Search

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.

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 La 1- x Sr x CoO 3-δ /La 1- x Sr x MnO 3-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 La 0.7 Sr 0.3 CoO 3-δ layers causes distortion of the CoO x polyhedra and the emergence of magnetically active Co 2+ ions. At the same time, an increase in the Sr concentration and a decrease in oxygen vacancies in the La 0.7 Sr 0.3 MnO 3-δ layers tend to retain MnO 6 octahedra and promote the formation of Mn 4+ 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 La 1- x Sr x CoO 3-δ /La 1- x Sr x MnO 3-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.

Spin qubits based on Si and Si 1- x Ge x 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 Si 1- x Ge x 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 Ge Si DBs in SiO 2 and strained Si layers in typical Si 1- x Ge x quantum dot heterostructures. Our results identify that Ge dangling bonds are more problematic charge-trapping centers both in typical Si 1- x Ge x 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 Si 1- x Ge x -based metal-oxide-semiconductor stacks and related devices.

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 SiV 2- . 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.

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.

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.

One route to decreasing the absorption in CdS buffer layers in Cu(In,Ga)Se 2 and Cu 2 ZnSn(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 (Cd 1-x Zn x O y S 1-y ) alloys within a regular solution model. Our results identify that full miscibility of most Cd 1-x Zn x O y S 1-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 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.

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.