Improving the reliability of machine learned potentials for modeling inhomogeneous liquids.

The atomic-scale response of inhomogeneous fluids at interfaces and surrounding solute particles plays a critical role in governing chemical, electrochemical, and biological processes. Classical molecular dynamics simulations have been applied extensively to simulate the response of fluids to inhomogeneities directly, but are limited by the accuracy of the underlying interatomic potentials. Here, we use neural network potentials (NNPs) trained to ab initio simulations to accurately predict the inhomogeneous responses of two distinct fluids: liquid water and molten NaCl. Although NNPs can be readily trained to model complex bulk systems across a range of state points, we show that to appropriately model a fluid's response at an interface, relevant inhomogeneous configurations must be included in the training data. In order to sufficiently sample appropriate configurations of such inhomogeneous fluids, we develop protocols based on molecular dynamics simulations in the presence of external potentials. We demonstrate that NNPs trained on inhomogeneous fluid configurations can more accurately predict several key properties of fluids-including the density response, surface tension and size-dependent cavitation free energies-for liquid water and molten NaCl, compared to both empirical interatomic potentials and NNPs that are not trained on such inhomogeneous configurations. This work therefore provides a first demonstration and framework to extract the response of inhomogeneous fluids from first principles for classical density-functional treatment of fluids free from empirical potentials.

Quantum-mechanical effects in photoluminescence from thin crystalline gold films.

Luminescence constitutes a unique source of insight into hot carrier processes in metals, including those in plasmonic nanostructures used for sensing and energy applications. However, being weak in nature, metal luminescence remains poorly understood, its microscopic origin strongly debated, and its potential for unraveling nanoscale carrier dynamics largely unexploited. Here, we reveal quantum-mechanical effects in the luminescence emanating from thin monocrystalline gold flakes. Specifically, we present experimental evidence, supported by first-principles simulations, to demonstrate its photoluminescence origin (i.e., radiative emission from electron/hole recombination) when exciting in the interband regime. Our model allows us to identify changes to the measured gold luminescence due to quantum-mechanical effects as the gold film thickness is reduced. Excitingly, such effects are observable in the luminescence signal from flakes up to 40 nm in thickness, associated with the out-of-plane discreteness of the electronic band structure near the Fermi level. We qualitatively reproduce the observations with first-principles modeling, thus establishing a unified description of luminescence in gold monocrystalline flakes and enabling its widespread application as a probe of carrier dynamics and light-matter interactions in this material. Our study paves the way for future explorations of hot carriers and charge-transfer dynamics in a multitude of material systems.

Fully Ab Initio Approach to Inelastic Atom-Surface Scattering.

We introduce a fully ab initio theory for inelastic scattering of any atom from any surface exciting single phonons, and apply the theory to helium scattering from Nb(100). The key aspect making our approach general is a direct first-principles evaluation of the scattering atom-electron vertex. By correcting misleading results from current state-of-the-art theories, this fully ab initio approach will be critical in guiding and interpreting experiments that adopt next-generation, nondestructive atomic beam scattering.

How spin relaxes and dephases in bulk halide perovskites.

Spintronics in halide perovskites has drawn significant attention in recent years, due to their highly tunable spin-orbit fields and intriguing interplay with lattice symmetry. Here, we perform first-principles calculations to determine the spin relaxation time (T1) and ensemble spin dephasing time ([Formula: see text]) in a prototype halide perovskite, CsPbBr3. To accurately capture spin dephasing in external magnetic fields we determine the Landé g-factor from first principles and take it into account in our calculations. These allow us to predict intrinsic spin lifetimes as an upper bound for experiments, identify the dominant spin relaxation pathways, and evaluate the dependence on temperature, external fields, carrier density, and impurities. We find that the Fröhlich interaction that dominates carrier relaxation contributes negligibly to spin relaxation, consistent with the spin-conserving nature of this interaction. Our theoretical approach may lead to new strategies to optimize spin and carrier transport properties.

Nanomolding of Two-Dimensional Materials.

