Revealing the intrinsic nature of the mid-gap defects in amorphous Ge2Sb2Te5

Accumulation of Long-Lived Photogenerated Holes at Indium Single-Atom Catalysts via Two Coordinate Nitrogen Vacancy Defect Engineering for Enhanced Photocatalytic Oxidation

Visible-light-driven photocatalytic oxidation by photogenerated holes has immense potential for environmental remediation applications. While the electron-mediated photoreduction reactions are often at the spotlight, active holes possess a remarkable oxidation capacity that can degrade recalcitrant organic pollutants, resulting in nontoxic byproducts. However, the random charge transfer and rapid recombination of electron-hole pairs hinder the accumulation of long-lived holes at the reaction center. Herein, a novel method employing defect-engineered indium (In) single-atom photocatalysts with nitrogen vacancy (Nv) defects, dispersed in carbon nitride foam (In-Nv-CNF), is reported to overcome these challenges and make further advances in photocatalysis. This Nv defect-engineered strategy produces a remarkable extension in the lifetime and an increase in the concentration of photogenerated holes in In-Nv-CNF. Consequently, the optimized In-Nv-CNF demonstrates a remarkable 50-fold increase in photo-oxidative degradation rate compared to pristine CN, effectively breaking down two widely used antibiotics (tetracycline and ciprofloxacin) under visible light. The contaminated water treated by In-Nv-CNF is completely nontoxic based on the growth of Escherichia coli. Structural-performance correlations between defect engineering and long-lived hole accumulation in In-Nv-CNF are established and validated through experimental and theoretical agreement. This work has the potential to elevate the efficiency of overall photocatalytic reactions from a hole-centric standpoint.

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.

A substitutional quantum defect in WS2 discovered by high-throughput computational screening and fabricated by site-selective STM manipulation

Point defects in two-dimensional materials are of key interest for quantum information science. However, the parameter space of possible defects is immense, making the identification of high-performance quantum defects very challenging. Here, we perform high-throughput (HT) first-principles computational screening to search for promising quantum defects within WS2, which present localized levels in the band gap that can lead to bright optical transitions in the visible or telecom regime. Our computed database spans more than 700 charged defects formed through substitution on the tungsten or sulfur site. We found that sulfur substitutions enable the most promising quantum defects. We computationally identify the neutral cobalt substitution to sulfur (CoS 0 ) and fabricate it with scanning tunneling microscopy (STM). The CoS 0 electronic structure measured by STM agrees with first principles and showcases an attractive quantum defect. Our work shows how HT computational screening and nanoscale synthesis routes can be combined to design promising quantum defects.

Toward Atomistic Understanding of Iron Phosphate Glass: A First-Principles-Based Density Functional Theory Modeling and Study of Its Physical Properties

Iron phosphate glasses (IPGs) have been proposed as futuristic materials for nuclear waste immobilization and anode materials for lithium batteries. Recently, many attempts have been made to propose atomistic models of IPGs to explain their properties from an atomistic viewpoint. In this paper, we seek to produce small scale models of IPG that can be handled within the scheme of density functional theory (DFT) to study its electronic structure. The starting models, generated using the Monte Carlo (MC) method, were subsequently annealed using ab initio molecular dynamics (AIMD) to minimize the coordination defects. The geometry of the equilibrated structure was then optimized at 0 K. This hybrid approach (MC + AIMD + DFT optimization) produced good atomistic models of IPG, which can reproduce the experimentally observed band gap, vibrational density of states, magnetic moment of Fe, and mechanical and optical properties. Computationally expensive melt-quench simulation can be avoided using the present approach, allowing the use of DFT for accurate calculations of properties.

