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01506 am a22001813u 4500 |
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132724 |
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|a Hsu, Yu-Chuan
|e author
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|a Yu, Chi-Hua
|e author
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|a Buehler, Markus J
|e author
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|a Using Deep Learning to Predict Fracture Patterns in Crystalline Solids
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|b Elsevier BV,
|c 2021-10-06T13:32:48Z.
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|z Get fulltext
|u https://hdl.handle.net/1721.1/132724
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|a Fracture is a catastrophic and complex process that involves various time and length scales. Scientists have devoted vast efforts toward understanding the underlying mechanisms for centuries, with much work left in terms of predictability of models and fundamental understanding. To this end, we present a machine-learning approach to predict fracture processes connecting molecular simulation into a physics-based artificial intelligence (AI) multiscale model. Our model exhibits predictive power not only regarding the computed fracture patterns but also for fracture toughness-the resistance of cracks to grow. The novel AI-based fracture predictor can also deal with complex loading conditions, here examined for both mode I (tensile) and mode II (shear). These results underscore the excellent predictive power of our model. Potential applications include the design of novel types of high-performance materials, composites design, surface coatings, or innovative bio-inspired structures.
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|a Article
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|t 10.1016/J.MATT.2020.04.019
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| 773 |
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|t Matter
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