Lateral confinement of layered, two-dimensional (2D) materials has uniquely enabled the exploration of several topological phenomena in electron transport due to the well-defined nanoscale cross-sections and perimeters. At present, research on laterally confined 2D materials is constrained by the lack of synthesis methods that can reliably and controllably produce nanostructures with narrow widths and high aspect ratios. We demonstrate the use of thermomechanical nanomolding (TMNM) to fabricate nanowires of six layered materials (Te, In2Se3, Bi2Te3, Bi2Se3, GaSe, and Sb2Te3) with widths of 40 nm and aspect ratios above 100. During molding, the van der Waals (vdW) layers rotate by 90° from the horizontal direction in the bulk feedstock to the vertical direction in the molded nanowire, such that the layers are aligned along the nanowire length. We find that interfacial diffusion and surface energy minimization drive nanowire formation during TMNM, often resulting in single-crystalline nanowires with consistent crystallographic orientation.

First-principles molten salt phase diagrams through thermodynamic integration.

Precise prediction of phase diagrams in molecular dynamics simulations is challenging due to the simultaneous need for long time and large length scales and accurate interatomic potentials. We show that thermodynamic integration from low-cost force fields to neural network potentials trained using density-functional theory (DFT) enables rapid first-principles prediction of the solid-liquid phase boundary in the model salt NaCl. We use this technique to compare the accuracy of several DFT exchange-correlation functionals for predicting the NaCl phase boundary and find that the inclusion of dispersion interactions is critical to obtain good agreement with experiment. Importantly, our approach introduces a method to predict solid-liquid phase boundaries for any material at an ab initio level of accuracy, with the majority of the computational cost at the level of classical potentials.

Transport and Interfacial Injection of d-Band Hot Holes Control Plasmonic Chemistry.

Harnessing nonequilibrium hot carriers from plasmonic metal nanostructures constitutes a vibrant research field with the potential to control photochemical reactions, particularly 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 among plasmon excitation, hot-carrier generation, transport, and interfacial collection in plasmonic photocatalytic devices, uniquely determining the charge injection efficiency at the solid/liquid interface. Measuring the internal quantum efficiency of ultrathin (14-33 nm) single-crystalline plasmonic gold (Au) nanoantenna arrays on titanium dioxide substrates, we find that the performance of the device is limited by hot hole collection at the metal/electrolyte interface. Our solid- and liquid-state experimental approach, combined with ab initio simulations, demonstrates more efficient collection of high-energy d-band holes traveling in the [111] orientation, enhancing oxidation reactions on {111} surfaces. These findings establish new guidelines for optimizing plasmonic photocatalytic systems and optoelectronic devices.

Design of Polymer Nanodielectrics for Capacitive Energy Storage.

Polymer nanodielectrics present a particularly challenging materials design problem for capacitive energy storage applications like polymer film capacitors. High permittivity and breakdown strength are needed to achieve high energy density and loss must be low. Strategies that increase permittivity tend to decrease the breakdown strength and increase loss. We hypothesize that a parameter space exists for fillers of modest aspect ratio functionalized with charge-trapping molecules that results in an increase in permittivity and breakdown strength simultaneously, while limiting increases in loss. In this work, we explore this parameter space, using physics-based, multiscale 3D dielectric property simulations, mixed-variable machine learning and Bayesian optimization to identify the compositions and morphologies which lead to the optimization of these competing properties. We employ first principle-based calculations for interface trap densities which are further used in breakdown strength calculations. For permittivity and loss calculations, we use continuum scale modelling and finite difference solution of Poisson's equation for steady-state currents. We propose a design framework for optimizing multiple properties by tuning design variables including the microstructure and interface properties. Finally, we employ mixed-variable global sensitivity analysis to understand the complex interplay between four continuous microstructural and two categorical interface choices to extract further physical knowledge on the design of nanodielectrics.

Simulating dielectric spectra: A demonstration of the direct electric field method and a new model for the nonlinear dielectric response.

We demonstrate a method to compute the dielectric spectra of fluids in molecular dynamics (MD) by directly applying electric fields to the simulation. We obtain spectra from MD simulations with low magnitude electric fields (≈0.01 V/Å) in agreement with spectra from the fluctuation-dissipation method for water and acetonitrile. We examine this method's trade-off between noise at low field magnitudes and the nonlinearity of the response at higher field magnitudes. We then apply the Booth equation to describe the nonlinear response of both fluids at low frequency (0.1 GHz) and high field magnitude (up to 0.5 V/Å). We develop a model of the frequency-dependent nonlinear response by combining the Booth description of the static nonlinear dielectric response of fluids with the frequency-dependent linear dielectric response of the Debye model. We find good agreement between our model and the MD simulations of the nonlinear dielectric response for both acetonitrile and water.

