Author Correction: Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

Dunn, Alexander; Wang, Qi; Ganose, Alex M.; Dopp, Daniel; Jain, Anubhav

doi:10.1038/s41524-020-00433-0

Cited by 9 publications

(3 citation statements)

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“…Featurization of the starting materials was performed in matminer using composition-based features such as atomic radius and electronegativity, as well as features based on the guessed oxidation states from the chemical formula, such as the number of valence electrons. The featurized matrix was cleaned, removing features for which data were missing for more than 3% of samples, using the automatminer pipeline . The features were downselected for the most important features in multiple steps.…”

Section: Methodsmentioning

confidence: 99%

“…This brought the initial >600 features down to 46. The starting matrix with 46 features was input into a genetic algorithm for further preprocessing and machine learning optimization, as an alternative to grid-based cross-validation methods. , Within the algorithm, half of the 46 features were removed with recursive feature elimination via an extra trees classifier. Tree-based feature reduction methods provide an advantage over principal component analysis (PCA) because they allow retention of the feature names.…”

Section: Methodsmentioning

confidence: 99%

“…The featurized matrix was cleaned, removing features for which data were missing for more than 3% of samples, using the automatminer pipeline. 30 The features were downselected for the most important features in multiple steps. First, features that were correlated >95% were removed.…”

Section: ■ Introductionmentioning

confidence: 99%

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AB₂X₆ Compounds and the Stabilization of Trirutile Oxides

et al. 2021

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The properties of crystalline materials tend to be strongly correlated with their structures, and the prediction of crystal structure from only the composition is a coveted goal in the field of inorganic materials. However, even for the simplest compositions, such prediction relies on a complex network of interactions, including atomic or ionic radii, ionicity, electronegativity, position in the periodic table, and magnetism, to name only a few important parameters. We focus here on the AB 2 X 6 (AB 2 O 6 and AB 2 F 6 ) composition space with the specific goal of finding new oxide compounds in the trirutile family, which is known for unusual 1D antiferromagnetic behavior.Through machine learning methods, we develop an understanding of how geometric and bonding constraints determine the crystallization of compounds in the trirutile structure as opposed to other other ternary structures in this space. In combination with density functional theory (DFT) calculations, we predict 18 candidate, previously unreported trirutile oxides. We successfully prepare one of these and show it forms in the disordered rutile structure, under the preparation conditions adopted here.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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AB₂X₆ Compounds and the Stabilization of Trirutile Oxides

et al. 2021

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Artificial Intelligence Guided Thermoelectric Materials Design and Discovery

Han

Sun

Feng

et al. 2023

Adv Elect Materials

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Materials discovery from the infinite earth repository is a major bottleneck for revolutionary technological progress. This labor-intensive and time-consuming process hinders the discovery of new materials. Although machine learning techniques show an excellent capability for speeding up materials discovery, obtaining effective material feature representations is still challenging, and making a precise prediction of the material properties is still tricky. This work focuses on developing an automatic material design and discovery framework enabled by data-driven artificial intelligence (AI) models. Multiple types of material descriptors are first developed to promote the representation and encoding of the materials' uniqueness, resulting in improved performance for different molecular properties predictions. The material's thermoelectric (TE) properties prediction is then utilized as a baseline to demonstrate the investigation logistic. The proposed framework achieves more than 90% accuracy for predicting materials' TE properties. Furthermore, the developed AI models identify 6 promising p-type TE materials and 8 promising n-type TE materials. The prediction results are evaluated by density functional theory calculations and agree with the material's TE property provided by experimental results. The proposed framework is expected to accelerate the design and discovery of the new functional materials.

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MolBench: A Benchmark of AI Models for Molecular Property Prediction

Jiang,

Tan,

Cen

et al. 2024

Lecture Notes in Computer Science

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Author Correction: Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Cited by 9 publications

References 0 publications

AB₂X₆ Compounds and the Stabilization of Trirutile Oxides

AB₂X₆ Compounds and the Stabilization of Trirutile Oxides

Artificial Intelligence Guided Thermoelectric Materials Design and Discovery

MolBench: A Benchmark of AI Models for Molecular Property Prediction

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Author Correction: Benchmarking materials property prediction methods: the Matbench test set and Automatminer reference algorithm

Abstract: An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Cited by 9 publications

References 0 publications

AB2X6 Compounds and the Stabilization of Trirutile Oxides

AB2X6 Compounds and the Stabilization of Trirutile Oxides

Artificial Intelligence Guided Thermoelectric Materials Design and Discovery

MolBench: A Benchmark of AI Models for Molecular Property Prediction

Contact Info

Product

Resources

About

AB₂X₆ Compounds and the Stabilization of Trirutile Oxides

AB₂X₆ Compounds and the Stabilization of Trirutile Oxides