MAGUS: machine learning and graph theory assisted universal structure searcher

J Wang, H Gao, Y Han, C Ding, S Pan… - National Science …, 2023 - academic.oup.com
Crystal structure predictions based on first-principles calculations have gained great
success in materials science and solid state physics. However, the remaining challenges …

Computational design of energy‐related materials: From first‐principles calculations to machine learning

H Xue, G Cheng, WJ Yin - Wiley Interdisciplinary Reviews …, 2024 - Wiley Online Library
Energy‐related materials are crucial for advancing energy technologies, improving
efficiency, reducing environmental impacts, and supporting sustainable development …

Recent advances in the application of machine learning to crystal behavior and crystallization process control

M Lu, S Rao, H Yue, J Han, J Wang - Crystal Growth & Design, 2024 - ACS Publications
Crystals are integral to a variety of industrial applications, such as the development of
pharmaceuticals and advancements in material science. To anticipate crystal behavior and …

Assessing the feasibility of near-ambient conditions superconductivity in the Lu-NH system

YW Fang, Đ Dangić, I Errea - Communications Materials, 2024 - nature.com
The report of near-ambient superconductivity in nitrogen-doped lutetium hydrides (Lu-NH)
has generated a great interest. However, conflicting results raised doubts regarding …

Metal–organic frameworks through the lens of artificial intelligence: a comprehensive review

K Neikha, A Puzari - Langmuir, 2024 - ACS Publications
Metal–organic frameworks (MOFs) are a class of hybrid porous materials that have gained
prominence as a noteworthy material with varied applications. Currently, MOFs are in …

Towards quantitative evaluation of crystal structure prediction performance

L Wei, Q Li, SS Omee, J Hu - Computational Materials Science, 2024 - Elsevier
Crystal structure prediction (CSP) is now increasingly used in the discovery of novel
materials with applications in diverse industries. However, despite decades of …

Compactness matters: Improving Bayesian optimization efficiency of materials formulations through invariant search spaces

SG Baird, JR Hall, TD Sparks - Computational Materials Science, 2023 - Elsevier
Would you rather search for a line inside a cube or a point inside a square? Physics-based
simulations and wet-lab experiments often have symmetries (degeneracies) that allow …

The MatHub‐3d first‐principles repository and the applications on thermoelectrics

L Liu, M Yao, Y Wang, Y **, J Ji, H Luo… - Materials Genome …, 2024 - Wiley Online Library
Abstract Following the Materials Genome Initiative project, materials research has embarked
a new research paradigm centered around material repositories, significantly accelerating …

Metolazone co-crystals-loaded oral fast dissolving films: Design, optimization, and in vivo evaluation

MS Mohamed, AA El-Shenawy, EA Mahmoud… - Journal of Drug Delivery …, 2023 - Elsevier
This study aimed to formulate and optimize metolazone (MLZ) co-crystals incorporating fast-
dissolving films (OFDFs) as an agreeable oral formulation for improving the bioavailability of …

Prediction of NdFe16-based permanent-magnet compounds with high magnetization

I Seo, S Tanaka, M Endo, Y Gohda - Applied Physics Express, 2024 - iopscience.iop.org
We find a candidate for new permanent-magnet materials with the 1–16 stoichiometry on the
basis of first-principles calculations utilizing a materials database. An extremely iron-rich …