Three-Dimensional Visualization of Adsorption Distribution in a Single Crystalline Particle of a Metal-Organic Framework.

Many unique adsorption properties of metal-organic frameworks (MOFs) have been revealed by diffraction crystallography, visualizing their vacant and guest-loaded crystal structures at the molecular scale. However, it has been challenging to see the spatial distribution of the adsorption behaviors throughout a single MOF particle in a transient equilibrium state. Here, we report three-dimensional (3D) visualization of molecular adsorption behaviors in a single crystalline particle of a MOF by in situ X-ray absorption fine structure spectroscopy combined with computed tomography for the first time. The 3D maps of water-coordinated Co sites in a 100 µm-scale MOF-74-Co crystal were obtained with 1 µm spatial resolution under several water vapor pressures. Through the visualization of the water vapor adsorption process, 3D spectroimaging revealed the mechanism and spatial heterogeneity of guest adsorption inside a single particle of a crystalline MOF.

Visualization of Structural Heterogeneities in Particles of Lithium Nickel Manganese Oxide Cathode Materials by Ptychographic X-ray Absorption Fine Structure.

A heterogeneous phase/structure distribution in the bulk of spinel lithium nickel manganese oxides (LNMOs) is the key to maximizing the performance and stability of the cathode materials of lithium-ion batteries. Herein, we report the use of two-dimensional ptychographic X-ray absorption fine structure (XAFS) to visualize the density and valence maps of manganese and nickel in as-prepared LNMO particles and unsupervised learning to classify the three-phase group in terms of different elemental compositions and chemical states. The described approach may increase the supply of information for nanoscale characterization and promote the design of suitable structural domains to maximize the performance and stability of batteries.

Evidence-based recommender system for high-entropy alloys.

Existing data-driven approaches for exploring high-entropy alloys (HEAs) face three challenges: numerous element-combination candidates, designing appropriate descriptors, and limited and biased existing data. To overcome these issues, here we show the development of an evidence-based material recommender system (ERS) that adopts Dempster-Shafer theory, a general framework for reasoning with uncertainty. Herein, without using material descriptors, we model, collect and combine pieces of evidence from data about the HEA phase existence of alloys. To evaluate the ERS, we compared its HEA-recommendation capability with those of matrix-factorization- and supervised-learning-based recommender systems on four widely known datasets of up-to-five-component alloys. The k-fold cross-validation on the datasets suggests that the ERS outperforms all competitors. Furthermore, the ERS shows good extrapolation capabilities in recommending quaternary and quinary HEAs. We experimentally validated the most strongly recommended Fe-Co-based magnetic HEA (namely, FeCoMnNi) and confirmed that its thin film shows a body-centered cubic structure.

Explainable machine learning for materials discovery: predicting the potentially formable Nd-Fe-B crystal structures and extracting the structure-stability relationship.

New Nd-Fe-B crystal structures can be formed via the elemental substitution of LA-T-X host structures, including lanthanides (LA), transition metals (T) and light elements, X = B, C, N and O. The 5967 samples of ternary LA-T-X materials that are collected are then used as the host structures. For each host crystal structure, a substituted crystal structure is created by substituting all lanthanide sites with Nd, all transition metal sites with Fe and all light-element sites with B. High-throughput first-principles calculations are applied to evaluate the phase stability of the newly created crystal structures, and 20 of them are found to be potentially formable. A data-driven approach based on supervised and unsupervised learning techniques is applied to estimate the stability and analyze the structure-stability relationship of the newly created Nd-Fe-B crystal structures. For predicting the stability for the newly created Nd-Fe-B structures, three supervised learning models: kernel ridge regression, logistic classification and decision tree model, are learned from the LA-T-X host crystal structures; the models achieved maximum accuracy and recall scores of 70.4 and 68.7%, respectively. On the other hand, our proposed unsupervised learning model based on the integration of descriptor-relevance analysis and a Gaussian mixture model achieved an accuracy and recall score of 72.9 and 82.1%, respectively, which are significantly better than those of the supervised models. While capturing and interpreting the structure-stability relationship of the Nd-Fe-B crystal structures, the unsupervised learning model indicates that the average atomic coordination number and coordination number of the Fe sites are the most important factors in determining the phase stability of the new substituted Nd-Fe-B crystal structures.

Boron cage effects on Nd-Fe-B crystal structure's stability.

In this study, we investigate the structure-stability relationship of hypothetical Nd-Fe-B crystal structures using descriptor-relevance analysis and the t-SNE dimensionality reduction method. 149 hypothetical Nd-Fe-B crystal structures are generated from 5967 LA-T-X host structures in the Open Quantum Materials Database by using the elemental substitution method, with LA denoting lanthanides, T denoting transition metals, and X denoting light elements such as B, C, N, and O. By borrowing the skeletal structure of each of the host materials, a hypothetical crystal structure is created by substituting all lanthanide sites with Nd, all transition metal sites with Fe, and all light element sites with B. High-throughput first-principle calculations are applied to evaluate the phase stability of these structures. Twenty of them are found to be potentially formable. As the first investigative result, the descriptor-relevance analysis on the orbital field matrix (OFM) materials' descriptor reveals the average atomic coordination number as the essential factor in determining the structure stability of these substituted Nd-Fe-B crystal structures. 19 among 20 hypothetical structures that are found potentially formable have an average coordination number larger than 6.5. By applying the t-SNE dimensionality reduction method, all the local structures represented by the OFM descriptors are integrated into a visible space to study the detailed correlation between their characteristics and the stability of the crystal structure to which they belong. We discover that unstable substituted structures frequently carry Nd and Fe local structures with two prominent points: low average coordination numbers and fully occupied B neighboring atoms. Moreover, there are only three popular forms of B local structures appearing on all potentially formable substituted structures: cage networks, planar networks, and interstitial sites. The discovered relationships are promising to speed up the screening process for the new formable crystal structures.

