Machine learning-assisted design of filler for laser welding of Al-Zn-Mg-Cu alloyThe Al-Zn-Mg-Cu alloy is extensively utilized in lightweight welding structures, thus the development of laser welding filler materials for this alloy holds significant importance. Here, a kriging assisted Two ...
This paper proposes an effective design approach based on machine learning. A feedforward neural network (FNN), in conjunction with a gradient descent algorithm, is employed to fast and accurately ascertain the SCP, offering a solution readily applicable in the system design. Both simulation and ...
This work uses quantum chemistry calculations and machine learning to explore design rules for singlet fission in a chemical space of four million indigoid derivatives. We identify ~400,000 derivatives of 2,2′-diethenyl cibalackrot, which theoretically fulfil the energy conditions for exoergic sing...
morphable applications, systematic methods to arrive at Kirigami motifs that fullfil a design requirement (i.e., inverse design problem) have been scarce, with trial-and-error (based on time-consuming experimental and computational iterations) remaining the prevalent approach. The inverse problem in t...
The rapid development of big data and artificial intelligence provides a new path for the efficient development of metallic materials, that is, machine learning-assisted design. In this paper, the basic strategy for the machine learning-assisted rational design of alloys was introduced. Research ...
We explore how machine-learning-powered memory design techniques help chip designers shift memory development left, improving turnaround time and PPA results.
Machine learning engineers design intelligent systems that learn from data and scale in production. Skilled in model development and algorithm tuning, these developers apply tools like TensorFlow and PyTorch to solve real-world problems in natural-language processing (NLP), computer vision, and predictiv...
In: Design Automation Conference (DAC) (2010) Google Scholar Wu, N., Xie, Y., Hao, C.: IronMan: GNN-assisted design space exploration in high-level synthesis via reinforcement learning. In: Great Lakes Symposium on VLSI (2021) Google Scholar Wu, Y., Wang, Q., Zheng, L., Liao,...
We formulate a materials design strategy combining a machine learning (ML) surrogate model with experimental design algorithms to search for high entropy alloys (HEAs) with large hardness in a model Al-Co-Cr-Cu-Fe-Ni system. We fabricated several alloys with hardness 10% higher than the best ...
By utilizing machine learning algorithms and predictive models, we have provided a route to design suitable wide bandgap perovskite materials for efficient light harvesting in indoor environments. The suitable absorber materials then can be applied in a specific device structure and the device performance...