Long-Lived Hot Electron in a Metallic Particle for Plasmonics and Catalysis: Ab Initio Nonadiabatic Molecular Dynamics with Machine Learning

MLatom Software Ecosystem for Surface Hopping Dynamics in Python with Quantum Mechanical and Machine Learning Methods

We present an open-source MLatom@XACS software ecosystem for on-the-fly surface hopping nonadiabatic dynamics based on the Landau-Zener-Belyaev-Lebedev algorithm. The dynamics can be performed via Python API with a wide range of quantum mechanical (QM) and machine learning (ML) methods, including ab initio QM (CASSCF and ADC(2)), semiempirical QM methods (e.g., AM1, PM3, OMx, and ODMx), and many types of ML potentials (e.g., KREG, ANI, and MACE). Combinations of QM and ML methods can also be used. While the user can build their own combinations, we provide AIQM1, which is based on Δ-learning and can be used out-of-the-box. We showcase how AIQM1 reproduces the isomerization quantum yield of trans-azobenzene at a low cost. We provide example scripts that, in dozens of lines, enable the user to obtain the final population plots by simply providing the initial geometry of a molecule. Thus, those scripts perform geometry optimization, normal mode calculations, initial condition sampling, parallel trajectories propagation, population analysis, and final result plotting. Given the capabilities of MLatom to be used for training different ML models, this ecosystem can be seamlessly integrated into the protocols building ML models for nonadiabatic dynamics. In the future, a deeper and more efficient integration of MLatom with Newton-X will enable a vast range of functionalities for surface hopping dynamics, such as fewest-switches surface hopping, to facilitate similar workflows via the Python API.

Nanocrystal Assemblies: Current Advances and Open Problems

We explore the potential of nanocrystals (a term used equivalently to nanoparticles) as building blocks for nanomaterials, and the current advances and open challenges for fundamental science developments and applications. Nanocrystal assemblies are inherently multiscale, and the generation of revolutionary material properties requires a precise understanding of the relationship between structure and function, the former being determined by classical effects and the latter often by quantum effects. With an emphasis on theory and computation, we discuss challenges that hamper current assembly strategies and to what extent nanocrystal assemblies represent thermodynamic equilibrium or kinetically trapped metastable states. We also examine dynamic effects and optimization of assembly protocols. Finally, we discuss promising material functions and examples of their realization with nanocrystal assemblies.

Construction of High Accuracy Machine Learning Interatomic Potential for Surface/Interface of Nanomaterials-A Review

The inherent discontinuity and unique dimensional attributes of nanomaterial surfaces and interfaces bestow them with various exceptional properties. These properties, however, also introduce difficulties for both experimental and computational studies. The advent of machine learning interatomic potential (MLIP) addresses some of the limitations associated with empirical force fields, presenting a valuable avenue for accurate simulations of these surfaces/interfaces of nanomaterials. Central to this approach is the idea of capturing the relationship between system configuration and potential energy, leveraging the proficiency of machine learning (ML) to precisely approximate high-dimensional functions. This review offers an in-depth examination of MLIP principles and their execution and elaborates on their applications in the realm of nanomaterial surface and interface systems. The prevailing challenges faced by this potent methodology are also discussed.

Tuning Optical Properties of Plasmonic Aerosols through Ligand-Solvent Interactions

Plasmonic nanoparticles are highly tunable light-harvesting materials with a wide array of applications in photonics and catalysis. More recently, there has been interest in using aerosolized plasmonic nanoparticles for cloud formation, airborne photocatalysts, and molecular sensors, all of which take advantage of the large scattering cross sections and the ability of these particles to support intense local field enhancement ("hot spots"). While extensive research has investigated properties of plasmonic particles in the solution phase, surfaces, and films, aerosolized plasmonics are relatively unexplored. Here, we demonstrate how the capping ligand, suspension solvent, and atomization conditions used for aerosol generation control the steady-state optical properties of aerosolized Silica@Au plasmonic nanoshells. Our experimental results, supported with spectral simulations, illustrate that ligand coverage and atomization conditions control the degree of solvent retention and thus the spectral characteristics and potential access to surfaces for catalysis in the aerosol phase, opening a new regime for tunable applications of plasmonic metamaterials.