Solvent effects determine the sign of the charges of maximum entropy and capacitance at silver electrodes.

Fully harnessing electrochemical interfaces for reactions requires a detailed understanding of solvent effects in the electrochemical double layer. Predicting the significant impact of solvents on entropic and electronic properties of electrochemical interfaces has remained an open challenge of computational electrochemistry. Using molecular dynamics simulations of silver-water and silver-acetonitrile interfaces, we show that switching the solvent changes the signs for both the charge of maximum capacitance (CMC) and charge of maximum entropy (CME). Contrasting the capacitance and CME behavior of these two interfaces, we demonstrate that the preferred orientation of the solvent molecule and the corresponding charge density determine the sign of the CMC and CME and, hence, the qualitatively different charge asymmetry of the electrochemical interface.

Dielectric Properties of Polymer Nanocomposite Interphases Using Electrostatic Force Microscopy and Machine Learning.

Knowing the dielectric properties of the interfacial region in polymer nanocomposites is critical to predicting and controlling dielectric properties. They are, however, difficult to characterize due to their nanoscale dimensions. Electrostatic force microscopy (EFM) provides a pathway to local dielectric property measurements, but extracting local dielectric permittivity in complex interphase geometries from EFM measurements remains a challenge. This paper demonstrates a combined EFM and machine learning (ML) approach to measuring interfacial permittivity in 50 nm silica particles in a PMMA matrix. We show that ML models trained to finite-element simulations of the electric field profile between the EFM tip and nanocomposite surface can accurately determine the interface permittivity of functionalized nanoparticles. It was found that for the particles with a polyaniline brush layer, the interfacial region was detectable (extrinsic interface). For bare silica particles, the intrinsic interface was detectable only in terms of having a slightly higher or lower permittivity. This approach fully accounts for the complex interplay of filler, matrix, and interface permittivity on the force gradients measured in EFM that are missed by previous semianalytic approaches, providing a pathway to quantify and design nanoscale interface dielectric properties in nanodielectric materials.

Topological Metal MoP Nanowire for Interconnect.

The increasing resistance of copper (Cu) interconnects for decreasing dimensions is a major challenge in continued downscaling of integrated circuits beyond the 7 nm technology node as it leads to unacceptable signal delays and power consumption in computing. The resistivity of Cu increases due to electron scattering at surfaces and grain boundaries at the nanoscale. Topological semimetals, owing to their topologically protected surface states and suppressed electron backscattering, are promising candidates to potentially replace current Cu interconnects. Here, we report the unprecedented resistivity scaling of topological metal molybdenum phosphide (MoP) nanowires, and it is shown that the resistivity values are superior to those of nanoscale Cu interconnects <500 nm2 cross-section areas. The cohesive energy of MoP suggests better stability against electromigration, enabling a barrier-free design . MoP nanowires are more resistant to surface oxidation than the 20 nm thick Cu. The thermal conductivity of MoP is comparable to those of Ru and Co. Most importantly, it is demonstrated that the dimensional scaling of MoP, in terms of line resistance versus total cross-sectional area, is competitive to those of effective Cu with barrier/liner and barrier-less Ru, suggesting MoP is an attractive alternative for the scaling challenge of Cu interconnects.

Improving the Accuracy of Atomistic Simulations of the Electrochemical Interface.

Atomistic simulation of the electrochemical double layer is an ambitious undertaking, requiring quantum mechanical description of electrons, phase space sampling of liquid electrolytes, and equilibration of electrolytes over nanosecond time scales. All models of electrochemistry make different trade-offs in the approximation of electrons and atomic configurations, from the extremes of classical molecular dynamics of a complete interface with point-charge atoms to correlated electronic structure methods of a single electrode configuration with no dynamics or electrolyte. Here, we review the spectrum of simulation techniques suitable for electrochemistry, focusing on the key approximations and accuracy considerations for each technique. We discuss promising approaches, such as enhanced sampling techniques for atomic configurations and computationally efficient beyond density functional theory (DFT) electronic methods, that will push electrochemical simulations beyond the present frontier.

