Periodic DFTB for Supported Clusters: Implementation and Application on Benzene Dimers Deposited on Graphene

Advances in Computational Methods for Modeling Photocatalytic Reactions: A Review of Recent Developments

Photocatalysis is a fascinating process in which a photocatalyst plays a pivotal role in driving a chemical reaction when exposed to light. Its capacity to harness light energy triggers a cascade of reactions that lead to the formation of intermediate compounds, culminating in the desired final product(s). The essence of this process is the interaction between the photocatalyst's excited state and its specific interactions with reactants, resulting in the creation of intermediates. The process's appeal is further enhanced by its cyclic nature-the photocatalyst is rejuvenated after each cycle, ensuring ongoing and sustainable catalytic action. Nevertheless, comprehending the photocatalytic process through the modeling of photoactive materials and molecular devices demands advanced computational techniques founded on effective quantum chemistry methods, multiscale modeling, and machine learning. This review analyzes contemporary theoretical methods, spanning a range of lengths and accuracy scales, and assesses the strengths and limitations of these methods. It also explores the future challenges in modeling complex nano-photocatalysts, underscoring the necessity of integrating various methods hierarchically to optimize resource distribution across different scales. Additionally, the discussion includes the role of excited state chemistry, a crucial element in understanding photocatalysis.

Addressing electronic and dynamical evolution of molecules and molecular clusters: DFTB simulations of energy relaxation in polycyclic aromatic hydrocarbons

We present a review of the capabilities of the density functional based Tight Binding (DFTB) scheme to address the electronic relaxation and dynamical evolution of molecules and molecular clusters following energy deposition via either collision or photoabsorption. The basics and extensions of DFTB for addressing these systems and in particular their electronic states and their dynamical evolution are reviewed. Applications to PAH molecules and clusters, carbonaceous systems of major interest in astrochemical/astrophysical context, are reported. A variety of processes are examined and discussed such as collisional hydrogenation, fast collisional processes and induced electronic and charge dynamics, collision-induced fragmentation, photo-induced fragmentation, relaxation in high electronic states, electronic-to-vibrational energy conversion and statistical versus non-statistical fragmentation. This review illustrates how simulations may help to unravel different relaxation mechanisms depending on various factors such as the system size, specific electronic structure or excitation conditions, in close connection with experiments.

QMLMaterial─A Quantum Machine Learning Software for Material Design and Discovery

Structural elucidation of chemical compounds is challenging experimentally, and theoretical chemistry methods have added important insight into molecules, nanoparticles, alloys, and materials geometries and properties. However, finding the optimum structures is a bottleneck due to the huge search space, and global search algorithms have been used successfully for this purpose. In this work, we present the quantum machine learning software/agent for materials design and discovery (QMLMaterial), intended for automatic structural determination in silico for several chemical systems: atomic clusters, atomic clusters and the spin multiplicity together, doping in clusters or solids, vacancies in clusters or solids, adsorption of molecules or adsorbents on surfaces, and finally atomic clusters on solid surfaces/materials or encapsulated in porous materials. QMLMaterial is an artificial intelligence (AI) software based on the active learning method, which uses machine learning regression algorithms and their uncertainties for decision making on the next unexplored structures to be computed, increasing the probability of finding the global minimum with few calculations as more data is obtained. The software has different acquisition functions for decision making (e.g., expected improvement and lower confidence bound). Also, the Gaussian process is available in the AI framework for regression, where the uncertainty is obtained analytically from Bayesian statistics. For the artificial neural network and support vector regressor algorithms, the uncertainty can be obtained by K-fold cross-validation or nonparametric bootstrap resampling methods. The software is interfaced with several quantum chemistry codes and atomic descriptors, such as the many-body tensor representation. QMLMaterial's capabilities are highlighted in the current work by its applications in the following systems: Na20, Mo6C3 (where the spin multiplicity was considered), H2O@CeNi3O5, Mg8@graphene, Na3Mg3@CNT (carbon nanotube).

Low-Energy Transformation Pathways between Naphthalene Isomers

The transformation pathways between low-energy naphthalene isomers are studied by investigating the topology of the energy landscape of this astrophysically relevant molecule. The threshold algorithm is used to identify the minima basins of the isomers on the potential energy surface of the system and to evaluate the probability flows between them. The transition pathways between the different basins and the associated probabilities were investigated for several lid energies up to 11 eV, this value being close to the highest photon energy in the interstellar medium (13.6 eV). More than a hundred isomers were identified and a set of 23 minima was selected among them, on the basis of their energy and probability of occurrence. The return probabilities of these 23 minima and the transition probabilities between them were computed for several lid energies up to 11 eV. The first connection appeared at 3.5 eV while all minima were found to be connected at 9.5 eV. The local density of state was also sampled inside their respective basins. This work gives insight into both energy and entropic barriers separating the different basins, which also provides information about the transition regions of the energy landscape.

Dedication: Commemorative Issue in Honor of Professor Karlheinz Schwarz on the Occasion of His 80th Birthday

Karlheinz Schwarz was born in January 1941 in Vienna (Austria), and he married Christine Schwarz in 1969 [...]

A Comprehensive DFT Investigation of the Adsorption of Polycyclic Aromatic Hydrocarbons onto Graphene

To better understand graphene and its interactions with polycyclic aromatic hydrocarbons (PAHs), density-functional-theory (DFT) computations were used. Adsorption energy is likely to rise with the number of aromatic rings in the adsorbates. The DFT results revealed that the distance between the PAH molecules adsorbed onto the G ranged between 2.47 and 3.98 Å depending on the structure of PAH molecule. The Non-Covalent Interactions (NCI) plot supports the concept that van der Waals interactions were involved in PAH adsorption onto the Graphene (G) structure. Based on the DFT-calculated adsorption energy data, a rapid and reliable method employing an empirical model of a quantitative structure–activity relationship (QSAR) was created and validated for estimating the adsorption energies of PAH molecules onto graphene.