Photocatalytic activity of dual defect modified graphitic carbon nitride is robust to tautomerism: machine learning assisted ab initio quantum dynamics

Two-dimensional graphitic carbon nitride (GCN) is a popular metal-free polymer for sustainable energy applications due to its unique structure and semiconductor properties. Dopants and defects are used to tune GCN, and dual defect modified GCN exhibits superior properties and enhanced photocatalytic efficiency in comparison to pristine or single defect GCN. We employ a multistep approach combining time-dependent density functional theory and nonadiabatic molecular dynamics (NAMD) with machine learning (ML) to investigate coupled structural and electronic dynamics in GCN over a nanosecond timescale, comparable to and exceeding the lifetimes of photo-generated charge carriers and photocatalytic events. Although frequent hydrogen hopping transitions occur among four tautomeric structures, the electron-hole separation and recombination processes are only weakly sensitive to the tautomerism. The charge separated state survives for about 10 ps, sufficiently long to enable photocatalysis. The employed ML-NAMD methodology provides insights into rare events that can influence excited state dynamics in the condensed phase and nanoscale materials and extends NAMD simulations from pico- to nanoseconds. The ab initio quantum dynamics simulation provides a detailed atomistic mechanism of photoinduced evolution of charge carriers in GCN and rationalizes how GCN remains photo-catalytically active despite its multiple isomeric and tautomeric forms.

Peeking into the Femtosecond Hot-Carrier Dynamics Reveals Unexpected Mechanisms in Plasmonic Photocatalysis

Plasmonic-driven photocatalysis may lead to reaction selectivity that cannot be otherwise achieved. A fundamental role is played by hot carriers, i.e., electrons and holes generated upon plasmonic decay within the metal nanostructure interacting with molecular species. Understanding the elusive microscopic mechanism behind such selectivity is a key step in the rational design of hot-carrier reactions. To accomplish that, we present state-of-the-art multiscale simulations, going beyond density functional theory, of hot-carrier injections for the rate-determining step of a photocatalytic reaction. We focus on carbon dioxide reduction, for which it was experimentally shown that the presence of a rhodium nanocube under illumination leads to the selective production of methane against carbon monoxide. We show that selectivity is due to a (predominantly) direct hole injection from rhodium to the reaction intermediate CHO. Unexpectedly, such an injection does not promote the selective reaction path by favoring proper bond breaking but rather by promoting bonding of the proper molecular fragment to the surface.

Ultrafast Charge Transfer and Recombination Dynamics in Monolayer-Multilayer WSe2 Junctions Revealed by Time-Resolved Photoemission Electron Microscopy

The ultrafast carrier dynamics of junctions between two chemically identical, but electronically distinct, transition metal dichalcogenides (TMDs) remains largely unknown. Here, we employ time-resolved photoemission electron microscopy (TR-PEEM) to probe the ultrafast carrier dynamics of a monolayer-to-multilayer (1L-ML) WSe2 junction. The TR-PEEM signals recorded for the individual components of the junction reveal the sub-ps carrier cooling dynamics of 1L- and 7L-WSe2, as well as few-ps exciton-exciton annihilation occurring on 1L-WSe2. We observe ultrafast interfacial hole (h) transfer from 1L- to 7L-WSe2 on an ∼0.2 ps time scale. The resultant excess h density in 7L-WSe2 decays by carrier recombination across the junction interface on an ∼100 ps time scale. Reminiscent of the behavior at a depletion region, the TR-PEEM image reveals the h density accumulation on the 7L-WSe2 interface, with a decay length ∼0.60 ± 0.17 μm. These charge transfer and recombination dynamics are in agreement with ab initio quantum dynamics. The computed orbital densities reveal that charge transfer occurs from the basal plane, which extends over both 1L and ML regions, to the upper plane localized on the ML region. This mode of charge transfer is distinctive to chemically homogeneous junctions of layered materials and constitutes an additional carrier deactivation pathway that should be considered in studies of 1L-TMDs found alongside their ML, a common occurrence in exfoliated samples.

Emerging Xene-Based Single-Atom Catalysts: Theory, Synthesis, and Catalytic Applications

In recent years, the emergence of novel 2D monoelemental materials (Xenes), e.g., graphdiyne, borophene, phosphorene, antimonene, bismuthene, and stanene, has exhibited unprecedented potentials for their versatile applications as well as addressing new discoveries in fundamental science. Owing to their unique physicochemical, optical, and electronic properties, emerging Xenes have been regarded as promising candidates in the community of single-atom catalysts (SACs) as single-atom active sites or support matrixes for significant improvement in intrinsic activity and selectivity. In order to comprehensively understand the relationships between the structure and property of Xene-based SACs, this review represents a comprehensive summary from theoretical predictions to experimental investigations. Firstly, theoretical calculations regarding both the anchoring of Xene-based single-atom active sites on versatile support matrixes and doping/substituting heteroatoms at Xene-based support matrixes are briefly summarized. Secondly, controlled synthesis and precise characterization are presented for Xene-based SACs. Finally, current challenges and future opportunities for the development of Xene-based SACs are highlighted.

