ remains poorly understood, with open questions surrounding its crystal structure, octahedral tilting, and the arrangement of formamidinium (FA) cations. Using our trained machine-learned potential in combination with l…
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FAPbI$_3$ is a material of interest for its potential in solar cell applications, driven by its remarkable optoelectronic properties. However, the low-temperature phase of FAPbI$_3$ remains poorly understood, with open questions surrounding its crystal structure, octahedral tilting, and the arrangement of formamidinium (FA) cations. Using our trained machine-learned potential in combination with large-scale molecular dynamics simulations, we provide a detailed investigation of this phase, uncovering its structural characteristics and dynamical behavior. Our analysis reveals the octahedral tilt pattern and sheds light on the rotational dynamics of FA cations in the low temperature phase. Strikingly, we find that the FA cations become frozen in a metastable configuration, unable to reach the thermodynamic ground state. By comparing our simulated results with experimental nuclear magnetic resonance (NMR) and inelastic neutron scattering (INS) spectra, we demonstrate good agreement, further validating our findings. This phenomenon mirrors experimental observations and offers a compelling explanation for the experimental challenges in accessing the true ground state. These findings provide critical insights into the fundamental physics of FAPbI$_3$ and its low-temperature behavior, advancing our understanding of this technologically important material.
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Submitted 2 April, 2025; v1 submitted 31 March, 2025;
originally announced March 2025.
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A green solvent system for precursor phase-engineered sequential deposition of stable formamidinium lead triiodide for perovskite solar cells
Authors:
Benjamin M. Gallant,
Philippe Holzhey,
Joel A. Smith,
Saqlain Choudhary,
Karim A. Elmestekawy,
Pietro Caprioglio,
Igal Levine,
Alex Sheader,
Fengning Yang,
Daniel T. W. Toolan,
Rachel C. Kilbride,
Augustin K. A. Zaininger,
James M. Ball,
M. Greyson Christoforo,
Nakita Noel,
Laura M. Herz,
Dominik J. Kubicki,
Henry J. Snaith
Abstract:
Perovskite solar cells (PSCs) offer an efficient, inexpensive alternative to current photovoltaic technologies, with the potential for manufacture via high-throughput coating methods. However, challenges for commercial-scale solution-processing of metal-halide perovskites include the use of harmful solvents, the expense of maintaining controlled atmospheric conditions, and the inherent instabiliti…
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Perovskite solar cells (PSCs) offer an efficient, inexpensive alternative to current photovoltaic technologies, with the potential for manufacture via high-throughput coating methods. However, challenges for commercial-scale solution-processing of metal-halide perovskites include the use of harmful solvents, the expense of maintaining controlled atmospheric conditions, and the inherent instabilities of PSCs under operation. Here, we address these challenges by introducing a high volatility, low toxicity, biorenewable solvent system to fabricate a range of 2D perovskites, which highly effective precursor phases for subsequent transformation to alpha-formamidinium lead triiodide (FAPbI3), fully processed under ambient conditions. PSCs utilising our FAPbI3 reproducibly show remarkable stability under illumination and elevated temperature (ISOS-L-2) and "damp heat" (ISOS-D-3) stressing, surpassing other state-of-the-art perovskite compositions. We determine that this enhancement is a consequence of the 2D precursor phase crystallisation route, which simultaneously avoids retention of residual low-volatility solvents (such as DMF and DMSO) and reduces the rate of degradation of FA+ in the material. Our findings highlight both the critical role of the initial crystallisation process in determining the operational stability of perovskite materials, and that neat FA+-based perovskites can be competitively stable despite the inherent metastability of the alpha-phase.
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Submitted 14 June, 2024; v1 submitted 12 June, 2024;
originally announced June 2024.
