Synthetic nanomotors powered by chemical reactions have been designed to act as vehicles for active cargo transport, drug delivery as well as a variety of other uses. Collections of such motors, acting in consort, can self-assemble to form swarms or clusters, providing opportunities for applications on various length scales. While such collective behavior has been studied when the motors move in a chemically inactive fluid environment, when the medium in which they move is a chemical network that supports complex spatial and temporal patterns we show that collective behavior changes. Spatial patterns in the environment can guide and control motor collective states, and interactions of the motors with their environment can give rise to distinctive spatiotemporal motor patterns. The results are illustrated by studies of the motor dynamics in systems that support Turing patterns and spiral waves. This work is relevant for potential applications that involve many active nanomotors moving in complex chemical or biological environments.

We present the development and implementation of the relativistic coupled cluster linear response theory (CC-LR) which allows the determination of molecular properties arising from time-dependent or time-independent electric, magnetic, or mixed electric-magnetic perturbations (within a common gauge origin), and take into account the finite lifetime of excited states via damped response theory. We showcase our implementation, which is capable to offload intensive tensor contractions onto graphical processing units (GPUs), in the calculation of: \textit{(a)} frequency-(in)dependent dipole-dipole polarizabilities of IIB atoms and selected diatomic molecules, with a emphasis on the calculation of valence absorption cross-sections for the I$_2$ molecule;\textit{(b)} indirect spin-spin coupling constants for benchmark systems such as the hydrogen halides (HX, X = F-I) as well the H$_2$Se-H$_2$O dimer as a prototypical system containing hydrogen bonds; and \textit{(c)} optical rotations at the sodium D line for hydrogen peroxide analogues (H$_{2}$Y$_{2}$, Y=O, S, Se, Te). Thanks to this implementation, we are able show the similarities in performance--but often the significant discrepancies--between CC-LR and approximate methods such as density functional theory (DFT). Comparing standard CC response theory with the equation of motion formalism, we find that, for valence properties such as polarizabilities, the two frameworks yield very similar results across the periodic table as found elsewhere in the literature; for properties that probe the core region such as spin-spin couplings, we show a progressive differentiation between the two as relativistic effects become more important. Our results also suggest that as one goes down the periodic table it may become increasingly difficult to measure pure optical rotation at the sodium D line, due to the appearance of absorbing states.

In this study, we employ a computational chemistry--based modeling approach to investigate the adsorption mechanisms of CH$_4$ and CO$_2$ on monolayer GPNL, with a specific focus on their effects on optical adsorption and electrical transport properties at room temperature. To simulate the adsorption dynamics as closely as possible to experimental conditions, we utilize the self--consistent charge tight--binding density functional theory (SCC--DFTB). Through semi--classical molecular dynamics (MD) simulations, we observe the formation of H$_2$ molecules from the dissociation of CH$_4$ and the formation of CO+O species from carbon dioxide molecules. This provides insights into the adsorption and dispersion mechanisms of CH$_4$ and CO$_2$ on GPNL. Furthermore, we explore the impact of molecular adsorption on optical absorption properties. Our results demonstrate that CH$_4$ and CH$_2$ affects drastically the optical adsorption of GPNL, while CO$_2$ does not significantly affect the optical properties of the two--dimensional material. To analyze electron transport, we employ the open--boundary non--equilibrium Green's function method. By studying the conductivity of GPNL and graphene under voltage bias up to 300 mV, we gain valuable insights into the electrical transport properties of GPNL under optical absorption conditions. The findings from our computational modeling approach might contribute to a deeper understanding of the potential applications of GPNL in hydrogen production and advanced electronic devices.

Harnessing non-equilibrium hot carriers from plasmonic metal nanostructures constitutes a vibrant research field. It promises to enable control of activity and selectivity of photochemical reactions, especially for solar fuel generation. However, a comprehensive understanding of the interplay of plasmonic hot carrier-driven processes in metal/semiconducting heterostructures has remained elusive. In this work, we reveal the complex interdependence between plasmon excitation, hot carrier generation, transport and interfacial collection in plasmonic photocatalytic devices, uniquely determining the charge injection efficiencies at the solid/solid and solid/liquid interfaces. Interestingly, by measuring the internal quantum efficiency of ultrathin (14 to 33 nm) single-crystalline plasmonic gold (Au) nanoantenna arrays on titanium dioxide substrates, we find that the performance of the device is governed by hot hole collection at the metal/electrolyte interface. In particular, by combining a solid- and liquid-state experimental approach with ab initio simulations, we show a more efficient collection of high-energy d-band holes traveling in [111] orientation, resulting in a stronger oxidation reaction at the {111} surfaces of the nanoantenna. These results thus establish new guidelines for the design and optimization of plasmonic photocatalytic systems and optoelectronic devices.

