Thermoelectric (TE) materials have received much attention due to their ability to harvest
waste heat energy. TE materials must exhibit a low thermal conductivity (κ) and a high power …

Raman spectroscopy is an important characterization tool with diverse applications in many
areas of research. We propose a machine learning (ML) method for predicting …

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 …

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 …

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 …

Abstract Machine Learned Interatomic Potentials (MLIPs) combine the predictive power of
Density Functional Theory (DFT) with the speed and scaling of interatomic potentials …

Raman flow cytometry (RFC) uniquely integrates the “label-free” capability of Raman
spectroscopy with the “high-throughput” attribute of traditional flow cytometry (FCM), offering …

We present a local and transferable machine-learning approach capable of predicting the
real-space density response of both molecules and periodic systems to homogeneous …

Theoretical prediction of vibrational Raman spectra enables a detailed interpretation of
experimental spectra, and the advent of machine learning techniques makes it possible to …

Raman spectroscopy is an indispensable technique for characterizing two-dimensional (2D)
material structures and interaction information in experiments. However, systematic …