A Universal Descriptor for Complicated Interfacial Effects on Electrochemical Reduction Reactions.

Supported catalysts have exhibited excellent performance in various reactions. However, the rational design of supported catalysts with high activity and certain selectivity remains a great challenge because of the complicated interfacial effects. Using recently emerged two-dimensional materials supported dual-atom catalysts (DACs@2D) as a prototype, we propose a simple and universal descriptor based on inherent atomic properties (electronegativity, electron type, and number), which can well evaluate the complicated interfacial effects on the electrochemical reduction reactions (i.e., CO2, O2, and N2 reduction reactions). Based on this descriptor, activity and selectivity trends in CO2 reduction reaction are successfully elucidated, in good agreement with available experimental data. Moreover, several potential catalysts with superior activity and selectivity for target products are predicted, such as CuCr/g-C3N4 for CH4 and CuSn/N-BN for HCOOH. More importantly, this descriptor can also be extended to evaluate the activity of DACs@2D for O2 and N2 reduction reactions, with very small errors between the prediction and reported experimental/computational results. This work provides feasible principles for the rational design of advanced electrocatalysts and the construction of universal descriptors based on inherent atomic properties.

LncRNA HOTTIP leads to osteoarthritis progression via regulating miR-663a/ Fyn-related kinase axis.

BACKGROUND: Long non-coding RNA (lncRNA) has been implicated in the progression of osteoarthritis (OA). This study was aimed to explore the role and molecular mechanism of lncRNA HOXA terminal transcriptional RNA (HOTTIP) in the development of OA. METHODS: The expression of HOTTIP, miR-663a and Fyn-related kinase (FRK) in the OA articular cartilage and OA chondrocyte model induced by IL-1ß was determined by qRT-PCR. CCK-8, colony formation and flow cytometry were used to determine the cell proliferation and apoptosis of OA chondrocytes. The specific molecular mechanism of HOTTIP in OA chondrocytes was determined by dual luciferase reporter assay, qRT-PCR, western blotting and RNA pull-down. RESULTS: The expression of HOTTIP and FRK were up-regulated, while miR-663a was down-regulated in OA cartilage tissues. Knockdown of HOTTIP decreased the proliferation and induced the apoptosis of OA cartilage model cells, while overexpression of HOTTIP increased the proliferation and reduced the apoptosis of OA cartilage model cells. Moreover, HOTTIP could bind to miR-663a as competitive endogenous RNA. Inhibition of miR-663a expression could alleviate the effect of HOTTIP knockdown on the proliferation and apoptosis of OA cartilage model cells. Furthermore, FRK was found to be a direct target of miR-663a, which could markedly down-regulate the expression of FRK in OA chondrocytes, while HOTTIP could remarkably up-regulate the expression of FRK. In addition, miR-663a inhibition increased the proliferation and reduced the apoptosis of OA cells, while FRK knockdown reversed the effect of miR-663a inhibition on the proliferation and apoptosis of OA cells. Meanwhile, overexpression of miR-663a decreased the proliferation and induced the apoptosis of OA cells, while overexpression of FRK reversed the effect of miR-663a overexpression on the proliferation and apoptosis of OA cells. CONCLUSION: HOTTIP was involved in the proliferation and apoptosis of OA chondrocytes via miR-663a/ FRK axis, and HOTTIP/miR-663a/FRK might be a potential target for the treatment of OA.

Magnetism and hybrid improper ferroelectricity in LaMO3/YMO3 superlattices.

Using first-principles calculations, we investigate the structural, electronic, and magnetic properties of perovskite LaMO3/YMO3 superlattices (M = Cr, Mn, Co and Ni). It is found that ferroelectricity can emerge in LaMO3/YMO3 superlattices (M = Cr, Mn, Co), allowing them to be promising multiferroic candidates, while no ferroelectricity is found in the LaNiO3/YNiO3 superlattice. The electronic structure calculations indicate that the LaCrO3/YCrO3, LaMnO3/YMnO3, and LaCoO3/YCoO3 superlattices are insulators, and their magnetic ground states exhibit G-type antiferromagnetic (AFM), A-type AFM, and G-type AFM order, respectively, while the LaNiO3/YNiO3 superlattice is however a half-metallic ferromagnet. The electronic structure and magnetic ground state are discussed, based on the projected density of states data and Heisenberg model, respectively, and the magnetic phase transition temperature is evaluated based on mean-field theory. In the meantime, the spontaneous ferroelectric polarization of the LaMO3/YMO3 superlattices (M = Cr, Mn, Co) is determined respectively using the Born effective charge model and Berry phase method, and their hybrid improper ferroelectric character is predicted, with the net polarization mainly from the different displacements of the LaO layers and YO layers along the b-axis. It is suggested that alternative multiferroic materials can be obtained by properly designing superlattices that consist of two non-polar magnetic materials but exhibit tunable magnetic ground states and transition temperature and hybrid improper ferroelectricity.

