Identify crystal structures by a new paradigm based on graph theory for building materials big data

Rapid Mining of Fast Ion Conductors via Subgraph Isomorphism Matching

The rapidly evolving field of inorganic solid-state electrolytes (ISSEs) has been driven in recent years by advances in data-mining techniques, which facilitates the high-throughput computational screening for candidate materials in the databases. The key to the mining process is the selection of critical features that underline the similarity of a material to an existing ISSE. Unfortunately, this selection is generally subjective and frequently under debate. Here we propose a subgraph isomorphism matching method that allows an objective evaluation of the similarity between two compounds according to the topology of the local atomic environment. The matching algorithm has been applied to discover four structure types that are highly analogous to the LiTi2(PO4)3 NASICON prototype. We demonstrate that the local atomic environments similar to LiTi2(PO4)3 endow these four structures with favorable Li diffusion tunnels and ionic conductivity on par with those of the prototype. By further taking into account the electronic structure and electrochemical stability window, 13 compounds are identified to be potential ISSEs. Our findings not only offer a promising approach toward rapid mining of fast ion conductors without limitation in the compositional range but also reveal insights into the design of ISSEs according to the topology of their framework structures.

Algebraic Graph-Based Machine Learning Model for Li-Cluster Prediction

In cluster research, determining the ground-state structure of medium-sized clusters is hindered by a large number of local minimum on potential energy surfaces. The global optimization heuristic algorithm is time-consuming due to the use of DFT to determine the relative size of the cluster energy. Although machine learning (ML) is proved to be a promising way to reduce the DFT computational costs, a suitable method to represent clusters as input vectors is one of the bottlenecks in the application of ML to cluster research. In this work, we proposed a multiscale weighted spectral subgraph (MWSS) as an effective low-dimension representation of clusters and build an MWSS-based ML model to discover the structure-energy relationships in lithium clusters. We combine this model with the particle swarm optimization algorithm and DFT calculations to search for globally stable structures of clusters. We have successfully predicted the ground-state structure of Li20.

Graph-based discovery and analysis of atomic-scale one-dimensional materials

Recent decades have witnessed an exponential growth in the discovery of low-dimensional materials (LDMs), benefiting from our unprecedented capabilities in characterizing their structure and chemistry with the aid of advanced computational techniques. Recently, the success of two-dimensional compounds has encouraged extensive research into one-dimensional (1D) atomic chains. Here, we present a methodology for topological classification of structural blocks in bulk crystals based on graph theory, leading to the identification of exfoliable 1D atomic chains and their categorization into a variety of chemical families. A subtle interplay is revealed between the prototypical 1D structural motifs and their chemical space. Leveraging the structure graphs, we elucidate the self-passivation mechanism of 1D compounds imparted by lone electron pairs, and reveal the dependence of the electronic band gap on the cationic percolation network formed by connections between structure units. This graph-theory-based formalism could serve as a source of stimuli for the future design of LDMs.

Topological representations of crystalline compounds for the machine-learning prediction of materials properties

Accurate theoretical predictions of desired properties of materials play an important role in materials research and development. Machine learning (ML) can accelerate the materials design by building a model from input data. For complex datasets, such as those of crystalline compounds, a vital issue is how to construct low-dimensional representations for input crystal structures with chemical insights. In this work, we introduce an algebraic topology-based method, called atom-specific persistent homology (ASPH), as a unique representation of crystal structures. The ASPH can capture both pairwise and many-body interactions and reveal the topology-property relationship of a group of atoms at various scales. Combined with composition-based attributes, ASPH-based ML model provides a highly accurate prediction of the formation energy calculated by density functional theory (DFT). After training with more than 30,000 different structure types and compositions, our model achieves a mean absolute error of 61 meV/atom in cross-validation, which outperforms previous work such as Voronoi tessellations and Coulomb matrix method using the same ML algorithm and datasets. Our results indicate that the proposed topology-based method provides a powerful computational tool for predicting materials properties compared to previous works.

Co13O8—metalloxocubes: a new class of perovskite-like neutral clusters with cubic aromaticity

Exploring stable clusters to understand structural evolution from atoms to macroscopic matter and to construct new materials is interesting yet challenging in chemistry. Utilizing our newly developed deep-ultraviolet laser ionization mass spectrometry technique, here we observe the reactions of neutral cobalt clusters with oxygen and find a very stable cluster species of Co₁₃O₈ that dominates the mass distribution in the presence of a large flow rate of oxygen gas. The results of global-minimum structural search reveal a unique cubic structure and distinctive stability of the neutral Co₁₃O₈ cluster that forms a new class of metal oxides that we named as ‘metalloxocubes’. Thermodynamics and kinetics calculations illustrate the structural evolution from icosahedral Co₁₃ to the metalloxocube Co₁₃O₈ with decreased energy, enhanced stability and aromaticity. This class of neutral oxygen-passivated metal clusters may be an ideal candidate for genetic materials because of the cubic nature of the building blocks and the stability due to cubic aromaticity.