Manuel Grumet
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View article: Machine learning accelerates Raman computations from molecular dynamics for materials science
Machine learning accelerates Raman computations from molecular dynamics for materials science Open
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 micro…
View article: Machine Learning Accelerates Raman Computations from Molecular Dynamics for Materials Science
Machine Learning Accelerates Raman Computations from Molecular Dynamics for Materials Science Open
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 micro…
View article: Rapid Characterization of Point Defects in Solid-State Ion Conductors Using Raman Spectroscopy, Machine-Learning Force Fields, and Atomic Raman Tensors
Rapid Characterization of Point Defects in Solid-State Ion Conductors Using Raman Spectroscopy, Machine-Learning Force Fields, and Atomic Raman Tensors Open
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-develop…
View article: Disentangling the effects of structure and lone-pair electrons in the lattice dynamics of halide perovskites
Disentangling the effects of structure and lone-pair electrons in the lattice dynamics of halide perovskites Open
View article: Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra
Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra Open
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…
View article: Temperature-transferable tight-binding model using a hybrid-orbital basis
Temperature-transferable tight-binding model using a hybrid-orbital basis Open
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 ma…
View article: The Critical Role of Anharmonic Lattice Dynamics for Macroscopic Properties of the Visible Light Absorbing Nitride Semiconductor CuTaN<sub>2</sub>
The Critical Role of Anharmonic Lattice Dynamics for Macroscopic Properties of the Visible Light Absorbing Nitride Semiconductor CuTaN<sub>2</sub> Open
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 charact…
View article: Accurate Description of Ion Migration in Solid-State Ion Conductors from Machine-Learning Molecular Dynamics
Accurate Description of Ion Migration in Solid-State Ion Conductors from Machine-Learning Molecular Dynamics Open
Solid-state ion conductors (SSICs) have emerged as a promising material class for electrochemical storage devices and novel compounds of this kind are continuously being discovered. High-throughout approaches that enable a rapid screening …
View article: Accurate description of ion migration in solid-state ion conductors from machine-learning molecular dynamics
Accurate description of ion migration in solid-state ion conductors from machine-learning molecular dynamics Open
Machine-learning molecular dynamics provides predictions of structural and anharmonic vibrational properties of solid-state ionic conductors with ab initio accuracy. This opens a path towards rapid design of novel battery materials.
View article: Temperature-transferable tight-binding model using a hybrid-orbital basis
Temperature-transferable tight-binding model using a hybrid-orbital basis Open
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 man…
View article: Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra
Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra Open
Raman spectroscopy is an important characterization tool with diverse applications in many areas of research. We propose a machine learning method for predicting polarizabilities with the goal of providing Raman spectra from molecular dyna…
View article: Anharmonic Lattice Dynamics in Sodium Ion Conductors
Anharmonic Lattice Dynamics in Sodium Ion Conductors Open
We employ THz-range temperature-dependent Raman spectroscopy and first-principles lattice-dynamical calculations to show that the undoped sodium ion conductors Na$_3$PS$_4$ and isostructural Na$_3$PSe$_4$ both exhibit anharmonic lattice dy…
View article: Beyond the quasiparticle approximation: Fully self-consistent <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>G</mml:mi><mml:mi>W</mml:mi></mml:mrow></mml:math> calculations
Beyond the quasiparticle approximation: Fully self-consistent calculations Open
We present quasiparticle (QP) energies from fully self-consistent $GW$\n(sc$GW$) calculations for a set of prototypical semiconductors and insulators\nwithin the framework of the projector-augmented wave methodology. To obtain\nconverged r…
View article: Self-Consistent $GW$ calculations for semiconductors and insulators
Self-Consistent $GW$ calculations for semiconductors and insulators Open
We present quasiparticle (QP) energies from fully self-consistent $GW$ (sc$GW$) calculations for a set of prototypical semiconductors and insulators within the framework of the projector-augmented wave methodology. To obtain converged resu…
View article: Self-consistent GW calculations for solids
Self-consistent GW calculations for solids Open
Die GW-Näherung ist eine Methode für ab-initio-Simulationen von Vielelektronensystemen, die auf dem formalen Gerüst des Green-Funktionen-Formalismus und auf den Hedin-Gleichungen beruht. Sie geht über Molekularfeld-Methoden wie die Hartree…