Inspired by rich physics and functionalities of graphenes, scientists have taken an intensive interest in two-dimensional (2D) crystals of h-BN (analogue of graphite, so-called "white" graphite). Recent calculations have predicted the exciting potentials of BN nanoribbons in spintronics due to tunable magnetic and electrical properties; however no experimental evidence has been provided since fabrication of such ribbons remains a challenge. Here, we show that few- and single-layered BN nanoribbons, mostly terminated with zigzag edges, can be produced under unwrapping multiwalled BN nanotubes through plasma etching. The interesting stepwise unwrapping and intermediate states were observed and analyzed. Opposed to insulating primal tubes, the nanoribbons become semiconducting due to doping-like conducting edge states and vacancy defects, as revealed by structural analyses and ab initio simulations. This study paves the way for BN nanoribbon production and usage as functional semiconductors with a wide range of applications in optoelectronics and spintronics.

"White graphenes": boron nitride nanoribbons via boron nitride nanotube unwrapping

Learning from data has led to paradigm shifts in a multitude of disciplines, including web, text and image search, speech recognition, as well as bioinformatics. Can machine learning enable similar breakthroughs in understanding quantum many-body systems? Here we develop an efficient deep learning approach that enables spatially and chemically resolved insights into quantum-mechanical observables of molecular systems. We unify concepts from many-body Hamiltonians with purpose-designed deep tensor neural networks, which leads to size-extensive and uniformly accurate (1 kcal mol−1) predictions in compositional and configurational chemical space for molecules of intermediate size. As an example of chemical relevance, the model reveals a classification of aromatic rings with respect to their stability. Further applications of our model for predicting atomic energies and local chemical potentials in molecules, reliable isomer energies, and molecules with peculiar electronic structure demonstrate the potential of machine learning for revealing insights into complex quantum-chemical systems.

/pdf/quantum-chemical-insights-from-deep-tensor-neural-networks-266u885pa4.pdf

Quantum-Chemical Insights from Deep Tensor Neural Networks

Metal–organic frameworks (MOFs), an emerging class of porous materials, have shown intriguing and promising properties in a wide range of applications due to their versatile structures, large surface areas, tunable porosity and tailorable chemistry. In recent years one of the most active research fields is to explore energy applications of MOF-based materials. In this review, we present a critical overview on the recent progress of the use of MOF-based materials for gaseous fuel storage, chemical hydrogen storage, solar and electrochemical energy storage and conversion. The challenges and opportunities towards advanced energy technologies with the MOF-based materials are discussed.

Metal–organic frameworks as a platform for clean energy applications

Developing efficient catalysts for nitrogen fixation is becoming increasingly important but is still challenging due to the lack of robust design criteria for tackling the activity and selectivity problems, especially for electrochemical nitrogen reduction reaction (NRR). Herein, by means of large-scale density functional theory (DFT) computations, we reported a descriptor-based design principle to explore the large composition space of two-dimensional (2D) biatom catalysts (BACs), namely, metal dimers supported on 2D expanded phthalocyanine (M2-Pc or MM'-Pc), toward the NRR at the acid conditions. We sampled both homonuclear (M2-Pc) and heteronuclear (MM'-Pc) BACs and constructed the activity map of BACs by using N2H* adsorption energy as the activity descriptor, which reduces the number of promising catalyst candidates from over 900 to less than 100. This strategy allowed us to readily identify 3 homonuclear and 28 heteronuclear BACs, which could break the metal-based activity benchmark toward the efficient NRR. Particularly, using the free energy difference of H* and N2H* as a selectivity descriptor, we screened out five systems, including Ti2-Pc, V2-Pc, TiV-Pc, VCr-Pc, and VTa-Pc, which exhibit a strong capability of suppressing the competitive hydrogen evolution reaction (HER) with favorable limiting potential of -0.75, -0.39, -0.74, -0.85, and -0.47 V, respectively. This work not only broadens the possibility of discovering more efficient BACs toward N2 fixation but also provides a feasible strategy for rational design of NRR electrocatalysts and helps pave the way to fast screening and design of efficient BACs for the NRR and other electrochemical reactions.

Tackling the Activity and Selectivity Challenges of Electrocatalysts toward the Nitrogen Reduction Reaction via Atomically Dispersed Biatom Catalysts

The combination of modern scientific computing with electronic structure theory can lead to an unprecedented amount of data amenable to intelligent data analysis for the identification of meaningful, novel, and predictive structure-property relationships. Such relationships enable high-throughput screening for relevant properties in an exponentially growing pool of virtual compounds that are synthetically accessible. Here, we present a machine learning (ML) model, trained on a data base of \textit{ab initio} calculation results for thousands of organic molecules, that simultaneously predicts multiple electronic ground- and excited-state properties. The properties include atomization energy, polarizability, frontier orbital eigenvalues, ionization potential, electron affinity, and excitation energies. The ML model is based on a deep multi-task artificial neural network, exploiting underlying correlations between various molecular properties. The input is identical to \emph{ab initio} methods, \emph{i.e.} nuclear charges and Cartesian coordinates of all atoms. For small organic molecules the accuracy of such a "Quantum Machine" is similar, and sometimes superior, to modern quantum-chemical methods---at negligible computational cost.

