Computational exfoliation of atomically thin one-dimensional materials with application to Majorana bound states

Abstract

General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights. Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  You may not further distribute the material or use it for any profit-making activity or commercial gain  You may freely distribute the URL identifying the publication in the public portal If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.

Hundreds of new, stable, one-dimensional materials from a generative machine learning model

“…We use a clustering algorithm based on the root-meansquare-distance (RMSD) to geometrically classify the materials similar to the analysis in Ref. [9]. The RMSD between two different structures with atomic coordinates…”

“…The CDVAE is trained on 508 one-dimensional materials from the C1DB database [9,36], which are found to be stable. The thermodynamic stability of the training materials are evaluated by requiring the heat of formation to be less than 0.2 eV/atom above the convex hull.…”

“…Low-dimensional materials are gaining considerable attention due to their distinctive physical and chemical properties, as well as a variety of possible applications, including lightabsorbers, single-photon emitters, and catalysts [1][2][3]. A number of one-and two-dimensional materials have been identified and their properties have been calculated and stored in publicly available databases [3][4][5][6][7][8][9]. The materials have mostly been identified based on experimental databases like the inorganic crystal structure database (ICSD) [10] or the crystallography open database (COD) [11], and new materials have been suggested using substitution of chemical elements with a high degree of similarity [8,9].…”

“…A number of one-and two-dimensional materials have been identified and their properties have been calculated and stored in publicly available databases [3][4][5][6][7][8][9]. The materials have mostly been identified based on experimental databases like the inorganic crystal structure database (ICSD) [10] or the crystallography open database (COD) [11], and new materials have been suggested using substitution of chemical elements with a high degree of similarity [8,9]. However, the question arises whether there could be many more low-dimensional materials with interesting properties, which just wait to be discovered and synthesized.…”

Hundreds of new, stable, one-dimensional materials from a generative machine learning model

“…Low-dimensional materials are gaining considerable attention due to their distinctive physical and chemical properties, as well as a variety of possible applications, including light-absorbers, single-photon emitters, and catalysts [1][2][3] . A number of one-and two-dimensional materials have been identified and their properties have been calculated and stored in publicly available databases [3][4][5][6][7][8][9] . The materials have mostly been identified based on experimental databases like the inorganic crystal structure database (ICSD) 10 or the crystallography open database (COD) 11 , and new materials have been suggested using substitution of chemical elements with a high degree of similarity 8,9 .…”