Related papers: Surface segregation in high-entropy alloys from al…
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…
The expansiveness of compositional phase space is too vast to fully search using current theoretical tools for many emergent problems in condensed matter physics. The reliance on a deep chemical understanding is one method to identify local…
Understanding material surfaces and interfaces is vital in applications like catalysis or electronics. By combining energies from electronic structure with statistical mechanics, ab initio simulations can in principle predict the structure…
Two-dimensional (2D) materials, such as transition metal dichalcogenides (TMDCs) in the 2H or 1T crystal phases, are promising (electro)catalyst candidates due to their high surface to volume ratio and composition of low-cost, abundant…
Low-temperature alloys are important for a wide spectrum of modern technologies ranging from liquid hydrogen, and superconductivity to quantum technology. These applications push the limit of material performance into extreme coldness,…
We introduce a local machine-learning method for predicting the electron densities of periodic systems. The framework is based on a numerical, atom-centred auxiliary basis, which enables an accurate expansion of the all-electron density in…
Machine Learning techniques can be used to represent high-dimensional potential energy surfaces for reactive chemical systems. Two such methods are based on a reproducing kernel Hilbert space representation or on deep neural networks. They…
Materials discovery is a computationally intensive process that requires exploring vast chemical spaces to identify promising candidates with desirable properties. In this work, we propose using quantum-enhanced machine learning algorithms…
To enhance the activity of photocatalysts for hydrogen production and CO2 conversion, noble metal cocatalysts as electron traps and/or acceptors such as platinum or gold are usually utilized. This study hypothesizes that mixing elements…
Magnesium hydride (MgH2) is the mostly used material for solid-state hydrogen storage. However, their slow kinetics and highly unfavorable thermodynamics make them unsuitable for the practical applications. The current study describes the…
We train an equivariant machine learning model to predict energies and forces for a real-world study of hydrogen combustion under conditions of finite temperature and pressure. This challenging case for reactive chemistry illustrates that…
The enthalpy of mixing in the liquid phase is an important property for predicting phase formation in alloys. It can be estimated in a large compositional space from pair wise interactions between elements, for which machine learning has…
In the drive towards higher strength alloys, a diverse range of alloying elements is employed to enhance their strength and ductility. Limited solid solubility of these elements in steel leads to segregation during casting which affects the…
Resolving the atomic-scale structure of defective high-entropy alloys (HEAs) containing interstitial species remains a major computational challenge due to the vast configurational space and the limitations of existing methods. Here we…
Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an…
Energy efficiency is motivating the search for new high-temperature metals. Some new body-centered-cubic random multicomponent "high entropy alloys (HEAs)" based on refractory elements (Cr-Mo-Nb-Ta-V-W-Hf-Ti-Zr) possess exceptional…
The "CO adsorption puzzle", a persistent failure of utilizing generalized gradient approximations (GGA) in density functional theory to replicate CO's experimental preference for top-site adsorption on transition-metal surfaces, remains a…
We use high-throughput first-principles sampling to investigate competitive factors that determine the crystal structure of high-entropy alloys (HEAs) and the energetics dependence of the stable phase on the atomic configuration of fully…
We present work flows and a software module for machine learning model building in surface science and heterogeneous catalysis. This includes fingerprinting atomic structures from 3D structure and/or connectivity information, it includes…
When metallic glasses (MGs) are subjected to mechanical loads, the plastic response of atoms is non-uniform. However, the extent and manner in which atomic environment signatures present in the undeformed structure determine this plastic…