Not as simple as we thought: a rigorous examination of data aggregation in materials informatics

Ottomano, Federico; De Felice, Giovanni; Gusev, Vladimir V.; Sparks, Taylor D.

doi:10.1039/d3dd00207a

Digital Discovery

2024

DOI: 10.1039/d3dd00207a

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Not as simple as we thought: a rigorous examination of data aggregation in materials informatics

Federico Ottomano,

Giovanni De Felice,

Vladimir V. Gusev

et al.

Abstract: Recent Machine Learning (ML) developments have opened new perspectives on accelerating the discovery of new materials. However, in the field of materials informatics, the performance of ML estimators is heavily...

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2024

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Autonomous materials research and design: Characterization

Kaufmann,

Vecchio

2024

Current Opinion in Solid State and Materials Science

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Autonomous materials research and design: Characterization

Kaufmann,

Vecchio

2024

Current Opinion in Solid State and Materials Science

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Supervised machine learning for multi-principal element alloy structural design

Berry,

Christofidou

2024

Materials Science and Technology

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The application of supervised Machine Learning (ML) in material science, especially towards the design of structural Multi-Principal Element Alloys (MPEAs) has rapidly accelerated over the past five years. However, several factors are limiting the impact that these ML methodologies can have, chief amongst them being the availability and fidelity of data. This review analyses how ML has been utilised to accelerate the design of novel structural MPEAs, outlining the standard procedures followed, and highlighting the successes and common pitfalls identified in current studies. The need for experimental validation and incorporation into closed loop ML pipelines is also discussed, including the influence and integration of manufacturing methodologies into the ML decision making process.

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Not as simple as we thought: a rigorous examination of data aggregation in materials informatics

Abstract: Recent Machine Learning (ML) developments have opened new perspectives on accelerating the discovery of new materials. However, in the field of materials informatics, the performance of ML estimators is heavily...

Cited by 2 publications

References 35 publications

Autonomous materials research and design: Characterization

Autonomous materials research and design: Characterization

Supervised machine learning for multi-principal element alloy structural design

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