Machine Learning in Materials Design

Faglige nøgleord: Density Functional Theory (DFT), Quantum Mechanics, Atomic-Scale Modeling, Artificial Intelligence, Machine Learning, Defect Formation.

Oplæg tilgængeligt på: Engelsk og hollandsk

My research is about studying materials at a very tiny, atomic level. Usually, we use something called density functional theory, which is a kind of quantum physics calculation done on a computer. It helps us understand how materials behave, but it’s very slow and needs a lot of computer power. The goal of my work is to teach an AI model to do the same kind of calculations much faster. If the AI works, we could watch how materials change over longer periods of time, which isn’t possible with the slow calculations. One thing we’re especially interested in is how defects, small imperfections in the material, form. By simulating this process, we hope to understand why and how these defects appear, which could help in designing better materials.

Opdateret af Sofie Marie Møller Nielsen den 9. april 2026

Fag og faglige nøgleord

Dette oplæg passer for eksempel godt til:

Fysik

Nøgleord:

Density Functional Theory(DFT)
Quantum Mechanics
Atomic-Scale Modeling
Artificial Intelligence
Machine Learning
Defect Formation.

Sprog og form på besøget

Klassebesøg eller foredrag?

Dette oplæg passer til en klasse (op til ca. 28 elever), hvor den ph.d.-studerende har mulighed for at have dialog med eleverne.

Sprog?

Dette oplæg er tilgængeligt på engelsk og hollandsk.

Digitalt eller fysisk besøg?

Den ph.d.-studerende kommer gerne ud på jeres skole til et fysisk besøg.

Øvrig information

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Kontakt

Taja Andersen Brenneche

Kommunikationsmedarbejder

AKM

45 25 10 57
tajab@adm.dtu.dk

Opdateret af Sofie Marie Møller Nielsen den 9. april 2026