Path integral simulations of condensed-phase vibrational spectroscopy
SC Althorpe - Annual Review of Physical Chemistry, 2024 - annualreviews.org
Recent theoretical and algorithmic developments have improved the accuracy with which
path integral dynamics methods can include nuclear quantum effects in simulations of …
path integral dynamics methods can include nuclear quantum effects in simulations of …
Machine learning the electric field response of condensed phase systems using perturbed neural network potentials
The interaction of condensed phase systems with external electric fields is of major
importance in a myriad of processes in nature and technology, ranging from the field …
importance in a myriad of processes in nature and technology, ranging from the field …
First-principles spectroscopy of aqueous interfaces using machine-learned electronic and quantum nuclear effects
Vibrational spectroscopy is a powerful approach to visualising interfacial phenomena.
However, extracting structural and dynamical information from vibrational spectra is a …
However, extracting structural and dynamical information from vibrational spectra is a …
[HTML][HTML] On the Fresnel factor correction of sum-frequency generation spectra of interfacial water
Insights into the microscopic structure of aqueous interfaces are essential for understanding
the chemical and physical processes on the water surface, including chemical synthesis …
the chemical and physical processes on the water surface, including chemical synthesis …
[HTML][HTML] Quantum dynamics using path integral coarse-graining
The vibrational spectra of condensed and gas-phase systems are influenced by thequantum-
mechanical behavior of light nuclei. Full-dimensional simulations of approximate quantum …
mechanical behavior of light nuclei. Full-dimensional simulations of approximate quantum …
Efficient Composite Infrared Spectroscopy: Combining the Double-Harmonic Approximation with Machine Learning Potentials
Vibrational spectroscopy is a cornerstone technique for molecular characterization and
offers an ideal target for the computational investigation of molecular materials. Building on …
offers an ideal target for the computational investigation of molecular materials. Building on …
Tensorial properties via the neuroevolution potential framework: Fast simulation of infrared and Raman spectra
Infrared and Raman spectroscopy are widely used for the characterization of gases, liquids,
and solids, as the spectra contain a wealth of information concerning, in particular, the …
and solids, as the spectra contain a wealth of information concerning, in particular, the …
High-dimensional neural network potentials for accurate vibrational frequencies: the formic acid dimer benchmark
In recent years, machine learning potentials (MLP) for atomistic simulations have attracted a
lot of attention in chemistry and materials science. Many new approaches have been …
lot of attention in chemistry and materials science. Many new approaches have been …
A complete description of thermodynamic stabilities of molecular crystals
Predictions of relative stabilities of (competing) molecular crystals are of great technological
relevance, most notably for the pharmaceutical industry. However, they present a long …
relevance, most notably for the pharmaceutical industry. However, they present a long …
Neural network-based sum-frequency generation spectra of pure and acidified water interfaces with air
The affinity of hydronium ions (H3O+) for the air–water interface is a crucial question in
environmental chemistry. While sum-frequency generation (SFG) spectroscopy has been …
environmental chemistry. While sum-frequency generation (SFG) spectroscopy has been …