Related papers: The phase stability of large-size nanoparticle all…
While nanoalloys are of paramount scientific and practical interests, the main processes leading to their formation are still poorly understood. Key structural features in the alloy systems, including crystal phase, chemical ordering, and…
We model the stabilization of clusters and lattices of cuboidal particles with long-ranged magnetic dipolar and short-ranged surface interactions. Two realistic systems were considered: one with magnetization orientated in the [001]…
Interparticle interactions and bulk properties of colloidal suspensions can be substantially modified by addition of nanoparticles. Extreme asymmetries in size and charge between colloidal particles and nanoparticles present severe…
Hydrogen is a promising energy carrier for replacing fossil fuels, and hydrogen production via hydrogen evolution reaction (HER) is an environmentally friendly option if electrocatalysts with low overpotentials and long-term stability are…
Sought-after ordered structures of mixtures of hard anisotropic nanoparticles can often be thermodynamically unfavorable due to the components' geometric incompatibility to densely pack into regular lattices. A simple compatibilization rule…
Simultaneously probing the electronic structure and morphology of materials at the nanometer or atomic scale while a chemical reaction proceeds is significant for understanding the underlying reaction mechanisms and optimizing a materials…
Due to their algorithmic simplicity and high accuracy, force-based model coupling techniques are an exciting development in computational physics. For example, the force-based quasicontinuum approximation is the only known pointwise…
Polyhedral nanocrystals are building blocks for nanostructured materials that find applications in catalysis and plasmonics. Synthesis efforts and self-assembly experiments have been assisted by computer simulations that predict phase…
Establishing a mapping between nanocatalysts structure and their catalytic properties is essential for efficient design. To this end, we demonstrate the accuracy of a general machine learning framework on a representative and challenging…
The ordered structure of binary alloy nanoparticles determines their magnetic and catalytic characteristics. In the alloys after annealing, one of the components preferentially segregates on the surface to reduce surface energy. This…
The prediction of energetically stable crystal structures formed by a given chemical composition is a central problem in solid-state physics. In principle, the crystalline state of assembled atoms can be determined by optimizing the energy…
Quaternary phases formed in copper alloys are investigated through a combination of quantum-mechanical and classical computer simulations and active machine learning. Focus is given on nickel, silicon, and chromium impurities in a copper…
Designing particles that are able to form icosahedral quasicrystals (IQCs) and that are as simple as possible is not only of fundamental interest but is also important to the potential realization of IQCs in materials other than metallic…
We present a general-purpose machine learning Gaussian approximation potential (GAP) for iron that is applicable to all bulk crystal structures found experimentally under diverse thermodynamic conditions, as well as surfaces and…
Prediction of the stable crystal structure for multinary (ternary or higher) compounds with unexplored compositions demands fast and accurate evaluation of free energies in exploring the vast configurational space. The machine-learning…
We investigate the phase ordering (pattern formation) of systems of two-dimensional core-shell particles using Monte-Carlo (MC) computer simulations and classical density functional theory (DFT). The particles interact via a pair potential…
Multicomponent transition metal carbides are promising for extreme-environment applications, but identifying compositions that are both synthesizable and hard remains challenging. We fine-tune the MACE machine-learned interatomic potential…
Experiments have reached a monumental capacity for designing and synthesizing microscopic particles for self-assembly, making it possible to precisely control particle concentrations, shapes, and interactions. However, more physical insight…
We present a systematic benchmark of MACE potentials for iron-nickel alloys, focusing on structural, elastic, magnetic, and finite-temperature properties relevant to phase stability. The reference dataset comprises spin-polarized PBE…
First-principles based crystal structure prediction (CSP) methods have revealed an essential tool for the discovery of new materials. However, in solids close to displacive phase transitions, which are common in ferroelectrics,…