ænet-PyTorch: A GPU-supported implementation for machine learning atomic potentials training

In this work, we present ænet-PyTorch, a PyTorch-based implementation for training artificial neural network-based machine learning interatomic potentials. Developed as an extension of the atomic energy network (ænet), ænet-PyTorch provides access to all the tools included in ænet for the application and usage of the potentials. The package has been designed as an alternative to the internal training capabilities of ænet, leveraging the power of graphic processing units to facilitate direct training on forces in addition to energies. This leads to a substantial reduction of the training time by one to two orders of magnitude compared to the central processing unit implementation, enabling direct training on forces for systems beyond small molecules. Here, we demonstrate the main features of ænet-PyTorch and show its performance on open databases. Our results show that training on all the force information within a dataset is not necessary, and including between 10% and 20% of the force information is sufficient to achieve optimally accurate interatomic potentials with the least computational resources.This work was supported by the “Departamento de Educación, Política Lingüística y Cultura del Gobierno Vasco” (IT1458-22), the “Ministerio de Ciencia e Innovación” (Grant No. PID2019-106644GB-I00), and the Project HPC-EUROPA3 (Grant No. INFRAIA-2016-1-730897), with the support of the EC Research Innovation Action under the H2020 Programme. The authors acknowledge technical and human support provided by SGIker (UPV/EHU/ERDF, EU) and the Duch National e-Infrastructure and the SURF Cooperative for computational resources (National Supercomputer Snellius). J.L.-Z. acknowledges financial support from the Basque Country Government (PRE_2019_1_0025). N.A. acknowledges funding from the Bayer AG Life Science Collaboration (“!AIQU”)

Atomistic prediction on the composition- and configuration-dependent bandgap of Ga(As,Sb) using cluster expansion and ab initio thermodynamics

© 2022 Elsevier B.V.The composition- and configuration-dependent bandgaps of pseudobinary Ga(As,Sb) are examined by the cluster expansion method and statistical thermodynamics based on density functional theory. The bandgaps and energetic stability of 330,000 configurations in the entire composition range show a consistent inverse relationship, in that a configuration with lower energy has a higher bandgap for a given composition. This inverse relation can be deduced from the opposite signs of effective cluster interaction coefficients for bandgap and energy, and can be quantified by the correlations of properties with short-range order parameters. The bandgap of GaAs0.5Sb0.5 varies from 0.02 to 0.93 eV depending on the atomic configuration, which suggests another tremendous chance to tune the bandgap by the configuration control. The average bandgap of a certain composition, calculated by the ab initio thermodynamics, decreases with increasing temperature. The calculated average bandgap shows feasible agreement with the experimental bandgap, reproducing the bandgap bowing.N

Ordered Electronic Reconstruction of the (112¯011ar2011ar{2}0) ZnO Single Crystal

Abstract Three‐dimensional (3D) charge‐written periodic peak and valley nanoarray surfaces are fabricated on a (112¯0$11ar{2}0$) ZnO single crystal grown via chemical vapor transport. Because the grown ZnO crystals exhibit uniform n‐type conduction, 3D periodic nanoarray patterns are formed via oxygen annealing. These periodically decorated structures show that the peak arrays are conducting at the nanoampere level, whereas the valley arrays are less conductive. Energy dispersive spectroscopy indicates that the valley arrays are deficient in zinc by ≈4–6 at%, and that the peak arrays are deficient in oxygen, respectively. Kelvin probe force microscopy reveals the presence of periodic wiggles featuring variations of ≈70–140‐meV between the peak and valley arrays. A significant decrease in the Fermi level of the valley region is observed (≈190 meV), which corresponds to a high zinc vacancy doping density of 2 × 1018 cm−3. This result indicates the periodic generation of an extremely large electric field (≈11 000 V cm−1) in the vicinity of the peak–valley arrays. Computational analysis corroborates the experimentally observed generation of VZn and the preferential formation of surface protrusions on ZnO (112¯0$11ar{2}0$) rather than on (0001), based on surface effects, along with the generation of peak and valley features

Resistance switching behavior of atomic layer deposited SrTiO3_{3} film through possible formation of Sr2_{2}Ti6_{6}O13_{13} or Sr1_{1}Ti11_{11}O20_{20} phases

Identification of microstructural evolution of nanoscale conducting phase, such as conducting filament (CF), in many resistance switching (RS) devices is a crucial factor to unambiguously understand the electrical behaviours of the RS-based electronic devices. Among the diverse RS material systems, oxide-based redox system comprises the major category of these intriguing electronic devices, where the local, along both lateral and vertical directions of thin films, changes in oxygen chemistry has been suggested to be the main RS mechanism. However, there are systems which involve distinctive crystallographic phases as CF; the Magnéli phase in TiO2 is one of the very well-known examples. The current research reports the possible presence of distinctive local conducting phase in atomic layer deposited SrTiO3 RS thin film. The conducting phase was identified through extensive transmission electron microscopy studies, which indicated that oxygen-deficient Sr2Ti6O13 or Sr1Ti11O20 phase was presumably present mainly along the grain boundaries of SrTiO3 after the unipolar set switching in Pt/TiN/SrTiO3/Pt structure. A detailed electrical characterization revealed that the samples showed typical bipolar and complementary RS after the memory cell was unipolar reset