Machine-learning approach for discovery of conventional superconductors

Huan, Tran Doan; Vu, Tuoc N.

doi:10.1103/physrevmaterials.7.054805

Phys. Rev. Materials

2023

DOI: 10.1103/physrevmaterials.7.054805

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Machine-learning approach for discovery of conventional superconductors

Tran Doan Huan

Tuoc N. Vu

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Cited by 9 publications

References 102 publications

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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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Superconductor Discovery in the Emerging Paradigm of Materials Informatics

Tran,

Dam,

Kuenneth

et al. 2024

Chem. Mater.

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Molecular hydrogen in the N-doped LuH3 system as a possible path to superconductivity

Tresca,

Forcella,

Angeletti

et al. 2024

Nat Commun

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The discovery of ambient superconductivity would mark an epochal breakthrough long-awaited for over a century, potentially ushering in unprecedented scientific and technological advancements. The recent findings on high-temperature superconducting phases in various hydrides under high pressure have ignited optimism, suggesting that the realization of near-ambient superconductivity might be on the horizon. However, the preparation of hydride samples tends to promote the emergence of various metastable phases, marked by a low level of experimental reproducibility. Identifying these phases through theoretical and computational methods entails formidable challenges, often resulting in controversial outcomes. In this paper, we consider N-doped LuH3 as a prototypical complex hydride: By means of machine-learning-accelerated force-field molecular dynamics, we have identified the formation of H2 molecules stabilized at ambient pressure by nitrogen impurities. Importantly, we demonstrate that this molecular phase plays a pivotal role in the emergence of a dynamically stable, low-temperature, experimental-ambient-pressure superconductivity. The potential to stabilize hydrogen in molecular form through chemical doping opens up a novel avenue for investigating disordered phases in hydrides and their transport properties under near-ambient conditions.

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Unleashing the power of artificial intelligence in phonon thermal transport: Current challenges and prospects

2024

Journal of Applied Physics

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The discovery of advanced thermal materials with exceptional phonon properties drives technological advancements, impacting innovations from electronics to superconductors. Understanding the intricate relationship between composition, structure, and phonon thermal transport properties is crucial for speeding up such discovery. Exploring innovative materials involves navigating vast design spaces and considering chemical and structural factors on multiple scales and modalities. Artificial intelligence (AI) is transforming science and engineering and poised to transform discovery and innovation. This era offers a unique opportunity to establish a new paradigm for the discovery of advanced materials by leveraging databases, simulations, and accumulated knowledge, venturing into experimental frontiers, and incorporating cutting-edge AI technologies. In this perspective, first, the general approach of density functional theory (DFT) coupled with phonon Boltzmann transport equation (BTE) for predicting comprehensive phonon properties will be reviewed. Then, to circumvent the extremely computationally demanding DFT + BTE approach, some early studies and progress of deploying AI/machine learning (ML) models to phonon thermal transport in the context of structure–phonon property relationship prediction will be presented, and their limitations will also be discussed. Finally, a summary of current challenges and an outlook of future trends will be given. Further development of incorporating AI/ML algorithms for phonon thermal transport could range from phonon database construction to universal machine learning potential training, to inverse design of materials with target phonon properties and to extend ML models beyond traditional phonons.

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Machine-learning approach for discovery of conventional superconductors

Cited by 9 publications

References 102 publications

Superconductor Discovery in the Emerging Paradigm of Materials Informatics

Superconductor Discovery in the Emerging Paradigm of Materials Informatics

Molecular hydrogen in the N-doped LuH3 system as a possible path to superconductivity

Unleashing the power of artificial intelligence in phonon thermal transport: Current challenges and prospects

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