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A computational approach to determine the equilibrium structures of nanoclusters in the whole temperature range from 0 K to melting is developed. Our approach relies on Parallel Tempering Molecular Dynamics (PTMD) simulations complemented…
Disordered elemental semiconductors, most notably a-C and a-Si, are ubiquitous in a myriad of different applications. These exploit their unique mechanical and electronic properties. In the past couple of decades, density functional theory…
Disordered forms of carbon are an important class of materials for applications such as thermal management. However, a comprehensive theoretical understanding of the structural dependence of thermal transport and the underlying microscopic…
Arrays of aligned nanorods oriented perpendicular to a support, which are accessible by top-down lithography or by means of shape-defining hard templates, have received increasing interest as sensor components, components for nanophotonics…
Disordered nanostructures with correlations on the scale of visible wavelengths can show angle-independent structural colors. These materials could replace dyes in some applications because the color is tunable and resists photobleaching.…
Aluminum-Copper (Al-Cu) alloys are essential materials for weight reduction critical structures in the aerospace and automotive industries, yet achieving their maximum ultrahigh-strength potential remains limited by nanoscale defect control…
High-throughput computational materials design promises to greatly accelerate the process of discovering new materials and compounds, and of optimizing their properties. The large databases of structures and properties that result from…
A complex study of aging kinetics for both coarse-grained and nanostructured by severe plastic deformation Cu -- 2 wt% Be alloy is reported. It is shown that aging of a coarse-grained alloy leads to continuous formation of nanosized CuBe…
Compositionally complex materials (CCMs) present a potential paradigm shift in the design of magnetic materials. These alloys exhibit long-range structural order coupled with limited or no chemical order. As a result, extreme local…
Reduced dimensionality leads to emergent phenomena in quantum materials and there is a need for accelerated materials discovery of nanoscale quantum materials in reduced dimensions. Thermomechanical nanomolding is a rapid synthesis method…
The topology of nanoporous metals is crucial for determining their mechanical response. In this work, we generated 6,000 gold and 422 silver nanoporous structures and calculated three components of elastic modulus with Molecular Dynamics…
First-principles computations are the driving force behind numerous discoveries of hydride-based superconductors, mostly at high pressures, during the last decade. Machine-learning (ML) approaches can further accelerate the future…
ANN (Artificial Neural Networks) modeling methodology was adopted for predicting mechanical properties of aluminum cast composite materials. For this purpose aluminum alloy were developed using conventional foundry method. The composite…
Knowledge of thermal properties is essential to design and evaluate thermal systems and processes using nanofluids. This paper presents different analytical models to predict thermal conductivity and viscosity. The efforts have been made to…
A method for automated quantification of the alignment of one-dimensional nanostructures from microscopy imaging is presented. Nanostructure alignment metrics are formulated and shown to able to rigorously quantify the orientational order…
The fabrication of nanomaterials involves self-ordering processes of functional molecules on inorganic surfaces. To obtain specific molecular arrangements, a common strategy is to equip molecules with functional groups. However, focusing on…
In Al2O3 suspensions, depending on the experimental conditions very different microstructures can be found, comprising fluid like suspensions, a repulsive structure, and a clustered microstructure. For technical processing in ceramics, the…
Data driven materials discovery and optimization requires databases that are error free and experimentally verified. Performing material measurements are time-consuming and often restricted by the fact that material sample preparations are…
We present a machine learning based model that can predict the electronic structure of quasi-one-dimensional materials while they are subjected to deformation modes such as torsion and extension/compression. The technique described here…
Interfaces have long been known to be the key to many mechanical and electric properties. To nickel base superalloys which have perfect creep and fatigue properties and have been widely used as materials of turbine blades, interfaces…