Related papers: Modelling atomic and nanoscale structure in the si…
Silicon nanowires have attracted considerable interest due to their wide-ranging applications in nanoelectromechanical systems and nanoelectronics. Molecular dynamics simulations are powerful tools for studying the mechanical properties of…
In this mini-review we summarize the progress of modeling, simulation and analysis of shock responses of heterogeneous materials in our group in recent years. The basic methodology is as below. We first decompose the problem into different…
Active learning (AL) can drastically accelerate materials discovery; its power has been shown in various classes of materials and target properties. Prior efforts have used machine learning models for the optimal selection of physical…
Oxynitrides are used in a variety of applications including photocatalysts, high-k dielectrics or wear-resistant coatings and often show intriguing multi-functionality. To accelerate the co-optimization of the relevant material properties…
The nature of the atomic defects on the hydrogen passivated Si (100) surface is analyzed using deep learning and scanning tunneling microscopy (STM). A robust deep learning framework capable of identifying atomic species, defects, in the…
The use of machine learning is becoming increasingly common in computational materials science. To build effective models of the chemistry of materials, useful machine-based representations of atoms and their compounds are required. We…
The molecular structures of amorphous silica and crystalline quartz are used to predict their intrinsic strength and fracture toughness together with the theoretical strength of silicon dioxide. At the atomic scale, the amorphous silica is…
Machine-learning of atomic-scale properties amounts to extracting correlations between structure, composition and the quantity that one wants to predict. Representing the input structure in a way that best reflects such correlations makes…
The first step in the construction of a regression model or a data-driven analysis, aiming to predict or elucidate the relationship between the atomic scale structure of matter and its properties, involves transforming the Cartesian…
In hydrogenated amorphous silicon oxide (a-SiO:H) films, incorporation of oxygen enhances optical gap due to a large number of St-O-Si bond formation, which lies deep into valence band states. An induction effect of this Si-O on other bonds…
The past years have witnessed impressive advances in electronic structure calculation, especially in the complexity and size of the systems studied, as well as in computation time. Linear scaling methods based on empirical tight-binding…
The nanostructure of hydrogenated amorphous silicon (a Si:H) is studied by a combination of small-angle X-ray (SAXS) and neutron scattering (SANS) with a spatial resolution of 0.8 nm. The a-Si:H materials were deposited using a range of…
Characteristic shock effects in silica serve as a key indicator of historical impacts at geological sites. Despite this geological significance, atomistic details of structural transformations under high pressure and shock compression…
Understanding structure-property relationships in complex materials requires integrating complementary measurements across multiple length scales. Here we propose an interpretable "multimodal" machine learning framework that unifies…
Classical molecular-dynamics simulations have been carried out to investigate densification mechanisms in silicon dioxide thin films deposited on an amorphous silica surface, according to a simplified ion-beam assisted deposition (IBAD)…
Predicting the spatial configuration of gas molecules in nanopores of shale formations is crucial for fluid flow forecasting and hydrocarbon reserves estimation. The key challenge in these tight formations is that the majority of the pore…
Owing to the advances in computational techniques and the increase in computational power, atomistic simulations of materials can simulate large systems with higher accuracy. Complex phenomena can be observed in such state-of-the-art…
In spite of remarkable developments in the field of advanced materials, silicon remains one of the foremost semiconductors of the day. Of enduring relevance to science and technology is silicon's nanomechanical behaviour including phase…
The Self-Assembly of Nano-Objects (SANO) code we implemented demonstrates the ability to predict the molecular self-assembly of different structural motifs by tuning the molecular building blocks as well as the metallic substrate. It…
We present a universal method for the large-scale prediction of the atomic structure of clusters. Our algorithm performs the joint evolutionary search for all clusters in a given area of the compositional space and takes advantage of…