Related papers: Navigating the Complex Compositional Landscape of …

Topological phases of matter$\unicode{x2013}$comprising both insulators and semimetals$\unicode{x2013}$offer great potential for quantum applications, but identifying new candidates remains challenging due to expensive first-principles…

High-entropy alloys (HEAs) have attracted growing attention for their exceptional mechanical and thermal properties arising from complex atomic configurations. In this paper, we propose crystal fractional graph neural network for predicting…

The development of machine learning sheds new light on the traditionally complicated problem of thermodynamics in multicomponent alloys. Successful application of such a method, however, strongly depends on the quality of the data and…

Here, we designed two promising schemes to realize the high-entropy structure in a series of quasi-two-dimensional compounds, transition metal dichalcogenides (TMDCs). In the intra-layer high-entropy plan, (HEM)X2 compounds with…

Conventional visualization tools such as phase diagrams and convex hulls are ill-suited to visualize multiple principal element alloys (MPEAs) due to their large compositional space that cannot be easily projected onto two dimensions. Here,…

One of the main drawbacks of the powder metallurgy route for High-Entropy Alloys (HEAs) is the unavailability of fully pre-alloyed powders in the market. Using commodity powders (commercial Ni, Fe and Co base fully pre-alloyed powders,…

The discovery of high-$T_c$ conventional superconductivity in high-pressure hydrides has helped establish computational methods as a formidable tool to guide material discoveries in a field traditionally dominated by serendipitous…

In computational materials science, a common means for predicting macroscopic (e.g., mechanical) properties of an alloy is to define a model using combinations of descriptors that depend on some material properties (elastic constants,…

On the premise that amorphous-HEA composites could demonstrate high toughness and resistance to embrittlement akin to the phase separating glassy-solid solution composites, we develop a thermodynamics based approach to identify chemical…

High entropy oxides (HEOs) are a rapidly growing class of compositionally complex ceramics in which configurational disorder is engineered to unlock novel functionality. While average crystallographic symmetry is often retained, local…

Mechanical cloaks are materials engineered to manipulate the elastic response around objects to make them indistinguishable from their homogeneous surroundings. Typically, methods based on material-parameter transformations are used to…

We propose machine learning (ML) models to predict the electron density -- the fundamental unknown of a material's ground state -- across the composition space of concentrated alloys. From this, other physical properties can be inferred,…

High entropy alloys (HEAs) are multicomponent compounds whose high configurational entropy allows them to solidify into a single phase, with a simple crystal lattice structure. Some HEA's exhibit desirable properties, such as high specific…

In a world abundant with diverse data arising from complex acquisition techniques, there is a growing need for new data analysis methods. In this paper we focus on high-dimensional data that are organized into several hierarchical datasets.…

Advanced engineering materials design involves the exploration of massive multidimensional feature spaces, the correlation of materials properties and the processing parameters derived from disparate sources. The search for alternative…

Algorithmic materials discovery is a multi-disciplinary domain that integrates insights from specialists in alloy design, synthesis, characterization, experimental methodologies, computational modeling, and optimization. Central to this…

Alloy discovery is constrained by vast compositional spaces, competing objectives, and prohibitive experimental costs. Although simulations and machine learning have each accelerated parts of this process, unifying scientific knowledge,…

The diffusion of large databases collecting different kind of material properties from high-throughput density functional theory calculations has opened new paths in the study of materials science thanks to data mining and machine learning…

High entropy alloys (HEAs) are promising magnetocaloric materials with tunable operating temperature conditions using compositional modifications. Here, we combine experiments and first principles based spin modelling to engineer…

We propose an approach to materials prediction that uses a machine-learning interatomic potential to approximate quantum-mechanical energies and an active learning algorithm for the automatic selection of an optimal training dataset. Our…