Manuel Grumet

Raman Signatures of Lithium Ion Dynamics in LLZO Garnet Electrolytes: Atomistic Insights from MD-Raman Calculations

Takeru Miyagawa, W. H. O'Leary, Manuel Grumet, Hyunwon Chu et autres

Lithium lanthanum zirconate (LLZO) garnets are among the most promising solid electrolytes for next-generation batteries owing to their high ionic conductivity, chemical stability, and compatibility with lithium metal. Raman spectroscopy is commonly employed to distinguish the highly conductive cubic phase from the …

Raman Signatures of Lithium Ion Dynamics in LLZO Garnet Electrolytes: Atomistic Insights from MD-Raman Calculations

Takeru Miyagawa, W. H. O'Leary, Manuel Grumet, Hyunwon Chu et autres

Lithium lanthanum zirconate (LLZO) garnets are among the most promising solid electrolytes for next-generation batteries owing to their high ionic conductivity, chemical stability, and compatibility with lithium metal. Raman spectroscopy is commonly employed to distinguish the highly conductive cubic phase from the …

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Revealing fast ionic conduction in solid electrolytes through machine learning accelerated Raman calculations

Manuel Grumet, Takeru Miyagawa, Olivier Pittet, Paolo Pegolo et autres

Revealing fast ionic conduction in solid electrolytes through machine learning accelerated Raman calculations, Grumet, Manuel, Miyagawa, Takeru, Pittet, Olivier, Pegolo, Paolo, Thalmann, Karin S, Kaiser, Waldemar, Egger, David A

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Revealing Fast Ionic Conduction in Solid Electrolytes through Machine Learning Accelerated Raman Calculations

Manuel Grumet, Takeru Miyagawa, Olivier Pittet, Paolo Pegolo et autres

Fast ionic conduction is a defining property of solid electrolytes for all-solid-state batteries. Previous studies have suggested that liquid-like cation motion associated with fast ionic transport can disrupt crystalline symmetry, thereby lifting Raman selection rules. Here, we exploit the resulting low-frequency, diffusive …

Machine learning accelerates Raman computations from molecular dynamics for materials science

David A. Egger, Manuel Grumet, Tomáš Bučko

Raman spectroscopy is a powerful experimental technique for characterizing molecules and materials that is used in many laboratories. First-principles theoretical calculations of Raman spectra are important because they elucidate the microscopic effects underlying Raman activity in these systems. These calculations are often …

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Machine Learning Accelerates Raman Computations from Molecular Dynamics for Materials Science

David A. Egger, Manuel Grumet, Tomáš Bučko

Raman spectroscopy is a powerful experimental technique for characterizing molecules and materials that is used in many laboratories. First-principles theoretical calculations of Raman spectra are important because they elucidate the microscopic effects underlying Raman activity in these systems. These calculations are often …

Rapid Characterization of Point Defects in Solid-State Ion Conductors Using Raman Spectroscopy, Machine-Learning Force Fields, and Atomic Raman Tensors

Willis O’Leary, Manuel Grumet, Waldemar Kaiser, Tomáš Bučko et autres

High Resolution Image Download MS PowerPoint Slide The successful design of solid-state photo- and electrochemical devices depends on the careful engineering of point defects in solid-state ion conductors. Characterization of point defects is critical to these efforts, but the best-developed techniques are …

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Disentangling the effects of structure and lone-pair electrons in the lattice dynamics of halide perovskites

Sebastián Caicedo‐Dávila, Adi Cohen, Silvia G. Motti, Masahiko Isobe et autres

Abstract Halide perovskites show great optoelectronic performance, but their favorable properties are paired with unusually strong anharmonicity. It was proposed that this combination derives from the n s 2 electron configuration of octahedral cations and associated pseudo-Jahn–Teller effect. We show that such …

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The Critical Role of Anharmonic Lattice Dynamics for Macroscopic Properties of the Visible Light Absorbing Nitride Semiconductor CuTaN2 (Adv. Energy Mater. 19/2024)

Franziska Simone Hegner, Adi Cohen, Stefan S. Rudel, Silva M. Kronawitter et autres

Solar Energy Harvesting In article number 2303059, Omer Yaffe, Ian D. Sharp, David A. Egger, and co-workers uncover pronounced anharmonic vibrational effects in the ternary nitride semiconductor, CuTaN2. These dynamic structural characteristics are found to have a large impact on the fundamental …

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Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra

Manuel Grumet, Clara von Scarpatetti, Tomáš Bučko, David A. Egger

High Resolution Image Download MS PowerPoint Slide Raman spectroscopy is an important characterization tool with diverse applications in many areas of research. We propose a machine learning (ML) method for predicting polarizabilities with the goal of providing Raman spectra from molecular dynamics …

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Temperature-transferable tight-binding model using a hybrid-orbital basis

Martin Schwade, Maximilian J. Schilcher, Christian Reverón Baecker, Manuel Grumet et autres

Finite-temperature calculations are relevant for rationalizing material properties, yet they are computationally expensive because large system sizes or long simulation times are typically required. Circumventing the need for performing many explicit first-principles calculations, tight-binding and machine-learning models for the electronic structure emerged …

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The Critical Role of Anharmonic Lattice Dynamics for Macroscopic Properties of the Visible Light Absorbing Nitride Semiconductor CuTaN 2

Franziska Simone Hegner, Adi Cohen, Stefan S. Rudel, Silva M. Kronawitter et autres

Abstract Ternary nitride semiconductors are rapidly emerging as a promising class of materials for energy conversion applications, offering an appealing combination of strong light absorption in the visible range, desirable charge transport characteristics, and good chemical stability. In this work, it is …

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