Modeling of nanomaterials for supercapacitors: Beyond carbon electrodes
Capacitive storage devices allow for fast charge and discharge cycles, making them the
perfect complements to batteries for high power applications. Many materials display …
perfect complements to batteries for high power applications. Many materials display …
Delta machine learning for predicting dielectric properties and Raman spectra
M Grumet, C von Scarpatetti, T Bučko… - The Journal of …, 2024 - ACS Publications
Raman spectroscopy is an important characterization tool with diverse applications in many
areas of research. We propose a machine learning (ML) method for predicting …
areas of research. We propose a machine learning (ML) method for predicting …
Polarizability models for simulations of finite temperature Raman spectra from machine learning molecular dynamics
Raman spectroscopy is a powerful and nondestructive method that is widely used to study
the vibrational properties of solids or molecules. Simulations of finite-temperature Raman …
the vibrational properties of solids or molecules. Simulations of finite-temperature Raman …
Accurate description of ion migration in solid-state ion conductors from machine-learning molecular dynamics
T Miyagawa, N Krishnan, M Grumet… - Journal of Materials …, 2024 - pubs.rsc.org
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 …
electrochemical storage devices and novel compounds of this kind are continuously being …
Raman spectra of amino acids and peptides from machine learning polarizabilities
E Berger, J Niemelä, O Lampela… - Journal of Chemical …, 2024 - ACS Publications
Raman spectroscopy is an important tool in the study of vibrational properties and
composition of molecules, peptides, and even proteins. Raman spectra can be simulated …
composition of molecules, peptides, and even proteins. Raman spectra can be simulated …
Predicting the charge density response in metal electrodes
The computational study of energy storage and conversion processes calls for simulation
techniques that can reproduce the electronic response of metal electrodes under electric …
techniques that can reproduce the electronic response of metal electrodes under electric …
Efficient Sampling for Machine Learning Electron Density and Its Response in Real Space
C Feng, Y Zhang, B Jiang - arXiv preprint arXiv:2410.04977, 2024 - arxiv.org
Electron density is a fundamental quantity, which can in principle determine all ground state
electronic properties of a given system. Although machine learning (ML) models for electron …
electronic properties of a given system. Although machine learning (ML) models for electron …
Machine Learning Potential for Electrochemical Interfaces with Hybrid Representation of Dielectric Response
JX Zhu, J Cheng - arXiv preprint arXiv:2407.17740, 2024 - arxiv.org
Understanding electrochemical interfaces at a microscopic level is essential for elucidating
important electrochemical processes in electrocatalysis, batteries and corrosion. While\textit …
important electrochemical processes in electrocatalysis, batteries and corrosion. While\textit …
Elucidating the Nature of -hydrogen Bonding in Liquid Water and Ammonia
K Brezina, H Beck, O Marsalek - arXiv preprint arXiv:2403.12937, 2024 - arxiv.org
Aromatic compounds form an unusual kind of hydrogen bond with water and ammonia
molecules, known as the $\pi $-hydrogen bond. In this work, we report ab initio path integral …
molecules, known as the $\pi $-hydrogen bond. In this work, we report ab initio path integral …