Ice Phase Classification Made Easy with Score-Based Denoising.

Accurate identification of ice phases is essential for understanding various physicochemical phenomena. However, such classification for structures simulated with molecular dynamics is complicated by the complex symmetries of ice polymorphs and thermal fluctuations. For this purpose, both traditional order parameters and data-driven machine learning approaches have been employed, but they often rely on expert intuition, specific geometric information, or large training data sets. In this work, we present an unsupervised phase classification framework that combines a score-based denoiser model with a subsequent model-free classification method to accurately identify ice phases. The denoiser model is trained on perturbed synthetic data of ideal reference structures, eliminating the need for large data sets and labeling efforts. The classification step utilizes the smooth overlap of atomic position (SOAP) descriptors as the atomic fingerprint, ensuring Euclidean symmetries and transferability to various structural systems. Our approach achieves a remarkable 100% accuracy in distinguishing ice phases of test trajectories using only seven ideal reference structures of ice phases as model inputs. This demonstrates the generalizability of the score-based denoiser model in facilitating phase identification for complex molecular systems. The proposed classification strategy can be broadly applied to investigate structural evolution and phase identification for a wide range of materials, offering new insights into the fundamental understanding of water and other complex systems.

Accuracy, transferability, and computational efficiency of interatomic potentials for simulations of carbon under extreme conditions.

Large-scale atomistic molecular dynamics (MD) simulations provide an exceptional opportunity to advance the fundamental understanding of carbon under extreme conditions of high pressures and temperatures. However, the fidelity of these simulations depends heavily on the accuracy of classical interatomic potentials governing the dynamics of many-atom systems. This study critically assesses several popular empirical potentials for carbon, as well as machine learning interatomic potentials (MLIPs), in their ability to simulate a range of physical properties at high pressures and temperatures, including the diamond equation of state, its melting line, shock Hugoniot, uniaxial compressions, and the structure of liquid carbon. Empirical potentials fail to accurately predict the behavior of carbon under high pressure-temperature conditions. In contrast, MLIPs demonstrate quantum accuracy, with Spectral Neighbor Analysis Potential (SNAP) and atomic cluster expansion (ACE) being the most accurate in reproducing the density functional theory results. ACE displays remarkable transferability despite not being specifically trained for extreme conditions. Furthermore, ACE and SNAP exhibit superior computational performance on graphics processing unit-based systems in billion atom MD simulations, with SNAP emerging as the fastest. In addition to offering practical guidance in selecting an interatomic potential with a fine balance of accuracy, transferability, and computational efficiency, this work also highlights transformative opportunities for groundbreaking scientific discoveries facilitated by quantum-accurate MD simulations with MLIPs on emerging exascale supercomputers.

Role of ripples in altering the electronic and chemical properties of graphene.

Ripples of graphene are known to manipulate electronic and hydrogenation properties of graphitic materials. More detailed work is needed to elucidate the structure-property relationship of these systems. In this work, the density functional theory is used to compute the energy and electronic structure of the graphene models with respect to variable curvatures and hydrogen adsorption sites. The magnitude of finite bandgap opening depends on the orientation of ripples, and the hydrogen adsorption energy depends on the local curvature of graphene. An adsorbed hydrogen alters the local curvature, resulting in relatively weakened adsorption on the neighboring three sites, which gives a rationale to experimentally observed dynamic equilibrium stoichiometry (H:C = 1:4) of hydrogenated graphene. The surface diffusion transition state energy of adsorbed hydrogen is computed, which suggests that the Eley-Rideal surface recombination mechanism may be important to establish the dynamic equilibrium, instead of the commonly assumed Langmuir-Hinshelwood mechanism.

Probing Charge Dynamics in Diamond with an Individual Color Center.

Control over the charge states of color centers in solids is necessary to fully utilize them in quantum technologies. However, the microscopic charge dynamics of deep defects in wide-band-gap semiconductors are complex, and much remains unknown. We utilize a single-shot charge-state readout of an individual nitrogen-vacancy (NV) center to probe the charge dynamics of the surrounding defects in diamond. We show that the NV center charge state can be converted through the capture of holes produced by optical illumination of defects many micrometers away. With this method, we study the optical charge conversion of silicon-vacancy (SiV) centers and provide evidence that the dark state of the SiV center under optical illumination is SiV2-. These measurements illustrate that charge carrier generation, transport, and capture are important considerations in the design and implementation of quantum devices with color centers and provide a novel way to probe and control charge dynamics in diamond.

TopoMS: Comprehensive topological exploration for molecular and condensed-matter systems.

