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Quantitative AFM Nanomechanics Data for Machine Learning and Materials Development

Explore new modes for AFM-based, machine learning (ML) augmented research on advanced materials with specific functions and desired properties

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Quantitative AFM Nanomechanics Data for Machine Learning and Materials Development

Towards the Quantitative Mapping of the Mechanical Properties of Materials Using Unsupervised Data Clustering Methods

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▶ Watch Recording | 26 Minutes

Quantitative AFM Nanomechanics Data for Machine Learning and Materials Development

Presented by Bede Pittenger, Ph.D., Sr. Staff Scientist AFM Applications, Bruker Nano Surfaces (March 16, 2021)

▶ Watch Recording | 27 Minutes

Towards the Quantitative Mapping of the Mechanical Properties of Materials Using Unsupervised Data Clustering Methods

Presented Philippe Leclere, Ph.d., Associate Professor, University of Mons (March 16, 2021)

▶ Watch Recording | 17 Minutes

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AFM modes and the broad capabilities from AFM beyond imaging

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AFM research on polymers

Polymers

Nanoscale imaging of polymers structure and morphology

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