Finding defects in glasses through machine learning.

Structural defects control the kinetic, thermodynamic and mechanical properties of glasses. For instance, rare quantum tunneling two-level systems (TLS) govern the physics of glasses at very low temperature. Due to their extremely low density, it is very hard to directly identify them in computer simulations. We introduce a machine learning approach to efficiently explore the potential energy landscape of glass models and identify desired classes of defects. We focus in particular on TLS and we design an algorithm that is able to rapidly predict the quantum splitting between any two amorphous configurations produced by classical simulations. This in turn allows us to shift the computational effort towards the collection and identification of a larger number of TLS, rather than the useless characterization of non-tunneling defects which are much more abundant. Finally, we interpret our machine learning model to understand how TLS are identified and characterized, thus giving direct physical insight into their microscopic nature.

Microscopic observation of two-level systems in a metallic glass model.

The low-temperature quasi-universal behavior of amorphous solids has been attributed to the existence of spatially localized tunneling defects found in the low-energy regions of the potential energy landscape. Computational models of glasses can be studied to elucidate the microscopic nature of these defects. Recent simulation work has demonstrated the means of generating stable glassy configurations for models that mimic metallic glasses using the swap Monte Carlo algorithm. Building on these studies, we present an extensive exploration of the glassy metabasins of the potential energy landscape of a variant of the most widely used model of metallic glasses. We carefully identify tunneling defects and reveal their depletion with increased glass stability. The density of tunneling defects near the experimental glass transition temperature appears to be in good agreement with experimental measurements.

Robust In-Zn-O Thin-Film Transistors with a Bilayer Heterostructure Design and a Low-Temperature Fabrication Process Using Vacuum and Solution Deposited Layers.

We report on the design, fabrication, and characterization of heterostructure In-Zn-O (IZO) thin-film transistors (TFTs) with improved performance characteristics and robust operation. The heterostructure layer is fabricated by stacking a solution-processed IZO film on top of a buffer layer, which is deposited previously using an electron beam (e-beam) evaporator. A thin buffer layer at the dielectric interface can help to template the structure of the channel. The control of the precursors and of the solvent used during the sol-gel process can help lower the temperature needed for the sol-gel condensation reaction to proceed cleanly. This boosts the overall performance of the device with a significantly reduced subthreshold swing, a four-fold mobility increase, and a two-order of magnitude larger on/off ratio. Atomistic simulations of the a-IZO structure using molecular dynamics (both classical and ab initio) and hybrid density functional theory (DFT) calculations of the electronic structure reveal the potential atomic origin of these effects.

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

Understanding the relation between the time-dependent resistance drift in the amorphous state of phase-change materials and the localised states in the band gap of the glass is crucial for the development of memory devices with increased storage density. Here a machine-learned interatomic potential is utilised to generate an ensemble of glass models of the prototypical phase-change alloy, Ge2Sb2Te5, to obtain reliable statistics. Hybrid density-functional theory is used to identify and characterise the geometric and electronic structures of the mid-gap states. 5-coordinated Ge atoms are the local defective bonding environments mainly responsible for these electronic states. The structural motif for the localisation of the mid-gap states is a crystalline-like atomic environment within the amorphous network. An extra electron is trapped spontaneously by these mid-gap states, creating deep traps in the band gap. The results provide significant insights that can help to rationalise the design of multi-level-storage memory devices.

Modeling the Phase-Change Memory Material, Ge2Sb2Te5, with a Machine-Learned Interatomic Potential.

The phase-change material, Ge2Sb2Te5, is the canonical material ingredient for next-generation storage-class memory devices used in novel computing architectures, but fundamental questions remain regarding its atomic structure and physicochemical properties. Here, we introduce a machine-learning (ML)-based interatomic potential that enables large-scale atomistic simulations of liquid, amorphous, and crystalline Ge2Sb2Te5 with an unprecedented combination of speed and density functional theory (DFT) level of accuracy. Two applications exemplify the usefulness of such an ML-driven approach: we generate a 7200-atom structural model, hitherto inaccessible with DFT simulations, that affords new insight into the medium-range structural order and we create an ensemble of uncorrelated, smaller structures, for studies of their chemical bonding with statistical significance. Our work opens the way for new atomistic insights into the fascinating and chemically complex class of phase-change materials that are used in real nonvolatile memory devices.

Similarity Between Amorphous and Crystalline Phases: The Case of TiO2.

Amorphous and crystalline materials differ in their long-range structural order. On the other hand, short-range order in amorphous and crystalline materials often appears similar. Here, we use a recently introduced method for obtaining quantitative measures for structural similarity to compare crystalline and amorphous materials. We compare seven common crystalline polymorphs of TiO2, all assembled out of TiO6 or TiO7 polyhedral building blocks, to liquid and amorphous TiO2 in a quantitative two-dimensional similarity plot. We find high structural similarity between a model of amorphous TiO2, obtained by ab initio molecular-dynamics, and the B-TiO2 crystalline polymorph. The general approach presented here sheds new light on a long-standing controversy in the structural theory of amorphous solids.

Origin of radiation tolerance in amorphous Ge2Sb2Te5 phase-change random-access memory material.

The radiation hardness of amorphous Ge2Sb2Te5 phase-change random-access memory material has been elucidated by ab initio molecular-dynamics simulations. Ionizing radiation events have been modeled to investigate their effect on the atomic and electronic structure of the glass. Investigation of the short- and medium-range order highlights a structural recovery of the amorphous network after exposure to the high-energy events modeled in this study. Analysis of the modeled glasses reveals specific structural rearrangements in the local atomic geometry of the glass, as well as an increase in the formation of large shortest-path rings. The electronic structure of the modeled system is not significantly affected by the ionizing radiation events, since negligible differences have been observed before and after irradiation. These results provide a detailed insight into the atomistic structure of amorphous Ge2Sb2Te5 after irradiation and demonstrate the radiation hardness of the glass matrix.