Fast and accurate nonadiabatic molecular dynamics enabled through variational interpolation of correlated electron wavefunctions

We build on the concept of eigenvector continuation to develop an efficient multi-state method for the rigorous and smooth interpolation of a small training set of many-body wavefunctions through chemical space at mean-field cost. The inferred states are represented as variationally optimal linear combinations of the training states transferred between the many-body bases of different nuclear geometries. We show that analytic multi-state forces and nonadiabatic couplings from the model enable application to nonadiabatic molecular dynamics, developing an active learning scheme to ensure a compact and systematically improvable training set. This culminates in application to the nonadiabatic molecular dynamics of a photoexcited 28-atom hydrogen chain, with surprising complexity in the resulting nuclear motion. With just 22 DMRG calculations of training states from the low-energy correlated electronic structure at different geometries, we infer the multi-state energies, forces and nonadiabatic coupling vectors at 12 000 geometries with provable convergence to high accuracy along an ensemble of molecular trajectories, which would not be feasible with a brute force approach. This opens up a route to bridge the timescales between accurate single-point correlated electronic structure methods and timescales of relevance for photo-induced molecular dynamics

Molecular graph transformer: stepping beyond ALIGNN into long-range interactions

Graph Neural Networks (GNNs) have revolutionized material property prediction by learning directly from the structural information of molecules and materials. However, conventional GNN models rely solely on local atomic interactions, such as bond lengths and angles, neglecting crucial long-range electrostatic forces that affect certain properties. To address this, we introduce the Molecular Graph Transformer (MGT), a novel GNN architecture that combines local attention mechanisms with message passing on both bond graphs and their line graphs, explicitly capturing long-range interactions. Benchmarking on MatBench and Quantum MOF (QMOF) datasets demonstrates that MGT's improved understanding of electrostatic interactions significantly enhances the prediction accuracy of properties like exfoliation energy and refractive index, while maintaining state-of-the-art performance on all other properties. This breakthrough paves the way for the development of highly accurate and efficient materials design tools across diverse applications

Performance of point charge embedding schemes for excited states in molecular organic crystals

Modeling excited state processes in molecular crystals is relevant for several applications. A popular approach for studying excited state molecular crystals is to use cluster models embedded in point charges. In this paper, we compare the performance of several embedding models in predicting excited states and S1-S0 optical gaps for a set of crystals from the X23 molecular crystal database. The performance of atomic charges based on ground or excited states was examined for cluster models, Ewald embedding, and self-consistent approaches. We investigated the impact of various factors, such as the level of theory, basis sets, embedding models, and the level of localization of the excitation. We consider different levels of theory, including time-dependent density functional theory and Tamm-Dancoff approximation (TDA) (DFT functionals: ωB97X-D and PBE0), CC2, complete active space self-consistent field, and CASPT2. We also explore the impact of selection of the QM region, charge leakage, and level of theory for the description of different kinds of excited states. We implemented three schemes based on distance thresholds to overcome overpolarization and charge leakage in molecular crystals. Our findings are compared against experimental data, G0W0-BSE, periodic TDA, and optimally tuned screened range-separated functionals

Computational screening of metalloporphyrin catalysts for the activation of carbon dioxide

Electrocatalytic CO2 reduction (eCO2R) to value-added chemicals offers a promising route for carbon capture and utilization. Metalloporphyrin (M-POR) is a class of catalysts for eCO2R that has drawn attention due to its tuneable electronic and structural properties. This work presents a computational screening, based on density functional theory calculations, of one of the key steps in the eCO2R: the adsorption of CO2 on 110 M-PORs with varying peripheral ligands, metal centres, and oxidation states, to understand how these factors can influence CO2 activation. A set of criteria was used to shortlist M-PORs based on their ability to lengthen the C–O bond, bend the O–C–O angle, bind CO2, and donate charge from the metal of the M-POR to the carbon of CO2. 16 systems were selected for their potential to activate CO2. These systems predominantly have the electron configuration of the metal centre in the d[6] and d[7] configurations. Natural bond orbital analysis revealed the impact of electron-withdrawing groups in the system, which increases orbital splitting and, consequently, lowers the ability of the M-POR to activate CO2. Second-order perturbation theory analysis confirms that the presence of electron-donating groups in the ligand structure enhances CO2 activation. This work demonstrates the interconnected effect of peripheral ligands, metal centres, and oxidation states in M-PORs on their ability to adsorb and activate CO2, thereby establishing structure-activity relationships within M-PORs

Communication: Accurate determination of side-chain torsion angle χ1 in proteins: Phenylalanine residues

The following article appeared in Journal of Chemical Physics 134.6 (2011): 061101 and may be found at http://scitation.aip.org/content/aip/journal/jcp/134/6/10.1063/1.3553204Quantitative side-chain torsion angle χ1 determinations of phenylalanine residues in Desulfovibrio vulgaris flavodoxin are carried out using exclusively the correlation between the experimental vicinal coupling constants and theoretically determined Karplus equations. Karplus coefficients for nine vicinal coupling related with the torsion angle χ1 were calculated using the B3LYP functional and basis sets of different size. Optimized χ1 angles are in outstanding agreement with those previously reported by employing x ray and NMR measurementsFinancial support from the MICINN of Spain (Project No. CTQ2007-66547 and CTQ2010-19232), the Comunidad de Madrid (Project Nos. S2009/ENE-1743) and AECID (Project No. D/023653/09) is gratefully acknowledged. Computational facilities have been provided by CCC-UA

