A regularized magnetotelluric inversion with a minimum support gradient constraint

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.

Predicting single-phase solid solutions in as-sputtered high entropy alloys:High-throughput screening with machine-learning model

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.

An efficient scheme for accelerating the calculation of stacking fault energy in multi-principal element alloys

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.