P
Peter Mahler Larsen
Researcher at Massachusetts Institute of Technology
Publications - 28
Citations - 1866
Peter Mahler Larsen is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Curse of dimensionality & Electron backscatter diffraction. The author has an hindex of 12, co-authored 26 publications receiving 1116 citations. Previous affiliations of Peter Mahler Larsen include Technical University of Denmark.
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Robust structural identification via polyhedral template matching
TL;DR: Polyhedral template matching (PTM) as discussed by the authors classifies structures according to the topology of the local atomic environment, without any ambiguity in the classification, and with greater reliability than e.g. common neighbour analysis in the presence of thermal fluctuations.
Journal ArticleDOI
The Computational 2D Materials Database: high-throughput modeling and discovery of atomically thin crystals
Sten Haastrup,Mikkel Strange,Mohnish Pandey,Thorsten Deilmann,Per Simmendefeldt Schmidt,N. F. Hinsche,Morten Niklas Gjerding,Daniele Torelli,Peter Mahler Larsen,Anders C. Riis-Jensen,Jakob Gath,Karsten Wedel Jacobsen,Jens Jørgen Mortensen,Thomas Olsen,Kristian Sommer Thygesen +14 more
TL;DR: The Computational 2D Materials Database (C2DB) as discussed by the authors is a large-scale database of 2D materials and van der Waals heterostructures, including tens of thousands of materials.
Journal ArticleDOI
Robust Structural Identification via Polyhedral Template Matching
TL;DR: Polyhedral Template Matching (PTM) as mentioned in this paper is a method that classifies structures according to the topology of the local atomic environment, without any ambiguity in the classification, and with greater reliability than e.g. Common Neighbour Analysis in the presence of thermal fluctuations.
Journal ArticleDOI
The Computational 2D Materials Database: High-Throughput Modeling and Discovery of Atomically Thin Crystals
Sten Haastrup,Mikkel Strange,Mohnish Pandey,Thorsten Deilmann,Per Simmendefeldt Schmidt,N. F. Hinsche,Morten Niklas Gjerding,Daniele Torelli,Peter Mahler Larsen,Anders C. Riis-Jensen,Jakob Gath,Karsten Wedel Jacobsen,Jens Jørgen Mortensen,Thomas Olsen,Kristian Sommer Thygesen +14 more
TL;DR: The Computational 2D Materials Database (C2DB) as discussed by the authors is a large-scale database of 2D materials and van der Waals heterostructures, including tens of thousands of materials.
Journal ArticleDOI
Learning grain boundary segregation energy spectra in polycrystals.
TL;DR: A machine learning framework is developed that can accurately predict the segregation tendency—quantified by the segregation enthalpy spectrum— of solute atoms at GB sites in polycrystals, based solely on the undecorated local atomic environment of such sites.