Lattice Distortion and Low-Frequency Anharmonic Phonons Suppress Charge Recombination in Lead Halide Perovskites upon Pseudohalide Doping: Time-Domain Ab Initio Analysis

Perovskite solar cells have witnessed a surge in interest as a promising technology for low-cost, high-efficiency photovoltaics with certified power conversion efficiencies beyond 25%. However, their commercial development is hindered by poor stability and nonradiative losses that restrict their approach to the theoretical efficiency limit. Using ab initio nonadiabatic molecular dynamics, we demonstrate that nonradiative charge recombination is suppressed when the iodide in formamidinium lead iodide (FAPbI3) is partially replaced with pseudohalide anions (SCN-, BF4-, and PF6-). The replacement breaks the symmetry of the system and creates local structural distortion and dynamic disorder, decreasing electron-hole overlap and nonadiabatic electron-vibrational coupling. The charge carrier lifetime is found to increase with increased structural distortion and is the longest for PF6-. This work is fundamentally relevant to the design of high-performance perovskite materials for optoelectronic applications.

Charting Nanocluster Structures via Convolutional Neural Networks

A general method to obtain a representation of the structural landscape of nanoparticles in terms of a limited number of variables is proposed. The method is applied to a large data set of parallel tempering molecular dynamics simulations of gold clusters of 90 and 147 atoms, silver clusters of 147 atoms, and copper clusters of 147 atoms, covering a plethora of structures and temperatures. The method leverages convolutional neural networks to learn the radial distribution functions of the nanoclusters and distills a low-dimensional chart of the structural landscape. This strategy is found to give rise to a physically meaningful and differentiable mapping of the atom positions to a low-dimensional manifold in which the main structural motifs are clearly discriminated and meaningfully ordered. Furthermore, unsupervised clustering on the low-dimensional data proved effective at further splitting the motifs into structural subfamilies characterized by very fine and physically relevant differences such as the presence of specific punctual or planar defects or of atoms with particular coordination features. Owing to these peculiarities, the chart also enabled tracking of the complex structural evolution in a reactive trajectory. In addition to visualization and analysis of complex structural landscapes, the presented approach offers a general, low-dimensional set of differentiable variables that has the potential to be used for exploration and enhanced sampling purposes.

Machine-Learning Accelerated First-Principles Accurate Modeling of the Solid-Liquid Phase Transition in MgO under Mantle Conditions

While accurate measurements of MgO under extreme high-pressure conditions are needed to understand and model planetary behavior, these studies are challenging from both experimental and computational modeling perspectives. Herein, we accelerate density functional theory (DFT) accurate calculations using deep neural network potentials (DNPs) trained over multiple phases and study the melting behavior of MgO via the two-phase coexistence (TPC) approach at 0-300 GPa and ≤9600 K. The resulting DNP-TPC melting curve is in excellent agreement with existing experimental studies. We show that the mitigation of finite-size effects that typically skew the predicted melting temperatures in DFT-TPC simulations in excess of several hundred kelvin requires models with ∼16 000 atoms and >100 ps molecular dynamics trajectories. In addition, the DNP can successfully describe MgO metallization well at increased pressures that are captured by DFT but missed by classical interatomic potentials.

Inverse design of chiral functional films by a robotic AI-guided system

Artificial chiral materials and nanostructures with strong and tuneable chiroptical activities, including sign, magnitude, and wavelength distribution, are useful owing to their potential applications in chiral sensing, enantioselective catalysis, and chiroptical devices. Thus, the inverse design and customized manufacturing of these materials is highly desirable. Here, we use an artificial intelligence (AI) guided robotic chemist to accurately predict chiroptical activities from the experimental absorption spectra and structure/process parameters, and generate chiral films with targeted chiroptical activities across the full visible spectrum. The robotic AI-chemist carries out the entire process, including chiral film construction, characterization, and testing. A machine learned reverse design model using spectrum embedded descriptors is developed to predict optimal structure/process parameters for any targeted chiroptical property. A series of chiral films with a dissymmetry factor as high as 1.9 (gabs ~ 1.9) are identified out of more than 100 million possible structures, and their feasible application in circular polarization-selective color filters for multiplex laser display and switchable circularly polarized (CP) luminescence is demonstrated. Our findings not only provide chiral films with the highest reported chiroptical activity, but also have great fundamental value for the inverse design of chiroptical materials.

Ultrafast and Nanoscale Energy Transduction Mechanisms and Coupled Thermal Transport across Interfaces

The coupled interactions among the fundamental carriers of charge, heat, and electromagnetic fields at interfaces and boundaries give rise to energetic processes that enable a wide array of technologies. The energy transduction among these coupled carriers results in thermal dissipation at these surfaces, often quantified by the thermal boundary resistance, thus driving the functionalities of the modern nanotechnologies that are continuing to provide transformational benefits in computing, communication, health care, clean energy, power recycling, sensing, and manufacturing, to name a few. It is the purpose of this Review to summarize recent works that have been reported on ultrafast and nanoscale energy transduction and heat transfer mechanisms across interfaces when different thermal carriers couple near or across interfaces. We review coupled heat transfer mechanisms at interfaces of solids, liquids, gasses, and plasmas that drive the resulting interfacial heat transfer and temperature gradients due to energy and momentum coupling among various combinations of electrons, vibrons, photons, polaritons (plasmon polaritons and phonon polaritons), and molecules. These interfacial thermal transport processes with coupled energy carriers involve relatively recent research, and thus, several opportunities exist to further develop these nascent fields, which we comment on throughout the course of this Review.

