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Billions of organic molecules are known, but only a tiny fraction of the functional inorganic materials have been discovered, a particularly relevant problem to the community searching for new quantum materials.
“Archimedean lattices”
Jorge Martinez · 1973
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“Metallic radii and electron configurations of the 5f- 6d metals”
WH Zachariasen · 1973
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“Archimedean lattices”
Jorge Martinez · 1973
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“Metallic radii and electron configurations of the 5f- 6d metals”
WH Zachariasen · 1973
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“Projector augmented-wave method”
Peter Blöchl · 1994
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“Efficient iterative schemes for ab initio total-energy calculations using a plane-wave basis set”
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“Archimedean lattices in the bound states of wave interacting particles”
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“Archimedean lattices in the bound states of wave interacting particles”
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“Topological Crystalline Insulators”
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“Commentary: The Materials Project: A materials genome approach to accelerating materials innovation”
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“Advances in theory and their application within the field of zeolite chemistry”
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“Interaction-driven topological and nematic phases on the Lieb lattice”
Wei-Feng Tsai, Chen Fang, Hong Yao and Jiangping Hu · 2015
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“Observation of a localized flat-band state in a photonic Lieb lattice”
Sebabrata Mukherjee et al · 2015
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“Observation of localized states in Lieb photonic lattices”
Rodrigo Vicencio et al · 2015
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“Thin-film ferroelectric materials and their applications”
Lane Martin and Andrew Rappe · 2016
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“Quantum spin liquids: a review”
Lucile Savary and Leon Balents · 2016
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“Experimental realization and characterization of an electronic Lieb lattice”
Marlou Slot et al · 2017
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“Weyl and Dirac semimetals in three-dimensional solids”
N.. Armitage, E.. Mele and Ashvin Vishwanath · 2018
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“Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties”
Tian Xie and Jeffrey Grossman · 2018
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“Benchmarking coordination number prediction algorithms on inorganic crystal structures”
Hillary Pan et al · 2021
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“Direct prediction of phonon density of states with Euclidean neural networks”
Zhantao Chen et al · 2021
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“Topological kagome magnets and superconductors”
Jia-Xin Yin, Biao Lian and M Hasan · 2022
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Andreas Lugmayr et al · 2022
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“e3nn: Euclidean neural networks”
Mario Geiger and Tess Smidt · 2022
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“Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties”
Tian Xie and Jeffrey Grossman · 2018
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Daniel Davies et al · 2019
Cited alongside, same era.
“Smact: Semiconducting materials by analogy and chemical theory”
Daniel Davies et al · 2019
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“Quantum spin liquids”
C Broholm et al · 2020
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“Topological flat bands in frustrated kagome lattice CoSn”
Mingu Kang et al · 2020
Cited alongside, same era.
“Local structure order parameters and site fingerprints for quantification of coordination environment and crystal structure similarity”
Nils Zimmermann and Anubhav Jain · 2020
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Mario Geiger and Tess Smidt · 2022
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“Scalable diffusion for materials generation”
Mengjiao Yang et al · 2023
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“Scaling deep learning for materials discovery”
Amil Merchant et al · 2023
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“Mattergen: a generative model for inorganic materials design”
Claudio Zeni et al · 2023
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Janosh Riebesell et al · 2023
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“Matbench Discovery–An evaluation framework for machine learning crystal stability prediction”
Janosh Riebesell et al · 2023
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“Rashba-like physics in condensed matter - Nature Reviews Physics — nature.com” [Accessed 11-06-2024], https://www.nature.com/articles/s42254-022-00490-y
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