We investigate the effect of nuclear quantum effects (NQEs) of hydrogen atoms on the elasticity of ice VII at high pressure and ambient temperature conditions using ab initio path-integral molecular dynamics (PIMD) calculations. We find that the NQEs of hydrogen contributes to the transition of ice VII from a static disordered structure to a dynamically disordered structure at pressures exceeding 40 GPa. This transition is marked by a discontinuous increase of the elastic constants. Comparison of ab initio molecular dynamics and PIMD calculations reveal that NQEs increase the elastic constants of ice by about 20% at 70 GPa and 300 K.

Surface-enhanced spectroscopy techniques are the method-of-choice to characterize adsorbed intermediates occurring during electrochemical reactions, which are crucial in realizing a green sustainable future. Characterizing species with low coverages or short lifetimes have so far been limited by low signal enhancement. Recently, metasurface-driven surface-enhanced infrared absorption spectroscopy (SEIRAS) has been pioneered as a promising narrowband technology to study single vibrational modes of electrochemical interfaces during CO oxidation. However, many reactions involve several species or configurations of adsorption that need to be monitored simultaneously requiring reproducible and broadband sensing platforms to provide a clear understanding of the underlying electrochemical processes. Here, we experimentally realize multi-band metasurface-driven SEIRAS for the in-situ study of electrochemical CO2 reduction on a Pt surface. We develop an easily reproducible and spectrally-tunable platinum nano-slot metasurface. Two CO adsorption configurations at 2030 cm-1 and 1840 cm-1 are locally enhanced as a proof of concept that can be extended to more vibrational bands. Our platform provides a 41-fold enhancement in the detection of characteristic absorption signals compared to conventional broadband electrochemically roughened platinum films. A straightforward methodology is outlined starting by baselining our system in CO saturated environment and clearly detecting both configurations of adsorption, in particular the hitherto hardly detectable CO bridge configuration. Then, thanks to the signal enhancement provided by our platform, we find that the CO bridge configuration on platinum does not play a significant role during CO2 reduction in an alkaline environment. We anticipate that our technology will guide researchers in developing similar sensing platforms.

Graph Neural Networks (GNNs) have emerged as a prominent class of data-driven methods for molecular property prediction. However, a key limitation of typical GNN models is their inability to quantify uncertainties in the predictions. This capability is crucial for ensuring the trustworthy use and deployment of models in downstream tasks. To that end, we introduce AutoGNNUQ, an automated uncertainty quantification (UQ) approach for molecular property prediction. AutoGNNUQ leverages architecture search to generate an ensemble of high-performing GNNs, enabling the estimation of predictive uncertainties. Our approach employs variance decomposition to separate data (aleatoric) and model (epistemic) uncertainties, providing valuable insights for reducing them. In our computational experiments, we demonstrate that AutoGNNUQ outperforms existing UQ methods in terms of both prediction accuracy and UQ performance on multiple benchmark datasets. Additionally, we utilize t-SNE visualization to explore correlations between molecular features and uncertainty, offering insight for dataset improvement. AutoGNNUQ has broad applicability in domains such as drug discovery and materials science, where accurate uncertainty quantification is crucial for decision-making.

Ultraviolet spectroscopy provides unique insights into the structure of matter with applications ranging from fundamental tests to photochemistry in the earth's atmosphere and astronomical observations from space telescopes. At longer wavelengths, dual-comb spectroscopy with two interfering laser frequency combs has evolved into a powerful technique that can offer simultaneously a broad spectral range and very high resolution. Here we demonstrate a photon-counting approach that can extend the unique advantages of this method into ultraviolet regions where nonlinear frequency-conversion tends to be very inefficient. Our spectrometer, based on two frequency combs of slightly different repetition frequencies, provides broad span, high resolution, frequency calibration within the accuracy of an atomic clock, and overall consistency of the spectra. We demonstrate a signal-to-noise ratio at the quantum limit and optimal use of the measurement time, provided by the multiplex recording of all spectral data on a single photo-counter. Our initial experiments are performed in the near-ultraviolet and in the visible spectral ranges with alkali-atom vapor, with a power per comb line as low as a femtowatt. This crucial step towards precision broadband spectroscopy at short wavelengths clears the path to extreme-ultraviolet dual-comb spectroscopy and, more generally, generates a new realm of applications for diagnostics at photon level, as encountered e.g., when driving single atoms or molecules.

