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.

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.