We introduce TopoMS, a computational tool enabling detailed topological analysis of molecular and condensed-matter systems, including the computation of atomic volumes and charges through the quantum theory of atoms in molecules, as well as the complete molecular graph. With roots in techniques from computational topology, and using a shared-memory parallel approach, TopoMS provides scalable, numerically robust, and topologically consistent analysis. TopoMS can be used as a command-line tool or with a GUI (graphical user interface), where the latter also enables an interactive exploration of the molecular graph. This paper presents algorithmic details of TopoMS and compares it with state-of-the-art tools: Bader charge analysis v1.0 (Arnaldsson et al., 01/11/17) and molecular graph extraction using Critic2 (Otero-de-la-Roza et al., Comput. Phys. Commun. 2014, 185, 1007). TopoMS not only combines the functionality of these individual codes but also demonstrates up to 4× performance gain on a standard laptop, faster convergence to fine-grid solution, robustness against lattice bias, and topological consistency. TopoMS is released publicly under BSD License. © 2018 Wiley Periodicals, Inc.

Identification of the local sources of paramagnetic noise in superconducting qubit devices fabricated on α-Al2O3 substrates using density-functional calculations.

Effective methods for decoupling superconducting qubits (SQs) from parasitic environmental noise sources are critical for increasing their lifetime and phase fidelity. While considerable progress has been made in this area, the microscopic origin of noise remains largely unknown. In this work, first principles density functional theory calculations are employed to identify the microscopic origins of magnetic noise sources in SQs on an α-Al2O3 substrate. The results indicate that it is unlikely that the existence of intrinsic point defects and defect complexes in the substrate are responsible for low frequency noise in these systems. Rather, a comprehensive analysis of extrinsic defects shows that surface aluminum ions interacting with ambient molecules will form a bath of magnetic moments that can couple to the SQ paramagnetically. The microscopic origin of this magnetic noise source is discussed and strategies for ameliorating the effects of these magnetic defects are proposed.

Unraveling the Correlation between the Interface Structures and Tunable Magnetic Properties of La1-xSrxCoO3-δ/La1-xSrxMnO3-δ Bilayers Using Deep Learning Models.

Perovskite oxides are gaining significant attention for use in next-generation magnetic and ferroelectric devices due to their exceptional charge transport properties and the opportunity to tune the charge, spin, lattice, and orbital degrees of freedom. Interfaces between perovskite oxides, exemplified by La1-xSrxCoO3-δ/La1-xSrxMnO3-δ (LSCO/LSMO) bilayers, exhibit unconventional magnetic exchange switching behavior, offering a pathway for innovative designs in perovskite oxide-based devices. However, the precise atomic-level stoichiometric compositions and chemophysical properties of these interfaces remain elusive, hindering the establishment of surrogate design principles. We leverage first-principles simulations, evolutionary algorithms, and neural network searches with on-the-fly uncertainty quantification to design deep learning model ensembles to investigate over 50,000 LSCO/LSMO bilayer structures as a function of oxygen deficiency (δ) and strontium concentration (x). Structural analysis of the low-energy interface structures reveals that preferential segregation of oxygen vacancies toward the interfacial La0.7Sr0.3CoO3-δ layers causes distortion of the CoOx polyhedra and the emergence of magnetically active Co2+ ions. At the same time, an increase in the Sr concentration and a decrease in oxygen vacancies in the La0.7Sr0.3MnO3-δ layers tend to retain MnO6 octahedra and promote the formation of Mn4+ ions. Electronic structure analysis reveals that the nonuniform distributions of Sr ions and oxygen vacancies on both sides of the interface can alter the local magnetization at the interface, showing a transition from ferromagnetic (FM) to local antiferromagnetic (AFM) or ferrimagnetic regions. Therefore, the exotic properties of La1-xSrxCoO3-δ/La1-xSrxMnO3-δ are strongly coupled to the presence of hard/soft magnetic layers, as well as the FM to AFM transition at the interface, and can be tuned by changing the Sr concentration and oxygen partial pressure during growth. These insights provide valuable guidance for the precise design of perovskite oxide multilayers, enabling tailoring of their functional properties to meet specific requirements for various device applications.

Dangling Bonds as Possible Contributors to Charge Noise in Silicon and Silicon-Germanium Quantum Dot Qubits.