A non-slip constraint between a particle and a wall is applied at the microscopic level of collision dynamics using the rough sphere model. We analyse the consequences of the translation-rotation coupling of the rough sphere confined between two parallel planar walls and establish that shearing the walls past each other i) preferentially deposits energy into the rotational degree of freedom and ii) results in a bounded oscillation of the energy of the confined particle.

Photoemission orbital tomography (POT) from photoelectron momentum maps (PMMs) has enabled detailed analysis of the shape and energy of molecular orbitals in the adsorbed state. This study proposes a new POT method based on the PhaseLift. Molecular orbitals, including three-dimensional phases, can be identified from a single PMM by actively providing atomic positions and basis. Moreover, our method is robust to noise and can perfectly discriminate adsorption-induced molecular deformations with an accuracy of 0.05 [angstrom]. Our new method enables simultaneous analysis of the three-dimensional shapes of molecules and molecular orbitals and thus paves the way for advanced quantum-mechanical interpretation of adsorption-induced electronic state changes and photo-excited inter-molecular interactions.

In this paper, we investigated the annealing experiments of poly(mercaptopropylmethylsiloxane, PMMS) confined within two types of porous templates (anodic aluminium oxide, AAO, and silica) characterized by different pore diameter, d= 8-120nm, using different thermal protocols (varying significantly in cooling/heating rate) by means of Broadband Dielectric Spectroscopy (BDS) supported by the complementary Differential Scanning Calorimetry (DSC) and temperature-dependent contact angle, {\theta}, measurements. It was found that relaxation times obtained from routine temperature-dependent dielectric investigations deviate from the bulk behavior when approaching the glass transition temperature. Importantly, this confinement induced effect can be easily removed by the annealing experiments performed at some specific range of temperatures. The analysis of the dielectric data collected during isothermal experiments of confined samples that was beforehand cooled with different rates revealed that (i) constant rates of annealing gets longer with cooling and weakly depend on the rate of cooling, and (ii) activation energy of the equilibration process, E_a, varies with the reduction of the pore diameter and material the porous template is made of. In fact, there is significant reduction in E_a from ~62 to ~23 kJ/mol obtained for the annealing process carried out in AAO (d= 10 nm) and silica (d= 8 nm) membranes, respectively. Such significant change in E_a can be explained taking into account temperature-dependence of {\theta} of PMMS indicating a notable change in wettability between both surfaces upon cooling. As a consequence, one can expect that the mass exchange between interfacial and core molecules as well as adsorption-desorption processes occurring at the interface at lower temperatures must be affected.

We introduce an efficient and robust method to compute alchemical free energy differences, resulting from the application of multiple walker Adaptive Biasing Force (ABF) in conjunction with strongly damped Langevin $\lambda$-dynamics. Unbiased alchemical free energy surfaces are naturally recovered by Thermodynamic Integration (TI). No manual optimization of the $\lambda$ schedule is required as the sampling of the $\lambda$ variable is continuous and converges towards a uniform distribution. Free diffusion of $\lambda$ improves orthogonal relaxation compared to fixed $\lambda$ methods such as standard TI or Free Energy Perturbation (FEP). Furthermore, the multiple walker strategy provides coverage of orthogonal space in a generic way with minimal user input and negligible computational overhead. Of practical importance, no adiabatic decoupling between the alchemical and Cartesian degrees of freedom is assumed, ensuring unbiased estimates for a wide envelope of numerical parameters. We present two high-performance implementations of the method in production molecular dynamics engines, namely NAMD and Tinker-HP, through coupling with the Colvars open source library. These interfaces enable the combination of the rich feature sets of those packages. We demonstrate the correctness and efficiency of the approach on several real-world cases: from solvation free energies up to ligand-receptor binding (using a recently proposed binding restraint scheme) with both fixed-charge and polarizable models. We find that, for a chosen accuracy, the computational cost is strongly reduced compared to state-of-the-art fixed-lambda methods and that results within 1~kcal/mol of experimental value are recovered for the most complex system. The implementation is publicly available and readily usable by practitioners of current alchemical methods.