Structure, bonding and electronic characteristics of amorphous Se

Ovonic threshold switching (OTS) selectors can effectively improve the storage density and suppress the leakage current of advanced phase-change memory devices. As a prototypical OTS material, amorphous GeSe is widely investigated. But the attention paid to amorphous Se (i.e., the functional constituent in amorphous GeSe) has been very limited up to now. Here we have explored the structure, bonding and electronic characteristics of amorphous Se using ab initio molecular dynamics simulations. The results reveal that the Se atoms in amorphous Se tend to form 2-coordinated configurations, and they connect with each other to form long chains. The fraction of the vibrational density of state located in the high frequency range is relatively large, and the formation energy of the Se-Se bond is as large as 4.44 eV, hinting that the Se-Se bonds in chains possess high stability. In addition, the mid-gap state related to the OTS behavior is also found in amorphous Se despite the small proportion. Our findings enrich the knowledge of amorphous Se, which aids the applications of Se-based OTS selectors.

First-principles study of lithium aluminosilicate glass scintillators

Radiation sensors are an important enabling technology in several fields, such as medicine, scientific research, energy, defence, meteorology, and homeland security. Glass-based scintillators have been in use for more than 50 years and offer many benefits, including their ability to respond to different types of radiation, and to be readily formed into various shapes. There is, however, the prospect to develop new and improved glass scintillators, with low self-absorption, low refractive indices, and high radiative recombination rates. To investigate the factors limiting the improvement of glass scintillator properties, this work provides insight from atomic scale simulations of the cerium-doped lithium aluminosilicate (SiO2-Al2O3-MgO-Li2O-Ce2O3) glass scintillator system. Three glass compositions were studied using molecular dynamics and density functional theory to investigate the effect of the ratio (with RAl/M = [0.1, 0.8 and 1.2]) on the structural and electronic properties. For a ratio RAl/M > 1, it has been shown that glasses with increased polymerization allow for more effective incorporation of Ce3+ cations. The structural analysis also showed that the bond order of Al-O can be affected in the presence of a lithium-rich environment. Electronic density of states and Bader charge analysis indicate a decline in the population of localized trapping states with increasing RAl/M. This suggests a higher probability of radiative recombination which can increase the photon yield of these scintillators. These findings provide valuable guidance for optimizing Li-glasses in neutron detection systems by highlighting the intricate challenges.

High-throughput identification of spin-photon interfaces in silicon

Color centers in host semiconductors are prime candidates as spin-photon interfaces for quantum applications. Finding an optimal spin-photon interface in silicon would move quantum information technologies toward a mature semiconducting host. However, the space of possible charged defects is vast, making the identification of candidates from experiments alone extremely challenging. Here, we use high-throughput first-principles computational screening to identify spin-photon interfaces among more than 1000 charged defects in silicon. The use of a single-shot hybrid functional approach is critical in enabling the screening of many quantum defects with a reasonable accuracy. We identify three promising spin-photon interfaces as potential bright emitters in the telecom band: [Formula: see text], [Formula: see text], and [Formula: see text]. These candidates are excited through defect-bound excitons, stressing the importance of such defects in silicon for telecom band operations. Our work paves the way to further large-scale computational screening for quantum defects in semiconductors.

The role of arsenic in the operation of sulfur-based electrical threshold switches

Arsenic is an essential dopant in conventional silicon-based semiconductors and emerging phase-change memory (PCM), yet the detailed functional mechanism is still lacking in the latter. Here, we fabricate chalcogenide-based ovonic threshold switching (OTS) selectors, which are key units for suppressing sneak currents in 3D PCM arrays, with various As concentrations. We discovered that incorporation of As into GeS brings >100 °C increase in crystallization temperature, remarkably improving the switching repeatability and prolonging the device lifetime. These benefits arise from strengthened As-S bonds and sluggish atomic migration after As incorporation, which reduces the leakage current by more than an order of magnitude and significantly suppresses the operational voltage drift, ultimately enabling a back-end-of-line-compatible OTS selector with >12 MA/cm2 on-current, ~10 ns speed, and a lifetime approaching 1010 cycles after 450 °C annealing. These findings allow the precise performance control of GeSAs-based OTS materials for high-density 3D PCM applications.

