Hydrogen fuel is considered as the cleanest renewable resource and the primary alternative to fossil fuels for future energy supply. Sustainable hydrogen generation is the major prerequisite to realize future hydrogen economy. The electrocatalytic hydrogen evolution reaction (HER), as the vital step of water electrolysis to H2 production, has been the subject of extensive study over the past decades. In this comprehensive review, we first summarize the fundamentals of HER and review the recent state-of-the-art advances in the low-cost and high-performance catalysts based on noble and non-noble metals, as well as metal-free HER electrocatalysts. We systemically discuss the insights into the relationship among the catalytic activity, morphology, structure, composition, and synthetic method. Strategies for developing an effective catalyst, including increasing the intrinsic activity of active sites and/or increasing the number of active sites, are summarized and highlighted. Finally, the challenges, perspectives, and research directions of HER electrocatalysis are featured.

Recent Advances in Electrocatalytic Hydrogen Evolution Using Nanoparticles.

2D MXenes: Electromagnetic property for microwave absorption and electromagnetic interference shielding

In recent years, a new large family of two dimensional transition metal carbides, carbonitrides, and nitrides, so-called MXenes, have grabbed considerable attention, owing to their many fascinating physical and chemical properties that are closely related to the rich diversity of their elemental compositions and surface terminations. In particular, it is easy for MXenes to form composites with other materials such as polymers, oxides, and carbon nanotubes, which further provides an effective way to tune the properties of MXenes for various applications. Not only have MXenes and MXene-based composites come into prominence as electrode materials in the energy storage field as is widely known, but they have also shown great potential in environment-related applications including electro/photocatalytic water splitting, photocatalytic reduction of carbon dioxide, water purification and sensors, thanks to their high conductivity, reducibility and biocompatibility. In this review, we summarize the synthesis and properties of MXenes and MXene-based composites and highlight their recent advances in environment-related applications. Challenges and perspectives for future research are also outlined.

MXene and MXene-based composites: synthesis, properties and environment-related applications

Two-dimensional MXenes: From morphological to optical, electric, and magnetic properties and applications

Surface and Heterointerface Engineering of 2D MXenes and Their Nanocomposites: Insights into Electro- and Photocatalysis

Ultrathin MXene nanosheets with rich fluorine termination groups realizing efficient electrocatalytic hydrogen evolution

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments.

pdf/deepmd-kit-v2-a-software-package-for-deep-potential-models-11b27oys.pdf

DeePMD-kit v2: A software package for Deep Potential models

In hybrid perovskites, the organic molecules and inorganic frameworks exhibit distinct static and dynamic characteristics. Their coupling will lead to unprecedented phenomena, which have attracted wide research interests. In this paper, we employed Deep Potential molecular dynamics (DPMD), a large-scale MD simulation scheme with DFT accuracy, to study FA / MAPbI 3 hybrid perovskites. A spontaneous hybrid nano-domain behavior, namely multiple molecular rotation nano-domains embedded into a single [PbI 6 ] 4 − octahedra rotation domain, was ﬁrstly discovered at low temperatures. The behavior originates from the interplay between the long range order of molecular rotation and local lattice deformation, and clariﬁes the puzzling diﬀraction patterns of FAPbI 3 at low temperatures. Our work provides new insights into the structural characteristics and stability of hybrid perovskite, as well as new ideas for the structural characterization of organic-inorganic coupled systems.

Hybrid nano-domain structures of organic-inorganic perovskites from molecule-cage coupling effects

Accurate prediction for the electronic structure properties of halide perovskites plays a significant role in the design of highly efficient and stable solar cells. While density functional theory (DFT) within the generalized gradient approximation (GGA) offers reliable prediction in terms of lattice constants and potential energy surface for halide perovskites, it severely underestimates the band gap due to the lack of non-local exact exchange term, which exists in computationally expensive hybrid functionals. In this work, a universal Deep Kohn-Sham (DeePKS) model based on neural network is trained so as to enable electronic structure calculations with the accuracy of hybrid functional HSE06 and the efficiency comparable to GGA functional, for a plethora of halide perovskites, i.e., ABX$_3$ (A=FA, MA, Cs; B=Sn, Pb; X=Cl, Br, I). Forces, band gaps, and density of states (DOS) predicted by our DeePKS model for all aforementioned perovskites are in good agreement with the HSE06 results, with significantly improved efficiency. In addition, even though the spin-orbit coupling (SOC) effect has not been taken into consideration during the training process, DeePKS+SOC offers highly consistent band gap and DOS as compared to HSE06+SOC for Pb-containing systems. We believe such DeePKS model can be readily applied for an accurate yet efficient prediction of various properties for the family of halide perovskites.

pdf/deepks-model-for-halide-perovskites-with-the-accuracy-of-2w4pmnr4.pdf

DeePKS Model for Halide Perovskites with the Accuracy of Hybrid Functional

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ping Tuo

Author Tools

Chat about Author

Papers

Ultrathin MXene nanosheets with rich fluorine termination groups realizing efficient electrocatalytic hydrogen evolution

DeePMD-kit v2: A software package for Deep Potential models

Hybrid nano-domain structures of organic-inorganic perovskites from molecule-cage coupling effects

DeePKS Model for Halide Perovskites with the Accuracy of Hybrid Functional