jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hui Song | Hengming Huang | Xianguang Meng | Qi Wang | Huilin Hu | Shengyao Wang | Hongwei Zhang | Wipakorn Jewasuwan | Naoki Fukata | Ningdong Feng | Jinhua Ye
Source: Angewandte Chemie International Edition (article, 2022-11-30)
Field: Materials Science — Materials Chemistry

Atomically Dispersed Nickel Anchored on a Nitrogen‐Doped Carbon/TiO<sub>2</sub>Composite for Efficient and Selective Photocatalytic CH<sub>4</sub>Oxidation to Oxygenates Direct photocatalytic oxidation of methane to liquid oxygenated products is a sustainable strategy for methane valorization at room temperature. However, in this reaction, noble metals are generally needed to function as cocatalysts for obtaining adequate activity and selectivity. Here, we report atomically dispersed nickel anchored on a nitrogen-doped carbon/TiO<sub>2</sub> composite (Ni-NC/TiO<sub>2</sub> ) as a highly active and selective catalyst for photooxidation of CH<sub>4</sub> to C1 oxygenates with O<sub>2</sub> as the only oxidant. Ni-NC/TiO<sub>2</sub> exhibits a yield of C1 oxygenates of 198 μmol for 4 h with a selectivity of 93 %, exceeding that of most reported high-performance photocatalysts. Experimental and theoretical investigations suggest that the single-atom Ni-NC sites not only enhance the transfer of photogenerated electrons from TiO<sub>2</sub> to isolated Ni atoms but also dominantly facilitate the activation of O<sub>2</sub> to form the key intermediate ⋅OOH radicals, which synergistically lead to a substantial enhancement in both activity and selectivity. Oxygenate ; Photocatalysis ; Selectivity ; Catalysis ; Nickel ; Methane ; Carbon fibers ; Nitrogen ; Inorganic chemistry ; Materials science ; Radical ; Photochemistry ; Yield (engineering) ; Quantum yield ; Electron transfer ; Composite number ; Chemistry ; Chemical engineering ; Organic chemistry ; Metallurgy Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques ; Catalysis and Oxidation Reactions
Authors: Clyde Fare | Peter Fenner | Matt Benatan | Alessandro Varsi | Edward O. Pyzer‐Knapp
Source: npj Computational Materials (article, 2022-12-19)
Field: Materials Science — Materials Chemistry

A multi-fidelity machine learning approach to high throughput materials screening Abstract The ever-increasing capability of computational methods has resulted in their general acceptance as a key part of the materials design process. Traditionally this has been achieved using a so-called computational funnel, where increasingly accurate - and expensive – methodologies are used to winnow down a large initial library to a size which can be tackled by experiment. In this paper we present an alternative approach, using a multi-output Gaussian process to fuse the information gained from both experimental and computational methods into a single, dynamically evolving design. Common challenges with computational funnels, such as mis-ordering methods, and the inclusion of non-informative steps are avoided by learning the relationships between methods on the fly. We show this approach reduces overall optimisation cost on average by around a factor of three compared to other commonly used approaches, through evaluation on three challenging materials design problems. Computer science ; Fuse (electrical) ; Process (computing) ; Key (lock) ; Fidelity ; Throughput ; Machine learning ; On the fly ; Factor (programming language) ; Computational model ; Gaussian process ; Artificial intelligence ; Computer engineering ; Gaussian ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Harold U. Escobar-Hernandez | Lisa M. Pérez | Pingfan Hu | Fernando Soto | Maria Papadaki | Hong‐Cai Zhou | Qingsheng Wang
Source: Industrial & Engineering Chemistry Research (article, 2022-04-21)
Field: Chemistry — Inorganic Chemistry