From bulk effective mass to 2D carrier mobility accurate prediction via adversarial transfer learning.

Data scarcity is one of the critical bottlenecks to utilizing machine learning in material discovery. Transfer learning can use existing big data to assist property prediction on small data sets, but the premise is that there must be a strong correlation between large and small data sets. To extend its applicability in scenarios with different properties and materials, here we develop a hybrid framework combining adversarial transfer learning and expert knowledge, which enables the direct prediction of carrier mobility of two-dimensional (2D) materials using the knowledge learned from bulk effective mass. Specifically, adversarial training ensures that only common knowledge between bulk and 2D materials is extracted while expert knowledge is incorporated to further improve the prediction accuracy and generalizability. Successfully, 2D carrier mobilities are predicted with the accuracy over 90% from only crystal structure, and 21 2D semiconductors with carrier mobilities far exceeding silicon and suitable bandgap are successfully screened out. This work enables transfer learning in simultaneous cross-property and cross-material scenarios, providing an effective tool to predict intricate material properties with limited data.

Universal machine learning aided synthesis approach of two-dimensional perovskites in a typical laboratory.

The past decade has witnessed the significant efforts in novel material discovery in the use of data-driven techniques, in particular, machine learning (ML). However, since it needs to consider the precursors, experimental conditions, and availability of reactants, material synthesis is generally much more complex than property and structure prediction, and very few computational predictions are experimentally realized. To solve these challenges, a universal framework that integrates high-throughput experiments, a priori knowledge of chemistry, and ML techniques such as subgroup discovery and support vector machine is proposed to guide the experimental synthesis of materials, which is capable of disclosing structure-property relationship hidden in high-throughput experiments and rapidly screening out materials with high synthesis feasibility from vast chemical space. Through application of our approach to challenging and consequential synthesis problem of 2D silver/bismuth organic-inorganic hybrid perovskites, we have increased the success rate of the synthesis feasibility by a factor of four relative to traditional approaches. This study provides a practical route for solving multidimensional chemical acceleration problems with small dataset from typical laboratory with limited experimental resources available.

Adaptive Design of Alloys for CO2 Activation and Methanation via Reinforcement Learning Monte Carlo Tree Search Algorithm.

Data-driven machine learning (ML) has earned remarkable achievements in accelerating materials design, while it heavily relies on high-quality data acquisition. In this work, we develop an adaptive design framework for searching for optimal materials starting from zero data and with as few DFT calculations as possible. This framework integrates automatic density functional theory (DFT) calculations with an improved Monte Carlo tree search via reinforcement learning algorithm (MCTS-PG). As a successful example, we apply it to rapidly identify the desired alloy catalysts for CO2 activation and methanation within 200 MCTS-PG steps. To this end, seven alloy surfaces with high theoretical activity and selectivity for CO2 methanation are screened out and further validated by comprehensive free energy calculations. Our adaptive design framework enables the fast computational exploration of materials with desired properties via minimal DFT calculations.

Inverse design with deep generative models: next step in materials discovery.

Data-driven inverse design for inorganic functional materials is a rapidly emerging field, which aims to automatically design innovative materials with target properties and to enable property-to-structure material discovery.

Coexistence of Semiconducting Ferromagnetics and Piezoelectrics down 2D Limit from Non van der Waals Antiferromagnetic LiNbO3-Type FeTiO3.

