Data Efficient and Stability Indicated Sampling for Developing Reactive Machine Learning Potential to Achieve Ultra-long Simulation in Lithium Metal Batteries

Modelling the formation of solid-liquid interphase (SEI) is challenging as its strict requirement with both simulation accuracy and length. Machine learning potential (MLP) based molecular dynamics (MD) simulation is expected to play a role in this field while currently its use is hindered by sampling efficiency and simulation stability. In this work, we tackle the two challenges together. We propose the stability-indicated sampling (SIS) algorithm for efficiently sampling training data using physical infor mation (temperature). Unlike previous strategies, our method does not need prior knowledge of reaction networks or training multiple MLPs for uncertainty estimation. Compared with the recent proposed methods HAIR and DP-GEN, our approach gives significant improvement of sampling efficiency with less requirements with the initial training data, to realize > 10 ns MLPMD simulation using ab initio MD (AIMD) trajectory of just a few ps. We introduce the concept underlying instability consis tency by showing the accuracy of reaction mechanisms and radial distribution function (RDF) can be improved by SIS-MLPMD, although their information is not explicitly used in our sampling decision. Furthermore, we show that long-time MLPMD simu lation of Lithium metal battery (LMB) can not only reproduce some well-known SEI components including LiF, Li2O, LiOH, LiS and the incomplete N-S breaking in high concentration systems, but also ionic aggregation structures of LiF, which is not shown in our AIMD training data but matches previous results of electrochemical impedance spectroscopy. Our work is expected to help accelerate future investigations, especially for studying long-time (≥ ns scale) reaction dynamics in interfacial problems

A multiple-fidelity Method for Accurate Simulation of MoS 2 Properties Using JAX-ReaxFF and Neural Network Potentials

Reactive force field (ReaxFF) is one of the most commonly used force field to model the chemical reactions on atomic level. Recently, JAX-ReaxFF, combined with auto- matic differentiation, has been used to efficiently parameterize ReaxFF. However, pre- dicted properties using parameterized ReaxFF may be inaccurate due to the inductive bias of its analytical formula. While neural network-based potentials (NNPs) trained on density functional theory (DFT)-labeled data offer a more accurate method, it re- quires a large amount of training data to be trained from scratch. To overcome these issues, we present a multiple-fidelity method that combines JAX-ReaxFF and NNP, and apply the method on MoS 2 , a promising two-dimensional (2D) semiconductor for flexible electronics due to its excellent mechanical, optical, and electronic properties. By optimizing ReaxFF for MoS 2 and incorporating implicit prior physical information in the functional forms, we show that ReaxFF can serve as a cost-effective way to generate pretraining data, facilitating more accurate simulations of MoS 2 properties, such as the convex hull diagram, sulfur vacancy formation, and interaction with S 8 using SchNet. Moreover, in the Mo-S-H multi-element system, the pretraining strat- egy can reduce root-mean-square errors(RMSE) of energy by 20%. This approach can be extended to a wide variety of material systems, accelerating their computational research

Characteristic Assessment of Angiographies at Different Depths with AS-OCTA: Implication for Functions of Post-Trabeculectomy Filtering Bleb

This study aimed to analyze the quantitative vascular biomarkers of filtering bleb function at different depths using anterior segment optical coherence tomography angiography (AS-OCTA). This cross-sectional study is registered on Clinicaltrails.gov (NCT 04515017). Forty-six eyes with primary open-angle glaucoma that had undergone trabeculectomy with mitomycin-C for more than six months were included. Vessel density (VD) and vessel diameter index (VDI) in the superficial layer (SL), Tenon’s layer (TL), and deep layer (DL) of the bleb were obtained. The VD and VDI were higher in the failure group (both p = 0.000). Significant correlations were found between the SL, TL, DL’s VDI, and IOP in the success group (p = 0.013, 0.016, 0.031, respectively). The VD of the TL and DL were related to IOP in the failure group (p = 0.012, 0.009). Tenon’s VD (TVD) and Tenon’s VDI (TVDI) correlated with IOP adjusting for TVD, TVDI, and the Indiana Bleb Appearance Grading Scale (IBAGS) (p = 0.009, 0.043) or Kenfeld grading system (KGS) (p = 0.011, 0.016). The area under curve (AUC) of the TVD, TVDI, IBAGS, and KGS to predict surgery failure were 0.960, 0.925, 0.770, and 0.850. AS-OCTA realized the quantitative evaluation of vessels, especially the invisible vascularity beneath the conjunctiva. TVD and TVDI as detected by AS-OCTA better reflected bleb function than conventional grading systems

Alloying gold with copper makes for a highly selective visible-light photocatalyst for the reduction of nitroaromatics to anilines

Finely control of product selectivity is an essential issue in organic chemical production. In the synthesis of functionalized anilines via reduction of the corresponding nitroarenes, the challenge is to selectively reduce only the nitro group in the presence of other reducible functional groups in nitroarene molecules at a high reaction rate. Normally, the nitroarene is reduced stepwise through a series of intermediates that remain as byproducts, increasing the aniline synthesis cost. Here we report that alloying small amounts of copper into gold nanoparticles can alter the reaction pathway of the catalytic reduction under visible-light irradiation at ambient temperature, allowing nitroaromatics to be transformed directly to anilines in a highly selective manner. The reasons for the high efficiency of the photocatalytic reduction under these comparatively benign conditions as well as the light-excited reaction mechanisms are discussed. This photocatalytic process avoids byproducts, exhibits a high reaction rate and excellent substituent tolerance, and can be used for the synthesis of many useful functionalized anilines under environmentally benign conditions. Switching of the reaction pathway simply by tailoring the bimetallic alloy NPs of the photocatalysts is effective for engineering of product chemoselectivity

Structural, Optical, and Catalytic Support Properties of γ‑Al₂O₃ Inverse Opals

Colloidal crystal templating is a versatile and inexpensive method for the fabrication of 3-dimensional photonic crystals. Here we describe the successful use of the method to fabricate γ-alumina (γ-Al₂O₃) inverse opal thin films and powders, possessing pseudo photonic bandgaps (PBGs) along the [111] direction at visible wavelengths. The optical properties of γ-Al₂O₃ inverse opal films were investigated in detail for the first time and closely obeyed a modified Bragg’s law expression. The PBGs red-shifted on immersion in organic solvents, with the magnitude of the shift being directly proportional to the solvent refractive index. Calcination of the γ-Al₂O₃ inverse opals (BET area 250–275 m² g^–1) at temperatures from 550 to 1200 °C resulted in the stepwise transformation γ-Al₂O₃ → δ-Al₂O₃ → θ-Al₂O₃ → α-Al₂O_3. The onset temperatures for the latter polymorphic transitions were ca. 50–100 °C higher for Al₂O₃ inverse opals compared to a sol–gel alumina nanopowder, suggesting that the inverse opal architecture imparts sintering resistance. Au/γ-Al₂O₃ catalysts synthesized using γ-Al₂O₃ inverse opal supports demonstrated excellent activity for CO oxidation, with 69% CO conversion being achieved at 20 °C and near-complete conversion at 150 °C. The hierarchical porosity and high specific surface areas of γ-Al₂O₃ inverse opal powders make them near ideal supports for catalytic applications that traditionally utilize γ-alumina