scholarly journals Crystal Structure Prediction of the Novel Cr2SiN4 Compound via Global Optimization, Data Mining, and the PCAE Method

Crystals ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 891
Author(s):  
Tamara Škundrić ◽  
Dejan Zagorac ◽  
Johann Christian Schön ◽  
Milan Pejić ◽  
Branko Matović
Keyword(s):  
Data Mining ◽  
Global Optimization ◽  
Structure Prediction ◽  
Experimental Studies ◽  
Energy Structure ◽  
The Novel ◽  
Crystal Structure Prediction ◽  
Primitive Cell ◽  
Structural Refinement ◽  
Two Phases

A number of studies have indicated that the implementation of Si in CrN can significantly improve its performance as a protective coating. As has been shown, the Cr-Si-N coating is comprised of two phases, where nanocrystalline CrN is embedded in a Si3N4 amorphous matrix. However, these earlier experimental studies reported only Cr-Si-N in thin films. Here, we present the first investigation of possible bulk Cr-Si-N phases of composition Cr2SiN4. To identify the possible modifications, we performed global explorations of the energy landscape combined with data mining and the Primitive Cell approach for Atom Exchange (PCAE) method. After ab initio structural refinement, several promising low energy structure candidates were confirmed on both the GGA-PBE and the LDA-PZ levels of calculation. Global optimization yielded six energetically favorable structures and five modifications possible to be observed in extreme conditions. Data mining based searches produced nine candidates selected as the most relevant ones, with one of them representing the global minimum in the Cr2SiN4. Additionally, employing the Primitive Cell approach for Atom Exchange (PCAE) method, we found three more promising candidates in this system, two of which are monoclinic structures, which is in good agreement with results from the closely related Si3N4 system, where some novel monoclinic phases have been predicted in the past.

scholarly journals Contact map based crystal structure prediction using global optimization

CrystEngComm ◽  
2021 ◽  
Author(s):  
Jianjun Hu ◽  
Wenhui Yang ◽  
Rongzhi Dong ◽  
Yuxin Li ◽  
Xiang Li ◽  
...  
Keyword(s):  
Crystal Structure ◽  
Global Optimization ◽  
Crystal Engineering ◽  
Structure Prediction ◽  
Crystal Structure Prediction ◽  
New Materials ◽  
Contact Map

Crystal structure prediction is now playing an increasingly important role in the discovery of new materials or crystal engineering.

scholarly journals Solid state forms of 4-aminoquinaldine – from void structures with and without solvent inclusion to close packing

CrystEngComm ◽  
2015 ◽  
Vol 17 (12) ◽  
pp. 2504-2516 ◽  
Author(s):  
Doris E. Braun ◽  
Thomas Gelbrich ◽  
Volker Kahlenberg ◽  
Ulrich J. Griesser
Keyword(s):  
Crystal Structure ◽  
Solid State ◽  
Carbon Tetrachloride ◽  
Structure Prediction ◽  
Experimental Studies ◽  
Close Packing ◽  
Crystal Structure Prediction ◽  
Role Of Solvent

Crystal structure prediction combined with experimental studies unveil the structural and thermodynamic features of three non-solvated forms and a carbon tetrachloride solvate of 4-aminoquinaldine and provide intriguing insights into void structures and the role of solvent inclusion.

Diffusion Equation and Distance Scaling Methods of Global Optimization:  Applications to Crystal Structure Prediction

1998 ◽  
Vol 102 (17) ◽  
pp. 2904-2918 ◽  
Author(s):  
Ryszard J. Wawak ◽  
Jaroslaw Pillardy ◽  
Adam Liwo ◽  
Kenneth D. Gibson ◽  
Harold A. Scheraga
Keyword(s):  
Crystal Structure ◽  
Global Optimization ◽  
Diffusion Equation ◽  
Structure Prediction ◽  
Crystal Structure Prediction ◽  
Scaling Methods

scholarly journals Distributed Multi-Objective Bayesian Optimization for the Intelligent Navigation of Energy Structure Function Maps For Efficient Property Discovery

2020 ◽  
Author(s):  
Edward Pyzer-Knapp ◽  
Graeme Day ◽  
Linjiang Chen ◽  
Andrew I. Cooper
Keyword(s):  
Structure Function ◽  
High Throughput ◽  
High Throughput Screening ◽  
Structure Prediction ◽  
Computational Cost ◽  
Bayesian Optimization ◽  
Energy Structure ◽  
Crystal Structure Prediction ◽  
Property Data ◽  
Prediction Techniques

