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Sequential Machine Learning in Material Science
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.).
2023 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Sekventiell maskininlärning inom materialvetenskap (Swedish)
Abstract [sv]

This report evaluates the possibility of using sequential learning in a material development setting to help predict material properties and speed up the development of new materials. To do this a Random forest model was built incorporating carefully calibrated prediction uncertainty estimates. The idea behind the model is to use the few data points available in this field and leverage that data to build a better representation of the input-output space as each experiment is performed. Having both predictions and uncertainties to evaluate, several different strategies were developed to investigate performance. Promising results regarding feasibility and potential cost-cutting were found using these strategies. It was found that within a specific performance region of the output space, the mean difference in alloying component price between the cheapest and most expensive material could be as high as 100 %. Also, the model performed fast extrapolation to previously unknown output regions, meaning new, differently performing materials could be found even with very poor initial data.

Abstract [sv]

I denna rapport utvärderas möjligheten att använda sekventiell maskininlärning inom materialutveckling för att kunna prediktera materials egenskaper och därigenom förkorta materialutvecklingsprocessen. För att göra detta byggdes en Random forest regressionsmodell som även innehöll en uppskattning av prediktionsosäkerheten. Tanken bakom modellen är att använda de relativt få datapunkter som generellt brukar vara tillgängliga inom materialvetenskap, och med hjälp av dessa bygga en bättre representation av input-output-rummet genom varje experiment som genomförs. Med både förutsägelser och osäkerheter att utvärdera utvecklades flera olika strategier för att undersöka prestanda för de olika kandidatmaterialen. Genom att använda dessa strategier kunde lovande resultat vad gäller genomförbarhet och potentiell kostnadsbesparing hittas. Det visade sig att, för specifika prestandakrav, den genomsnittliga skillnaden i pris mellan den billigaste och den dyraste materialkemin kan vara så hög som 100 %. Vad gäller övriga resultat klarade modellen av att snabbt extrapolera initial data till tidigare okända regioner av output-rummet. Detta innebär att nya material med ny typ av prestanda kunde hittas även med mycket missanpassad initial träningsdata.

Place, publisher, year, edition, pages
2023. , p. 46
Series
TRITA-SCI-GRU ; 2023:068
Keywords [en]
Machine learning, Random forest, Uncertainty measure, Material development, Empirical Bayes
Keywords [sv]
Maskininlärning, Random forest, Osäkerhetsmått, Materialutveckling, Empirical Bayes
National Category
Other Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-337133OAI: oai:DiVA.org:kth-337133DiVA, id: diva2:1800253
External cooperation
Swerim AB
Subject / course
Mathematical Statistics
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2023-09-27 Created: 2023-09-26 Last updated: 2023-09-27Bibliographically approved

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CiteExportLink to record
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