Suppressing Structural Relaxation in Nanoscale Antimony to Enable Ultralow-Drift Phase-Change Memory Applications

Phase-change random-access memory (PCRAM) devices suffer from pronounced resistance drift originating from considerable structural relaxation of phase-change materials (PCMs), which hinders current developments of high-capacity memory and high-parallelism computing that both need reliable multibit programming. This work realizes that compositional simplification and geometrical miniaturization of traditional GeSbTe-like PCMs are feasible routes to suppress relaxation. While to date, the aging mechanisms of the simplest PCM, Sb, at nanoscale, have not yet been unveiled. Here, this work demonstrates that in an optimal thickness of only 4 nm, the thin Sb film can enable a precise multilevel programming with ultralow resistance drift coefficients, in a regime of ≈10-4 -10-3 . This advancement is mainly owed to the slightly changed Peierls distortion in Sb and the less-distorted octahedral-like atomic configurations across the Sb/SiO2 interfaces. This work highlights a new indispensable approach, interfacial regulation of nanoscale PCMs, for pursuing ultimately reliable resistance control in aggressively-miniaturized PCRAM devices, to boost the storage and computing efficiencies substantially.

Review: understanding the properties of amorphous materials with high-performance computing methods

Amorphous materials have no long-range order in their atomic structure. This makes much of the formalism for the study of crystalline materials irrelevant, and so elucidating their structure and properties is challenging. The use of computational methods is a powerful complement to experimental studies, and in this paper we review the use of high-performance computing methods in the simulation of amorphous materials. Five case studies are presented to showcase the wide range of materials and computational methods available to practitioners in this field. This article is part of a discussion meeting issue 'Supercomputing simulations of advanced materials'.

Nanoscale Chemical Heterogeneity Ensures Unprecedently Low Resistance Drift in Cache-Type Phase-Change Memory Materials

Phase-change random access memory is a promising technique to realize universal memory and neuromorphic computing, where the demand for robust multibit programming drives exploration for high-accuracy resistance control in memory cells. Here in Sc_xSb₂Te₃ phase-change material films, we demonstrate thickness-independent conductance evolution, presenting an unprecedently low resistance-drift coefficient in the range of ∼10^-4-10^-3, ∼3-2 orders of magnitude lower compared to conventional Ge₂Sb₂Te₅. By atom probe tomography and ab initio simulations, we unveiled that nanoscale chemical inhomogeneity and constrained Peierls distortion together suppress structural relaxation, rendering an almost invariant electronic band structure and thereby the ultralow resistance drift of Sc_xSb₂Te₃ films upon aging. Associated with subnanosecond crystallization speed, Sc_xSb₂Te₃ serves as the most appropriate candidate for developing high-accuracy cache-type computing chips.

Atomic-Layer-Deposited Aluminum Oxide Thin Films Probed with X-ray Scattering and Compared to Molecular Dynamics and Density Functional Theory Models

A better understanding of amorphous aluminum oxide's structure and electronic properties is obtained through combined experimental and computational approaches. Grazing incidence X-ray scattering measurements were carried out on aluminum oxide thin films grown using thermal atomic layer deposition. The corresponding pair distribution functions (PDFs) showed structures similar to previously reported PDFs of solid-state amorphous alumina and molten alumina. Structural models based on crystalline alumina polymorphs (PDFgui) and amorphous alumina (molecular dynamics, MD) were examined for structural comparisons to the experimental PDF data. Smaller MD models were optimized and verified against larger models to allow for quantum chemical electronic structure calculations. The electronic structure of the amorphous alumina models yields additional insight into the band structure and electronic defects present in amorphous alumina that are not present in crystalline samples.

