Position Paper on Materials Design -- A Modern Approach

• URL: http://arxiv.org/abs/2312.10996v1
• Date: Mon, 18 Dec 2023 07:46:30 GMT
• Title: Position Paper on Materials Design -- A Modern Approach
• Authors: Willi Grossmann and Sebastian Eilermann and Tim Rensmeyer and Artur Liebert and Michael Hohmann and Christian Wittke and Oliver Niggemann
• Abstract summary: We show how machine learning can speed up the design process for new materials and assemblies. ML approaches can synthesize possible morphologies of the materials based on defined conditions. This modern approach accelerates the design process for new materials and enables the prediction and interpretation of realistic materials behavior.
• Score: 1.6668914921312827
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Traditional design cycles for new materials and assemblies have two fundamental drawbacks. The underlying physical relationships are often too complex to be precisely calculated and described. Aside from that, many unknown uncertainties, such as exact manufacturing parameters or materials composition, dominate the real assembly behavior. Machine learning (ML) methods overcome these fundamental limitations through data-driven learning. In addition, modern approaches can specifically increase system knowledge. Representation Learning allows the physical, and if necessary, even symbolic interpretation of the learned solution. In this way, the most complex physical relationships can be considered and quickly described. Furthermore, generative ML approaches can synthesize possible morphologies of the materials based on defined conditions to visualize the effects of uncertainties. This modern approach accelerates the design process for new materials and enables the prediction and interpretation of realistic materials behavior.

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