A density-functional-theory-based and machine-learning-accelerated hybrid method for intricate system catalysis

Being progressively applied in the design of highly active catalysts for energy devices,machine learning(ML)technology has shown attractive ability of dramatically reducing the computational cost of the traditional density functional theory(DFT)method,showing a particular advantage for the simulation of intricate system catalysis.Starting with a basic description of the whole workflow of the novel DFT-based and ML-accelerated(DFT-ML)scheme,and the common algorithms useable for machine learning,we presented in this paper our work on the development and performance test of a DFT-based ML method for catalysis program(DMCP)to implement the DFT-ML scheme.DMCP is an efficient and user-friendly program with the flexibility to accommodate the needs of performing ML calculations based on the data generated by DFT calculations or from materials database.We also employed an example of transition metal phthalocyanine double-atom catalysts as electrocatalysts for carbon reduction reaction to exhibit the general workflow of the DFT-ML hybrid scheme and our DMCP program.

Insight into the nitrogen-vacancy center formation in type-Ib diamond by irradiation and annealing approach

Comprehending the microscopic formation of nitrogen vacancy(NV)centers in nitrogen-doped diamonds is crucial for enhancing the controllable preparation of NV centers and quantum applications.Irradiation followed by annealing simulations for a type-Ib diamond with a 900 ppm concentration of isolated nitrogen is conducted along different orientations and at different annealing temperatures.In these simulations,molecular dynamics(MD)with smoothly connected potential functions are implemented.MD simulations revealed the dynamic formation process of the NV center,which was subsequently verified by first-principles calculations and experiments.The results indicate that vacancies undergo one or multiple migrations by exchanging sites with neighboring atoms.There are three mechanisms for the formation of NV centers:direct irradiation-induced NV formation,irradiation with further annealing to form NV and vacancy migration(VM)during the annealing process.Furthermore,the results show that both VM and NV center formations are affected by orientations.This study clarifies the formation of NV centers across multiple scales and provides a solid foundation for the targeted preparation of NV centers.

Phase engineering of Pd–Te nanoplates via potential energy trapping

Phase modulation of noble metal alloys(NMAs)is critically important in nanoscience since the distinct atomic arrangements can largely determine their physicochemical properties.However,the precise modulation of NMAs is formidably challenging,because thermodynamically stable phases are generally preferential compared to those metastable ones.Herein,we proposed a potential energy trapping strategy for phase modulation of Pd–Te alloys with solvents.Thereinto,ethylene glycol can increase the energy barrier for both Pd leaching and Te introduction,forming metastable Pd20Te7 phase.Inversely,N,N-dimethylformamide is unable to trap metastable phase,inducing the phase evolution to thermodynamically stable PdTe phase,and the precise phase modulation was realized including Pd20Te7,PdTe and PdTe2 phases.The Pd–Te alloys displayed phase-dependent formic acid oxidation catalytic performance with PdTe phase showing the best.This work proposes a strategy for creating metastable phase with potential energy trap,which may deepen the understanding of phase engineering for noble metal-based nanocrystals.

Prospect of Hexagonal CsMg(I_(1-x)Br_(x))_(3) Alloys for Deep-Ultraviolet Light Emission

Materials for deep-ultraviolet(DUV)light emission are extremely rare,significantly limiting the development of efficient DUV light-emitting diodes.Here we report CsMg(I_(1−x)Br_(x))_(3) alloys as potential DUV light emitters.Based on rigorous first-principles hybrid functional calculations,we find that CsMgI_(3) has an indirect bandgap,while CsMgBr_(3) has a direct bandgap.Further,we employ a band unfolding technique for alloy supercell calculations to investigate the critical Br concentration in CsMg(I_(1−x)Br_(x))_(3) associated with the crossover from an indirect to a direct bandgap,which is found to be∼0.36.Thus,CsMg(I_(1−x)Br_(x))_(3) alloys with 0.366≤6≤1 cover a wide range of direct bandgap(4.38–5.37 eV;284–231 nm),falling well into the DUV regime.Our study will guide the development of efficient DUV light emitters.

Phase boundary engineering of metal-organic-framework-derived carbonaceous nickel selenides for sodium-ion batteries

Nano Research volume 13,pages2289–2298(2020)Cite this article 347 Accesses 1 Altmetric Metrics details Abstract Sodium-ion batteries(SIBs)are promising power sources due to the low cost and abundance of battery-grade sodium resources,while practical SIBs suffer from intrinsically sluggish diffusion kinetics and severe volume changes of electrode materials.Metal-organic framework(MOFs)derived carbonaceous metal compound offer promising applications in electrode materials due to their tailorable composition,nanostructure,chemical and physical properties.Here,we fabricated hierarchical MOF-derived carbonaceous nickel selenides with bi-phase composition for enhanced sodium storage capability.As MOF formation time increases,the pyrolyzed and selenized products gradually transform from a single-phase Ni3Se4 into bi-phase NiSex then single-phase NiSe2,with concomitant morphological evolution from solid spheres into hierarchical urchin-like yolk-shell structures.As SIBs anodes,bi-phase NiSex@C/CNT-10h(10 h of hydrothermal synthesis time)exhibits a high specific capacity of 387.1 mAh/g at 0.1 A/g,long cycling stability of 306.3 mAh/g at a moderately high current density of 1 A/g after 2,000 cycles.Computational simulation further proves the lattice mismatch at the phase boundary facilitates more interstitial space for sodium storage.Our understanding of the phase boundary engineering of transformed MOFs and their morphological evolution is conducive to fabricate novel composites/hybrids for applications in batteries,catalysis,sensors,and environmental remediation.

Machine-learning-based interatomic potentials for advanced manufacturing

This paper summarizes the progress of machine-learning-based interatomic potentials and their applications in advanced manufacturing.Interatomic potential is essential for classical molecular dynamics.The advancements made in machine learning(ML)have enabled the development of fast interatomic potential with ab initio accuracy.The accelerated atomic simulation can greatly transform the design principle of manufacturing technology.The most widely used supervised and unsupervised ML methods are summarized and compared.Then,the emerging interatomic models based on ML are discussed:Gaussian approximation potential,spectral neighbor analysis potential,deep potential molecular dynamics,SCHNET,hierarchically interacting particle neural network,and fast learning of atomistic rare events.