A Tungsten Deep Potential with High Accuracy and Generalization Ability based on a Newly Designed Three-body Embedding Formalism

Tungsten is a promising candidate material in fusion energy facilities. Molecular dynamics (MD)simulations reveal the atomisttic scale mechanisms, so they are crucial for the understanding ofthe macroscopic property deterioration of tungsten under harsh and complex service environment.The interatomic potential used in the MD simulations is required to accurately describe a widespectrum of relevant defect properties, which is by far challenging to the existing interatomicpotentials. In this paper, we propose a new three-body embedding descriptor and hybridize it intothe Deep-Potential (DP) framework, an end-to-end deep learning interatomic potential model.Trained with the dataset generated by a concurrent learning method, the potential model fortungsten, named by DP-HYB, is able to accurately predict a wide range of properties includingelastic constants, the formation energies of free surfaces, grain boundaries, point defects and defectclusters, stacking fault energies, the core structure of screw dislocation, the energy barrier and thetransition path of the screw dislocation migration. Since most of the properties are not explicitlyincluded in the training dataset, the strong generalizability of the DP-HYB model indicates thatit is a good candidate for the atomistic simulations of tungsten property deterioration, especiallythose involving the mechanical property changing under the harsh service environment

A Spin-dependent Machine Learning Framework for Transition Metal Oxide Battery Cathode Materials

Owing to the trade-off between the accuracy and efficiency, machine-learning-potentials (MLPs) have been widely applied in the battery materials science, enabling atomic-level dynamics description for various critical processes. However, the challenge arises when dealing with complex transition metal (TM) oxide cathode materials, as multiple possibilities of d-orbital electrons localization often lead to convergence to different spin states (or equivalently local minimums with respect to the spin configurations) after ab initio self-consistent-field calculations, which causes a significant obstacle for training MLPs of cathode materials. In this work, we introduce a solution by incorporating an additional feature - atomic spins - into the descriptor, based on the pristine deep potential (DP) model, to address the above issue by distinguishing different spin states of TM ions. We demonstrate that our proposed scheme provides accurate descriptions for the potential energies of a variety of representative cathode materials, including the traditional Li$_x$TMO$_2$ (TM=Ni, Co, Mn, $x$=0.5 and 1.0), Li-Ni anti-sites in Li$_x$NiO$_2$ ($x$=0.5 and 1.0), cobalt-free high-nickel Li$_x$Ni$_{1.5}$Mn$_{0.5}$O$_4$ ($x$=1.5 and 0.5), and even a ternary cathode material Li$_x$Ni$_{1/3}$Co$_{1/3}$Mn$_{1/3}$O$_2$ ($x$=1.0 and 0.67). We highlight that our approach allows the utilization of all ab initio results as a training dataset, regardless of the system being in a spin ground state or not. Overall, our proposed approach paves the way for efficiently training MLPs for complex TM oxide cathode materials

A Study on an Energy Conservation and Interconnection Scheme between WSN and Internet Based on the 6LoWPAN

Wireless sensor network (WSN), which has broad application prospects, consists of small nodes with sensing, computation, and communications capabilities. IPv6 is used over low power WPAN (wireless personal area network) which is run by 6LoWPAN technology on the LoWPAN devices. In order to establish seamless connection of two heterogeneous networks, that is, WSN and IPv6 network, this paper proposes an improved energy conservation and interconnection scheme (ECIS) based on the analysis of the current schemes. Detailed design of each functional component in the new scheme is proposed, and an expansion of the SSCS module in NS2 802.15.4 simulator is examined. Comprehensive experiments on the simulation platform show that the newly proposed scheme is advantageous over existing results

Automatic Screen-out of Ir(III) Complex Emitters by Combined Machine Learning and Computational Analysis

Organic light-emitting diodes (OLEDs) have gained widespread commercial use, yet there is a continuous need to identify innovative emitters that offer higher efficiency and broader color gamut. To effectively screen out promising OLED molecules that are yet to be synthesized, we perform a representation learning aided high throughput virtual screening (HTVS) over millions of Ir(III) complexes, a prototypical type of phosphorescent OLED material, constructed via a random combination of 278 reported ligands. We successfully screen out a decent amount of promising candidates for both display and lighting purposes, which are worth further experimental investigation. The high efficiency and accuracy of our model are largely attributed to the pioneering attempt of using representation learning to organic luminescent molecules, which is initiated by a pre-training procedure with over 1.6 million 3D molecular structures and frontier orbital energies predicted via semi-empirical methods, followed by a fine-tune scheme via the quantum mechanical computed properties over around 1500 candidates. Such workflow enables an effective model construction process that is otherwise hindered by the scarcity of labeled data, and can be straightforwardly extended to the discovery of other novel materials

Optimization of the Energy Level Alignment between the Photoactive Layer and the Cathode Contact Utilizing Solution-Processed Hafnium Acetylacetonate as Buffer Layer for Efficient Polymer Solar Cells

The insertion of an appropriate interfacial buffer layer between the photoactive layer and the contact electrodes makes a great impact on the performance of polymer solar cells (PSCs). Ideal interfacial buffer layers could minimize the interfacial traps and the interfacial barriers caused by the incompatibility between the photoactive layer and the electrodes. In this work, we utilized solution-processed hafnium­(IV) acetylacetonate (Hf­(acac)₄) as an effective cathode buffer layer (CBL) in PSCs to optimize the energy level alignment between the photoactive layer and the cathode contact, with the short-circuit current density (<i>J</i>_sc), open-circuit voltage (<i>V</i>_oc), and fill factor (FF) all simultaneously improved with Hf­(acac)₄ CBL, leading to enhanced power conversion efficiencies (PCEs). Ultraviolet photoemission spectroscopy (UPS) and scanning Kelvin probe microscopy (SKPM) were performed to confirm that the interfacial dipoles were formed with the same orientation direction as the built-in potential between the photoactive layer and Hf­(acac)₄ CBL, benefiting the exciton separation and electron transport/extraction. In addition, the optical characteristics and surface morphology of the Hf­(acac)₄ CBL were also investigated