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RESEARCH PAPER · APL Materials · 2020

Machine learning depinning of dislocation pileups

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11 direct citing papers in this saved network. Explore the next layer with second-order citations.

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42 direct citing works

Machine learning depinning of dislocation pileupsOriginalFrom mechanism-based to data-driven approaches in materials science9Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges67Machine learning reveals strain-rate-dependent predictability of discrete dislocation plasticity7Machine learning of nonlocal micro-structural defect evolutions in crystalline materials19Bayesian optimization of discrete dislocation plasticity of two-dimensional precipitation-hardened crystals4Predicting elastic and plastic properties of small iron polycrystals by machine learning10From Data Science to Materials Data Science0Nonequilibrium phase diagram for the serration statistics during slow deformation of refractory high-entropy alloys2Estimating predictability of depinning dynamics by machine learning1Effectiveness of pulsed vs continuous electric current field in uniaxial deformation of Ti-6Al-4V5Influence of microstructural features on the size effect in confined grains: a study using dislocation dynamics simulations and machine learning methods0

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  1. From mechanism-based to data-driven approaches in materials science

    Materials Theory · 2021-09-01

    Stefan Hiemer, Stefano Zapperi

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  2. Materials Informatics for Mechanical Deformation: A Review of Applications and Challenges

    Materials · 2021-10-02

    Karol Frydrych, Kamran Karimi, Michal Pecelerowicz, Rene Alvarez, Francesco Javier Dominguez-Gutiérrez, Fabrizio Rovaris, Stefanos Papanikolaou

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  3. Machine learning reveals strain-rate-dependent predictability of discrete dislocation plasticity

    Physical Review Materials · 2022-02-16

    Marcin Mińkowski, David Kurunczi-Papp, Lasse Laurson

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  4. Machine learning of nonlocal micro-structural defect evolutions in crystalline materials

    Computer Methods in Applied Mechanics and Engineering · 2022-11-18

    Eduardo A. Barros de Moraes, Marta D’Elia, Mohsen Zayernouri

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  5. Bayesian optimization of discrete dislocation plasticity of two-dimensional precipitation-hardened crystals

    Physical Review Materials · 2022-12-06

    Mika Sarvilahti, Lasse Laurson

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  6. Predicting elastic and plastic properties of small iron polycrystals by machine learning

    Scientific Reports · 2023-08-26

    Marcin Mińkowski, Lasse Laurson

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  7. From Data Science to Materials Data Science

    ˜The œMaterials Research Society series · 2023-11-17

    Stefan Sandfeld

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  8. Nonequilibrium phase diagram for the serration statistics during slow deformation of refractory high-entropy alloys

    Scientific Reports · 2025-02-28

    Mingwei Liu, Wei-Chen Hsu, Tzu‐Yang Chiang, Che‐Wei Tsai, Karin A. Dahmen

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  9. Estimating predictability of depinning dynamics by machine learning

    Journal of Statistical Mechanics Theory and Experiment · 2025-03-03

    Valtteri Haavisto, Marcin Mińkowski, Lasse Laurson

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  10. Effectiveness of pulsed vs continuous electric current field in uniaxial deformation of Ti-6Al-4V

    Materials Science and Engineering A · 2025-03-12

    Ahmad Abdul Kadir, Lorenzo Zani, Konstantinos P. Baxevanakis, Anish Roy

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  11. Influence of microstructural features on the size effect in confined grains: a study using dislocation dynamics simulations and machine learning methods

    Modelling and Simulation in Materials Science and Engineering · 2026-07-02

    Shichen Li, Shuang Xu, Jun Li, Lisheng Liu, Maoyuan Jiang

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