Thermal Stability of Metal–Organic Frameworks (MOFs): Concept, Determination, and Model Prediction Using Computational Chemistry and Machine Learning The indubitable rise of metal–organic framework (MOF) technology has opened the potential for commercialization as alternative materials with a versatile number of applications that range from catalysis to greenhouse gas capture. However, there are several factors that constrain the direct scale-up of MOFs from laboratory to industrial plant given the insufficient knowledge about the overall safety in synthesis processes. This article focuses on the study of MOF thermal stability, from concept to prediction, and the factors that influence such stability. The core of this work is a thermal stability prediction model for MOFs. This model can be applied to existing and new MOF structures, and it will allow for an estimation of the thermal stability temperature range of MOFs. This work contributes to the overall advancement of MOF technology and the efforts for its commercial use at industrial scale, combining both experimental data and computational techniques. Commercialization ; Metal-organic framework ; Process engineering ; Stability (learning theory) ; Work (physics) ; Scale (ratio) ; Thermal stability ; Computer science ; Range (aeronautics) ; Thermal ; Biochemical engineering ; Nanotechnology ; Materials science ; Mechanical engineering ; Chemistry ; Machine learning ; Engineering ; Thermodynamics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Thermal and Kinetic Analysis
Authors: Ruocheng Han | Rangsiman Ketkaew | Sandra Luber
Source: The Journal of Physical Chemistry A (review, 2022-02-08)
Field: Materials Science — Materials Chemistry

A Concise Review on Recent Developments of Machine Learning for the Prediction of Vibrational Spectra Machine learning has become more and more popular in computational chemistry, as well as in the important field of spectroscopy. In this concise review, we walk the reader through a short summary of machine learning algorithms and a comprehensive discussion on the connection between machine learning methods and vibrational spectroscopy, particularly for the case of infrared and Raman spectroscopy. We also briefly discuss state-of-the-art molecular representations which serve as meaningful inputs for machine learning to predict vibrational spectra. In addition, this review provides an overview of the transferability and best practices of machine learning in the prediction of vibrational spectra as well as possible future research directions. Transferability ; Machine learning ; Artificial intelligence ; Computer science ; Infrared spectroscopy ; Field (mathematics) ; Raman spectroscopy ; Spectroscopy ; Chemistry ; Physics ; Mathematics ; Optics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Lorenzo Gigli | Max Veit | Michele Kotiuga | Giovanni Pizzi | Nicola Marzari | Michele Ceriotti
Source: npj Computational Materials (article, 2022-09-29)
Field: Materials Science — Materials Chemistry

Thermodynamics and dielectric response of BaTiO3 by data-driven modeling Abstract Modeling ferroelectric materials from first principles is one of the successes of density-functional theory and the driver of much development effort, requiring an accurate description of the electronic processes and the thermodynamic equilibrium that drive the spontaneous symmetry breaking and the emergence of macroscopic polarization. We demonstrate the development and application of an integrated machine learning model that describes on the same footing structural, energetic, and functional properties of barium titanate (BaTiO 3 ), a prototypical ferroelectric. The model uses ab initio calculations as a reference and achieves accurate yet inexpensive predictions of energy and polarization on time and length scales that are not accessible to direct ab initio modeling. These predictions allow us to assess the microscopic mechanism of the ferroelectric transition. The presence of an order-disorder transition for the Ti off-centered states is the main driver of the ferroelectric transition, even though the coupling between symmetry breaking and cell distortions determines the presence of intermediate, partly-ordered phases. Moreover, we thoroughly probe the static and dynamical behavior of BaTiO 3 across its phase diagram without the need to introduce a coarse-grained description of the ferroelectric transition. Finally, we apply the polarization model to calculate the dielectric response properties of the material in a full ab initio manner, again reproducing the correct qualitative experimental behavior. Ferroelectricity ; Ab initio ; Dielectric ; Polarization (electrochemistry) ; Phase transition ; Barium titanate ; Phase diagram ; Condensed matter physics ; Materials science ; Symmetry breaking ; Density functional theory ; Statistical physics ; Ab initio quantum chemistry methods ; Physics ; Phase (matter) ; Chemistry ; Quantum mechanics ; Physical chemistry ; Optoelectronics ; Molecule Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices ; Electronic and Structural Properties of Oxides
Authors: Binling Chen | Zhuxian Yang | Quanli Jia | Richard Ball | Yanqiu Zhu | Yongde Xia
Source: Materials Science and Engineering R Reports (article, 2022-12-22)
Field: Chemistry — Inorganic Chemistry

