Mapping Materials and Molecules

Abstract

The visualization of data is indispensable in scientific research, from the early stages when human insight forms, to the final step of communicating results. In computational physics, 1 chemistry and materials science, it can be as simple as making a scatter plot, or as straightforward as looking through the snapshots of atomic positions manually. However, as a result of the "big data" revolution these conventional approaches are often inadequate. The widespread adoption of high-throughput computation for materials discovery and the associated community-wide repositories have given rise to data sets that contain an enormous number of compounds and atomic configurations. A typical data set contains thousands to millions of atomic structures, along with a diverse range of properties such as formation energies, band gaps, or bio-activities.It would thus be desirable to have a data-driven and automated framework for visualizing and analyzing such structural datasets. The key idea is to construct a low-dimensional representation of the data, which facilitates navigation, reveals underlying patterns, and helps to identify data points with unusual attributes. Such data-intensive maps, often employing machine learning methods, are appearing more and more frequently in the literature. However, to the wider community, it is not always transparent how these maps are made and how they should be interpreted. Furthermore, while these maps undoubtedly serve a decorative purpose in academic publications, it is not always apparent what extra information can be garnered from reading or making them. This Account attempts to answer such questions. We start with a concise summary of the theory of representing chemical environments, followed by the introduction of a simple yet practical conceptual approach for generating structure maps in a generic and automated manner. Such analysis and mapping is made nearly effortless by employing the newly developed software tool, ASAP. To showcase the applicability to a wide variety of systems in chemistry and materials science, we provide several illustrative examples, including crystalline and amorphous materials, interfaces, and organic molecules. In these examples, the maps not only help to sift through large datasets, but also reveal hidden patterns that could be easily missed using conventional analyses.The explosion in the amount of computed information in chemistry and materials science 2 has made visualization into a science in itself. Not only have we benefited from exploiting these visualization methods in previous works, we also believe that the automated mapping of datasets will in turn stimulate further creativity and exploration, as well as ultimately feed back into future advances in the respective fields.

Ab initio molecular dynamics and materials design for embedded phase-change memory

“…The SOAP formalism has been applied in structural analyses of a variety of materials 74 , including alloyed Sb 2 Te 3 19 , and in the fitting of machine learning interatomic potentials (ref. 75 and references therein) including one for liquid, amorphous, and crystalline Ge 2 Sb 2 Te 5 76 .…”

Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C60

“…1, where we use a new method to embed high-dimensional data in two dimensions, based on a hierarchical combination of cluster-based data classification and multidimensional scaling [34,35]. This is a popular tool for visualizing structural databases in the context of ML applied to the study of atomic systems [36][37][38][39].…”

Visualization and Quantification of Geometric Diversity in Metal–Organic Frameworks

“…To interpret (cg-) SOAP analysis results, the dataset is often visualized as a two-dimensional projection. 29,32,39 A large number of algorithms are available to carry out this projection (or "embedding"), and a central aspect of the present work will be to compare different widely used embedding schemes. Our implementation stores the similarity of all structures with one another in the form of a symmetric similarity matrix, K, which we construct using the per-cell averaged similarity (Eq.…”

“…28 Subsequently, it was shown how similarity kernels based on the SOAP formalism may be used to analyze the structural similarity for molecular and bulk periodic structures. 29 By coupling to ML techniques such as dimensionality reduction and data clustering, one can begin to navigate complex configurational spaces, [30][31][32] and search for underlying structure-property relationships. [33][34][35][36][37][38] We have recently demonstrated that a combination of coarse-graining, re-scaling, and SOAP analysis enables geometric comparison between very different classes of materials, exemplified us-ing a database of AB 2 hybrid and inorganic networks.…”