Passivation of Mid-Gap Electronic States at Calcium Aluminosilicate Glass Surfaces upon Water Exposure: An Ab Initio Study

When a clean glass surface is exposed to humid air, a thin water layer forms on the hydrophilic surface. Using ab initio molecular dynamics, we simulate the changes in the electronic structure of a CaO-Al₂O₃-SiO₂ glass model upon vacuum fracture and subsequent exposure to H₂O. When the glass is fractured, dangling bonds form, which lower the band gap of the surface by ∼1.8 eV compared to the bulk value due to mid-gap surface states. When H₂O adsorbs onto the vacuum-fractured surface, the band gap increases to a value closer to that of the bulk band gap. Using two different hydroxylation methods, we find that the calculated band gap of the glass surface depends on the hydroxylation state. Surfaces with ∼4.5 OH/nm² have smaller band gaps due to unfilled surface states, and surfaces with ∼2.5 OH/nm² have larger band gaps with no apparent unfilled surface states. The resulting changes in the electronic structure, quantified by electron affinity and work function values, are hypothesized to play an important role in the electrostatic charge transfer based on the principles of surface state theory, which posit that the density of electronic surface states determines the amount of electronic charge transfer to or from material surfaces.

Accelerating the theoretical study of Li-polysulfide adsorption on single-atom catalysts via machine learning approaches

Li-S batteries are a promising alternative to Li-ion batteries, offering large energy storage capacity and wide operating temperature range. However, their performance is heavily affected by the Li-polysulfide (LiPS) shuttling. Computational screening of LiPS adsorption on single-atom catalyst (SAC) substrates is of great aid to the design of Li-S batteries which are robust against the LiPS shuttling from the cathode to the anode and the electrolyte. To facilitate this process, we develop a machine learning (ML) protocol to accelerate the systematic mapping of dominant local energy minima found with calculations based on the density functional theory (DFT), and, in turn, fast screening of LiPS adsorption properties on SACs. We first validate the approach by probing the potential energy surface for LiPS adsorbed on graphene decorated with a Fe-N₄-C SAC. We identify minima whose binding energies are better or on par with the one previously reported in the literature. We then move to analyze the adsorption trends on Zn-N₄-C SAC and observe similar adsorption strength and behavior with the Fe-N₄-C SAC, highlighting the good predictive power of our protocol. Our approach offers a comprehensive and computationally efficient alternative to conventional approaches studying LiPS adsorption.

Different N-Glycosylation Sites Reduce the Activity of Recombinant DSPAα2

Bat plasminogen activators α2 (DSPAα2) has extremely high medicinal value as a powerful natural thrombolytic protein. However, wild-type DSPAα2 has two N-glycosylation sites (N185 and N398) and its non-human classes of high-mannose-type N-glycans may cause immune responses in vivo. By mutating the N-glycosylation sites, we aimed to study the effect of its N-glycan chain on plasminogen activation, fibrin sensitivity, and to observe the physicochemical properties of DSPAα2. A logical structure design was performed in this study. Four single mutants and one double mutant were constructed and expressed in Pichia pastoris. When the N398 site was eliminated, the plasminogen activator in the mutants had their activities reduced to ~40%. When the N185 site was inactivated, there was a weak decrease in the plasminogen activation of its mutant, while the fibrin sensitivity significantly decreased by ~10-fold. Neither N-glycosylation nor deglycosylation mutations changed the pH resistance or heat resistance of DSPAα2. This study confirms that N-glycosylation affects the biochemical function of DSPAα2, which provides a reference for subsequent applications of DSPAα2.

Unraveling Crystallization Mechanisms and Electronic Structure of Phase-Change Materials by Large-Scale Ab Initio Simulations

Ge-Sb-Te ("GST") alloys are leading phase-change materials for digital memories and neuro-inspired computing. Upon fast crystallization, these materials form rocksalt-like phases with large structural and vacancy disorder, leading to an insulating phase at low temperature. Here, a comprehensive description of crystallization, structural disorder, and electronic properties of GeSb₂ Te₄ based on realistic, quantum-mechanically based ("ab initio") computer simulations with system sizes of more than 1000 atoms is provided. It is shown how an analysis of the crystallization mechanism based on the smooth overlap of atomic positions kernel reveals the evolution of both geometrical and chemical order. The connection between structural and electronic properties of the disordered, as-crystallized models, which are relevant to the transport properties of GST, is then studied. Furthermore, it is shown how antisite defects and extended Sb-rich motifs can lead to Anderson localization in the conduction band. Beyond memory applications, these findings are therefore more generally relevant to disordered rocksalt-like chalcogenides that exhibit self-doping, since they can explain the origin of Anderson insulating behavior in both p- and n-doped chalcogenide materials.

