In:
APL Machine Learning, AIP Publishing, Vol. 1, No. 2 ( 2023-06-01)
Abstract:
Machine learning (ML) is gaining popularity as a tool for materials scientists to accelerate computation, automate data analysis, and predict materials properties. The representation of input material features is critical to the accuracy, interpretability, and generalizability of data-driven models for scientific research. In this Perspective, we discuss a few central challenges faced by ML practitioners in developing meaningful representations, including handling the complexity of real-world industry-relevant materials, combining theory and experimental data sources, and describing scientific phenomena across timescales and length scales. We present several promising directions for future research: devising representations of varied experimental conditions and observations, the need to find ways to integrate machine learning into laboratory practices, and making multi-scale informatics toolkits to bridge the gaps between atoms, materials, and devices.
Type of Medium:
Online Resource
ISSN:
2770-9019
Language:
English
Publisher:
AIP Publishing
Publication Date:
2023
detail.hit.zdb_id:
3161950-2
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