Spin qubits based on Si and Si1-xGex quantum dot architectures exhibit among the best coherence times of competing quantum computing technologies, yet they still suffer from charge noise that limit their qubit gate fidelities. Identifying the origins of these charge fluctuations is therefore a critical step toward improving Si quantum-dot-based qubits. Here, we use hybrid functional calculations to investigate possible atomistic sources of charge noise, focusing on charge trapping at Si and Ge dangling bonds (DBs). We evaluate the role of global and local environment in the defect levels associated with DBs in Si, Ge, and Si1-xGex alloys, and consider their trapping and excitation energies within the framework of configuration coordinate diagrams. We additionally consider the influence of strain and oxidation in charge-trapping energetics by analyzing Si and GeSi DBs in SiO2 and strained Si layers in typical Si1-xGex quantum dot heterostructures. Our results identify that Ge dangling bonds are more problematic charge-trapping centers both in typical Si1-xGex alloys and associated oxidation layers, and they may be exacerbated by compositional inhomogeneities. These results suggest the importance of alloy homogeneity and possible passivation schemes for DBs in Si-based quantum dot qubits and are of general relevance to mitigating possible trap levels in other Si, Ge, and Si1-xGex-based metal-oxide-semiconductor stacks and related devices.

Simultaneous control of ionic and electronic conductivity in materials: thallium bromide case study.

Achieving simultaneous control of ionic and electronic conductivity in materials is one of the great challenges in solid state ionics. Since these properties are intertwined, optimizing one often results in degrading the other. In this Letter, we propose a method to limit ionic current without impacting the electronic properties of a general class of materials, based on codoping with oppositely charged ions. We describe a set of analyses, based on parameter-free quantum mechanical simulations, to assess the efficacy of the approach and determine optimal dopants. For illustration, we discuss the case of thallium bromide, a wide band gap ionic crystal whose promise as a room-temperature radiation detector has been hampered by ionic migration. We find that acceptors and donors bind strongly with the charged vacancies that mediate ionic transport, forming neutral complexes that render them immobile. Analysis of carrier recombination and scattering by the complexes allows the identification of specific dopants that do not degrade electronic transport in the crystal.

Anomalous diffusion along metal/ceramic interfaces.

Interface diffusion along a metal/ceramic interface present in numerous energy and electronic devices can critically affect their performance and stability. Hole formation in a polycrystalline Ni film on an α-Al2O3 substrate coupled with a continuum diffusion analysis demonstrates that Ni diffusion along the Ni/α-Al2O3 interface is surprisingly fast. Ab initio calculations demonstrate that both Ni vacancy formation and migration energies at the coherent Ni/α-Al2O3 interface are much smaller than in bulk Ni, suggesting that the activation energy for diffusion along coherent Ni/α-Al2O3 interfaces is comparable to that along (incoherent/high angle) grain boundaries. Based on these results, we develop a simple model for diffusion along metal/ceramic interfaces, apply it to a wide range of metal/ceramic systems and validate it with several ab initio calculations. These results suggest that fast metal diffusion along metal/ceramic interfaces should be common, but is not universal.

Perfect Strain Relaxation in Metamorphic Epitaxial Aluminum on Silicon through Primary and Secondary Interface Misfit Dislocation Arrays.

Understanding the atomically precise arrangement of atoms at epitaxial interfaces is important for emerging technologies such as quantum materials that have function and performance dictated by bonds and defects that are energetically active on the micro-electronvolt scale. A combination of atomistic modeling and dislocation theory analysis describes both primary and secondary dislocation networks at the metamorphic Al/Si (111) interface, which is experimentally validated by atomic resolution scanning transmission electron microscopy. The electron microscopy images show primary misfit dislocations for the majority of the strain relief and evidence of a secondary structure allowing for complete relaxation of the Al-Si misfit strain. This study demonstrates the equilibrium interface that represents the lowest energy structure of a highly mismatched and semicoherent single-crystal interface with complete strain relief in an atomically abrupt structure.

Incident wavelength and polarization dependence of spectral shifts in ß-Ga2O3 UV photoluminescence.

We report polarization dependent photoluminescence studies on unintentionally-, Mg-, and Ca-doped ß-Ga2O3 bulk crystals grown by the Czochralski method. In particular, we observe a wavelength shift of the highest-energy UV emission which is dependent on the pump photon energy and polarization. For 240 nm (5.17 eV) excitation almost no shift of the UV emission is observed between E||b and E||c, while a shift of the UV emission centroid is clearly observed for 266 nm (4.66 eV), a photon energy lying between the band absorption onsets for the two polarizations. These results are consistent with UV emission originating from transitions between conduction band electrons and two differentially-populated self-trapped hole (STH) states. Calcuations based on hybrid and self-interaction-corrected density functional theories further validate that the polarization dependence is consistent with the relative stability of two STHs. This observation implies that the STHs form primarily at the oxygen atoms involved in the original photon absorption event, thus providing the connection between incident polarization and emission wavelength. The data imposes a lower bound on the energy separation between the self-trapped hole states of ~70-160 meV, which is supported by the calculations.

