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Journal ArticleDOI

First Step Towards Planning of Syntheses in Solid-State Chemistry: Determination of Promising Structure Candidates by Global Optimization

J. Christian Schön, +1 more
- 09 Jul 1996 - 
- Vol. 35, Iss: 12, pp 1286-1304
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TLDR
In this paper, the Hartree-Fock algorithm is used to predict the existence and structure of (meta)stable solid compounds based on a set of adjustable modules that are applied to the study of the energy function of the chemical system of interest.
Abstract
A method is presented here that allows, in principle, the prediction of the existence and structure of (meta)stable solid compounds. It is based on a set of adjustable modules that are applied to the study of the energy function of the chemical system of interest. The main elements are a set of routines for global optimization and local minimization, as well as algorithms for the investigation of the phase space structure near local minima of the potential energy, and the analysis and characterization of the structure candidates. The current implementation focuses on ionic compounds, for which empirical potentials are used for the evaluation of the energy function in the first stage, and a Hartree–Fock algorithm for refinements. The global optimization is performed with a stochastic simulated annealing algorithm, and the local minimization employs stochastic quenches and gradient methods. The neighborhoods of the local minima are studied with the threshold algorithm. The results of this approach are illustrated with a number of examples: compounds of binary noble gases, and binary and ternary ionic compounds. These include several substances that have not been synthesized yet, but should stand a fair chance of being kinetically stable, for example further alkali metal nitrides besides Li3N, as well as Ca3SiBr2 or SrTi2O5.

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Journal ArticleDOI

Crystal structure prediction using ab initio evolutionary techniques: principles and applications.

TL;DR: An efficient and reliable methodology for crystal structure prediction, merging ab initio total-energy calculations and a specifically devised evolutionary algorithm, which allows one to predict the most stable crystal structure and a number of low-energy metastable structures for a given compound at any P-T conditions without requiring any experimental input.
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Crystal structure prediction using ab initio evolutionary techniques: principles and applications

TL;DR: In this paper, an efficient and reliable methodology for crystal structure prediction, merging ab initio total energy calculations and a specifically devised evolutionary algorithm, was developed, which allows one to predict the most stable crystal structure and a number of low-energy metastable structures for a given compound at any P-T conditions without requiring any experimental input.
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Recent advances and applications of machine learning in solid-state materials science

TL;DR: A comprehensive overview and analysis of the most recent research in machine learning principles, algorithms, descriptors, and databases in materials science, and proposes solutions and future research paths for various challenges in computational materials science.
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USPEX—Evolutionary crystal structure prediction

TL;DR: Starting from chemical composition, USPEX is tested on numerous systems for which the stable structure is known and has observed a success rate of nearly 100%, simultaneously finding large sets of crystals.
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How Evolutionary Crystal Structure Prediction Works—and Why

TL;DR: It is demonstrated that the energy landscapes of chemical systems have an overall shape and explore their intrinsic dimensionalities and the power of evolutionary CSP is illustrated through applications that examine matter at high pressure, where new, unexpected phenomena take place.
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