Substrate-Selective Adhesion of Metal Nanoparticles to Graphene Devices

Nanostructured electronic devices, such as those based on graphene, are typically grown on top of the insulator SiO₂. Their exposure to a flux of small size-selected silver nanoparticles has revealed remarkably selective adhesion: the graphene channel can be made fully metallized, while the insulating substrate remains coverage-free. This conspicuous contrast derives from the low binding energy between the metal nanoparticles and a contaminant-free passivated silica surface. In addition to providing physical insight into nanoparticle adhesion, this effect may be of value in applications involving deposition of metallic layers on device working surfaces: it eliminates the need for masking the insulating region and the associated extensive and potentially deleterious pre- and postprocessing.

Active Site Engineering on Plasmonic Nanostructures for Efficient Photocatalysis

Plasmonic nanostructures have shown immense potential in photocatalysis because of their distinct photochemical properties associated with tunable photoresponses and strong light-matter interactions. The introduction of highly active sites is essential to fully exploit the potential of plasmonic nanostructures in photocatalysis, considering the inferior intrinsic activities of typical plasmonic metals. This review focuses on active site-engineered plasmonic nanostructures with enhanced photocatalytic performance, wherein the active sites are classified into four types (i.e., metallic sites, defect sites, ligand-grafted sites, and interface sites). The synergy between active sites and plasmonic nanostructures in photocatalysis is discussed in detail after briefly introducing the material synthesis and characterization methods. Active sites can promote the coupling of solar energy harvested by plasmonic metal to catalytic reactions in the form of local electromagnetic fields, hot carriers, and photothermal heating. Moreover, efficient energy coupling potentially regulates the reaction pathway by facilitating the excited state formation of reactants, changing the status of active sites, and creating additional active sites using photoexcited plasmonic metals. Afterward, the application of active site-engineered plasmonic nanostructures in emerging photocatalytic reactions is summarized. Finally, a summary and perspective of the existing challenges and future opportunities are presented. This review aims to deliver some insights into plasmonic photocatalysis from the perspective of active sites, expediting the discovery of high-performance plasmonic photocatalysts.

Machine learning for nanoplasmonics

Plasmonic nanomaterials have outstanding optoelectronic properties potentially enabling the next generation of catalysts, sensors, lasers and photothermal devices. Owing to optical and electron techniques, modern nanoplasmonics research generates large datasets characterizing features across length scales. Furthermore, optimizing syntheses leading to specific nanostructures requires time-consuming multiparametric approaches. These complex datasets and trial-and-error practices make nanoplasmonics research ripe for the application of machine learning (ML) and advanced data processing methods. ML algorithms capture relationships between synthesis, structure and performance in a way that far exceeds conventional simulation and theory approaches, enabling effective performance optimization. For example, neural networks can tailor the nanostructure morphology to target desired properties, identify synthetic conditions and extract quantitative information from complex data. Here we discuss the nascent field of ML for nanoplasmonics, describe the opportunities and limitations of ML in nanoplasmonic research, and conclude that ML is potentially transformative, especially if the community curates and shares its big data.

Investigating the factors that influence sacrificial hydrogen evolution activity for three structurally-related molecular photocatalysts: thermodynamic driving force, excited-state dynamics, and surface interaction with cocatalysts

The design of molecular organic photocatalysts for reactions such as water splitting requires consideration of factors that go beyond electronic band gap and thermodynamic driving forces. Here, we carried out a theoretical investigation of three molecular photocatalysts (1-3) that are structurally similar but that show different hydrogen evolution activities (25, 23 & 0 μmol h^-1 for 1-3, respectively). We used density functional theory (DFT) and time-dependent DFT calculations to evaluate the molecules' optoelectronic properties, such as ionization potential, electron affinity, and exciton potentials, as well as the interaction between the molecular photocatalysts and an idealized platinum cocatalyst surface. The 'static' picture thus obtained was augmented by probing the nonadiabatic dynamics of the molecules beyond the Born-Oppenheimer approximation, revealing a different picture of exciton recombination and relaxation for molecule 3. Our results suggest that slow exciton recombination, fast relaxation to the lowest-energy excited state, and a shorter charge transfer distance between the photocatalyst and the metal cocatalyst are important features that contribute to the photocatalytic hydrogen evolution activity of 1 and 2, and may partly rationalize the observed inactivity of 3, in addition to its lower light absorption profile.

