Machine learning has emerged as an invaluable tool in many research areas. In the present
work, we harness this power to predict highly accurate molecular infrared spectra with …

Specialized computational chemistry packages have permanently reshaped the landscape
of chemical and materials science by providing tools to support and guide experimental …

Dynamic polymers with both physical interactions and dynamic covalent bonds exhibit
superior performance, but achieving such dry polymers in an efficient manner remains a …

Atmospheric particulate matter (PM) is a complex mixture of many different substances and
requires a suite of instruments for chemical characterization. Fourier transform infrared (FT …

Computational simulation of nonadiabatic molecular dynamics is an indispensable tool for
understanding complex photoinduced processes such as internal conversion, energy …

Excited-state intramolecular proton transfer (ESIPT) is a fundamental chemical process with
several applications. Ultrafast ESIPT involves coupled electronic and atomic motions and …

The recently developed ab initio multiple cloning (AIMC) approach based on the
multiconfigurational Ehrenfest (MCE) method provides a powerful and accurate way of …

Due to the recently uncovered health benefits and anti-HIV activities of dicaffeoylquinic acids
(diCQAs), understanding their structures and functions is of great interest for drug discovery …

An on-the-fly fragment-based machine learning (ML) approach was developed to construct
machine learning force fields for large complex systems. In this approach, the energy, forces …

We record nanoscale chemical images of thiobenzonitrile (TBN)-functionalized plasmonic
gold nanocubes via tip-enhanced Raman spectroscopy (TERS). The spatially averaged …