Emerging applications of metal-organic frameworks and derivatives in solar cells: Recent advances and challenges The utilization of naturally existed energy is an indispensable solution to realise carbon neutral society in the future. Solar energy in the Sun is a tremendously abundant renewable and green energy resource which can provide more than enough energy to meet the whole world’s demand. The biggest challenge is to develop cost-effective and durable materials to achieve efficient transformation of solar energy to electricity. As the most promising energy harvesting devices that based on the photovoltaic effect, solar cells can effectively transfer sunlight into electricity and the technologies have expanded rapidly over recent decades. Materials with optimal properties are the key to achieving efficient solar energy-driven performance for a variety of solar cells. Metal-organic frameworks (MOFs) and their derivatives, featured a great symbiosis of morphological, physicochemical and semiconducting properties that are difficult to achieve through the conventional materials, were intensively explored for energy storage and conversion applications in the past decade due to their intrinsic porous structures, tremendous inorganic-organic nature and fantastic diversity in functionalities. MOFs and derivatives based photovoltaic devices, especially solar cells, have demonstrated exceptional potential to enhance solar-to-electricity conversion performance with increased stability. In this review, the state-of-the-art progress on the applications of MOFs and their derivatives in a diverse range of solar cell devices including dye-sensitized solar cells, perovskite solar cells and organic solar cells is systematically documented. Important aspects on material compositions and structures, material synthesis strategies, device processing techniques, and photovoltaic performances of MOFs-based and MOF-derived materials used in solar cells are discussed and analyzed. The utilizations of MOFs and their derivatives as electrodes, photoactive materials, charge carriers and additives in different solar cells are highlighted. In addition, current challenges and perspectives on further development of MOF based materials for solar cell applications are also discussed. Metal-organic framework ; Nanotechnology ; Organic solar cell ; Materials science ; Astrobiology ; Environmental science ; Data science ; Computer science ; Engineering ; Photovoltaic system ; Chemistry ; Physics ; Electrical engineering Metal-Organic Frameworks: Synthesis and Applications ; Conducting polymers and applications ; Perovskite Materials and Applications
Authors: Andrew Tarzia | Kim E. Jelfs
Source: Chemical Communications (review, 2022-01-01)
Field: Chemistry — Organic Chemistry

Unlocking the computational design of metal–organic cages Metal-organic cages are macrocyclic structures that can possess an intrinsic void that can hold molecules for encapsulation, adsorption, sensing, and catalysis applications. As metal-organic cages may be comprised from nearly any combination of organic and metal-containing components, cages can form with diverse shapes and sizes, allowing for tuning toward targeted properties. Therefore, their near-infinite design space is almost impossible to explore through experimentation alone and computational design can play a crucial role in exploring new systems. Although high-throughput computational design and screening workflows have long been known as powerful tools in drug and materials discovery, their application in exploring metal-organic cages is more recent. We show examples of structure prediction and host-guest/catalytic property evaluation of metal-organic cages. These examples are facilitated by advances in methods that handle metal-containing systems with improved accuracy and are the beginning of the development of automated cage design workflows. We finally outline a scope for how high-throughput computational methods can assist and drive experimental decisions as the field pushes toward functional and complex metal-organic cages. In particular, we highlight the importance of considering realistic, flexible systems. Metal-organic framework ; Metal ; Encapsulation (networking) ; Nanotechnology ; Catalysis ; Design elements and principles ; Materials science ; Computer science ; Chemistry ; Organic chemistry ; Metallurgy ; Adsorption Supramolecular Chemistry and Complexes ; Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection
Authors: Lukáš Ohnoutek | Ji‐Young Kim | Jun Lu | Ben J. Olohan | Dora M. Răsădean | G. Dan Pantoş | Nicholas A. Kotov | Ventsislav K. Valev
Source: Nature Photonics (article, 2022-01-13)
Field: Chemistry — Spectroscopy

Third-harmonic Mie scattering from semiconductor nanohelices Mie scattering ; Scattering ; Semiconductor ; Materials science ; Photonics ; Harmonic ; Second-harmonic generation ; Optics ; High harmonic generation ; Nonlinear optics ; Chirality (physics) ; Light scattering ; Optoelectronics ; Physics ; Laser ; Quantum mechanics Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Yael Gutiérrez | Anna P. Ovvyan | Gonzalo Santos | Dilson Juan | Saúl A. Rosales | Javier Junquera | Pablo García‐Fernández | Stefano Dicorato | Maria M. Giangregorio | Elena Dilonardo | Fabio Palumbo | M. Modreanu | Josef Resl | Ольга Анатоліївна Іщенко | G. Garry | Tigers Jonuzi | Marin Georghe | C. Cobianu | Kurt Hingerl | Christoph Cobet | Fernando Moreno | Wolfram H. P. Pernice | María Losurdo
Source: iScience (article, 2022-05-10)
Field: Materials Science — Materials Chemistry

