Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Since the discovery of mechanically exfoliated graphene in 2004, research on ultrathin two-dimensional (2D) nanomaterials has grown exponentially in the fields of condensed matter physics, material science, chemistry, and nanotechnology. Highlighting their compelling physical, chemical, electronic, and optical properties, as well as their various potential applications, in this Review, we summarize the state-of-art progress on the ultrathin 2D nanomaterials with a particular emphasis on their recent advances. First, we introduce the unique advances on ultrathin 2D nanomaterials, followed by the description of their composition and crystal structures. The assortments of their synthetic methods are then summarized, including insights on their advantages and limitations, alongside some recommendations on suitable characterization techniques. We also discuss in detail the utilization of these ultrathin 2D nanomaterials for wide ranges of potential applications among the electronics/optoelectronics, electrocat...

Recent Advances in Ultrathin Two-Dimensional Nanomaterials

Photoreduction of CO2 into sustainable and green solar fuels is generally believed to be an appealing solution to simultaneously overcome both environmental problems and energy crisis. The low selectivity of challenging multi-electron CO2 photoreduction reactions makes it one of the holy grails in heterogeneous photocatalysis. This Review highlights the important roles of cocatalysts in selective photocatalytic CO2 reduction into solar fuels using semiconductor catalysts. A special emphasis in this review is placed on the key role, design considerations and modification strategies of cocatalysts for CO2 photoreduction. Various cocatalysts, such as the biomimetic, metal-based, metal-free, and multifunctional ones, and their selectivity for CO2 photoreduction are summarized and discussed, along with the recent advances in this area. This Review provides useful information for the design of highly selective cocatalysts for photo(electro)reduction and electroreduction of CO2 and complements the existing reviews on various semiconductor photocatalysts.

Cocatalysts for Selective Photoreduction of CO2 into Solar Fuels.

One of the most exciting tools that have entered the material science toolbox in recent years is machine learning. This collection of statistical methods has already proved to be capable of considerably speeding up both fundamental and applied research. At present, we are witnessing an explosion of works that develop and apply machine learning to solid-state systems. We provide a comprehensive overview and analysis of the most recent research in this topic. As a starting point, we introduce machine learning principles, algorithms, descriptors, and databases in materials science. We continue with the description of different machine learning approaches for the discovery of stable materials and the prediction of their crystal structure. Then we discuss research in numerous quantitative structure–property relationships and various approaches for the replacement of first-principle methods by machine learning. We review how active learning and surrogate-based optimization can be applied to improve the rational design process and related examples of applications. Two major questions are always the interpretability of and the physical understanding gained from machine learning models. We consider therefore the different facets of interpretability and their importance in materials science. Finally, we propose solutions and future research paths for various challenges in computational materials science.

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Recent advances and applications of machine learning in solid-state materials science

Single-atom catalysts offer a pathway to cost-efficient catalysis using the minimal amount of precious metals. However, preparing and keeping them stable during operation remains a challenge. Here we report the synthesis of double transition metal MXene nanosheets—Mo2TiC2Tx, with abundant exposed basal planes and Mo vacancies in the outer layers—by electrochemical exfoliation, enabled by the interaction between protons and the surface functional groups of Mo2TiC2Tx. The as-formed Mo vacancies are used to immobilize single Pt atoms, enhancing the MXene’s catalytic activity for the hydrogen evolution reaction. The developed catalyst exhibits a high catalytic ability with low overpotentials of 30 and 77 mV to achieve 10 and 100 mA cm−2 and a mass activity about 40 times greater than the commercial platinum-on-carbon catalyst. The strong covalent interactions between positively charged Pt single atoms and the MXene contribute to the exceptional catalytic performance and stability. Single-atom catalysts are very attractive due to their ability to maintain high activities at the lowest possible precious metal loading. Here, a double transition metal MXene that effectively anchors single Pt atoms is reported, and exhibits superior performance and stability towards the hydrogen evolution reaction.

Single platinum atoms immobilized on an MXene as an efficient catalyst for the hydrogen evolution reaction

Lacking strategies to simultaneously address the intrinsic activity, site density, electrical transport, and stability problems of chalcogels is restricting their application in catalytic hydrogen production. Herein, we resolve these challenges concurrently through chemically activating the molybdenum disulfide (MoS2) surface basal plane by doping with a low content of atomic palladium using a spontaneous interfacial redox technique. Palladium substitution occurs at the molybdenum site, simultaneously introducing sulfur vacancy and converting the 2H into the stabilized 1T structure. Theoretical calculations demonstrate the sulfur atoms next to the palladium sites exhibit low hydrogen adsorption energy at –0.02 eV. The final MoS2 doped with only 1wt% of palladium demonstrates exchange current density of 805 μA cm−2 and 78 mV overpotential at 10 mA cm−2, accompanied by a good stability. The combined advantages of our surface activating technique open the possibility of manipulating the catalytic performance of MoS2 to rival platinum. While water reduction may provide a carbon-neutral means to produce hydrogen gas, there is a scarcity of efficient, earth-abundant electrocatalysts. Here, the authors add palladium into MoS2 materials to activate and stabilize the conductive basal plane to improve the electrocatalytic activity.

