| 1 | Kei2 ECR离子源C^(4+)、C^(5+)束流的最佳结果(英文)显示文摘Earlier we reported an ion current jump which was observed at a fixed negative biased disc potential in the 6.4GHz ECR ion source at VECC,Kolkata.In a recent experiment with neon ions,we measured the time spectra of the ion current and observed the presence of a burst frequency in the kilohertz range.This frequency shows a correlated jump with the ion current jump described above.Another interesting feature is that the observed burst frequency shows a good linear correlation with the extracted ion current.The higher the ion current,the higher is the burst frequency.This means that current per burst is a constant factor;when there are more number of bursts,the current also increases. | G.S.Taki P.R.Sarma A.G.Drentje T.Nakagawa P.K.Ray R.K.Bhandari | 2007 | Chinese Physics C2007，31，S1： | 0 |
| 2 | Distilling physical origins of hardness in multi-principal element alloys directly from ensemble neural network models显示文摘Despite a plethora of data being generated on the mechanical behavior of multi-principal element alloys,a systematic assessment remains inaccessible via Edisonian approaches.We approach this challenge by considering the specific case of alloy hardness,and present a machine-learning framework that captures the essential physical features contributing to hardness and allows high-throughput exploration of multi-dimensional compositional space.The model,tested on diverse datasets,was used to explore and successfully predict hardness in Al_(x)Ti_(y)(CrFeNi)_(1-x-y),Hf_(x)Co_(y)(CrFeNi)_(1-x-y)and Al_(x)(TiZrHf)_(1-x)systems supported by data from density-functional theory predicted phase stability and ordering behavior.The experimental validation of hardness was done on TiZrHfAlx.The selected systems pose diverse challenges due to the presence of ordering and clustering pairs,as well as vacancy-stabilized novel structures.We also present a detailed model analysis that integrates local partial-dependencies with a compositional-stimulus and model-response study to derive material-specific insights from the decision-making process. | D.Beniwal P.Singh S.Gupta M.J.Kramer D.D.Johnson P.K.Ray | 2022 | npj Computational Materials2022，，1： | 0 |
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