Engineering the electronic and optical properties of 2D porphyrin paddlewheel metal-organic frameworks

Metal organic frameworks (MOFs) are promising photocatalytic materials due to their high surface area and tuneability of their electronic structure. We discuss here how to engineer the band structures and optical properties of a family of two-dimensional (2D) porphyrin-based MOFs, consisting of M tetrakis(4 carboxyphenyl) porphyrin structures (M TCPP, where M = Zn or Co) and metal (Co, Ni, Cu or Zn) paddlewheel clusters, with the aim of optimising their photocatalytic behaviour in solar fuel synthesis reactions (water splitting and/or CO2 reduction). Based on density functional theory (DFT) and time-dependent DFT simulations with a hybrid functional, we studied three types of composition/structural modifications: a) varying the metal centre at the paddlewheel or at the porphyrin centre to modify the band alignment; b) partially reducing the porphyrin unit to chlorin, which leads to stronger absorption of visible light; and c) substituting the benzene bridging between the porphyrin and paddlewheel, by ethyne or butadiyne bridges, with the aim of modifying the linker to metal charge transfer behaviour. Our work offers new insights on how to improve the photocatalytic behaviour of porphyrin- and paddlewheel-based MOFs

New Insights into the State Trapping of UV-Excited Thymine

Ljiljana Stojanović, Shuming Bai, and Mario Barbatti thank the support of the Aix-Marseille Initiative d’Excellence (A*MIDEX) grant (No. ANR-11-IDEX-0001-02) funded by the French Government “Investissements d’Avenir” program supervised by the Agence Nationale de la Recherche. This work was granted access to the HPC resources of Aix-Marseille Université financed by the project Equip@Meso (ANR-10-EQPX-29-01) also within the “Investissements d’Avenir” program. Artur F. Izmaylov acknowledges funding from a Sloan Research Fellowship and the Natural Sciences and Engineering Research Council of Canada (NSERC) through the Discovery Grants Program

Theoretical insights into the role of defects in the optimization of the electrochemical capacitance of graphene

Graphene-based frameworks suffer from a low quantum capacitance due to graphene’s Dirac point at the Fermi level. This theoretical study investigated the effect structural defects, nitrogen and boron doping, and surface epoxy/hydroxy groups have on the electronic structure and capacitance of graphene. Density functional theory calculations reveal that the lowest energy configurations for nitrogen or boron substitutional doping occur when the dopant atoms are segregated. This elucidates why the magnetic transition for nitrogen doping is experimentally only observed at higher doping levels. We also highlight that the lowest energy configuration for a single vacancy defect is magnetic. Joint density functional theory calculations show that the fixed band approximation becomes increasingly inaccurate for electrolytes with lower dielectric constants. The introduction of structural defects rather than nitrogen or boron substitutional doping, or the introduction of adatoms leads to the largest increase in density of states and capacitance around graphene’s Dirac point. However, the presence of adatoms or substitutional doping leads to a larger shift of the potential of zero charge away from graphene’s Dirac point

Silicon Radical-Induced CH4 Dissociation for Uniform Graphene Coating on Silica Surface

Due to the manufacturability of highly well-defined structures and wide-range versatility in its microstructure, SiO2 is an attractive template for synthesizing graphene frameworks with the desired pore structure. However, its intrinsic inertness constrains the graphene formation via methane chemical vapor deposition. This work overcomes this challenge by successfully achieving uniform graphene coating on a trimethylsilyl-modified SiO2 (denote TMS-MPS). Remarkably, the onset temperature for graphene growth dropped to 720 °C for the TMS-MPS, as compared to the 885 °C of the pristine SiO2 . This is found to be mainly from the Si radicals formed from the decomposition of the surface TMS groups. Both experimental and computational results suggest a strong catalytic effect of the Si radicals on the CH4 dissociation. The surface engineering of SiO2 templates facilitates the synthesis of high-quality graphene sheets. As a result, the graphene-coated SiO2 composite exhibits a high electrical conductivity of 0.25 S cm-1 . Moreover, the removal of the TMP-MPS template has released a graphene framework that replicates the parental TMS-MPS template on both micro- and nano- scales. This study provides tremendous insights into graphene growth chemistries as well as establishes a promising methodology for synthesizing graphene-based materials with pre-designed microstructures and porosity

A thermodynamically favorable route to the synthesis of nanoporous graphene templated on CaO via chemical vapor deposition

Template-assisted chemical vapor deposition (CVD) is a promising approach for fabricating nanoporous materials based on graphene walls. Among conventional metal oxide templates, CaO, produced through the thermal decomposition of CaCO3, offers improved environmental sustainability and lower production costs, thereby potentially making it a viable candidate for green template materials. Nevertheless, the underlying reaction mechanisms of the interaction on the CaO surface during the CVD process remain indeterminate, giving rise to challenges in regulating graphene formation and obtaining high-quality materials. In this work, a comprehensive experimental-theoretical investigation has unveiled the CVD mechanism on CaO. CaO exhibits efficient catalytic activity in the dissociation of CH4, thereby facilitating a thermodynamically favorable conversion of CH4 to graphene. These findings highlight the potential of using CaO as a substrate for graphene growth, combining both sustainability and cost-effectiveness. When the shell-like graphene layer deposited on CaO particles is released through the dissolution of CaO with HCl, the resulting nanoporous graphene-based materials can be readily compacted by the capillary force of the liquid upon drying. The folded surfaces, however, can become available for electric double-layer capacitance via electrochemical exfoliation under a low applied potential (<1.2 V vs. Ag/AgClO4)