Interlaboratory study on Sb2S3 interplay between structure, dielectric function, and amorphous-to-crystalline phase change for photonics Antimony sulfide, Sb<sub>2</sub>S<sub>3</sub>, is interesting as the phase-change material for applications requiring high transmission from the visible to telecom wavelengths, with its band gap tunable from 2.2 to 1.6 eV, depending on the amorphous and crystalline phase. Here we present results from an interlaboratory study on the interplay between the structural change and resulting optical contrast during the amorphous-to-crystalline transformation triggered both thermally and optically. By statistical analysis of Raman and ellipsometric spectroscopic data, we have identified two regimes of crystallization, namely 250°C ≤ <i>T</i> < 300°C, resulting in <i>Type-I</i> spherulitic crystallization yielding an optical contrast Δ<i>n</i> ∼ 0.4, and 300 ≤ <i>T</i> < 350°C, yielding <i>Type-II</i> crystallization bended spherulitic structure with different dielectric function and optical contrast Δ<i>n</i> ∼ 0.2 below 1.5 eV. Based on our findings, applications of on-chip reconfigurable nanophotonic phase modulators and of a reconfigurable high-refractive-index core/phase-change shell nanoantenna are designed and proposed. Photonics ; Amorphous solid ; Dielectric function ; Phase change ; Dielectric ; Materials science ; Engineering physics ; Nanotechnology ; Chemical physics ; Optoelectronics ; Chemistry ; Physics ; Crystallography Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Nonlinear Optical Materials Studies
Authors: Valentina Maestrello | Pavel Solovyev | Luana Bontempo | Luisa Mannina | Federica Camin
Source: Comprehensive Reviews in Food Science and Food Safety (review, 2022-07-25)
Field: Chemistry — Analytical Chemistry

Nuclear magnetic resonance spectroscopy in extra virgin olive oil authentication Extra virgin olive oil (EVOO) is a high-quality product that has become one of the stars in the food fraud context in recent years. EVOO can encounter different types of fraud, from adulteration with cheaper oils to mislabeling, and for this reason, the assessment of its authenticity and traceability can be challenging. There are several officially recognized analytical methods for its authentication, but they are not able to unambiguously trace the geographical and botanical origin of EVOOs. The application of nuclear magnetic resonance (NMR) spectroscopy to EVOO is reviewed here as a reliable and rapid tool to verify different aspects of its adulteration, such as undeclared blends with cheaper oils and cultivar and geographical origin mislabeling. This technique makes it possible to use both targeted and untargeted approaches and to determine the olive oil metabolomic profile and the quantification of its constituents. Olive oil ; Traceability ; Context (archaeology) ; Authentication (law) ; Food science ; Cultivar ; Computer science ; Chemistry ; Biotechnology ; Environmental science ; Biochemical engineering ; Biology ; Botany ; Engineering ; Computer security Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Identification and Quantification in Food
Authors: D. Josephine Selvarani Ruth | Jungwoo Sohn | K. Dhanalakshmi | Seung‐Bok Choi
Source: Sensors (review, 2022-06-27)
Field: Materials Science — Materials Chemistry

