Chemical substitution assisted ion sensing with organic molecules: a case study of naphthalene

RSC Advances ◽  
2016 ◽  
Vol 6 (8) ◽  
pp. 6191-6195
Author(s):  
Wei-Jie Min ◽  
Hua Hao ◽  
Xian-Long Wang ◽  
Xiao-Hong Zheng ◽  
Zhi Zeng
Keyword(s):  
First Principles ◽  
Organic Molecules ◽  
First Principles Calculations ◽  
Good System ◽  
Chemical Substitution ◽  
Ion Sensing ◽  
Naphthalene Molecule

Using first-principles calculations, we predict that a naphthalene molecule with N substitutions for the –CH groups is a good system for H+ sensing.

scholarly journals Using first-principles calculations to screen for fragile magnetism: Case study of LaCrGe3 and LaCrSb3

2018 ◽  
Vol 97 (18) ◽  
Author(s):  
Manh Cuong Nguyen ◽  
Valentin Taufour ◽  
Sergey L. Bud'ko ◽  
Paul C. Canfield ◽  
Vladimir P. Antropov ◽  
...  
Keyword(s):  
First Principles ◽  
First Principles Calculations

First Principles Calculations of NMR Parameters in Biocompatible Materials Science: The Case Study of Calcium Phosphates, β- and γ-Ca(PO3)2. Combination with MAS-J Experiments

2007 ◽  
Vol 19 (26) ◽  
pp. 6367-6369 ◽  
Author(s):  
Frédérique Pourpoint ◽  
Adi Kolassiba ◽  
Christel Gervais ◽  
Thierry Azaïs ◽  
Laure Bonhomme-Coury ◽  
...  
Keyword(s):  
First Principles ◽  
Materials Science ◽  
Calcium Phosphates ◽  
Biocompatible Materials ◽  
First Principles Calculations ◽  
Nmr Parameters

scholarly journals ZenGen, a tool to generate ordered configurations for systematic first-principles calculations: The Cr–Mo–Ni–Re system as a case study

Calphad ◽  
2015 ◽  
Vol 51 ◽  
pp. 233-240 ◽  
Author(s):  
J.-C. Crivello ◽  
R. Souques ◽  
A. Breidi ◽  
N. Bourgeois ◽  
J.-M. Joubert
Keyword(s):  
First Principles ◽  
First Principles Calculations

scholarly journals Active discovery of organic semiconductors

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Christian Kunkel ◽  
Johannes T. Margraf ◽  
Ke Chen ◽  
Harald Oberhofer ◽  
Karsten Reuter
Keyword(s):  
Organic Semiconductors ◽  
First Principles ◽  
Organic Molecules ◽  
Design Space ◽  
Chemical Space ◽  
Search Space ◽  
First Principles Calculations ◽  
Test Space ◽  
Predictive Quality ◽  
Conduction Properties

AbstractThe versatility of organic molecules generates a rich design space for organic semiconductors (OSCs) considered for electronics applications. Offering unparalleled promise for materials discovery, the vastness of this design space also dictates efficient search strategies. Here, we present an active machine learning (AML) approach that explores an unlimited search space through consecutive application of molecular morphing operations. Evaluating the suitability of OSC candidates on the basis of charge injection and mobility descriptors, the approach successively queries predictive-quality first-principles calculations to build a refining surrogate model. The AML approach is optimized in a truncated test space, providing deep methodological insight by visualizing it as a chemical space network. Significantly outperforming a conventional computational funnel, the optimized AML approach rapidly identifies well-known and hitherto unknown molecular OSC candidates with superior charge conduction properties. Most importantly, it constantly finds further candidates with highest efficiency while continuing its exploration of the endless design space.

Novel Graphene-like Co2VAl (111): Case Study on Magnetoelectronic and Optical Properties by First-Principles Calculations

2017 ◽  
Vol 121 (7) ◽  
pp. 3978-3986 ◽  
Author(s):  
Arash Boochani ◽  
Bromand Nowrozi ◽  
Jabbar Khodadadi ◽  
Shahram Solaymani ◽  
Saeid Jalali-Asadabadi
Keyword(s):  
Optical Properties ◽  
First Principles ◽  
First Principles Calculations
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