Open-shell molecules rarely fluoresce, due to their typically faster non-radiative relaxation rates compared to closed-shell ones. Even rarer is the fluorescence from states that have two more unpaired electrons than the open-shell ground state, for example tripdoublet states (a triplet excitation antiferromagnetically coupled to a doublet state). The description of the latter states by U-TDDFT is notoriously inaccurate due to large spin contamination. In this work, we applied our spin-adapted TDDFT method, X-TDDFT, and the static-dynamic-static second order perturbation theory (SDSPT2), to the study of the excited states as well as their relaxation pathways of copper(II) porphyrin; previous experimental works suggested that the photoluminescence of some substituted copper(II) porphyrins originate from a tripdoublet state, formed by a triplet ligand $\pi\to\pi^*$ excitation. Our results demonstrated favorable agreement between the X-TDDFT, SDSPT2 and experimental excitation energies, and revealed noticeable improvements of X-TDDFT compared to U-TDDFT, suggesting that X-TDDFT is a reliable tool for the study of tripdoublet fluorescence. Intriguingly, the aforementioned tripdoublet state is the lowest doublet excited state and lies only slightly higher than the lowest quartet state, which explains why the tripdoublet of copper(II) porphyrin is long-lived enough to fluoresce; an explanation for this unusual state ordering is given. Indeed, thermal vibration correlation function (TVCF)-based calculations of internal conversion, intersystem crossing, and radiative transition rates confirm that copper(II) porphyrin emits thermally activated delayed fluorescence (TADF) and a small amount of phosphorescence at low temperature (83 K), in accordance with experiment. The present contribution is concluded by a few possible approaches of designing new molecules that fluoresce from tripdoublet states.

We introduce a practical and efficient approach for calculating the all-electron full potential bandstructure in real space, employing a finite element basis. As an alternative to the k-space method, the method involves the self-consistent solution of the Kohn-Sham equation within a larger finite system that encloses the unit-cell. It is based on the fact that the net potential of the unit-cell converges at a certain radius point. Bandstructure results are then obtained by performing non-self-consistent calculations in the Brillouin zone. Numerous numerical experiments demonstrate that the obtained valence and conduction bands are in excellent agreement with the pseudopotential k-space method. Moreover, we successfully observe the band bending of core electrons.

How does an electrochemical interface respond to changes in the electrode potential? How does the response affect the key properties of the system - energetics, excess charge, capacitance? Essential questions key to ab-initio simulations of electrochemical systems, which we address in this work on the basis of a rigorous mathematical evaluation of the interfacial energetics at constant applied potential. By explicitly taking into account the configurational and electronic degrees of freedom we derive important statements about stationary points in the electronically grand canonical ensemble. We analyze their geometric response to changes in electrode potential and show that it can be mapped identically onto an additional contribution to the system's capacitance. We draw similar conclusions for the constant charge ensemble which equally allows to assess the respective stationary points. Our analysis of the relation between the canonical and grand canonical energetics reveals, however, one key difference between both ensembles. While the constant potential ensemble yields in general positive capacitances at local minima, the capacitance of local minima in the constant charge ensemble might become negative. We trace back this feature to the possibility of character switching of stationary points when switching between the ensembles causing the differences in the response to perturbations. Our systematical analysis not only provides a detailed qualitative and quantitative understanding of the interplay between electronic and configurational degrees of freedom and their contributions to the energetics of electrified interfaces but also highlights the similarities and subtle dissimilarities between the canonical and grand canonical description of the electronic degrees of freedom, which is crucial for a better understanding of theoretical calculations with and without potentiostat.

It has recently been shown that the SOiCI approach [J. Phys.: Condens. Matter 34 (2022) 224007], in conjunction with the spin-separated exact two-component relativistic Hamiltonian, can provide very accurate fine structures of systems containing heavy elements by treating electron correlation and spin-orbit coupling (SOC) on an equal footing. Nonetheless, orbital relaxations/polarizations induced by SOC are not yet fully accounted for, due to the use of scalar relativistic orbitals. This issue can be resolved by further optimizing the still real-valued orbitals self-consistently in the presence of SOC, as done in the spin-orbit coupled CASSCF approach [J. Chem. Phys. 138 (2013) 104113] but with the iCISCF algorithm [J. Chem. Theory Comput. 17 (2021) 7545] for large active spaces. The resulting SOiCISCF employs both double group and time reversal symmetries for computational efficiency and assignment of target states. The fine structures of $p$-block elements are taken as showcases to reveal the efficacy of SOiCISCF.

