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| 1 | High-throughput phase-field simulations and machine learning of resistive switching in resistive random-access memory显示文摘Metal oxide-based Resistive Random-Access Memory(RRAM)exhibits multiple resistance states,arising from the activation/deactivation of a conductive filament(CF)inside a switching layer.Understanding CF formation kinetics is critical to achieving optimal functionality of RRAM.Here a phase-field model is developed,based on materials properties determined by ab initio calculations,to investigate the role of electrical bias,heat transport and defect-induced Vegard strain in the resistive switching behavior. | Kena Zhang Jianjun Wang Yuhui Huang Long-Qing Chen P.Ganesh Ye Cao | 2020 | npj Computational Materials2020，，1： | 1 |
| 2 | Predicting synthesizable multi-functional edge reconstructions in two-dimensional transition metal dichalcogenides显示文摘Two-dimensional(2D)transition metal dichalcogenides(TMDCs)have attracted tremendous interest as functional materials due to their exceptionally diverse and tunable properties,especially in their edges.In addition to the conventional armchair and zigzag edges common to hexagonal 2D materials,more complex edge reconstructions can be realized through careful control over the synthesis conditions.However,the whole family of synthesizable,reconstructed edges remains poorly studied.Here,we develop a computational approach integrating ensemble-generation,force-relaxation,and electronic-structure calculations to systematically and efficiently discover additional reconstructed edges and screen their functional properties. | Guoxiang Hu Victor Fung Xiahan Sang Raymond R.Unocic P.Ganesh | 2020 | npj Computational Materials2020，，1： | 1 |
| 3 | Inverse design of two-dimensional materials with invertible neural networks显示文摘The ability to readily design novel materials with chosen functional properties on-demand represents a next frontier in materials discovery.However,thoroughly and efficiently sampling the entire design space in a computationally tractable manner remains a highly challenging task.To tackle this problem,we propose an inverse design framework(MatDesINNe)utilizing invertible neural networks which can map both forward and reverse processes between the design space and target property.This approach can be used to generate materials candidates for a designated property,thereby satisfying the highly sought-after goal of inverse design.We then apply this framework to the task of band gap engineering in two-dimensional materials,starting with MoS_(2).Within the design space encompassing six degrees of freedom in applied tensile,compressive and shear strain plus an external electric field,we show the framework can generate novel,high fidelity,and diverse candidates with near-chemical accuracy.We extend this generative capability further to provide insights regarding metal-insulator transition in MoS_(2)which are important for memristive neuromorphic applications,among others.This approach is general and can be directly extended to other materials and their corresponding design spaces and target properties. | Victor Fung Jiaxin Zhang Guoxiang Hu P.Ganesh Bobby G.Sumpter | 2021 | npj Computational Materials2021，，1： | 0 |
| 4 | Author Correction:Inverse design of two-dimensional materials with invertible neural networks显示文摘The original version of this Article contained errors in Fig.4,in which Fig.4a and Fig.4b were swapped. | Victor Fung Jiaxin Zhang Guoxiang Hu P.Ganesh Bobby G.Sumpter | 2021 | npj Computational Materials2021，，1： | 0 |
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