{
  "id": "2207.09444",
  "version": "v1",
  "published": "2022-07-19T17:58:30.000Z",
  "updated": "2022-07-19T17:58:30.000Z",
  "title": "Machine learning approach to genome of two-dimensional materials with flat electronic bands",
  "authors": [
    "Anupam Bhattacharya",
    "Ivan Timokhin",
    "Ratnamala Chatterjee",
    "Qian Yang",
    "Artem Mishchenko"
  ],
  "categories": [
    "cond-mat.mes-hall"
  ],
  "abstract": "Many-body physics of electron-electron correlations plays a central role in condensed mater physics, it governs a wide range of phenomena, stretching from superconductivity to magnetism, and is behind numerous technological applications. To explore this rich interaction-driven physics, two-dimensional (2D) materials with flat electronic bands provide a natural playground thanks to their highly localised electrons. Currently, thousands of 2D materials with computed electronic bands are available in open science databases, awaiting such exploration. Here we used a new machine learning algorithm combining both supervised and unsupervised machine intelligence to automate the otherwise daunting task of materials search and classification, to build a genome of 2D materials hosting flat electronic bands. To this end, a feedforward artificial neural network was employed to identify 2D flat band materials, which were then classified by a bilayer unsupervised learning algorithm. Such a hybrid approach of exploring materials databases allowed us to reveal completely new material classes outside the known flat band paradigms, offering new systems for in-depth study on their electronic interactions.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2022-07-19T17:58:30.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "machine learning approach",
      "two-dimensional materials",
      "2d flat band materials",
      "materials hosting flat electronic",
      "hosting flat electronic bands"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}