다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

This presentation is a case study taken from the travel and holiday industry. Paxport/Multicom, based in UK and Sweden, have recently adopted a recommendation system for holiday accommodation bookings. Machine learning techniques such as Collaborative Filtering have been applied using Python (3.5.1), with Jupyter (4.0.6) as the main framework. Data scale and sparsity present significant challenges in the case study, and so the effectiveness of various techniques are described as well as the performance of Python-based libraries such as Python Data Analysis Library (Pandas), and Scikit-learn (built on NumPy, SciPy and matplotlib). The presentation is suitable for all levels of programmers.

Machine learning with Python

Predicting the stability of the perovskite structure remains a long-standing challenge for the discovery of new functional materials for many applications including photovoltaics and electrocatalysts. We developed an accurate, physically interpretable, and one-dimensional tolerance factor, τ, that correctly predicts 92% of compounds as perovskite or nonperovskite for an experimental dataset of 576 ABX 3 materials ( X = O 2− , F − , Cl − , Br − , I − ) using a novel data analytics approach based on SISSO (sure independence screening and sparsifying operator). τ is shown to generalize outside the training set for 1034 experimentally realized single and double perovskites (91% accuracy) and is applied to identify 23,314 new double perovskites ( A 2 BB′X 6 ) ranked by their probability of being stable as perovskite. This work guides experimentalists and theorists toward which perovskites are most likely to be successfully synthesized and demonstrates an approach to descriptor identification that can be extended to arbitrary applications beyond perovskite stability predictions.

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New tolerance factor to predict the stability of perovskite oxides and halides

Metal-halide perovskites have rapidly emerged as one of the most promising materials of the 21st century, with many exciting properties and great potential for a broad range of applications, from photovoltaics to optoelectronics and photocatalysis. The ease with which metal-halide perovskites can be synthesized in the form of brightly luminescent colloidal nanocrystals, as well as their tunable and intriguing optical and electronic properties, has attracted researchers from different disciplines of science and technology. In the last few years, there has been a significant progress in the shape-controlled synthesis of perovskite nanocrystals and understanding of their properties and applications. In this comprehensive review, researchers having expertise in different fields (chemistry, physics, and device engineering) of metal-halide perovskite nanocrystals have joined together to provide a state of the art overview and future prospects of metal-halide perovskite nanocrystal research.

State of the Art and Prospects for Halide Perovskite Nanocrystals.

Statistical learning of materials properties or functions so far starts with a largely silent, nonchallenged step: the choice of the set of descriptive parameters (termed descriptor). However, when the scientific connection between the descriptor and the actuating mechanisms is unclear, the causality of the learned descriptor-property relation is uncertain. Thus, a trustful prediction of new promising materials, identification of anomalies, and scientific advancement are doubtful. We analyze this issue and define requirements for a suitable descriptor. For a classic example, the energy difference of zinc blende or wurtzite and rocksalt semiconductors, we demonstrate how a meaningful descriptor can be found systematically.

Big Data of Materials Science -- Critical Role of the Descriptor

Searching for novel functional materials represents an important direction in the research and development of renewable energy. Due to their unique structural and compositional flexibility and high material stability, oxide perovskites and their derivatives have recently been extensively explored as a class of versatile materials for applications in electrocatalysis (EC), photocatalysis (PC) and photovoltaics (PV), showing great promise in terms of catalytic activity and device stability. In this review, we firstly discuss the extreme flexibilities of oxide perovskites in terms of their structures and compositions, which lead to a treasure trove of materials for diverse applications. Secondly, the current status of their applications and challenges in EC, PC and PV are reviewed. We attempt to build the connections between the structural and compositional flexibility and the tunable material properties desirable for various applications.

Oxide perovskites, double perovskites and derivatives for electrocatalysis, photocatalysis, and photovoltaics

Stability is of central importance in current perovskite solar cell research and applications. Goldschmidt tolerance factor (t) recently provided qualitative guidance for experimentalists to engineer stable ABX3 perovskite by tuning effective ionic size with mixing cations or anions and for theorists to search emerging perovskites. Through first-principles calculations, we have calculated decomposition energies of 138 perovskite compounds of potential solar cell applications. Instead of t, we have found that (μ + t)η, where μ and η are the octahedral factor and the atomic packing fraction, respectively, demonstrates a remarkably linear correlation with thermodynamic stability. As a stability descriptor, (μ + t)η is able to predict the relative stability among any two perovskites with an accuracy of ∼90%. This trend is then used to predict decomposition energies of another 69 perovskites, and the results are in excellent agreement with first-principles calculations, indicating the generalization of the tre...

Thermodynamic Stability Trend of Cubic Perovskites

Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor, μ/t, where μ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, Cs0.4La0.6Mn0.25Co0.75O3, Cs0.3La0.7NiO3, SrNi0.75Co0.25O3, and Sr0.25Ba0.75NiO3, are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties. Symbolic regression holds big promise for guiding materials design, yet its application in materials science is still limited. Here the authors use symbolic regression to introduce an activity descriptor predicting new oxide perovskites with improved oxygen evolution activity as corroborated by experimental validation.

/pdf/simple-descriptor-derived-from-symbolic-regression-2d1fh3lpnp.pdf

Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts.

Thermodynamic Stability Landscape of Halide Double Perovskites via High-Throughput Computing and Machine Learning

Development of lead-free inorganic perovskite material, such as Cs2AgBiBr6, is of great importance to solve the toxicity and stability issues of traditional lead halide perovskite solar cells. However, due to a wide bandgap of Cs2AgBiBr6 film, its light absorption ability is largely limited and the photoelectronic conversion efficiency is normally lower than 4.23%. In this text, by using a hydrogenation method, the bandgap of Cs2AgBiBr6 films could be tunable from 2.18 eV to 1.64 eV. At the same time, the highest photoelectric conversion efficiency of hydrogenated Cs2AgBiBr6 perovskite solar cell has been improved up to 6.37% with good environmental stability. Further investigations confirmed that the interstitial doping of atomic hydrogen in Cs2AgBiBr6 lattice could not only adjust its valence and conduction band energy levels, but also optimize the carrier mobility and carrier lifetime. All these works provide an insightful strategy to fabricate high performance lead-free inorganic perovskite solar cells. 

/pdf/hydrogenated-cs2agbibr6-for-significantly-improved-mgur2k2v.pdf

Qingde Sun

Papers

Oxide perovskites, double perovskites and derivatives for electrocatalysis, photocatalysis, and photovoltaics

Thermodynamic Stability Trend of Cubic Perovskites

Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts.

Thermodynamic Stability Landscape of Halide Double Perovskites via High-Throughput Computing and Machine Learning

Hydrogenated Cs2AgBiBr6 for significantly improved efficiency of lead-free inorganic double perovskite solar cell