With the rapid advancement of biotechnology, it is foreseeable that fusion protein technology will have increasing importance in creating novel protein therapeutics and in improving the performance of current protein drugs. In the present study, we propose a machine learning design system to realize property-oriented compositional design for high-performance complex alloys. For a given property requirement, the P2C model is utilized to screen the composition space, and then the C2P model is employed to efficiently recommend the targeted composition within a certain confidential interval.
- Ozerdem et al.24 built a multi-layer BP NN model to predict the yield strength, UTS and elongation of the Cu–Sn–Pb–Zn–Ni alloy.
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- In the present study, we propose a machine learning design system involving three features of machine learning modeling, compositional design and property prediction, which can accelerate the discovery of new materials.
The Cu–2.20Ni–0.42Si–0.08Mg–0.30Zn alloy has been used to evaluate the C2P model in Table 1. The concentration of its main components is close to that of the 4# alloy. The error between the measured properties (UTS of 720 MPa and EC of 49.5% IACS) and the targeted properties of 4# alloy (UTS of 750 MPa and EC of 50.0% IACS) is 4.
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