Descriptor-Based Approach for the Prediction of Cation Vacancy Formation Energies and Transition Levels.

Point defects largely determine the observed optical and electrical properties of a given material, yet the characterization and identification of defects has remained a slow and tedious process, both experimentally and theoretically. We demonstrate a computationally-cheap model that can reliably predict the formation energies of cation vacancies as well as the location of their electronic states in a large set of II-VI and III-V materials using only parameters obtained from the bulk primitive unit cell (2-4 atoms). We apply our model to ordered alloys within the CdZnSeTe, CdZnS, and ZnMgO systems and predict the positions of cation vacancy charge-state transition levels with a mean absolute error of < 0.2 eV compared to the explicitly calculated values, showing useful accuracy without the need for the expensive and large-scale calculations typically required. This suggests the properties of other point defects may also be predicted with useful accuracy from only bulk-derived properties.

Stability of Cd1-xZnxOyS1-y Quaternary Alloys Assessed with First-Principles Calculations.

One route to decreasing the absorption in CdS buffer layers in Cu(In,Ga)Se2 and Cu2ZnSn(S,Se)4 thin-film photovoltaics is by alloying. Here we use first-principles calculations based on hybrid functionals to assess the energetics and stability of quaternary Cd, Zn, O, and S (Cd1-xZnxOyS1-y) alloys within a regular solution model. Our results identify that full miscibility of most Cd1-xZnxOyS1-y compositions and even binaries like Zn(O,S) is outside typical photovoltaic processing conditions. The results suggest that the tendency for phase separation of the oxysulfides may drive the nucleation of other phases such as sulfates that have been increasingly observed in oxygenated CdS and ZnS.

Lithium ion solvation and diffusion in bulk organic electrolytes from first-principles and classical reactive molecular dynamics.

Lithium-ion battery performance is strongly influenced by the ionic conductivity of the electrolyte, which depends on the speed at which Li ions migrate across the cell and relates to their solvation structure. The choice of solvent can greatly impact both the solvation and diffusivity of Li ions. In this work, we used first-principles molecular dynamics to examine the solvation and diffusion of Li ions in the bulk organic solvents ethylene carbonate (EC), ethyl methyl carbonate (EMC), and a mixture of EC and EMC. We found that Li ions are solvated by either carbonyl or ether oxygen atoms of the solvents and sometimes by the PF6(-) anion. Li(+) prefers a tetrahedrally coordinated first solvation shell regardless of which species are involved, with the specific preferred solvation structure dependent on the organic solvent. In addition, we calculated Li diffusion coefficients in each electrolyte, finding slightly larger diffusivities in the linear carbonate EMC compared to the cyclic carbonate EC. The magnitude of the diffusion coefficient correlates with the strength of Li(+) solvation. Corresponding analysis for the PF6(-) anion shows greater diffusivity associated with a weakly bound, poorly defined first solvation shell. These results can be used to aid in the design of new electrolytes to improve Li-ion battery performance.

Atomistic design of thermoelectric properties of silicon nanowires.

We present predictions of the thermoelectric figure of merit ( ZT) of Si nanowires with diameter up to 3 nm, based upon the Boltzman transport equation and ab initio electronic structure calculations. We find that ZT depends significantly on the wire growth direction and surface reconstruction, and we discuss how these properties can be tuned to select silicon based nanostructures with combined n-type and p-type optimal ZT. Our calculations show that only by reducing the ionic thermal conductivity by about 2 or 3 orders of magnitudes with respect to bulk values, one may attain ZT larger than 1, for 1 or 3 nm wires, respectively. We also find that ZT of p-doped wires is considerably smaller than that of their n-doped counterparts with the same size and geometry.

Nearest-neighbor configuration in (GaIn)(NAs) probed by x-ray absorption spectroscopy.

Ga(1-x)In(x)N(y)As(1-y) is a promising material system for the fabrication of inexpensive "last-mile" optoelectronic components. However, details of its atomic arrangement and the relationship to observed optical properties is not fully known. Particularly, a blueshift of emission wavelength is observed after annealing. In this work, we use x-ray absorption fine structure to study the chemical environment around N atoms in the material before and after annealing. We find that as-grown molecular beam epitaxy material consists of a nearly random distribution of atoms, while postannealed material shows segregation of In toward N. Ab initio simulations show that this short-range ordering creates a more thermodynamically stable alloy and is responsible for blueshifting the emission.