Cluster Fragments in Amorphous Phosphorus and their Evolution under Pressure

Amorphous phosphorus (a-P) has long attracted interest because of its complex atomic structure, and more recently as an anode material for batteries. However, accurately describing and understanding a-P at the atomistic level remains a challenge. Here, it is shown that large-scale molecular-dynamics simulations, enabled by a machine-learning (ML)-based interatomic potential for phosphorus, can give new insights into the atomic structure of a-P and how this structure changes under pressure. The structural model so obtained contains abundant five-membered rings, as well as more complex seven- and eight-atom clusters. Changes in the simulated first sharp diffraction peak during compression and decompression indicate a hysteresis in the recovery of medium-range order. An analysis of cluster fragments, large rings, and voids suggests that moderate pressure (up to about 5 GPa) does not break the connectivity of clusters, but higher pressure does. The work provides a starting point for further computational studies of the structure and properties of a-P, and more generally it exemplifies how ML-driven modeling can accelerate the understanding of disordered functional materials.

Multi-level phase-change memory with ultralow power consumption and resistance drift

By controlling the amorphous-to-crystalline relative volume, chalcogenide phase-change memory materials can provide multi-level data storage (MLS), which offers great potential for high-density storage-class memory and neuro-inspired computing. However, this type of MLS system suffers from high power consumption and a severe time-dependent resistance increase ("drift") in the amorphous phase, which limits the number of attainable storage levels. Here, we report a new type of MLS system in yttrium-doped antimony telluride, utilizing reversible multi-level phase transitions between three states, i.e., amorphous, metastable cubic and stable hexagonal crystalline phases, with ultralow power consumption (0.6-4.3 pJ) and ultralow resistance drift for the lower two states (power-law exponent < 0.007). The metastable cubic phase is stabilized by yttrium, while the evident reversible cubic-to-hexagonal transition is attributed to the sequential and directional migration of Sb atoms. Finally, the decreased heat dissipation of the material and the increase in crystallinity contribute to the overall high performance. This study opens a new way to achieve advanced multi-level phase-change memory without the need for complicated manufacturing procedures or iterative programming operations.

Gaussian Process Regression for Materials and Molecules

We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come.

Understanding the structure, bonding and reactions of nanocrystalline semiconductors: a novel high-resolution instrumental method of solid-state synchronous luminescence spectroscopy

This Perspective Article describes the recent advancements in studies of nanocrystalline metal oxides using a novel ultra-high resolution method, solid-state synchronous luminescence spectroscopy (SS-SLS). Semiconductors notably include titanium dioxide and these studies shed light on the detailed electronic structure, composition, and their reactions. First, we critically discuss the limitations of the major existing non-spectroscopic and spectroscopic methods of characterization of electronic structure of nanocrystalline semiconductors and insulators. Second, we describe the foundations and the setup of SS-SLS as an enhanced-resolution, facile, non-contact, non-destructive, and highly capable method of studies of nanomaterials. Third, the following insights are featured which are obtained by SS-SLS, but are not available by other methods: (a) detection of traps of electric charge (specific mid-gap states); (b) discrimination between "surface" and "bulk" sites; (c) in situ studies of composite nanomaterials and mechanisms of reactions, (d) the derivative SS-SLS for accurate determination of energies of absorption and emission. The specific advantages of SS-SLS versus other methods and in direct comparison with "conventional" photoluminescence spectroscopy are highlighted. Finally, new opportunities and challenges of SS-SLS are presented. SS-SLS is an advanced spectroscopic method with significant potential to aid academia and industry in studies of chemo-sensing, photocatalysis, optoelectronic materials, applied surface science, development of instrumental analysis, and studies of mechanisms of surface and "bulk" chemical reactions.

