In this Overview, we critically examine the role of informatics in several important materials subfields, highlighting significant contributions to date and identifying known shortcomings. Its practitioners employ the methods of multivariate statistics and machine learning in conjunction with standard computational tools (e.g., density-functional theory) to, for example, visualize and dimensionally reduce large data sets, identify patterns in hyperspectral data, parse microstructural images of polycrystals, characterize vortex structures in ferroelectrics, design batteries and, in general, establish correlations to extract important physics and infer structure-property-processing relationships. This relatively new field is already having a significant impact on the interpretation of data for a variety of materials systems, including those used in thermoelectrics, ferroelectrics, battery anodes and cathodes, hydrogen storage materials, polymer dielectrics, etc. In recent years materials informatics, which is the application of data science to problems in materials science and engineering, has emerged as a powerful tool for materials discovery and design. The experiment result shows that this prototype achieved a significant improvement in efficiency by reducing the amount of invalid computation remarkably. To search for diamond-like structures with higher group velocity in a space of 254 compounds, a SEHC-based prototype was implemented. Combined with the public service like data storage and system monitoring, the SEHC with a “Stage-Pipeline-Framework” three-tier structure is formed. Multiple high-throughput Stages with the same standard design specifications can be assembled into a Pipeline model. The time-consuming high-throughput computing process is disassembled into several finer-grained high-throughput Stages. The framework introduces an automatic self-evaluation filtering mechanism, which is based on machine learning, for high-throughput computing architectures to stop unexpected materials calculation tasks in advance during high-throughput calculation. In this paper, we provide a Self-Evaluation High-throughput Computing framework (SEHC). To illustrate those models, an analysis of atomic orbital participation in Ni–Ni and Ni–O bonds of diatomic molecules and in Ni 5–O cluster were performed by using a parametric method.Efficiency is one of the key problems in the design of high-throughput materials computing. A complementary picture is also introduced by analyzing inter-atomic electron–atom bonding interactions. Thus, diatomic binding energy (DBE) is expressed as a sum of atomic orbital binding energies (AOBEs). It is possible to distinguish the energetic changes or rearrangements that occur on each atom (monoatomic terms) and those that are associated to direct inter-atomic orbital–orbital interactions (diatomic terms).
The proposed partition model allows a detailed analysis of the contribution of each atomic orbital in the formation of the bond. In a similar way, changes of intra-atomic energy per atomic orbital were also considered.
This approach is based on partition of inter-atomic energy in its components: resonance ( R), electron–electron repulsion ( J), attractive electron–nucleus ( V), exchange ( K), and nucleus–nucleus repulsion ( N) for pairs of interacting orbitals. The participation of atomic orbitals in bond formation is presented here in terms of energy contributions.
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