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Based on the first-principles calculations, we perform an initiatory statistical assessment on the reliability level of theoretical positron lifetime of bulk material. We found the original generalized gradient approximation (GGA) form of the enhancement factor and correlation potentials overestimates the effect of the gradient factor. Furthermore, an excellent agreement between model and data with the difference being the noise level of the data is found in this work. In addition, we suggest a new GGA form of the correlation scheme which gives the best performance. This work demonstrates that a brand-new reliability level is achieved for the theoretical prediction on positron lifetime of bulk material and the accuracy of the best theoretical scheme can be independent on the type of materials.
In this work, we present a highly accurate spectral neighbor analysis potential (SNAP) model for molybdenum (Mo) developed through the rigorous application of machine learning techniques on large materials data sets. Despite Mos importance as a struc
Recent studies illustrate how machine learning (ML) can be used to bypass a core challenge of molecular modeling: the tradeoff between accuracy and computational cost. Here, we assess multiple ML approaches for predicting the atomization energy of or
In this paper, we have built a numerical p-n Si/GaAs heterojunction model using a quantum-mechanical tunneling theory with various quantum tunneling interfacial materials including two-dimensional semiconductors such as hexagonal boron nitride (h-BN)
Antiferromagnetism in stacked nanographite is investigated with using the Hubbard-type model. We find that the open shell electronic structure can be an origin of the decreasing magnetic moment with the decrease of the inter-graphene distance, as exp
Exciting advances have been made in artificial intelligence (AI) during the past decades. Among them, applications of machine learning (ML) and deep learning techniques brought human-competitive performances in various tasks of fields, including imag