Universal Fragment Descriptors for Predicting Elec
Post# of 22465
Posted On: 08/19/2016 10:36:15 AM
Universal Fragment Descriptors for Predicting Electronic Properties of Inorganic Crystals
Olexandr Isayev, Corey Oses, Stefano Curtarolo, Alexander Tropsha
(Submitted on 16 Aug 2016)
Historically, materials discovery is driven by a laborious trial-and-error process. The growth of materials databases and emerging informatics approaches finally offer the opportunity to transform this practice into data- and knowledge-driven rational design−accelerating discovery of novel materials exhibiting desired properties. By using data from the AFLOW repository for high-throughput ab-initio calculations, we have generated Q−uantitative M−−aterials S−tructure-P−roperty R−elationship (QMSPR) models to predict three critical material properties, namely the metal/insulator classification, Fermi energy, and band gap energy. The prediction accuracy obtained with these QMSPR models approaches training data for virtually any stoichiometric inorganic crystalline material. We attribute the success and universality of these models to the construction of new material descriptors−referred to as the universal p−roperty-l−abeled m−−aterial f−ragments (PLMF). This representation affords straightforward model interpretation in terms of simple heuristic design rules that could guide rational materials design. This proof-of-concept study demonstrates the power of materials informatics to dramatically accelerate the search for new materials.
http://arxiv.org/abs/1608.04782
Olexandr Isayev, Corey Oses, Stefano Curtarolo, Alexander Tropsha
(Submitted on 16 Aug 2016)
Historically, materials discovery is driven by a laborious trial-and-error process. The growth of materials databases and emerging informatics approaches finally offer the opportunity to transform this practice into data- and knowledge-driven rational design−accelerating discovery of novel materials exhibiting desired properties. By using data from the AFLOW repository for high-throughput ab-initio calculations, we have generated Q−uantitative M−−aterials S−tructure-P−roperty R−elationship (QMSPR) models to predict three critical material properties, namely the metal/insulator classification, Fermi energy, and band gap energy. The prediction accuracy obtained with these QMSPR models approaches training data for virtually any stoichiometric inorganic crystalline material. We attribute the success and universality of these models to the construction of new material descriptors−referred to as the universal p−roperty-l−abeled m−−aterial f−ragments (PLMF). This representation affords straightforward model interpretation in terms of simple heuristic design rules that could guide rational materials design. This proof-of-concept study demonstrates the power of materials informatics to dramatically accelerate the search for new materials.
http://arxiv.org/abs/1608.04782
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