Unsupervised learning is becoming an essential tool to analyze the increasingly large
amounts of data produced by atomistic and molecular simulations, in material science, solid …

Batteries are of paramount importance for the energy storage, consumption, and
transportation in the current and future society. Recently machine learning (ML) has …

The development of new catalyst materials for energy-efficient chemical synthesis is critical
as over 80% of industrial processes rely on catalysts, with many of the most energy-intensive …

Electrochemical systems function via interconversion of electric charge and chemical
species and represent promising technologies for our cleaner, more sustainable future …

We present a computational screening of experimental structural repositories for fast Li-ion
conductors, with the goal of finding new candidate materials for application as solid-state …

Understanding the dynamical processes that govern the performance of functional materials
is essential for the design of next generation materials to tackle global energy and …

Solid-state electrolyte materials with superior lithium ionic conductivities are vital to the next-
generation Li-ion batteries. Molecular dynamics could provide atomic scale information to …

Abstract Rhombohedral NaZr2 (PO4) 3 is the prototype of all the NASICON‐type materials.
The ionic diffusion in these rhombohedral NASICON materials is highly influenced by the …

The discovery of the lithium superionic conductor Li10GeP2S12 (LGPS) has led to
significant research activity on solid electrolytes for high-performance solid-state batteries …

The restructuring of interfaces plays a crucial role in materials science and heterogeneous
catalysis. Bimetallic systems, in particular, often adopt very different compositions and …