Equivariant Networks for Crystal Structures

Kaba, Sékou-Oumar; Ravanbakhsh, Siamak

doi:10.48550/arxiv.2211.15420

Cited by 3 publications

(2 citation statements)

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“…Equivariant graph-based NNs show promise, as they leverage group representation theory to design architectures that are equivariant to specified symmetry groups, making them well-suited for analysing chemical systems with underlying symmetries. 131 We expect another impact to come from interpretable and explainable ML which enables researchers to understand the underlying mechanisms behind predictions, build trust in ML model outcomes, and uncover unexpected correlations that may lead to scientific insights. For those interested, we refer to a recent review paper by Oviedo et al 30 for more about interpretable and explainable ML in materials chemistry.…”

Section: Remaining Challenges and Future Outlookmentioning

confidence: 99%

Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry

Anker,

Butler,

Selvan

et al. 2023

Chem. Sci.

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Section: Remaining Challenges and Future Outlookmentioning

confidence: 99%

Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry

Anker,

Butler,

Selvan

et al. 2023

Chem. Sci.

View full text Add to dashboard Cite

show abstract

“…Equivariant graph-based NNs show promise, as they leverage group representation theory to design architectures that are equivariant to specied symmetry groups, making them wellsuited for analysing chemical systems with underlying symmetries. 131 We expect another impact to come from interpretable and explainable ML which enables researchers to understand the underlying mechanisms behind predictions, build trust in ML model outcomes, and uncover unexpected correlations that may lead to scientic insights. For those interested, we refer to a recent review paper by Oviedo et al 30 for more about interpretable and explainable ML in materials chemistry.…”

Section: Remaining Challenges and Future Outlookmentioning

confidence: 99%

Machine Learning for Analysis of Experimental Scattering and Spectroscopy Data in Materials Chemistry

Anker,

Butler,

Selvan

et al. 2023

Preprint

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The rapid growth of materials chemistry data, driven by advancements in large-scale radiation facilities as well as laboratory instruments, has outpaced conventional data analysis and modelling methods, which can require enormous manual effort. To address this bottleneck, we investigate the application of supervised and unsupervised machine learning (ML) techniques for scattering and spectroscopy data analysis in materials chemistry research. Our perspective focuses on ML applications in powder diffraction (PD), pair distribution function (PDF), small-angle scattering (SAS), inelastic neutron scattering (INS), and X-ray absorption spectroscopy (XAS) data, but the lessons that we learn are generally applicable across materials chemistry. We review the ability of ML to efficiently and accurately identify physical and structural models and extract information from experimental data. Furthermore, we discuss the challenges associated with supervised ML and highlight how unsupervised ML can mitigate these limitations, thus enhancing experimental materials chemistry data analysis. Our perspective emphasises the transformative potential of ML in materials chemistry characterisation and identifies promising directions for future applications. The perspective aims to guide newcomers to ML-based experimental data analysis, alerting them to the potential pitfalls and offering guidance for success.

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Superconductor Discovery in the Emerging Paradigm of Materials Informatics

Tran,

Dam,

Kuenneth

et al. 2024

Chem. Mater.

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The past two decades have witnessed a tremendous number of computational predictions of hydride-based (phonon-mediated) superconductors, mostly at extremely high pressures, i.e., hundreds of gigapascals. These discoveries were strongly driven by Migdal−E ́liashberg theory (and its first-principles computational implementations) for electron−phonon interactions, the key concept of phonon-mediated superconductivity. Dozens of predictions were experimentally synthesized and characterized, triggering not only enormous excitement in the community but also some debates. In this work, we review the computationally driven discoveries and the recent developments in the field from various essential aspects, including the theoretically based, computationally based, and, specifically, artificial intelligence/machine learning (AI/ML)-based approaches emerging within the paradigm of materials informatics. While challenges and critical gaps can be found in all of these approaches, AI/ML efforts specifically remain in their infancy for good reasons. However, there are opportunities in which these approaches can be further developed and integrated in concerted efforts, in which AI/ML approaches could play more important roles.

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Equivariant Networks for Crystal Structures

Cited by 3 publications

References 0 publications

Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry

Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry

Machine Learning for Analysis of Experimental Scattering and Spectroscopy Data in Materials Chemistry

Superconductor Discovery in the Emerging Paradigm of Materials Informatics

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