Recent advances and applications of machine learning in solid-state materials science
One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …
is machine learning. This collection of statistical methods has already proved to be capable …
Machine learning for high-entropy alloys: Progress, challenges and opportunities
High-entropy alloys (HEAs) have attracted extensive interest due to their exceptional
mechanical properties and the vast compositional space for new HEAs. However …
mechanical properties and the vast compositional space for new HEAs. However …
Machine learning assisted materials design and discovery for rechargeable batteries
Y Liu, B Guo, X Zou, Y Li, S Shi - Energy Storage Materials, 2020 - Elsevier
Abstract Machine learning plays an important role in accelerating the discovery and design
process for novel electrochemical energy storage materials. This review aims to provide the …
process for novel electrochemical energy storage materials. This review aims to provide the …
Structure prediction drives materials discovery
Progress in the discovery of new materials has been accelerated by the development of
reliable quantum-mechanical approaches to crystal structure prediction. The properties of a …
reliable quantum-mechanical approaches to crystal structure prediction. The properties of a …
The highscore suite
T Degen, M Sadki, E Bron, U König, G Nénert - Powder diffraction, 2014 - cambridge.org
HighScore with the Plus option (HighScore Plus) is the commercial powder diffraction
analysis software from PANalytical. It has been in constant development over the last 13 …
analysis software from PANalytical. It has been in constant development over the last 13 …
High-throughput electronic band structure calculations: Challenges and tools
The article is devoted to the discussion of the high-throughput approach to band structures
calculations. We present scientific and computational challenges as well as solutions relying …
calculations. We present scientific and computational challenges as well as solutions relying …
AFLOW: An automatic framework for high-throughput materials discovery
Recent advances in computational materials science present novel opportunities for
structure discovery and optimization, including uncovering of unsuspected compounds and …
structure discovery and optimization, including uncovering of unsuspected compounds and …
AFLOWLIB. ORG: A distributed materials properties repository from high-throughput ab initio calculations
Empirical databases of crystal structures and thermodynamic properties are fundamental
tools for materials research. Recent rapid proliferation of computational data on materials …
tools for materials research. Recent rapid proliferation of computational data on materials …
Data‐Driven Materials Innovation and Applications
Owing to the rapid developments to improve the accuracy and efficiency of both
experimental and computational investigative methodologies, the massive amounts of data …
experimental and computational investigative methodologies, the massive amounts of data …
AiiDA: automated interactive infrastructure and database for computational science
Computational science has seen in the last decades a spectacular rise in the scope,
breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still …
breadth, and depth of its efforts. Notwithstanding this prevalence and impact, it is often still …