Data mined ionic substitutions for the discovery of new compounds.

By Geoffroy Hautier, C. Fischer, V. Ehrlacher, Anubhav Jain, Gerbrand Ceder. 2011

389 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Materials Chemistry

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In Materials Chemistry

Citations in the corpus

Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals (2018, methodology): “Hautier et al. 59 previously developed an ionic substitution prediction algorithm using data mining, which has been used successfully in the discovery of several new materials.”
Crystal Diffusion Variational Autoencoder for Periodic Material Generation (2021, background): “…include random sampling (Pickard & Needs, 2011), evolutionary algorithms (Wang et al., 2012; Glass et al., 2006), substituting elements in known materials (Hautier et al., 2011), etc., but they generally have low success rates and require extensive computation even on relatively small problems.”
Computation-Accelerated Design of Materials and Interfaces for All-Solid-State Lithium-Ion Batteries (2018, methodology): “New candidate structures have been uncovered by computational approaches such as structure screening using the bond valence method,(91,92) machine-learning algorithms,(93) structure prediction algorithms,(94) and structural substitution algorithms.(95,96) As motivated by the spirit of MGI,(21) this ”
Key computational modeling issues in Integrated Computational Materials Engineering (2013, background): “Computational materials science and multiscale mechanics models play key roles in evaluating performance metrics necessary to support materials design.”
Mining Unexplored Chemistries for Phosphors for High-Color-Quality White-Light-Emitting Diodes (2018, methodology): “New crystal structure candidates for the target chemistries were generated by applying a retrained version of the ionic substitution algorithm developed by Hautier et al.(21) on the ICSD.”
Accelerating the discovery of materials for clean energy in the era of smart automation (2018, methodology): “…new classes of phosphate and carbonophosphate Li-ion cathodes were generated as ‘virtual’ compounds by substituting ions in known compounds from the Inorganic Crystal Structure Database, using a data-mined model 161 , to form new possible compounds containing Li, P, O and a redox-active metal.”
The thermodynamic scale of inorganic crystalline metastability (2016, background): “(47), as implemented in the pymatgen package.”
Scaling deep learning for materials discovery (2023, methodology): “Combining ab initio calculations with simple substitutions has allowed researchers to improve to 48,000 computationally stable materials according to our own recalculations 22–24 (see Methods).”