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- Ahlawat, Paramvir, et al.
(author)
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A combined molecular dynamics and experimental study of two-step process enabling low-temperature formation of phase-pure alpha-FAPbI3
- 2021
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In: Science Advances. - : American Association for the Advancement of Science (AAAS). - 2375-2548. ; 7:17
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Journal article (peer-reviewed)abstract
- It is well established that the lack of understanding the crystallization process in a two-step sequential deposition has a direct impact on efficiency, stability, and reproducibility of perovskite solar cells. Here, we try to understand the solid-solid phase transition occurring during the two-step sequential deposition of methylammonium lead iodide and formamidinium lead iodide. Using metadynamics, x-ray diffraction, and Raman spectroscopy, we reveal the microscopic details of this process. We find that the formation of perovskite proceeds through intermediate structures and report polymorphs found for methylammonium lead iodide and formamidinium lead iodide. From simulations, we discover a possible crystallization pathway for the highly efficient metastable alpha phase of formamidinium lead iodide. Guided by these simulations, we perform experiments that result in the low-temperature crystallization of phase-pure alpha-formamidinium lead iodide.
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| 2. |
- Müllender, Lukas, et al.
(author)
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Effective data-driven collective variables for free energy calculations from metadynamics of paths
- 2024
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In: PNAS Nexus. - : Oxford University Press (OUP). - 2752-6542. ; 3:4
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Journal article (peer-reviewed)abstract
- A variety of enhanced sampling (ES) methods predict multidimensional free energy landscapes associated with biological and other molecular processes as a function of a few selected collective variables (CVs). The accuracy of these methods is crucially dependent on the ability of the chosen CVs to capture the relevant slow degrees of freedom of the system. For complex processes, finding such CVs is the real challenge. Machine learning (ML) CVs offer, in principle, a solution to handle this problem. However, these methods rely on the availability of high-quality datasets—ideally incorporating information about physical pathways and transition states—which are difficult to access, therefore greatly limiting their domain of application. Here, we demonstrate how these datasets can be generated by means of ES simulations in trajectory space via the metadynamics of paths algorithm. The approach is expected to provide a general and efficient way to generate efficient ML-based CVs for the fast prediction of free energy landscapes in ES simulations. We demonstrate our approach with two numerical examples, a 2D model potential and the isomerization of alanine dipeptide, using deep targeted discriminant analysis as our ML-based CV of choice.
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