Stable two-dimensional (2D) ferromagnetic semiconductors (FMSs) with multifunctional properties have attracted extensive attention in device applications. Non van der Waals (vdW) transition-metal oxides with excellent environmental stability, if ferromagnetic (FM), may open up an unconventional and promising avenue for this subject, but they are usually antiferromagnetic or ferrimagnetic. Herein, we predict an FMS, monolayer Fe2Ti2O9, which can be obtained from LiNbO3-type FeTiO3 antiferromagnetic bulk, has a moderate band gap of 0.87 eV, large perpendicular magnetization (6 µB/fu) and a Curie temperature up to 110 K. The intriguing magnetic properties are derived from the double exchange and negative charge transfer between O_p orbitals and Fe_d orbitals. In addition, a large in-plane piezoelectric (PE) coefficient d11 of 5.0 pm/V is observed. This work offers a competitive candidate for multifunctional spintronics and may stimulate further experimental exploration of 2D non-vdW magnets.

Accelerated design of promising mixed lead-free double halide organic-inorganic perovskites for photovoltaics using machine learning.

Mixed double halide organic-inorganic perovskites (MDHOIPs) exhibit both good stability and high power conversion efficiency and have been regarded as attractive photovoltaic materials. Nevertheless, due to the complexity of structures, large-scale screening of thousands of possible candidates remains a great challenge. In this work, advanced machine learning (ML) techniques and first-principles calculations were combined to achieve a rapid screening of MDHOIPs for solar cells. Successfully, 204 stable lead-free MDHOIPs with optimal bandgaps were selected out of 11 370 candidates. The accuracy of ML models for perovskite structure formability and bandgap is over 94% and 97%, respectively. Moreover, representative MDHOIP candidates, MA2GeSnI4Br2 and MA2InBiI2Br4, stand out with suitable direct bandgaps, light carrier effective masses, small exciton binding energies, strong visible light absorption, and good stability against decomposition.

Property-Oriented Material Design Based on a Data-Driven Machine Learning Technique.

Property-oriented material design is a persistent pursuit for material scientists. Recently, machine learning (ML) as a powerful new tool has attracted worldwide attention in the material design field. Based on statistics instead of solving physical equations, ML can predict material properties faster with lower cost. Because of its data-driven characteristics, the quantity and quality of material data become the keys to the practical applications of this technique. In this Perspective, problems caused by lack of data and diversity of data are discussed. Various approaches, including high-throughput calculations, database construction, feedback loop algorithms, and better descriptors, have been exploited to address these problems. It is expected that this Perspective will bring data itself to the forefront of ML-based material design.

Coupling a Crystal Graph Multilayer Descriptor to Active Learning for Rapid Discovery of 2D Ferromagnetic Semiconductors/Half-Metals/Metals.

2D ferromagnetic (FM) semiconductors/half-metals/metals are the key materials toward next-generation spintronic devices. However, such materials are still rather rare and the material search space is too large to explore exhaustively. Here, an adaptive framework to accelerate the discovery of 2D intrinsic FM materials is developed, by combining advanced machine-learning (ML) techniques with high-throughput density functional theory calculations. Successfully, about 90 intrinsic FM materials with desirable bandgap and excellent thermodynamic stability are screened out and a database containing 1459 2D magnetic materials is set up. To improve the performance of ML models on small-scale datasets like diverse 2D materials, a crystal graph multilayer descriptor using the elemental property is proposed, with which ML models achieve prediction accuracy over 90% on thermodynamic stability, magnetism, and bandgap. This study not only provides dozens of compelling FM candidates for future spintronics, but also paves a feasible route for ML-based rapid screening of diverse structures and/or complex properties.

Perspective on theoretical methods and modeling relating to electro-catalysis processes.

Electro-catalysis is expected to be a promising clean alternative for energy conversion, and the search for effective and stable electro-catalysts is fundamental. Theoretical calculations play an important role in the rational design and optimization of the performance of electro-catalysts by revealing active sites for reactions and corresponding reaction mechanisms. However, the simulation of electrochemical processes under realistic conditions, for instance, electrode-electrolyte interface structures and the dynamic movement of species around the interface, is still limited. In this review, we summarize advances in theoretical methods and models for the description of thermodynamics and kinetics in electro-catalysis, including solvent effects, externally applied potentials, and many-body interactions. Multiple innovative methods and models are covered with specific examples, and the scope for future development is discussed.

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

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.