Materials Screening for Disorder-Controlled Chalcogenide Crystals for Phase-Change Memory Applications

Tailoring the degree of disorder in chalcogenide phase-change materials (PCMs) plays an essential role in nonvolatile memory devices and neuro-inspired computing. Upon rapid crystallization from the amorphous phase, the flagship Ge-Sb-Te PCMs form metastable rocksalt-like structures with an unconventionally high concentration of vacancies, which results in disordered crystals exhibiting Anderson-insulating transport behavior. Here, ab initio simulations and transport experiments are combined to extend these concepts to the parent compound of Ge-Sb-Te alloys, viz., binary Sb₂ Te₃ , in the metastable rocksalt-type modification. Then a systematic computational screening over a wide range of homologous, binary and ternary chalcogenides, elucidating the critical factors that affect the stability of the rocksalt structure is carried out. The findings vastly expand the family of disorder-controlled main-group chalcogenides toward many more compositions with a tunable bandgap size for demanding phase-change applications, as well as a varying strength of spin-orbit interaction for the exploration of potential topological Anderson insulators.

Ultrahigh drive current and large selectivity in GeS selector

Selector devices are indispensable components of large-scale nonvolatile memory and neuromorphic array systems. Besides the conventional silicon transistor, two-terminal ovonic threshold switching device with much higher scalability is currently the most industrially favored selector technology. However, current ovonic threshold switching devices rely heavily on intricate control of material stoichiometry and generally suffer from toxic and complex dopants. Here, we report on a selector with a large drive current density of 34 MA cm-2 and a ~106 high nonlinearity, realized in an environment-friendly and earth-abundant sulfide binary semiconductor, GeS. Both experiments and first-principles calculations reveal Ge pyramid-dominated network and high density of near-valence band trap states in amorphous GeS. The high-drive current capacity is associated with the strong Ge-S covalency and the high nonlinearity could arise from the synergy of the mid-gap traps assisted electronic transition and local Ge-Ge chain growth as well as locally enhanced bond alignment under high electric field.

Quantitative prediction of grain boundary thermal conductivities from local atomic environments

Quantifying the dependence of thermal conductivity on grain boundary (GB) structure is critical for controlling nanoscale thermal transport in many technologically important materials. A major obstacle to determining such a relationship is the lack of a robust and physically intuitive structure descriptor capable of distinguishing between disparate GB structures. We demonstrate that a microscopic structure metric, the local distortion factor, correlates well with atomically decomposed thermal conductivities obtained from perturbed molecular dynamics for a wide variety of MgO GBs. Based on this correlation, a model for accurately predicting thermal conductivity of GBs is constructed using machine learning techniques. The model reveals that small distortions to local atomic environments are sufficient to reduce overall thermal conductivity dramatically. The method developed should enable more precise design of next-generation thermal materials as it allows GB structures exhibiting the desired thermal transport behaviour to be identified with small computational overhead.

Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

Atomic-scale modeling and understanding of materials have made remarkable progress, but they are still fundamentally limited by the large computational cost of explicit electronic-structure methods such as density-functional theory. This Progress Report shows how machine learning (ML) is currently enabling a new degree of realism in materials modeling: by "learning" electronic-structure data, ML-based interatomic potentials give access to atomistic simulations that reach similar accuracy levels but are orders of magnitude faster. A brief introduction to the new tools is given, and then, applications to some select problems in materials science are highlighted: phase-change materials for memory devices; nanoparticle catalysts; and carbon-based electrodes for chemical sensing, supercapacitors, and batteries. It is hoped that the present work will inspire the development and wider use of ML-based interatomic potentials in diverse areas of materials research.