Schottky barrier effect on plasmon-induced charge transfer

Plasmon-induced charge transfer causes electron-hole spatial separation at the metal-semiconductor interface, which plays a key role in photocatalytic and photovoltaic applications. The Schottky barrier formed at the metal-semiconductor interface can modify the hot carrier dynamics. Taking the Ag-TiO₂ system as an example, we have investigated plasmon-induced charge transfer at the Schottky junction using quantum mechanical simulations. We find that the Schottky barrier induced by n-type doping enhances the electron transfer and that induced by p-type doping enhances the hole transfer, which is attributed to the shift of the Fermi energy and the band bending of the Schottky junction at the interface. The Schottky barrier also modifies the layer distribution of hot carriers. In particular, for the system with a large band bending, there exists electron-hole spatial separation inside the TiO₂ substrate. Our results reveal the mechanism and dynamics of charge transfer at the Schottky junction, and pave the way for manipulating plasmon-assisted photocatalytic and photovoltaic applications.

Development and Validation of Versatile Deep Atomistic Potentials for Metal Oxides

Machine learning interatomic potentials powered by neural networks have been shown to readily model a gradient of compositions in metallic systems. However, their application to date on ionic systems tends to focus on specific compositions and oxidation states owing to their more heterogeneous chemical nature. Herein we show that a deep neural network potential (DNP) can model various properties of metal oxides with different oxidation states without additional charge information. We created and validated DNPs for Ag_xO_y, Cu_xO_y Mg_xO_y, Pt_xO_y, and Zn_xO_y, whereby each system was trained without any limitations on oxidation states. We illustrate how the database can be augmented to enhance the DNP transferability for a new polymorph, surface energies, and thermal expansion. In addition, we show that these potentials can correctly interpolate significant pressure and temperature ranges, exhibit stability over long molecular dynamics simulation time scales, and replicate nonharmonic thermal expansion, consistent with experimental results.

Where Nanosensors Meet Machine Learning: Prospects and Challenges in Detecting Disease X

Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive portable devices that can swiftly screen disease biomarkers on site, reducing the reliance on laboratory-based analyses. However, conventional data analytics limit the progress of nanosensor research. In this Perspective, we highlight the integral role of machine learning (ML) algorithms in advancing nanosensing strategies toward Disease X detection. We first summarize recent progress in utilizing ML algorithms for the smart design and fabrication of custom nanosensor platforms as well as realizing rapid on-site prediction of infection statuses. Subsequently, we discuss promising prospects in further harnessing the potential of ML algorithms in other aspects of nanosensor development and biomarker detection.

Electron-Volt Fluctuation of Defect Levels in Metal Halide Perovskites on a 100 ps Time Scale

Metal halide perovskites (MHPs) have gained considerable attention due to their excellent optoelectronic performance, which is often attributed to unusual defect properties. We demonstrate that midgap defect levels can exhibit very large and slow energy fluctuations associated with anharmonic acoustic motions. Therefore, care should be taken classifying MHP defects as deep or shallow, since shallow defects may become deep and vice versa. As a consequence, charges from deep levels can escape into bands, and light absorption can be extended to longer wavelengths, improving material performance. The phenomenon, demonstrated with iodine vacancy in CH₃NH₃PbI₃ using a machine learning force field, can be expected for a variety of defects and dopants in many MHPs and other soft inorganic semiconductors. Since large-scale anharmonic motions can be precursors to chemical decomposition, a known problem with MHPs, we propose that materials that are stiffer than MHPs but softer than traditional inorganic semiconductors, such as Si and TiO₂, may simultaneously exhibit excellent performance and stability.

How Hole Injection Accelerates Both Ion Migration and Nonradiative Recombination in Metal Halide Perovskites

Ion migration, hole trapping, and electron-hole recombination are common processes in metal halide perovskites. We demonstrate using ab initio non-adiabatic molecular dynamics and time-domain density functional theory that they are intricately related and strongly influence each other. The hole injection accelerates ion migration by decreasing the diffusion barrier and shortening the migration length. The injected hole also promotes the nonradiative charge recombination by strengthening electron-phonon interactions in the low-frequency region and prolonging the quantum coherence time. The synergy stems from the soft perovskite lattice and response of the valence band maximum to the Pb-I lattice distortion induced by the hole. This work provides important insights into the influence of ion mobility and hole injection on the performance of perovskite solar cells and suggests that high concentration of holes should be avoided.

