UV-Visible Absorption Spectra of Solvated Molecules by Quantum Chemical Machine Learning.

Predicting UV-visible absorption spectra is essential to understand photochemical processes and design energy materials. Quantum chemical methods can deliver accurate calculations of UV-visible absorption spectra, but they are computationally expensive, especially for large systems or when one computes line shapes from thermal averages. Here, we present an approach to predict UV-visible absorption spectra of solvated aromatic molecules by quantum chemistry (QC) and machine learning (ML).

Engineering Thermal Transport across Layered Graphene-MoS Superlattices.

Layering two-dimensional van der Waals materials provides a high degree of control over atomic placement, which could enable tailoring of vibrational spectra and heat flow at the sub-nanometer scale. Here, using spatially resolved ultrafast thermoreflectance and spectroscopy, we uncover the design rules governing cross-plane heat transport in superlattices assembled from monolayers of graphene (G) and MoS (M). Using a combinatorial experimental approach, we probe nine different stacking sequences, G, GG, MG, GGG, GMG, GGMG, GMGG, GMMG, and GMGMG, and identify the effects of vibrational mismatch, interlayer adhesion, and junction asymmetry on thermal transport.

Influence of thermostatting on nonequilibrium molecular dynamics simulations of heat conduction in solids.

Nonequilibrium molecular dynamics (NEMD) has been extensively used to study thermal transport at various length scales in many materials. In this method, two local thermostats at different temperatures are used to generate a nonequilibrium steady state with a constant heat flux. Conventionally, the thermal conductivity of a finite system is calculated as the ratio between the heat flux and the temperature gradient extracted from the linear part of the temperature profile away from the local thermostats.