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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

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Predicting material properties by integrating high-throughput experiments, high-throughput ab-initio calculations,

Yuma Iwasaki^1,2, Masahiko Ishida¹, Masayuki Shirane^1,3

¹Central Research Laboratories, NEC Corporation, Tsukuba, Japan.

Science and Technology of Advanced Materials

|February 22, 2020

Summary

This summary is machine-generated.

This study combines simple high-throughput experiments (HTEs), high-throughput ab-initio calculation (HTC), and machine learning to predict material properties. This approach accelerates materials discovery by enabling rapid and accurate property prediction without costly experiments.

Keywords:

404 Materials informatics / Genomics Materials informatics ab-initio combinatorial high-throughput machine learning

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Last Updated: Dec 28, 2025

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Area of Science:

Materials Science
Computational Materials Science
Data Science

Background:

High-throughput experiments (HTEs) generate valuable materials data but are often equipment-intensive and costly.
High-throughput ab-initio calculation (HTC) offers a scalable approach to materials data but may not fully capture experimental realities.

Purpose of the Study:

To develop a hybrid methodology integrating HTEs, HTC, and machine learning for accurate materials property prediction.
To overcome the limitations of individual HTE and HTC approaches in materials big data generation.

Main Methods:

A combined approach utilizing simple HTEs, HTC, and machine learning algorithms was employed.
The methodology was validated by predicting the Kerr rotation mapping of an Fe_xCo_yNi_1-x-y composition spread alloy.

Main Results:

The proposed method achieved accurate and rapid prediction of Kerr rotation mapping.
Demonstrated the efficacy of combining experimental and computational data with machine learning for materials characterization.

Conclusions:

This integrated approach accelerates materials development by enabling efficient prediction of material properties.
It offers a cost-effective alternative to expensive HTEs for exploring vast material spaces.