Machine Learning of Molecular Electronic Properties in Chemical Compound Space

As modern society develops, the need for clean energy becomes increasingly important on a global scale. Because of this, the exploration of novel materials for energy storage and utilization is urgently needed to achieve low-carbon economy and sustainable development. Among these novel materials, metal–organic frameworks (MOFs), a class of porous materials, have gained increasing attention for utilization in energy storage and conversion systems because of ultra-high surface areas, controllable structures, large pore volumes and tunable porosities. In addition to pristine MOFs, MOF derivatives such as porous carbons and nanostructured metal oxides can also exhibit promising performances in energy storage and conversion applications. In this review, the latest progress and breakthrough in the application of MOF and MOF-derived materials for energy storage and conversion devices are summarized, including Li-based batteries (Li-ion, Li–S and Li–O2 batteries), Na-ion batteries, supercapacitors, solar cells and fuel cells.

Metal–Organic Frameworks (MOFs) and MOF-Derived Materials for Energy Storage and Conversion

Nitrogen fixation is one of the most essential processes in chemistry Developing more effective nitrogen fixation systems to catalyze the reaction under mild conditions is one of the most attractive but long-standing challenges In this work, by means of first-principles computations, we systematically investigated the catalytic performance of transition metal atom-anchored C2N monolayer electrocatalysts (TMx@C2N, x = 1 or 2; TM = Ti, Mn, Fe, Co, Cu, Mo, Ru, Rh, Pd, Ag, Ir, Pt, or Au) for N2 fixation The natively N edged uniform holes could fix the TM atoms firmly in TM–Nx configurations, which is beneficial for the N2 fixation Among them, Mo2@C2N exhibits the best catalytic performance for the reduction of N2 to NH3 with a maximum free energy change of 041 eV and an energy barrier of 051 eV, indicating that Mo2@C2N is a promising catalyst with high catalytic activity for the reduction of N2 to NH3 We believe that this work could provide a new idea for the design of N2 fixation catalysts and shed light on C2N monolayers as excellent substrates for catalysis

Double-atom catalysts: transition metal dimer-anchored C2N monolayers as N2 fixation electrocatalysts

Developing highly active non-noble catalysts for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) is essential for overall water splitting. In this work, by means of first-principles computations, we screened a series of transition metal atom anchored C2N monolayers (TMx@C2N, TM = Ti, Mn, Fe, Co, Ni, Cu, Mo, Ru, Rh, Pd, Ag, Ir, Pt, or Au) as electrocatalysts for both HER and OER. Almost all TMx@C2N composites show metallic properties, indicating outstanding charge transfer for efficient electrochemical procedures. Ti1@C2N, Mn1@C2N, Co2@C2N, Ni2@C2N, Cu2@C2N, Mo1@C2N, Ru2@C2N and Ir1@C2N exhibit high catalytic activity toward the HER. Among them, Ti1@C2N would be the best HER catalyst since both N and Ti atoms are active sites. Unfortunately, Ti1@C2N exhibits no OER activity. Instead, only Mn1@C2N could perform as a bifunctional electrocatalyst with N and Mn atoms as active sites for the HER and OER, respectively. This work would open a new door for the development of non-noble metal bifunctional electrocatalysts for overall water splitting and also shed light on C2N-supported nanomaterials as advanced catalysts.

Transition metal anchored C2N monolayers as efficient bifunctional electrocatalysts for hydrogen and oxygen evolution reactions

https://onlinelibrary.wiley.com/doi/pdf/10.1002/inf2.12094

Machine learning: Accelerating materials development for energy storage and conversion

Electroreduction of CO2 is one of the most potential ways to realize CO2 recycle and energy regeneration. The key to promoting this technology is the development of high-performance electrocatalyst...

An Chen

Papers

Metal–Organic Frameworks (MOFs) and MOF-Derived Materials for Energy Storage and Conversion

Double-atom catalysts: transition metal dimer-anchored C2N monolayers as N2 fixation electrocatalysts

Transition metal anchored C2N monolayers as efficient bifunctional electrocatalysts for hydrogen and oxygen evolution reactions

Machine learning: Accelerating materials development for energy storage and conversion

A Machine Learning Model on Simple Features for CO2 Reduction Electrocatalysts