A Fuzzy Classification Framework to Identify Equivalent Atoms in Complex Materials and Molecules

Abstract

The nature of an atom in a bonded structure -- such as in molecules, in nanoparticles or solids, at surfaces or interfaces -- depends on its local atomic environment. In atomic-scale modeling and simulation, identifying groups of atoms with equivalent environments is a frequent task, to gain an understanding of the material function, to interpret experimental results or to simply restrict demanding first-principles calculations. While routine, this task can often be challenging for complex molecules or non-ideal materials with breaks of symmetries or long-range order. To automatize this task, we here present a general machine-learning framework to identify groups of (nearly) equivalent atoms. The initial classification rests on the representation of the local atomic environment through a high-dimensional smooth overlap of atomic positions (SOAP) vector. Recognizing that not least thermal vibrations may lead to deviations from ideal positions, we then achieve a fuzzy classification by mean-shift clustering within a low-dimensional embedded representation of the SOAP points as obtained through multidimensional scaling. The performance of this classification framework is demonstrated for simple aromatic molecules and crystalline Pd surface examples.

arXiv commentAccepted manuscript in Journal of Chemical Physics. Repositories of the package (DECAF): DOI:10.17617/3.U7VKBM or https://gitlab.mpcdf.mpg.de/klai/decaf

References in corpus (16)

• Scikit-learn: Machine Learning in Python
• On representing chemical environments
• Ab initio Random Structure Searching
• Big Data of Materials Science - Critical Role of the Descriptor
• DScribe: Library of Descriptors for Machine Learning in Materials Science
• Comparing molecules and solids across structural and alchemical space
• Machine Learning Unifies the Modelling of Materials and Molecules
• Physics-inspired structural representations for molecules and materials
• A Practical Guide to Surface Kinetic Monte Carlo Simulations
• Unified Representation of Molecules and Crystals for Machine Learning
• Recognizing molecular patterns by machine learning: an agnostic structural definition of the hydrogen bond
• Recognizing Local and Global Structural Motifs at the Atomic Scale
• Predicting binding motifs of complex adsorbates using machine learning with a physics-inspired graph representation
• Efficient Simulation of Near-Edge X-ray Absorption Fine Structure (NEXAFS) in Density-Functional Theory: Comparison of Core-Level Constraining Approaches
• Machine learning enhanced global optimization by clustering local environments to enable bundled atomic energies
• DADApy: Distance-based Analysis of DAta-manifolds in Python