Photosystem II (PSII) reaction center is a unique protein-chromophore complex that is capable of efficiently separating electronic charges across the membrane after photoexcitation. In the PSII reaction center, the primary energy- and charge-transfer (CT) processes occur on comparable ultrafast timescales, which makes it extremely challenging to understand the fundamental mechanism responsible for the near-unity quantum efficiency of the transfer. Here, we elucidate the role of quantum coherences in the ultrafast energy and CT in the PSII reaction center by performing two-dimensional (2D) electronic spectroscopy at the cryogenic temperature of 20 K, which captures the distinct underlying quantum coherences. Specifically, we uncover the electronic and vibrational coherences along with their lifetimes during the primary ultrafast processes of energy and CT. We also examine the functional role of the observed quantum coherences. To gather further insight, we construct a structure-based excitonic model that provided evidence for coherent energy and CT at low temperature in the 2D electronic spectra. The principles, uncovered by this combination of experimental and theoretical analyses, could provide valuable guidelines for creating artificial photosystems with exploitation of system-bath coupling and control of coherences to optimize the photon conversion efficiency to specific functions.

Zeolites are inorganic materials known for their diversity of applications, synthesis conditions, and resulting polymorphs. Although their synthesis is controlled both by inorganic and organic synthesis conditions, computational studies of zeolite synthesis have focused mostly on organic template design. In this work, we use a strong distance metric between crystal structures and machine learning (ML) to create inorganic synthesis maps in zeolites. Starting with 253 known zeolites, we show how the continuous distances between frameworks reproduce inorganic synthesis conditions from the literature without using labels such as building units. An unsupervised learning analysis shows that neighboring zeolites according to our metric often share similar inorganic synthesis conditions, even in template-based routes. In combination with ML classifiers, we find synthesis-structure relationships for 14 common inorganic conditions in zeolites, namely Al, B, Be, Ca, Co, F, Ga, Ge, K, Mg, Na, P, Si, and Zn. By explaining the model predictions, we demonstrate how (dis)similarities towards known structures can be used as features for the synthesis space. Finally, we show how these methods can be used to predict inorganic synthesis conditions for unrealized frameworks in hypothetical databases and interpret the outcomes by extracting local structural patterns from zeolites. In combination with template design, this work can accelerate the exploration of the space of synthesis conditions for zeolites.

Coordinate denoising is a promising 3D molecular pre-training method, which has achieved remarkable performance in various downstream drug discovery tasks. Theoretically, the objective is equivalent to learning the force field, which is revealed helpful for downstream tasks. Nevertheless, there are two challenges for coordinate denoising to learn an effective force field, i.e. low coverage samples and isotropic force field. The underlying reason is that molecular distributions assumed by existing denoising methods fail to capture the anisotropic characteristic of molecules. To tackle these challenges, we propose a novel hybrid noise strategy, including noises on both dihedral angel and coordinate. However, denoising such hybrid noise in a traditional way is no more equivalent to learning the force field. Through theoretical deductions, we find that the problem is caused by the dependency of the input conformation for covariance. To this end, we propose to decouple the two types of noise and design a novel fractional denoising method (Frad), which only denoises the latter coordinate part. In this way, Frad enjoys both the merits of sampling more low-energy structures and the force field equivalence. Extensive experiments show the effectiveness of Frad in molecular representation, with a new state-of-the-art on 9 out of 12 tasks of QM9 and on 7 out of 8 targets of MD17.

Defects determine many important properties and applications of materials, ranging from doping in semiconductors, to conductivity in mixed ionic-electronic conductors used in batteries, to active sites in catalysts. The theoretical description of defect formation in crystals has evolved substantially over the past century. Advances in supercomputing hardware, and the integration of new computational techniques such as machine learning, provide an opportunity to model longer length and time-scales than previously possible. In this Tutorial Review, we cover the description of free energies for defect formation at finite temperatures, including configurational (structural, electronic, spin) and vibrational terms. We discuss challenges in accounting for metastable defect configurations, progress such as machine learning force fields and thermodynamic integration to directly access entropic contributions, and bottlenecks in going beyond the dilute limit of defect formation. Such developments are necessary to support a new era of accurate defect predictions in computational materials chemistry.