Artificial intelligence and specifically machine learning applications are nowadays used in a
variety of scientific applications and cutting-edge technologies, where they have a …

In material science, traditional experimental and computational approaches require
investing enormous time and resources, and the experimental conditions limit the …

Amid the global call for decarbonization efforts, uranium and lithium are two important metal
resources critical for securing a sustainable energy future. Extraction of uranium and lithium …

Organic chemistry has seen colossal progress due to machine learning (ML). However, the
translation of artificial intelligence (AI) into materials science is challenging, where biological …

Understanding the thermodynamic stability and metastability of materials can help us to, for
example, gauge whether crystalline polymorphs in pharmaceutical formulations are likely to …

Fast and accurate crystal structure prediction (CSP) algorithms and web servers are highly
desirable for the exploration and discovery of new materials out of the infinite chemical …

Materials informatics is reaching the transition point and is evolving from its early stages of
adoption and development and moving toward its golden age. Here, the transformation of …

Predicting crystal structure from the chemical composition is one of the most challenging and
long-standing problems in condensed matter physics. This problem resides at the interface …

With the emergence of big data initiatives and the wealth of available chemical data, data-
driven approaches are becoming a vital component of materials discovery pipelines or …

Na-ion solid-state electrolytes (Na-SSEs) exhibit high potential for electrical energy storage
owing to their high energy densities and low manufacturing cost. However, their mechanical …