A Mixed Deterministic-Stochastic Algorithm of the Branching Corrected Mean Field Method for Nonadiabatic Dynamics

We present a new algorithm of the branching corrected mean field (BCMF) method for nonadiabatic dynamics [J. Xu and L. Wang, J. Phys. Chem. Lett. 11, 8283 (2020)], which combines the key advantages of the two existed algorithms, i.e., the deterministic BCMF algorithm based on weights of trajectory branches (BCMF-w) and the stochastic BCMF algorithm with random collapse of the electronic wavefunction (BCMF-s). The resulting mixed deterministic-stochastic BCMF algorithm (BCMF-ws) is benchmarked in a series of standard scattering problems with potential wells on the excited-state surfaces, which are common in realistic systems. In all investigated cases, BCMF-ws holds the same high accuracy while the computational time is reduced about two orders of magnitude compared to the original BCMF-w and BCMF-s algorithms, thus promising for nonadiabatic dynamics simulations of general systems.

Interpolating Nonadiabatic Molecular Dynamics Hamiltonian with Inverse Fast Fourier Transform

Nonadiabatic (NA) molecular dynamics (MD) allows one to investigate far-from-equilibrium processes in nanoscale and molecular materials at the atomistic level and in the time domain, mimicking time-resolved spectroscopic experiments. Ab initio NAMD is limited to about 100 atoms and a few picoseconds, due to computational cost of excitation energies and NA couplings. We develop a straightforward methodology that can extend ab initio quality NAMD to nanoseconds and thousands of atoms. The ab initio NAMD Hamiltonian is sampled and interpolated along a trajectory using a Fourier transform, and then, it is used to perform NAMD with known algorithms. The methodology relies on the classical path approximation, which holds for many materials and processes. To achieve a complete ab initio quality description, the trajectory can be obtained using an ab initio trained machine learning force field. The method is demonstrated with charge carrier trapping and relaxation in hybrid organic-inorganic and all-inorganic metal halide perovskites that exhibit complex dynamics and are actively studied for optoelectronic applications.

Modeling Non-adiabatic Dynamics in Nanoscale and Condensed Matter Systems

Rapid, far-from-equilibrium processes involving excitation of electronic, vibrational, spin, photon, topological, and other degrees of freedom form the basis of modern technologies, including electronics and optoelectronics, solar energy harvesting and conversion to electrical and chemical energy, quantum information processing, spin- and valleytronics, chemical detection, and medical therapies. Such processes are studied experimentally with various time-resolved spectroscopies that allow scientists to track system's evolution on ultrafast time scales and at close to atomistic level of detail. The availability of various forms of lasing has made such measurements easily accessible to many experimental groups worldwide, to study atoms and small molecules, nanoscale and condensed matter systems, proteins, cells, and mesoscopic materials. The experimental work necessitates parallel theoretical efforts needed to interpret the experiments and to provide insights that cannot be gained through measurements due to experimental limitations.Non-adiabatic (NA) molecular dynamics (MD) allows one to study processes at the atomistic level and in the time domain most directly mimicking the time-resolved experiments. Atomistic modeling takes full advantage of chemical intuition and principles that guide design and fabrication of molecules and materials. It provides atomistic origins of quasi-particles, such as holes, excitons, trions, plasmons, phonons, polarons, polaritons, spin-waves, momentum-resolved and topological states, electrically and magnetically polarized structures, and other abstract concepts. An atomistic description enables one to study realistic aspects of materials, which necessarily contain defects, dopants, surfaces, interfaces, passivating ligands, and solvent layers. Often, such realistic features govern material properties and are hard to account for phenomenologically. NA-MD requires few approximations and assumptions. It does not need to assume that atomic motions are harmonic, that electrons are Drude oscillators, that coupling between different degrees of freedom is weak, that dynamics is Markovian or has short memory, or that evolution occurs by exponential kinetics of transitions between few states. The classical or semiclassical treatment of atomic motions constitutes the main approximation of NA-MD and is used because atoms are 3-5 orders of magnitude heavier than electrons. NA-MD is limited by system size, typically hundreds or thousands of atoms, and time scale, picoseconds to nanoseconds. The quality of NA-MD simulations depends on the electronic structure method used to obtain excited state energies and NA couplings.NA-MD has been largely popularized and advanced in the chemistry community that focuses on molecules. Modeling far-from-equilibrium dynamics in nanoscale and condensed matter systems often has to account for other types of physics. At the same time, condensed phase NA-MD allows for approximations that may not work in molecules. Focusing on the recent NA-MD developments aimed at studying excited state processes in nanoscale and condensed phases, this Account considers how the phenomena important on the nanoscale can be incorporated into NA-MD and what approximations can be made to increase its efficiency with complex systems and processes.

