[PDF][PDF] Recent developments in the Inorganic Crystal Structure Database: theoretical crystal structure data and related features

D Zagorac, H Müller, S Ruehl, J Zagorac… - Journal of applied …, 2019 - journals.iucr.org
The Inorganic Crystal Structure Database (ICSD) is the world's largest database of fully
evaluated and published crystal structure data, mostly obtained from experimental results …

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 …

[HTML][HTML] Universal fragment descriptors for predicting properties of inorganic crystals

O Isayev, C Oses, C Toher, E Gossett… - Nature …, 2017 - nature.com
Although historically materials discovery has been driven by a laborious trial-and-error
process, knowledge-driven materials design can now be enabled by the rational …

[HTML][HTML] Machine-enabled inverse design of inorganic solid materials: promises and challenges

J Noh, GH Gu, S Kim, Y Jung - Chemical Science, 2020 - pubs.rsc.org
Developing high-performance advanced materials requires a deeper insight and search into
the chemical space. Until recently, exploration of materials space using chemical intuitions …

Current trends in finite‐time thermodynamics

B Andresen - Angewandte Chemie International Edition, 2011 - Wiley Online Library
The cornerstone of finite‐time thermodynamics is all about the price of haste and how to
minimize it. Reversible processes may be ultimately efficient, but they are unrealistically …

Structure-based synthesizability prediction of crystals using partially supervised learning

J Jang, GH Gu, J Noh, J Kim, Y Jung - Journal of the American …, 2020 - ACS Publications
Predicting the synthesizability of inorganic materials is one of the major challenges in
accelerated material discovery. A widely employed approximate approach is to consider the …

The XtalOpt evolutionary algorithm for crystal structure prediction

Z Falls, P Avery, X Wang, KP Hilleke… - The Journal of Physical …, 2020 - ACS Publications
Significant progress has been made in the field of a priori crystal structure prediction, with a
number of recent remarkable success stories. Herein, we briefly outline the methods that …

Into the unknown: how computation can help explore uncharted material space

AM Mroz, V Posligua, A Tarzia… - Journal of the …, 2022 - ACS Publications
Novel functional materials are urgently needed to help combat the major global challenges
facing humanity, such as climate change and resource scarcity. Yet, the traditional …

In‐situ monitoring of the formation of crystalline solids

N Pienack, W Bensch - Angewandte Chemie International …, 2011 - Wiley Online Library
The processes occurring during the early stages of the formation of crystalline solids are not
well understood thus preventing the rational synthesis of new solids. The investigation of the …

[HTML][HTML] Physics guided deep learning for generative design of crystal materials with symmetry constraints

Y Zhao, EMD Siriwardane, Z Wu, N Fu… - npj Computational …, 2023 - nature.com
Discovering new materials is a challenging task in materials science crucial to the progress
of human society. Conventional approaches based on experiments and simulations are …