Energy-structure-function (ESF) maps have emerged as a powerful tool for in silico materials design, coupling crystal structure prediction techniques with property simulations to assess the potential for new candidate materials to display desirable properties. Despite continuing increases to accessible computational power, however, the computational cost of acquiring an ESF map often remains too high to allow integration into true high-throughput virtual screening techniques. In this paper, we propose the next evolution of the ESF map, which uses parallel Bayesian optimization to selectively acquire energy and property data, generating the same levels of insight at a fraction of the computational cost by limiting the expensive property calculations to a small fraction of the predicted crystal structures associated with a molecule. We utilize this approach to obtain a two orders of magnitude speedup on a previous ESF study that focused on methane capture materials, saving over 500,000 CPUh from the original protocol. Through acceleration of the acquisition of ESF-type insight, we pave the way for the use of ESF maps in automated ultra-high throughput screening pipelines. This greatly reduce the opportunity risk associated with the choice of system to calculate. For example, it will allow researchers to use ESF maps in the search for physical properties where the computational costs are currently just intractable, or to investigate orders of magnitude more systems for a given computational cost.<br>

scholarly journals Distributed Multi-Objective Bayesian Optimization for the Intelligent Navigation of Energy Structure Function Maps For Efficient Property Discovery

2020 ◽  
Author(s):  
Edward Pyzer-Knapp ◽  
Graeme Day ◽  
Linjiang Chen ◽  
Andrew I. Cooper
Keyword(s):  
Structure Function ◽  
High Throughput ◽  
High Throughput Screening ◽  
Structure Prediction ◽  
Computational Cost ◽  
Bayesian Optimization ◽  
Energy Structure ◽  
Crystal Structure Prediction ◽  
Property Data ◽  
Prediction Techniques

Energy-structure-function (ESF) maps have emerged as a powerful tool for in silico materials design, coupling crystal structure prediction techniques with property simulations to assess the potential for new candidate materials to display desirable properties. Despite continuing increases to accessible computational power, however, the computational cost of acquiring an ESF map often remains too high to allow integration into true high-throughput virtual screening techniques. In this paper, we propose the next evolution of the ESF map, which uses parallel Bayesian optimization to selectively acquire energy and property data, generating the same levels of insight at a fraction of the computational cost by limiting the expensive property calculations to a small fraction of the predicted crystal structures associated with a molecule. We utilize this approach to obtain a two orders of magnitude speedup on a previous ESF study that focused on methane capture materials, saving over 500,000 CPUh from the original protocol. Through acceleration of the acquisition of ESF-type insight, we pave the way for the use of ESF maps in automated ultra-high throughput screening pipelines. This greatly reduce the opportunity risk associated with the choice of system to calculate. For example, it will allow researchers to use ESF maps in the search for physical properties where the computational costs are currently just intractable, or to investigate orders of magnitude more systems for a given computational cost.<br>

scholarly journals Control and prediction of the organic solid state: a challenge to theory and experiment

2018 ◽  
Vol 474 (2217) ◽  
pp. 20180351 ◽  
Author(s):  
Sarah L. Price
Keyword(s):  
Solid State ◽  
Crystal Structures ◽  
Structure Prediction ◽  
Computer Modelling ◽  
Experimental Studies ◽  
Functional Materials ◽  
Crystal Structure Prediction ◽  
Organic Solid ◽  
Kinetic Effects ◽  
Organic Solid State

The ability of theoretical chemists to quantitatively model the weak forces between organic molecules is being exploited to predict their crystal structures and estimate their physical properties. Evolving crystal structure prediction methods are increasingly being used to aid the design of organic functional materials and provide information about thermodynamically plausible polymorphs of speciality organic materials to aid, for example, pharmaceutical development. However, the increasingly sophisticated experimental studies for detecting the range of organic solid-state behaviours provide many challenges for improving quantitative theories that form the basis for the computer modelling. It is challenging to calculate the relative thermodynamic stability of different organic crystal structures, let alone understand the kinetic effects that determine which polymorphs can be observed and are practically important. However, collaborations between experiment and theory are reaching the stage of devising experiments to target the first crystallization of new polymorphs or create novel organic molecular materials.

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