Machine Learning Design of Aluminum-Lithium Alloys with High Strength

Due to the large unexplored compositional space,long development cycle,and high cost of traditional trial-anderror experiments,designing high strength aluminum-lithium alloys is a great challenge.This work establishes a performance-oriented machine learning design strategy for aluminum-lithium alloys to simplify and shorten the development cycle.The calculation results indicate that radial basis function(RBF)neural networks exhibit better predictive ability than back propagation(BP)neural networks.The RBF neural network predicted tensile and yield strengths with determination coefficients of 0.90 and 0.96,root mean square errors of 30.68 and 25.30,and mean absolute errors of 28.15 and 19.08,respectively.In the validation experiment,the comparison between experimental data and predicted data demonstrated the robustness of the two neural network models.The tensile and yield strengths of Al-2Li-1Cu-3Mg-0.2Zr(wt.%)alloy are 17.8 and 3.5 MPa higher than those of the Al-1Li4.5Cu-0.2Zr(wt.%)alloy,which has the best overall performance,respectively.It demonstrates the reliability of the neural network model in designing high strength aluminum-lithium alloys,which provides a way to improve research and development efficiency.

Coalescence of Al_(0.3)CoCrFeNi polycrystalline high-entropy alloy in hot-pressed sintering: a molecular dynamics and phase- field study

Existing hot sintering models based on molecular dynamics focus on single-crystal alloys.This work proposes a new multiparticle model based on molecular dynamics to investigate coalescence kinetics during the hot-pressed sintering of a polycrystalline Al_(0.3)CoCrFeNi high-entropy alloy.The accuracy and effectiveness of the multiparticle model are verified by a phase-field model.Using this model,it is found that when the particle contact zones undergo pressure-induced evolution into exponential power creep zones,the occurrences of phenomena,such as necking,pore formation/filling,dislocation accumulation/decomposition,and particle rotation/rearrangement are accelerated.Based on tensile test results,Young’s modulus of the as-sintered Al_(0.3)CoCrFeNi high-entropy alloy is calculated to be 214.11±1.03 GPa,which deviates only 0.82%from the experimental value,thus further validating the feasibility and accuracy of the multiparticle model.

Response of ammonia-oxidizing archaea to heavy metal contamination in freshwater sediment

It has been well-documented that the distribution of ammonia-oxidizing bacteria(AOB) and archaea(AOA) in soils can be affected by heavy metal contamination, whereas information about the impact of heavy metal on these ammonia-oxidizing microorganisms in freshwater sediment is still lacking. The present study explored the change of sediment ammonia-oxidizing microorganisms in a freshwater reservoir after being accidentally contaminated by industrial discharge containing high levels of metals. Bacterial amoA gene was found to be below the quantitative PCR detection and was not successfully amplified by conventional PCR. The number of archaeal amoA gene in reservoir sediments were 9.62 × 10~2–1.35 × 10~7 copies per gram dry sediment. AOA abundance continuously decreased, and AOA richness, diversity and community structure also considerably varied with time. Therefore, heavy metal pollution could have a profound impact on freshwater sediment AOA community. This work could expand our knowledge of the effect of heavy metal contamination on nitrification in natural ecosystems.