Tunable electron and hole injection channels at plasmonic Al-TiO2 interfaces

Metallic nanostructures can strongly absorb light through their plasmon excitations, whose nonradiative decay generates hot electron-hole pairs. When the metallic nanostructure is interfaced with a semiconductor, the spatial separation of hot carriers plays the central and decisive roles in photovoltaic and photocatalytic applications. In recent years, free-electron metals like Al have attracted tremendous attentions due to the much higher plasmon frequencies that could extend to the ultraviolet regime. Here, the plasmon excitations and charge separations at the Al-TiO₂ interfaces have been investigated using quantum-mechanical calculations, where the atomic structures and electronic dynamics are all treated from first-principles. It is found that the high-frequency plasmon of Al produces abundant and broad-band hot-carrier distributions, where the electron-hole symmetry is broken by the presence of the semiconductor band gap. Such an asymmetric hot-carrier distribution provides two competing channels, which can be controlled either by tuning the laser frequency, or by harnessing the plasmon frequency through the geometry and shape of the metallic nanostructure. Our study suggests that the Al plasmon offers a versatile and tunable pathway for the charge transfer and separation, and has general implications in plasmon-assisted photovoltaics and photocatalysis.

Thermal-Driven Dynamic Shape Change of Bimetallic Nanoparticles Extends Hot Electron Lifetime of Pt/MoS2 Catalysts

Using a combination of time-domain density functional theory and nonadiabatic (NA) molecular dynamics, we demonstrate that the replacement of noble Pt with cheap Sn in the Pt nanoparticles sensitized MoS₂ greatly retards the photoexcited "hot" electron relaxation. The simulations show that Sn substitution causes significant geometry distortion associated with the Sn dopant detaching from the Pt nanoparticle base, which decreases the NA coupling and creates an isolated trap state distant from the electron donor state. Generally, smaller NA coupling delays "hot" electron relaxation. At the same time, the photoexcited electron on MoS₂ first populates the nanoparticles state and then slowly goes to the trap state, following relaxation to the nanoparticle acceptor state over 1 ps. As a result, the "hot" electron lives over 3.5 times longer than that in pristine Pt/MoS₂ system. The long-lived "hot" electron associated with the reduced cost establishes a novel concept for developing high-efficient and cost-effective photocatalysts and photovoltaics.

Accelerated mapping of electronic density of states patterns of metallic nanoparticles via machine-learning

Within first-principles density functional theory (DFT) frameworks, it is challenging to predict the electronic structures of nanoparticles (NPs) accurately but fast. Herein, a machine-learning architecture is proposed to rapidly but reasonably predict electronic density of states (DOS) patterns of metallic NPs via a combination of principal component analysis (PCA) and the crystal graph convolutional neural network (CGCNN). With the PCA, a mathematically high-dimensional DOS image can be converted to a low-dimensional vector. The CGCNN plays a key role in reflecting the effects of local atomic structures on the DOS patterns of NPs with only a few of material features that are easily extracted from a periodic table. The PCA-CGCNN model is applicable for all pure and bimetallic NPs, in which a handful DOS training sets that are easily obtained with the typical DFT method are considered. The PCA-CGCNN model predicts the R2 value to be 0.85 or higher for Au pure NPs and 0.77 or higher for Au@Pt core@shell bimetallic NPs, respectively, in which the values are for the test sets. Although the PCA-CGCNN method showed a small loss of accuracy when compared with DFT calculations, the prediction time takes just ~ 160 s irrespective of the NP size in contrast to DFT method, for example, 13,000 times faster than the DFT method for Pt147. Our approach not only can be immediately applied to predict electronic structures of actual nanometer scaled NPs to be experimentally synthesized, but also be used to explore correlations between atomic structures and other spectrum image data of the materials (e.g., X-ray diffraction, X-ray photoelectron spectroscopy, and Raman spectroscopy).

Molecular excited states through a machine learning lens

Theoretical simulations of electronic excitations and associated processes in molecules are indispensable for fundamental research and technological innovations. However, such simulations are notoriously challenging to perform with quantum mechanical methods. Advances in machine learning open many new avenues for assisting molecular excited-state simulations. In this Review, we track such progress, assess the current state of the art and highlight the critical issues to solve in the future. We overview a broad range of machine learning applications in excited-state research, which include the prediction of molecular properties, improvements of quantum mechanical methods for the calculations of excited-state properties and the search for new materials. Machine learning approaches can help us understand hidden factors that influence photo-processes, leading to a better control of such processes and new rules for the design of materials for optoelectronic applications.

Concentric Approximation for Fast and Accurate Numerical Evaluation of Nonadiabatic Coupling with Projector Augmented-Wave Pseudopotentials

We develop an efficient and accurate method for numerical evaluation of nonadiabatic (NA) coupling in the Kohn-Sham representation with projector augmented-wave (PAW) pseudopotentials that are commonly used in electronic structure calculations on nanoscale, condensed matter, and molecular systems. Without additional cost, the method provides an order of magnitude improvement in accuracy compared to the current technique, while it is 3-4 orders of magnitude faster than the exact evaluation. Atomic displacements over typical time steps in molecular dynamics (MD) simulations are much smaller than the size of the PAW core region, and therefore, evaluation of the NA in the core is simplified. The accuracy is demonstrated with three condensed matter systems. The method is robust to variation in the MD time step. The accurate NA coupling evaluation also helps in maintaining phase-consistency of the NA coupling and identifying trivial crossings of adiabatic states. The approach stimulates NAMD applications to modeling of modern materials and processes.