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Chemically activating MoS2 via spontaneous atomic palladium interfacial doping towards efficient hydrogen evolution.

Rapidly discovering functional materials remains an open challenge because the traditional trial-and-error methods are usually inefficient especially when thousands of candidates are treated. Here, we develop a target-driven method to predict undiscovered hybrid organic-inorganic perovskites (HOIPs) for photovoltaics. This strategy, combining machine learning techniques and density functional theory calculations, aims to quickly screen the HOIPs based on bandgap and solve the problems of toxicity and poor environmental stability in HOIPs. Successfully, six orthorhombic lead-free HOIPs with proper bandgap for solar cells and room temperature thermal stability are screened out from 5158 unexplored HOIPs and two of them stand out with direct bandgaps in the visible region and excellent environmental stability. Essentially, a close structure-property relationship mapping the HOIPs bandgap is established. Our method can achieve high accuracy in a flash and be applicable to a broad class of functional material design.

/pdf/accelerated-discovery-of-stable-lead-free-hybrid-organic-4b27cnusj6.pdf

Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning

Nanoscale molybdenum disulfide (MoS2) has attracted ever-growing interest as one of the most promising nonprecious catalysts for hydrogen evolution reaction (HER). However, the active sites of pristine MoS2 are located at the edges, leaving a large area of basal planes useless. Here, we systematically evaluate the capabilities of 16 kinds of structural defects including point defects (PDs) and grain boundaries (GBs) to activate the basal plane of MoS2 monolayer. Our first-principle calculations show that six types of defects (i.e., Vs, VMoS3, MoS2 PDs; 4|8a, S bridge, and Mo–Mo bond GBs) can greatly improve the HER performance of the in-plane domains of MoS2. More importantly, Vs and MoS2 PDs and S bridge and 4|8a GBs exhibit outstanding activity in both Heyrovsky and Tafel reactions as well. Moreover, the different HER activities of defects are well-understood by an amendatory band-center model, which is applicable to a broad class of systems with localized defect states. Our study provides a comprehensi...

Activating Inert Basal Planes of MoS2 for Hydrogen Evolution Reaction through the Formation of Different Intrinsic Defects

Nanosheet supported single-atom catalysts (SACs) can make full use of metal atoms and yet entail high selectivity and activity, and bifunctional catalysts can enable higher performance while lowering the cost than two separate unifunctional catalysts. Supported single-atom bifunctional catalysts are therefore of great economic interest and scientific importance. Here, on the basis of first-principles computations, we report a design of the first single-atom bifunctional eletrocatalyst, namely, isolated nickel atom supported on β12 boron monolayer (Ni1/β12-BM), to achieve overall water splitting. This nanosheet supported SAC exhibits remarkable electrocatalytic performance with the computed overpotential for oxygen/hydrogen evolution reaction being just 0.40/0.06 V. The ab initio molecular dynamics simulation shows that the SAC can survive up to 800 K elevated temperature, while enacting a high energy barrier of 1.68 eV to prevent isolated Ni atoms from clustering. A viable experimental route for the synth...

Nanosheet Supported Single-Metal Atom Bifunctional Catalyst for Overall Water Splitting

Electrocatalytic or photocatalytic N2 reduction holds great promise for green and sustainable NH3 production under ambient conditions, where an efficient catalyst plays a crucial role but remains a long‐standing challenge. Here, a high‐throughput screening of catalysts for N2 reduction among (nitrogen‐doped) graphene‐supported single atom catalysts is performed based on a general two‐step strategy. 10 promising candidates with excellent performance are extracted from 540 systems. Most strikingly, a single W atom embedded in graphene with three C atom coordination (W1C3) exhibits the best performance with an extremely low onset potential of 0.25 V. This study not only provides a series of promising catalysts for N2 fixation, but also paves a new way for the rational design of catalysts for N2 fixation under ambient conditions.

Yixin Ouyang

Papers

Chemically activating MoS2 via spontaneous atomic palladium interfacial doping towards efficient hydrogen evolution.

Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning

Activating Inert Basal Planes of MoS2 for Hydrogen Evolution Reaction through the Formation of Different Intrinsic Defects

Nanosheet Supported Single-Metal Atom Bifunctional Catalyst for Overall Water Splitting

A General Two-Step Strategy–Based High-Throughput Screening of Single Atom Catalysts for Nitrogen Fixation