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DOI:10.1016/j.addma.2024.104195
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Machine learning aided prediction of martensite transformation temperature of NiTi-based shape memory alloy
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Towards quantifying (meta-)stability of multi-principal element alloys: From configurational entropy to characteristic temperatures
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Leveraging machine learning to minimize experimental trials and predict hot deformation behaviour in dual phase high entropy alloys
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Interpretable machine learning for simultaneous designing martensitic transformation temperature and thermal hysteresis of high-entropy shape memory alloys
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Using machine learning to go beyond potential energy surface benchmarking for chemical reactivity
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Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous graphs
8个月前
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