Ab initio prediction of low-temperature phase diagrams in the Al–Ga–In–As system, MAs–M′As (M, M′ = Al, Ga or In) and AlAs–GaAs–InAs, via the global study of energy landscapes

文献信息

发布日期 2010-07-05
DOI 10.1039/C004040C
影响因子 3.676
作者

Ilya V. Pentin, J. Christian Schön, Martin Jansen


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摘要

Traditionally, the determination of phase diagrams has followed the inductive paradigm, where experimental observations provide the phase boundaries in more or less detail and phenomenological and semi-phenomenological models are employed to interpolate between the experimental data points, and by extrapolation to predict the shape of the phase boundaries in experimentally inaccessible regions. Over the past fifteen years, a new methodology has been developing, the aim of which is the prediction, determination and validation of phase diagrams in chemical systems without any recourse to experimental information. The foundation stone of this deductive approach is the global study of the energy landscape of the chemical system. In this study, we present an application of this approach to the prediction of the low-temperature part of the phase diagram of the quasi-ternary semiconductors (Al, Ga, In)As.

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来源期刊

Physical Chemistry Chemical Physics

Physical Chemistry Chemical Physics
CiteScore: 5.5
自引率: 10.3%
年发文量: 3036

Physical Chemistry Chemical Physics (PCCP) is an international journal co-owned by 19 physical chemistry and physics societies from around the world. This journal publishes original, cutting-edge research in physical chemistry, chemical physics and biophysical chemistry. To be suitable for publication in PCCP, articles must include significant innovation and/or insight into physical chemistry; this is the most important criterion that reviewers and Editors will judge against when evaluating submissions. The journal has a broad scope and welcomes contributions spanning experiment, theory, computation and data science. Topical coverage includes spectroscopy, dynamics, kinetics, statistical mechanics, thermodynamics, electrochemistry, catalysis, surface science, quantum mechanics, quantum computing and machine learning. Interdisciplinary research areas such as polymers and soft matter, materials, nanoscience, energy, surfaces/interfaces, and biophysical chemistry are welcomed if they demonstrate significant innovation and/or insight into physical chemistry. Joined experimental/theoretical studies are particularly appreciated when complementary and based on up-to-date approaches.

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