Unravelling a Zigzag Pathway for Hot Carrier Collection with Graphene Electrode

The capture of photoexcited deep-band hot carriers, excited by photons with energies far above the bandgap, is of significant importance for photovoltaic and photoelectronic applications because it is directly related to the quantum efficiency of photon-to-electron conversion. By employing time-resolved photoluminescence and state-of-the-art time-domain density functional theory, we reveal that photoexcited hot carriers in organic-inorganic hybrid perovskites prefer a zigzag interfacial charge-transfer pathway, i.e., the hot carriers transfer back and forth between CH₃NH₃PbI₃ and graphene electrode, before they reach a charge-separated state. Driven by quantum coherence and interlayer vibrational modes, this pathway at the semiconductor-graphene interface takes about 400 fs, much faster than the relaxation process within CH₃NH₃PbI₃ (several picoseconds). Our work provides new insight into the fundamental understanding and precise manipulation of hot carrier dynamics at the complex interfaces, paving the way for highly efficient photovoltaic and photoelectric device optimization.

Efficient Photoexcited Charge Separation at the Interface of a Novel 0D/2D Heterojunction: A Time-Dependent Ultrafast Dynamic Study

To achieve efficient conversion and avoid loss of solar energy, ultrafast charge separation and slow electron-hole recombination are desired. Combining time-dependent density functional theory (TD-DFT) with nonadiabatic molecular dynamics, Au₉(PH₃)₈/MoS₂, as a prototype for zero-dimensional/two-dimensional (0D/2D) heterojunction, has been demonstrated to present excellent light absorption capacity and effective charge separation characteristics. In the heterojunction, photoexcitation of the Au₉(PH₃)₈ nanocluster drives an ultrafast electron transfer from Au₉(PH₃)₈ to MoS₂ within 20 fs, whereas photoexcitation of the MoS₂ nanosheet leads to hole transfer from MoS₂ to Au₉(PH₃)₈ within 680 fs. The strong nonadiabatic coupling and prominent density overlap are responsible for the faster electron separation relative to hole separation. In competition with the charge separation, electron-hole recombination requires 205 ns, ensuring an effective carrier separation. Our atomistic TD-DFT simulation provides valuable insights into the photocarrier dynamics at the Au₉(PH₃)₈/MoS₂ interface, which would stimulate the exploration of 0D/2D hybrid materials for photovoltaic and optoelectronic devices.

Modeling Auger Processes with Nonadiabatic Molecular Dynamics

Auger-type energy exchange plays key roles in the carrier dynamics in nanomaterials due to strong carrier-carrier interactions. However, theoretical descriptions are limited to perturbative calculations of scattering rates on static structures. We develop an accurate and efficient ab initio technique to model Auger scattering with nonadiabatic molecular dynamics. We incorporate the many-body Coulomb matrix into several surface hopping methods and describe simultaneously charge-charge and charge-phonon scattering in the time-domain and in a nonperturbative, configuration-dependent manner. The approach is illustrated with a CdSe quantum dot. Auger scattering between electrons and holes breaks the phonon bottleneck to electron relaxation. The bottleneck is recovered when electrons and holes are decoupled. The simulations correctly reproduce all experimental processes and time scales, including Auger- and phonon-assisted cooling of hot electrons, intraband carrier relaxation, and carrier recombination. Providing detailed insights into the energy flow, the developed method allows studies of carrier dynamics in nanomaterials with strong carrier-carrier interactions.

Isotopic Exchange Extends Charge Carrier Lifetime in Metal Lead Perovskites by Quantum Dynamics Simulations

One may expect that isotopic exchange has no influence on charge carrier lifetime and perovskite solar cell performance because isotopic effects do not affect the fundamental electronic structure of materials. Experiments defy this expectation. By performing nonadiabatic (NA) molecular dynamics simulations, we demonstrate that hydrogen and deuterium exchange significantly enhances the excited-state lifetime and stability of CH₃NH₃PbI₃. Replacing lighter hydrogen with heavier deuterium suppresses the collective motions of organic and inorganic components, thus enhancing lattice stiffness and decreasing the NA coupling. Isotopic exchange further reduces NA coupling by localizing electron wave functions for separation of electrons and holes, which beats the extended coherence time, slowing down nonradiative electron-hole recombination from CH₃ND₃PbI₃ to CD₃ND₃PbI₃ with respect to the pristine system. The unchanged fundamental electronic structure together with the prolonged carrier lifetime and enhanced stability rationalize the improvement of the deuterated CH₃NH₃PbI₃ solar cells. Our work provides valuable insights into isotope effects for the design of high-performance perovskite photovoltaic and optoelectronic devices.