RESUMO
Exploring potential energy surfaces (PES) is essential for unraveling the underlying mechanisms of chemical reactions and material properties. While the activation-relaxation technique (ARTn) is a state-of-the-art method for identifying saddle points on PES, it often faces challenges in complex energy landscapes, especially on surfaces. In this study, we introduce iso-ARTn, an enhanced ARTn method that incorporates constraints on an orthogonal hyperplane and employs an adaptive active volume. By leveraging a neural network potential (NNP) to conduct an exhaustive saddle point search on the Pt(111) surface with 0.3 monolayers of surface oxygen coverage, iso-ARTn achieves a success rate that is 8.2% higher than the original ARTn, with 40% fewer force calls. Moreover, this method effectively finds various saddle points without compromising the success rate. Combined with kinetic Monte Carlo simulations for event table construction, iso-ARTn with NNP demonstrates the capability to reveal structures consistent with experimental observations. This work signifies a substantial advancement in the investigation of PES, enhancing both the efficiency and breadth of saddle point searches.
RESUMO
An atomistic understanding of dry-etching processes with reactive molecules is crucial for achieving geometric integrity in highly scaled semiconductor devices. Molecular dynamics (MD) simulations are instrumental, but the lack of reliable force fields hinders the widespread use of MD in etching simulations. In this work, we develop an accurate neural network potential (NNP) for simulating the etching process of amorphous Si3N4 with HF molecules. The surface reactions in diverse local environments are considered by incorporating several types of training sets: baseline structures, reaction-specific data, and general-purpose training sets. Furthermore, the NNP is refined through iterative comparisons with the density functional theory results. Using the trained NNP, we carry out etching simulations, which allow for detailed observation and analysis of key processes such as preferential sputtering, surface modification, etching yield, threshold energy, and the distribution of etching products. Additionally, we develop a simple continuum model, built from the MD simulation results, which effectively reproduces the surface composition obtained with MD simulations. By establishing a computational framework for atomistic etching simulation and scale bridging, this work will pave the way for more accurate and efficient design of etching processes in the semiconductor industry, enhancing device performance and manufacturing precision.
RESUMO
Semiconducting inorganic materials with band gaps ranging between 0 and 5 eV constitute major components in electronic, optoelectronic and photovoltaic devices. Since the band gap is a primary material property that affects the device performance, large band-gap databases are useful in selecting optimal materials in each application. While there exist several band-gap databases that are theoretically compiled by density-functional-theory calculations, they suffer from computational limitations such as band-gap underestimation and metastable magnetism. In this data descriptor, we present a computational database of band gaps for 10,481 materials compiled by applying a hybrid functional and considering the stable magnetic ordering. For benchmark materials, the root-mean-square error in reference to experimental data is 0.36 eV, significantly smaller than 0.75-1.05 eV in the existing databases. Furthermore, we identify many small-gap materials that are misclassified as metals in other databases. By providing accurate band gaps, the present database will be useful in screening materials in diverse applications.
