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.

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.

Adaptive Design of Alloys for CO 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).

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.

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.

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

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 FeTiO, which can be obtained from LiNbO-type FeTiO antiferromagnetic bulk, has a moderate band gap of 0.

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.

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-1β was determined by qRT-PCR.

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.

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.

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.

Magnetism and hybrid improper ferroelectricity in LaMO/YMO 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.

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.