A finite-displacement workflow is established to isolate numerical artifacts causing imaginary phonon modes in MOF-5 and to analyze remaining modes via mapping and Monte Carlo distortions for dynamical stability assessment.
AIM2DAT: A Python-based Automated Ab Initio Material Modeling and Data Analysis Toolkit
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abstract
The emergence of data-driven computational materials science offers unprecedented opportunities to explore complex material landscapes, complementing experimental research with the discovery of novel compounds. To enable these developments, it is essential to establish robust, reliable, and easy-to-use software supporting workflow automation and large dataset processing. Herein, we introduce the Automated Ab Initio Materials Modeling and Data Analysis Toolkit (aim2dat), a Python package offering a user-friendly interface to generate and handle big data, design high-throughput workflows based on density functional theory calculations, and analyze the output. Its key features include interfaces to online databases for structure query and analysis, high-throughput screening routines, and seamless integration of machine learning models. The capabilities of aim2dat are showcased with a variety of use-cases, ranging from photocathode materials to metal-organic frameworks.
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cond-mat.mtrl-sci 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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A Practical Guide for Diagnosing Imaginary Phonon Modes in Metal--Organic Frameworks: The Case of MOF-5
A finite-displacement workflow is established to isolate numerical artifacts causing imaginary phonon modes in MOF-5 and to analyze remaining modes via mapping and Monte Carlo distortions for dynamical stability assessment.