Committee machine that votes for similarity between materials.

A method has been developed to measure the similarity between materials, focusing on specific physical properties. The information obtained can be utilized to understand the underlying mechanisms and support the prediction of the physical properties of materials. The method consists of three steps: variable evaluation based on nonlinear regression, regression-based clustering, and similarity measurement with a committee machine constructed from the clustering results. Three data sets of well characterized crystalline materials represented by critical atomic predicting variables are used as test beds. Herein, the focus is on the formation energy, lattice parameter and Curie temperature of the examined materials. Based on the information obtained on the similarities between the materials, a hierarchical clustering technique is applied to learn the cluster structures of the materials that facilitate interpretation of the mechanism, and an improvement in the regression models is introduced to predict the physical properties of the materials. The experiments show that rational and meaningful group structures can be obtained and that the prediction accuracy of the materials' physical properties can be significantly increased, confirming the rationality of the proposed similarity measure.

Learning structure-property relationship in crystalline materials: A study of lanthanide-transition metal alloys.

We have developed a descriptor named Orbital Field Matrix (OFM) for representing material structures in datasets of multi-element materials. The descriptor is based on the information regarding atomic valence shell electrons and their coordination. In this work, we develop an extension of OFM called OFM1. We have shown that these descriptors are highly applicable in predicting the physical properties of materials and in providing insights on the materials space by mapping into a low embedded dimensional space. Our experiments with transition metal/lanthanide metal alloys show that the local magnetic moments and formation energies can be accurately reproduced using simple nearest-neighbor regression, thus confirming the relevance of our descriptors. Using kernel ridge regressions, we could accurately reproduce formation energies and local magnetic moments calculated based on first-principles, with mean absolute errors of 0.03 µB and 0.10 eV/atom, respectively. We show that meaningful low-dimensional representations can be extracted from the original descriptor using descriptive learning algorithms. Intuitive prehension on the materials space, qualitative evaluation on the similarities in local structures or crystalline materials, and inference in the designing of new materials by element substitution can be performed effectively based on these low-dimensional representations.

Novel mixture model for the representation of potential energy surfaces.

We demonstrate that knowledge of chemical physics on a materials system can be automatically extracted from first-principles calculations using a data mining technique; this information can then be utilized to construct a simple empirical atomic potential model. By using unsupervised learning of the generative Gaussian mixture model, physically meaningful patterns of atomic local chemical environments can be detected automatically. Based on the obtained information regarding these atomic patterns, we propose a chemical-structure-dependent linear mixture model for estimating the atomic potential energy. Our experiments show that the proposed mixture model significantly improves the accuracy of the prediction of the potential energy surface for complex systems that possess a large diversity in their local structures.

Data mining for materials design: a computational study of single molecule magnet.

We develop a method that combines data mining and first principles calculation to guide the designing of distorted cubane Mn(4+)Mn3(3+) single molecule magnets. The essential idea of the method is a process consisting of sparse regressions and cross-validation for analyzing calculated data of the materials. The method allows us to demonstrate that the exchange coupling between Mn(4+) and Mn(3+) ions can be predicted from the electronegativities of constituent ligands and the structural features of the molecule by a linear regression model with high accuracy. The relations between the structural features and magnetic properties of the materials are quantitatively and consistently evaluated and presented by a graph. We also discuss the properties of the materials and guide the material design basing on the obtained results.

Alternative splicing produces structural and functional changes in CUGBP2.

BACKGROUND: CELF/Bruno-like proteins play multiple roles, including the regulation of alternative splicing and translation. These RNA-binding proteins contain two RNA recognition motif (RRM) domains at the N-terminus and another RRM at the C-terminus. CUGBP2 is a member of this family of proteins that possesses several alternatively spliced exons. RESULTS: The present study investigated the expression of exon 14, which is an alternatively spliced exon and encodes the first half of the third RRM of CUGBP2. The ratio of exon 14 skipping product (R3δ) to its inclusion was reduced in neuronal cells induced from P19 cells and in the brain. Although full length CUGBP2 and the CUGBP2 R3δ isoforms showed a similar effect on the inclusion of the smooth muscle (SM) exon of the ACTN1 gene, these isoforms showed an opposite effect on the skipping of exon 11 in the insulin receptor gene. In addition, examination of structural changes in these isoforms by molecular dynamics simulation and NMR spectrometry suggested that the third RRM of R3δ isoform was flexible and did not form an RRM structure. CONCLUSION: Our results suggest that CUGBP2 regulates the splicing of ACTN1 and insulin receptor by different mechanisms. Alternative splicing of CUGBP2 exon 14 contributes to the regulation of the splicing of the insulin receptor. The present findings specifically show how alternative splicing events that result in three-dimensional structural changes in CUGBP2 can lead to changes in its biological activity.