Control Aspects of Shape Memory Alloys in Robotics Applications: A Review over the Last Decade This paper mainly focuses on various types of robots driven or actuated by shape memory alloy (SMA) element in the last decade which has created the potential functionality of SMA in robotics technology, that is classified and discussed. The wide spectrum of increasing use of SMA in the development of robotic systems is due to the increase in the knowledge of handling its functional characteristics such as large actuating force, shape memory effect, and super-elasticity features. These inherent characteristics of SMA can make robotic systems small, flexible, and soft with multi-functions to exhibit different types of moving mechanisms. This article comprehensively investigates three subsections on soft and flexible robots, driving or activating mechanisms, and artificial muscles. Each section provides an insight into literature arranged in chronological order and each piece of literature will be presented with details on its configuration, control, and application. SMA* ; Shape-memory alloy ; Robotics ; Artificial intelligence ; Soft robotics ; Robot ; Computer science ; Control engineering ; Human–computer interaction ; Engineering Shape Memory Alloy Transformations ; Soft Robotics and Applications
Authors: Xue Cai | Zhangzhi Xue | Chunlong Wu | Rui Sun | Liujia Qian | Liang Yue | Weigang Ge | Xiao Yi | Wei Liu | Chen Chen | Huanhuan Gao | Jing Yu | Luang Xu | Yi Zhu | Tiannan Guo
Source: Nature Protocols (review, 2022-08-05)
Field: Chemistry — Spectroscopy

High-throughput proteomic sample preparation using pressure cycling technology Lysis ; Lysis buffer ; Proteomics ; Sample preparation ; Mass spectrometry ; Chromatography ; Digestion (alchemy) ; Chemistry ; Materials science ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: Rico Friedrich | Mahdi Ghorbani‐Asl | Stefano Curtarolo | Arkady V. Krasheninnikov
Source: Nano Letters (article, 2022-01-20)
Field: Materials Science — Materials Chemistry

Data-Driven Quest for Two-Dimensional Non-van der Waals Materials Two-dimensional (2D) materials are frequently associated with the sheets forming bulk layered compounds bonded by van der Waals (vdW) forces. The anisotropy and weak interaction between the sheets have also been the main criteria in the computational search for new 2D systems, predicting ∼2000 exfoliable compounds. However, some representatives of a new type of non-vdW 2D systems, without layered 3D analogues, were recently manufactured. For this novel materials class, data-driven design principles are still missing. Here, we outline a set of 8 binary and 20 ternary candidates by filtering the AFLOW-ICSD database according to structural prototypes. The oxidation state of the surface cations regulates the exfoliation energy with low oxidation numbers leading to weak bonding─a useful descriptor to obtain novel 2D materials also providing clear guidelines for experiments. A vast range of appealing electronic, optical, and magnetic properties make the candidates attractive for various applications and particularly spintronics. van der Waals force ; Spintronics ; Ternary operation ; Materials science ; Binary number ; Anisotropy ; Chemical physics ; Exfoliation joint ; Nanotechnology ; Graphene ; Computer science ; Chemistry ; Condensed matter physics ; Ferromagnetism ; Physics ; Molecule ; Quantum mechanics 2D Materials and Applications ; MXene and MAX Phase Materials ; Graphene research and applications
Authors: Ban Wang | Cristina Ascenzi Pettenuzzo | Jujhar Singh | Gavin E. Mccabe | Logan Clark | Ryan M. Young | Jingzhi Pu | Yongming Deng
Source: ACS Catalysis (article, 2022-08-09)
Field: Chemistry — Organic Chemistry

Photoinduced Site-Selective Functionalization of Aliphatic C–H Bonds by Pyridine <i>N</i> -oxide Based HAT Catalysts Readily available and facilely tunable pyridine N-oxides have been developed as effective photoinduced hydrogen-atom-transfer (HAT) catalysts for site-selective C–H functionalizations of a broad range of C–H substrates, including unactivated alkanes. Pyridine N-oxide radicals, catalytically generated from N-oxides by photoredox catalyzed single-electron oxidation, are the key intermediates that enable an effective HAT process for carbon radical generation to achieve alkylation, amination, azidation, allylation, and cyanation. Additionally, the fine-tuning of reactivity and selectivity of pyridine N-oxides through operationally simple structural modification was investigated and showed promising capability for selective functionalization of unactivated C(sp3)–H bonds. Chemistry ; Catalysis ; Amination ; Surface modification ; Pyridine ; Radical ; Photochemistry ; Cyanation ; Alkylation ; Photoredox catalysis ; Selectivity ; Reactivity (psychology) ; Combinatorial chemistry ; Oxide ; Organic chemistry ; Photocatalysis Radical Photochemical Reactions ; Oxidative Organic Chemistry Reactions ; Catalytic C–H Functionalization Methods