Machine learning assisted design of new lattice core for sandwich structures with superior load carrying capacityHerein new lattice unit cells with buckling load 261–308% higher than the classical octet unit cell were reported. Lattice structures have been widely used in sandwich structures as light...
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 phrase "work-life balance" can elicit eye rolls from architects. But machine learning–assisted design will allow greater artistry in the future of architecture.
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 ...
Body-centered cubic (BCC) alloy systems can theoretically store double amounts of hydrogen compared with commercial metal hydrides at room temperature, and BCC high entropy alloys (HEAs) have shown the potential to reach this theoretic limit. However, the high thermodynamic stability of the dihydrid...
Machine learning assisted design of high-entropy alloys with ultra-high microhardness and unexpected low density Active learning cycle system was designed to explore high-entropy alloys with exceptional hardness.Screened 30 Al-Co-Cr-Cu-Fe-Ni compositions exhibit a pre... S Zhao,B Jiang,K Song,....
Recently, machine-learning-assisted optimization (MLAO) has been widely introduced to accelerate the design process of antennas and arrays. Machine learning (ML) methods, including Gaussian process regression, support vector machine (SVM) and artificial neural networks (ANNs), have been applied to ...
CLADE: Cluster learning-assisted directed evolution Directed evolution (DE), a strategy for protein engineering, optimizes protein properties (i.e. fitness) by expensive and time-consuming screen or selection of a large combinatorial sequence space. Machine learning-assisted directed evol... YQ Qiu,...
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
Wen, C. et al. Machine learning assisted design of high entropy alloys with desired property.Acta Mater.170, 109–117 (2019). CASGoogle Scholar Wang, C., Fu, H., Jiang, L., Xue, D. & Xie, J. A property-oriented design strategy for high performance copper alloys via machine learning...