Behavior of Linear and Nonlinear Dimensionality Reduction for Collective Variable Identification of Small Molecule Solution-Phase Reactions.

Identifying collective variables (CVs) for chemical reactions is essential to reduce the 3N-dimensional energy landscape into lower dimensional basins and barriers of interest. However, in condensed phase processes, the nonmeaningful motions of bulk solvent often overpower the ability of dimensionality reduction methods to identify correlated motions that underpin collective variables. Yet solvent can play important indirect or direct roles in reactivity, and much can be lost through treatments that remove or dampen solvent motion. This has been amply demonstrated within principal component analysis (PCA), although less is known about the behavior of nonlinear dimensionality reduction methods, e.g., uniform manifold approximation and projection (UMAP), that have become recently utilized. The latter presents an interesting alternative to linear methods though often at the expense of interpretability. This work presents distance-attenuated projection methods of atomic coordinates that facilitate the application of both PCA and UMAP to identify collective variables in the presence of explicit solvent and further the specific identity of solvent molecules that participate in chemical reactions. The performance of both methods is examined in detail for two reactions where the explicit solvent plays very different roles within the collective variables. When applied to raw molecular dynamics data in solution, both PCA and UMAP representations are dominated by bulk solvent motions. On the other hand, when applied to data preprocessed by our attenuated projection methods, both PCA and UMAP identify the appropriate collective variables (though varying sensitivity is observed due to the presence of explicit solvent that results from the projection method). Importantly, this approach allows identification of specific solvent molecules that are relevant to the CVs and their importance.

Interfacial water asymmetry at ideal electrochemical interfaces.

Controlling electrochemical reactivity requires a detailed understanding of the charging behavior and thermodynamics of the electrochemical interface. Experiments can independently probe the overall charge response of the electrochemical double layer by capacitance measurements and the thermodynamics of the inner layer with potential of maximum entropy measurements. Relating these properties by computational modeling of the electrochemical interface has so far been challenging due to the low accuracy of classical molecular dynamics (MD) for capacitance and the limited time and length scales of ab initio MD. Here, we combine large ensembles of long-time-scale classical MD simulations with charge response from electronic density functional theory to predict the potential-dependent capacitance of a family of ideal aqueous electrochemical interfaces with different peak capacitances. We show that while the potential of maximum capacitance varies, this entire family exhibits an electrode charge of maximum capacitance (CMC) between -2.9 and -2.2 µC/cm2, regardless of the details in the electronic response. Simulated heating of the same interfaces reveals that the entropy peaks at a charge of maximum entropy (CME) of -5.1 ± 0.6 µC/cm2, in agreement with experimental findings for metallic electrodes. The CME and CMC both indicate asymmetric response of interfacial water that is stronger for negatively charged electrodes, while the difference between CME and CMC illustrates the richness in behavior of even the ideal electrochemical interface.

Giant Spin Lifetime Anisotropy and Spin-Valley Locking in Silicene and Germanene from First-Principles Density-Matrix Dynamics.

Through first-principles real-time density-matrix (FPDM) dynamics simulations, we investigated spin relaxation due to electron-phonon and electron-impurity scatterings with spin-orbit coupling (SOC) in two-dimensional Dirac materials silicene and germanene at finite temperatures. We discussed the applicability of conventional descriptions of spin relaxation mechanisms by Elliott-Yafet (EY) and D'yakonov-Perel' (DP) compared to the FPDM method, which is determined by a complex interplay of intrinsic SOC, external fields, and scattering strength. For example, the electric field dependence of the spin lifetime by FPDM is close to the DP mechanism for silicene at room temperature but similar to the EY mechanism for germanene. Because of its stronger SOC strength and buckled structure in contrast to graphene, germanene has a giant spin lifetime anisotropy and spin-valley locking effect under nonzero Ez and low temperatures. More importantly, germanene has a long spin lifetime (∼100 ns at 50 K) and an ultrahigh carrier mobility, making it advantageous for spin-valleytronic applications.

Quantifying Uncertainties in Solvation Procedures for Modeling Aqueous Phase Reaction Mechanisms.

