Searching for single-phase solid solutions(SPSSs)in high-entropy alloys(HEAs)is a prerequisite for the intentional design and manipulation of microstructures of alloys in vast composition space.However,to date,reporte...Searching for single-phase solid solutions(SPSSs)in high-entropy alloys(HEAs)is a prerequisite for the intentional design and manipulation of microstructures of alloys in vast composition space.However,to date,reported SPSS HEAs are still rare due to the lack of reliable guiding principles for the synthesis of new SPSS HEAs.Here,we demonstrate an ensemble machine-learning method capable of discovering SPSS HEAs by directly predicting quinary phase diagrams based only on atomic composition.A total of 2198 experimental structure data are extracted from as-sputtered quinary HEAs in the literature and used to train a random forest classifier(termed AS-RF)utilizing bagging,achieving a prediction accuracy of 94.6%compared with experimental results.The AS-RF model is then utilized to predict 224 quinary phase diagrams including∼32,000 SPSS HEAs in Cr-Co-Fe-Ni-Mn-Cu-Al composition space.The extrapolation capability of the AS-RF model is then validated by performing first-principle calculations using density functional theory as a benchmark for the predicted phase transition of newly predicted HEAs.Finally,interpretation of the AS-RF model weighting of the input parameters also sheds light on the driving forces behind HEA formation in sputtered systems with the main contributors being:valance electron concentration,work function,atomic radius difference and elementary symmetries.展开更多
We present the High-Throughput Computing and Statistical Analysis(HCSA)scheme,which efficiently and accurately predicts the stacking fault energies(SFEs)of multi-principal element alloys(MPEAs).Our approach estimates ...We present the High-Throughput Computing and Statistical Analysis(HCSA)scheme,which efficiently and accurately predicts the stacking fault energies(SFEs)of multi-principal element alloys(MPEAs).Our approach estimates the SFE of a single complex supercell by averaging numerous SFEs from small supercells,resulting in superior accuracy compared to traditional density functional theory(DFT)calculations.To validate our scheme,we applied it to NiFe and Ni_(10)Co_(60)Cr_(25)W_(5)alloys,achieving an SFE error of only 11%,in contrast to the 45%error obtained from traditional DFT calculations for NiFe.We observed a strong correlation between the average SFEs of samples with the same valence electron concentration as that of the experimental data.Our scheme provides an efficient and reliable tool for predicting SFEs in MPEAs and holds the potential to significantly accelerate materials design and discovery processes.展开更多
Magnetotelluric(MT)inversion is an illposed problem and the standard way to address it is through regularization,by adding a stabilizing functional to the data objective functional in order to obtain a stable solution...Magnetotelluric(MT)inversion is an illposed problem and the standard way to address it is through regularization,by adding a stabilizing functional to the data objective functional in order to obtain a stable solution.The traditional stabilizing functionals,in which a low-order differential operator is used,yield a smooth solution that may not be appropriate when anomalies occur in block patterns.In some cases the focused imaging of a sharp electrical boundary is necessary.Even though various experiments have used stabilizing functionals that are suitable to obtain a clear and sharp boundary,such as the minimum support(MS)and the minimum gradient support(MGS)functionals,there are still some limitations in practice.In this paper,the minimum support gradient(MSG)is proposed as the stabilizing functional.Under the uniform regularization framework,a regularized inversion with a variety of stabilizing functionals is performed and the inversion results are compared.This study shows that MSG inversion can not only obtain a clearly focused inversion but also a quite stable and robust one.展开更多
With continued exploration and the increased need for energy resources,deep reservoirs have gradually become the main target of oil and gas exploration in recent years.The Lower Wuerhe Formation on the northern slope ...With continued exploration and the increased need for energy resources,deep reservoirs have gradually become the main target of oil and gas exploration in recent years.The Lower Wuerhe Formation on the northern slope of the Central Depression of the Junggar Basin has a highquality,deep,glutenite(coarse-grained clastic)reservoir at depths greater than 4500 m.However,its genetic mechanism remains unclear.Here,we improve our understanding of the origin of this deep reservoir by performing comprehensive investigations via thin section analysis,field emission scanning electron microscopy,electron probe analysis,X-ray diffraction analysis,and whole-rock carbon and oxygen isotope analysis.The results reveal that the deep reservoir lithology within the study area comprises primarily gray-white gravelly gritstone and conglomerate.Zeolite cement is predominant,and secondary dissolution pores are the primary type of reservoir space in deep reservoirs.The Lower Wuerhe Formation has experienced significant compaction in the study area.Debris flow microfacies serve as the prevailing sedimentary microfacies containing substantial amounts of laumontite.The effect of dissolution of organic acids on laumontite is pivotal in the formation of high-quality deep reservoirs in the study area.These findings serve as valuable references for the genesis of deep zeolite-rich reservoirs in the Central Depression of the Junggar Basin and other areas worldwide.展开更多
基金We acknowledge support from the National Natural Science Foundation of China(Nos.52271006,22173047)the Fundamental Research Funds for the Central Universities(Nos.30922010716,30920041116,0920021159,and 30919011405).