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Ultrafast two-colour X-ray emission spectroscopy reveals excited state landscape in a base metal dyad
Authors:
Michal Nowakowski,
Marina Huber-Gedert,
Hossam Elgabarty,
Jacek Kubicki,
Ahmet Kertem,
Natalia Lindner,
Dmitry Khakhulin,
Frederico Alves Lima,
Tae-Kyu Choi,
Mykola Biednov,
Natalia Piergies,
Peter Zalden,
Katerina Kubicek,
Angel Rodriguez-Fernandez,
Mohammad Alaraby Salem,
Thomas Kühne,
Wojciech Gawelda,
Matthias Bauer
Abstract:
Effective photoinduced charge transfer makes molecular bimetallic assemblies attractive for applications as active light induced proton reduction systems. For a more sustainable future, development of competitive base metal dyads is mandatory. However, the electron transfer mechanisms from the photosensitizer to the proton reduction catalyst in base metal dyads remain so far unexplored. We study a…
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Effective photoinduced charge transfer makes molecular bimetallic assemblies attractive for applications as active light induced proton reduction systems. For a more sustainable future, development of competitive base metal dyads is mandatory. However, the electron transfer mechanisms from the photosensitizer to the proton reduction catalyst in base metal dyads remain so far unexplored. We study a Fe-Co dyad that exhibits photocatalytic H2 production activity using femtosecond X-ray emission spectroscopy, complemented by ultrafast optical spectroscopy and theoretical time-dependent DFT calculations, to understand the electronic and structural dynamics after photoexcitation and during the subsequent charge transfer process from the FeII photosensitizer to the cobaloxime catalyst. Using this novel approach, the simultaneous measurement of the transient Kalpha X-ray emission at the iron and cobalt K-edges in a two-colour experiment is enabled making it possible to correlate the excited state dynamics to the electron transfer processes. The methodology, therefore, provides a clear and direct spectroscopic evidence of the Fe->Co electron transfer responsible for the proton reduction activity.
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Submitted 11 January, 2023;
originally announced January 2023.
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Lattice phonon modes of the spin crossover crystal [Fe(phen)2(NCS)2] studied by THz, IR, Raman spectroscopies and DFT calculations
Authors:
Eric Collet,
Giovanni Azzolina,
Tomoaki Ichii,
Laurent Guerin,
Roman Bertoni,
Alain Moreac,
Marco Cammarata,
Nathalie Daro,
Guillaume Chastanet,
Jacek Kubicki,
Koichiro Tanaka,
Samir F. Matar
Abstract:
[Fe(phen)2(NCS)2] is a prototype transition metal complex material, which undergoes a phase transition between low-spin (LS) and high-spin (HS) phases, induced by temperature, pressure or light. Vibrational modes play a key role for spin-state switching both in thermal and photo-induced cases, by contributing to vibrational entropy for thermal equilibrium transitions or driving the fast structural…
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[Fe(phen)2(NCS)2] is a prototype transition metal complex material, which undergoes a phase transition between low-spin (LS) and high-spin (HS) phases, induced by temperature, pressure or light. Vibrational modes play a key role for spin-state switching both in thermal and photo-induced cases, by contributing to vibrational entropy for thermal equilibrium transitions or driving the fast structural trapping of the photoinduced high spin state. Here we study the crystal phonon modes of [Fe(phen)2(NCS)2], by combining THz, IR, and Raman spectroscopies sensitive to modes in different frequency ranges and different symmetries. We compare the experimental results to DFT calculations performed in a periodic 3D crystal for understanding the phonon modes in the crystal, compared to molecular vibrations. Indeed, each vibrational mode of the isolated molecule combines into several modes of different symmetry and frequency in the crystal, as the unit cell contains four molecules. We focus our attention on the HS symmetric and anti-symmetric breathing modes in the crystal as well as on the N-CS stretching modes.
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Submitted 23 October, 2018;
originally announced October 2018.
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An Investigation of Machine Learning Methods Applied to Structure Prediction in Condensed Matter
Authors:
William J. Brouwer,
James D. Kubicki,
Jorge O. Sofo,
C. Lee Giles
Abstract:
Materials characterization remains a significant, time-consuming undertaking. Generally speaking, spectroscopic techniques are used in conjunction with empirical and ab-initio calculations in order to elucidate structure. These experimental and computational methods typically require significant human input and interpretation, particularly with regards to novel materials. Recently, the application…
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Materials characterization remains a significant, time-consuming undertaking. Generally speaking, spectroscopic techniques are used in conjunction with empirical and ab-initio calculations in order to elucidate structure. These experimental and computational methods typically require significant human input and interpretation, particularly with regards to novel materials. Recently, the application of data mining and machine learning to problems in material science have shown great promise in reducing this overhead. In the work presented here, several aspects of machine learning are explored with regards to characterizing a model material, titania, using solid-state Nuclear Magnetic Resonance (NMR). Specifically, a large dataset is generated, corresponding to NMR $^{47}$Ti spectra, using ab-initio calculations for generated TiO$_2$ structures. Principal Components Analysis (PCA) reveals that input spectra may be compressed by more than 90%, before being used for subsequent machine learning. Two key methods are used to learn the complex mapping between structural details and input NMR spectra, demonstrating excellent accuracy when presented with test sample spectra. This work compares Support Vector Regression (SVR) and Artificial Neural Networks (ANNs), as one step towards the construction of an expert system for solid state materials characterization.
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Submitted 14 May, 2014;
originally announced May 2014.