Crystal structures are characterised by repeating atomic patterns within unit cells across three-dimensional space, posing unique challenges for graph-based representation learning. Current methods ...

For those who tried (or were even afraid to begin) to learn VEX but failed and stopped because it was too hard. Here you will learn VEX and applied math starting from the basics. From general to ...

The development of universal machine-learning interatomic potentials capable of simulating magnetic ordering is vital for the in silico discovery of indispensable magnetic materials across vast ...

Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. Graph Neural Networks (GNNs) have emerged as powerful tools for predicting material ...

The PaStiX (Parallel Sparse matriX package) is a open-source scientific library that provides a high performance parallel solver for very large sparse linear systems based on direct methods. Numerical ...

A particle in quantum mechanics can escape thermalization in the presence of a random potential, a phenomenon known as Anderson localization. The properties of the Anderson localization transition ...

Shear wave velocity is an essential elastic rock parameter for reservoir characterization, fluid identification, and rock physics model building. However, S-wave velocity logging data are often ...

Abstract: In recent times due to the advancement in perceptual applications, focus in computer vision has been inclined towards tasks that require a significant level of semantic understanding of ...

Structural defects are abundant in solids, and vital to the macroscopic materials properties. However, a defect-property linkage typically requires significant efforts from experiments or simulations, ...

The cubic polycrystal of SiC (3C-SiC) coating on the quartz glass (QG) surface was successfully prepared via a two-step chemical vapor deposition (CVD) by introducing a thin PyC coating as a buffer ...