Computational quantum chemistry provides fundamental chemical and physical insights into solvated reaction mechanisms across many areas of chemistry, especially in homogeneous and heterogeneous renewable energy catalysis. Such reactions may depend on explicit interactions with ions and solvent molecules that are nontrivial to characterize. Rigorously modeling explicit solvent effects with molecular dynamics usually brings steep computational costs while the performance of continuum solvent models such as polarizable continuum model (PCM), charge-asymmetric nonlocally determined local-electric (CANDLE), conductor-like screening model for real solvents (COSMO-RS), and effective screening medium method with the reference interaction site model (ESM-RISM) are less well understood for reaction mechanisms. Here, we revisit a fundamental aqueous hydride transfer reaction-carbon dioxide (CO2) reduction by sodium borohydride (NaBH4)-as a test case to evaluate how different solvent models perform in aqueous phase charge migrations that would be relevant to renewable energy catalysis mechanisms. For this system, quantum mechanics/molecular mechanics (QM/MM) molecular dynamics simulations almost exactly reproduced energy profiles from QM simulations, and the Na+ counterion in the QM/MM simulations plays an insignificant role over ensemble averaged trajectories that describe the reaction pathway. However, solvent models used on static calculations gave much more variability in data depending on whether the system was modeled using explicit solvent shells and/or the counterion. We pinpoint this variability due to unphysical descriptions of charge-separated states in the gas phase (i.e., self-interaction errors), and we show that using more accurate hybrid functionals and/or explicit solvent shells lessens these errors. This work closes with recommended procedures for treating solvation in future computational efforts in studying renewable energy catalysis mechanisms.

Resolving the Geometry/Charge Puzzle of the c(2 × 2)-Cl Cu(100) Electrode.

Potential-induced changes in charge and surface structure are significant drivers of the reactivity of electrochemical interfaces but are frequently difficult to decouple from the effects of surface solvation. Here, we consider the Cu(100) surface with a c(2 × 2)-Cl adlayer, a model surface with multiple geometry measurements under both ultrahigh vacuum and electrochemical conditions. Under aqueous electrochemical conditions, the measured Cu-Cl interplanar separation (dCu-Cl) increases by at least 0.3 Å relative to that under ultrahigh vacuum conditions. This large geometry change is unexpected for a hydrophobic surface, and it requires invoking a negative charge on the Cl-covered surface which is much greater than expected from the work function and our capacitance measurements. To resolve this inconsistency we employ ab initio calculations and find that the Cu-Cl separation increases with charging at a rate of 0.7 Å/e- per Cl atom. The larger Cu-Cl bond distance increases the surface dipole and, therefore, the work function of the interface, contributing to the negative charge under fixed potential electrochemical conditions. Interactions with water are not needed to explain either the large charge or large Cu-Cl interplanar spacing of this surface under electrochemical conditions.

Coupled Electromagnetic and Reaction Kinetics Simulation of Super-Resolution Interference Lithography.

Inspired by the ability of super-resolved fluorescence microscopy to circumvent the diffraction barrier, two-color super-resolution interference lithography exploits nonequilibrium kinetics in materials to achieve large-area nanopatterning while using visible light. Periodic patterns with super-resolved features down to tens of nanometers have been demonstrated in thin films and monolayers. Extending these advances to the bulk nanopatterning of thick films requires a quantitative understanding of the time-dependent interactions of optical dynamics, including absorption, diffraction, and intensity modulation at two wavelengths, with the photoactivated and inhibited reaction kinetics. Here, we develop an efficient electromagnetic (EM) perturbation theory approach that facilitates for the first time fully coupled simulations of EM and chemical kinetics in two-color interference lithography. Applied to a spirothiopyran-functionalized photoresist system, these simulations show that diffraction and absorption effects are negligible (<0.1%) for depths up to 10 µm, and that tuning exposure time and intensities can lead to concentration contrast up to 80%. We investigate multiple exposure strategies to reduce the pitch of the line pattern including sequential exposures with different times to achieve uniform lines and multiplexed exposures with equal periods. This capability to rapidly and accurately predict the coupled optical and chemical dynamics facilitates the computational design of high-precision patterns in two-color interference lithography.