{
  "abstract": "Controlling phonon-mediated heat transport in multilayer nanostructures is central to thermal management and energy-conversion technologies. Yet identifying architectures exhibiting extreme lattice thermal conductivity ( κ l ) remains challenging due to the combinatorial complexity of interface arrangements and the high computational cost of transport simulations. In superlattices, thermal transport arises from competing wave effects: coherent phonon propagation enhances transport, whereas interface scattering and localization suppress it. We study Si-Ge superlattices using an iterative machine-learning-guided non-equilibrium Green’s function (NEGF) approach, where a convolutional neural network trained on NEGF data efficiently explores an experimentally constrained design space to identify both low- and high- κ l limits. Dense interface clustering induces cumulative elastic scattering and reduces κ l by up to 35%, while sparse, locally ordered motifs preserve extended phonon modes and enhance κ l by up to 33% beyond periodic designs. These results are obtained at 200 K in a regime where phonon transport is predominantly coherent and elastic; thus, the identified structural trends are representative of the coherent transport regime. Our work reveals that local motif-level order, rather than global periodicity, governs extreme thermal transport in multilayer systems.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 6,
  "authors": [
    {
      "name": "Shubham Tyagi"
    },
    {
      "name": "Julian G Fernandez"
    },
    {
      "name": "Anass Sebbar"
    },
    {
      "name": "Natalia Seoane"
    },
    {
      "name": "Antonio Garcia-Loureiro"
    },
    {
      "name": "Marc Bescond"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/2bwwy25df8pvj.bib",
  "cited_by": 0,
  "doi": "10.1038/s41524-026-02172-0",
  "fulltext_url": null,
  "id": "2bwwy25df8pvj",
  "issns": [
    "2057-3960"
  ],
  "landing_url": "https://hal.science/hal-05732366v1",
  "language": "en",
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.phys"
    ],
    [
      "openalex",
      "2505|Thermal properties of materials"
    ]
  ],
  "openalex": "W7163509390",
  "pdf_url": "https://hal.science/hal-05732366/document",
  "pmcid": null,
  "pmid": null,
  "published": "2026-06-04",
  "publisher": "Springer Nature",
  "ris": "https://research.draconitic.com/paper/2bwwy25df8pvj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:21Z",
      "native_id": "hal-05732366",
      "source": "hal",
      "url": "https://hal.science/hal-05732366v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2505|Thermal properties of materials",
      "subfield": "materials-polymers"
    }
  ],
  "title": "Machine-learning discovery of extreme coherent thermal transport governed by motif-level order in Si-Ge superlattices",
  "title_text": "Machine-learning discovery of extreme coherent thermal transport governed by motif-level order in Si-Ge superlattices",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:21Z",
  "url": "https://research.draconitic.com/paper/2bwwy25df8pvj",
  "venue": "npj Computational Materials",
  "versions": []
}