文摘Searching for single-phase solid solutions(SPSSs)in high-entropy alloys(HEAs)is a prerequisite for the intentional design and manipulation of microstructures of alloys in vast composition space.However,to date,reported SPSS HEAs are still rare due to the lack of reliable guiding principles for the synthesis of new SPSS HEAs.Here,we demonstrate an ensemble machine-learning method capable of discovering SPSS HEAs by directly predicting quinary phase diagrams based only on atomic composition.A total of 2198 experimental structure data are extracted from as-sputtered quinary HEAs in the literature and used to train a random forest classifier(termed AS-RF)utilizing bagging,achieving a prediction accuracy of 94.6%compared with experimental results.The AS-RF model is then utilized to predict 224 quinary phase diagrams including∼32,000 SPSS HEAs in Cr-Co-Fe-Ni-Mn-Cu-Al composition space.The extrapolation capability of the AS-RF model is then validated by performing first-principle calculations using density functional theory as a benchmark for the predicted phase transition of newly predicted HEAs.Finally,interpretation of the AS-RF model weighting of the input parameters also sheds light on the driving forces behind HEA formation in sputtered systems with the main contributors being:valance electron concentration,work function,atomic radius difference and elementary symmetries.
基金financially supported by the National Natural Science Foundation of China(Nos.22173047 and 51931003)the Natural Science Foundation of Jiangsu Province(No.BK20211198)+1 种基金the Sino-German Mobility Program of the Sino-German Center for Research Promotion(Grant M-0147)the Fundamental Research Funds for the Central Universities(Nos.30920041116,30919011254,and 30919011405).
文摘We present the High-Throughput Computing and Statistical Analysis(HCSA)scheme,which efficiently and accurately predicts the stacking fault energies(SFEs)of multi-principal element alloys(MPEAs).Our approach estimates the SFE of a single complex supercell by averaging numerous SFEs from small supercells,resulting in superior accuracy compared to traditional density functional theory(DFT)calculations.To validate our scheme,we applied it to NiFe and Ni_(10)Co_(60)Cr_(25)W_(5)alloys,achieving an SFE error of only 11%,in contrast to the 45%error obtained from traditional DFT calculations for NiFe.We observed a strong correlation between the average SFEs of samples with the same valence electron concentration as that of the experimental data.Our scheme provides an efficient and reliable tool for predicting SFEs in MPEAs and holds the potential to significantly accelerate materials design and discovery processes.
基金the National Natural Science Foundation of China(No.41630317)the National Key Research and Development Program of China(No.2017YFC0602405).
文摘Magnetotelluric(MT)inversion is an illposed problem and the standard way to address it is through regularization,by adding a stabilizing functional to the data objective functional in order to obtain a stable solution.The traditional stabilizing functionals,in which a low-order differential operator is used,yield a smooth solution that may not be appropriate when anomalies occur in block patterns.In some cases the focused imaging of a sharp electrical boundary is necessary.Even though various experiments have used stabilizing functionals that are suitable to obtain a clear and sharp boundary,such as the minimum support(MS)and the minimum gradient support(MGS)functionals,there are still some limitations in practice.In this paper,the minimum support gradient(MSG)is proposed as the stabilizing functional.Under the uniform regularization framework,a regularized inversion with a variety of stabilizing functionals is performed and the inversion results are compared.This study shows that MSG inversion can not only obtain a clearly focused inversion but also a quite stable and robust one.
基金financially supported by the National Natural Science Foundation of China(Grant No.42372150)Provincial University Basic Scientific Research Operating Cost Projects(No.2023RCZX-02).
文摘With continued exploration and the increased need for energy resources,deep reservoirs have gradually become the main target of oil and gas exploration in recent years.The Lower Wuerhe Formation on the northern slope of the Central Depression of the Junggar Basin has a highquality,deep,glutenite(coarse-grained clastic)reservoir at depths greater than 4500 m.However,its genetic mechanism remains unclear.Here,we improve our understanding of the origin of this deep reservoir by performing comprehensive investigations via thin section analysis,field emission scanning electron microscopy,electron probe analysis,X-ray diffraction analysis,and whole-rock carbon and oxygen isotope analysis.The results reveal that the deep reservoir lithology within the study area comprises primarily gray-white gravelly gritstone and conglomerate.Zeolite cement is predominant,and secondary dissolution pores are the primary type of reservoir space in deep reservoirs.The Lower Wuerhe Formation has experienced significant compaction in the study area.Debris flow microfacies serve as the prevailing sedimentary microfacies containing substantial amounts of laumontite.The effect of dissolution of organic acids on laumontite is pivotal in the formation of high-quality deep reservoirs in the study area.These findings serve as valuable references for the genesis of deep zeolite-rich reservoirs in the Central Depression of the Junggar Basin and other areas worldwide.
