jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Siwar Chibani | François‐Xavier Coudert
Source: APL Materials (article, 2020-08-01)
Field: Materials Science — Materials Chemistry

Machine learning approaches for the prediction of materials properties We give here a brief overview of the use of machine learning (ML) in our field, for chemists and materials scientists with no experience with these techniques. We illustrate the workflow of ML for computational studies of materials, with a specific interest in the prediction of materials properties. We present concisely the fundamental ideas of ML, and for each stage of the workflow, we give examples of the possibilities and questions to be considered in implementing ML-based modeling. Workflow ; Field (mathematics) ; Computer science ; Materials science ; Data science ; Machine learning ; Artificial intelligence ; Management science ; Engineering ; Mathematics ; Database Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Rosa M. Arán‐Ais | Rubén Rizo | Philipp Grosse | Gerardo Algara‐Siller | Kassiogé Dembélé | Milivoj Plodinec | Thomas Lunkenbein | See Wee Chee | Beatriz Roldán Cuenya
Source: Nature Communications (article, 2020-07-13)
Field: Materials Science — Materials Chemistry

Imaging electrochemically synthesized Cu2O cubes and their morphological evolution under conditions relevant to CO2 electroreduction Copper is a widely studied catalyst material for the electrochemical conversion of carbon dioxide to valuable hydrocarbons. In particular, copper-based nanostructures expressing predominantly {100} facets have shown high selectivity toward ethylene formation, a desired reaction product. However, the stability of such tailored nanostructures under reaction conditions remains poorly understood. Here, using liquid cell transmission electron microscopy, we show the formation of cubic copper oxide particles from copper sulfate solutions during direct electrochemical synthesis and their subsequent morphological evolution in a carbon dioxide-saturated 0.1 M potassium bicarbonate solution under a reductive potential. Shape-selected synthesis of copper oxide cubes was achieved through: (1) the addition of chloride ions and (2) alternating the potentials within a narrow window where the deposited non-cubic particles dissolve, but cubic ones do not. Our results indicate that copper oxide cubes change their morphology rapidly under carbon dioxide electroreduction-relevant conditions, leading to an extensive re-structuring of the working electrode surface. Copper ; Electrochemistry ; Inorganic chemistry ; Copper oxide ; Oxide ; Materials science ; Catalysis ; Electrochemical reduction of carbon dioxide ; Chemical engineering ; Transmission electron microscopy ; Nanoparticle ; Electrocatalyst ; Carbon fibers ; Electrode ; Chemistry ; Nanotechnology ; Carbon monoxide ; Organic chemistry ; Metallurgy ; Composite material ; Physical chemistry Copper-based nanomaterials and applications ; CO2 Reduction Techniques and Catalysts ; Electronic and Structural Properties of Oxides
Authors: Yu Han | Bijun Tang | Liang Wang | Hong Bao | Yuhao Lu | Cuntai Guan | Liang Zhang | Mengying Le | Zheng Liu | Minghong Wu
Source: ACS Nano (article, 2020-09-22)
Field: Materials Science — Materials Chemistry

Machine-Learning-Driven Synthesis of Carbon Dots with Enhanced Quantum Yields Knowing the correlation of reaction parameters in the preparation process of carbon dots (CDs) is essential for optimizing the synthesis strategy, exploring exotic properties, and exploiting potential applications. However, the integrated screening experimental data on the synthesis of CDs are huge and noisy. Machine learning (ML) has recently been successfully used for the screening of high-performance materials. Here, we demonstrate how ML-based techniques can offer insight into the successful prediction, optimization, and acceleration of CDs' synthesis process. A regression ML model on hydrothermal-synthesized CDs is established capable of revealing the relationship between various synthesis parameters and experimental outcomes as well as enhancing the process-related properties such as the fluorescent quantum yield (QY). CDs exhibiting a strong green emission with QY up to 39.3% are obtained through the combined ML guidance and experimental verification. The mass of precursors and the volume of alkaline catalysts are identified as the most important features in the synthesis of high-QY CDs by the trained ML model. The CDs are applied as an ultrasensitive fluorescence probe for monitoring the Fe<sup>3+</sup> ion because of their superior optical behaviors. The probe exhibits the linear response to the Fe<sup>3+</sup> ion with a wide concentration range (0-150 μM), and its detection limit is 0.039 μM. Our findings demonstrate the great capability of ML to guide the synthesis of high-quality CDs, accelerating the development of intelligent material. Quantum yield ; Fluorescence ; Quantum dot ; Detection limit ; Materials science ; Yield (engineering) ; Carbon fibers ; Nanotechnology ; Hydrothermal synthesis ; Linear range ; Hydrothermal circulation ; Computer science ; Chemistry ; Chemical engineering ; Physics ; Chromatography Carbon and Quantum Dots Applications ; Nanocluster Synthesis and Applications ; Advanced Nanomaterials in Catalysis
Authors: Maria S. Sokolikova | Cecilia Mattevi
Source: Chemical Society Reviews (review, 2020-01-01)
Field: Materials Science — Materials Chemistry

Direct synthesis of metastable phases of 2D transition metal dichalcogenides The different polymorphic phases of transition metal dichalcogenides (TMDs) have attracted enormous interest in the last decade. The metastable metallic and small band gap phases of group VI TMDs displayed leading performance for electrocatalytic hydrogen evolution, high volumetric capacitance and some of them exhibit large gap quantum spin Hall (QSH) insulating behaviour. Metastable 1T(1T') phases require higher formation energy, as compared to the thermodynamically stable 2H phase, thus in standard chemical vapour deposition and vapour transport processes the materials normally grow in the 2H phases. Only destabilization of their 2H phase via external means, such as charge transfer or high electric field, allows the conversion of the crystal structure into the 1T(1T') phase. Bottom-up synthesis of materials in the 1T(1T') phases in measurable quantities would broaden their prospective applications and practical utilization. There is an emerging evidence that some of these 1T(1T') phases can be directly synthesized via bottom-up vapour- and liquid-phase methods. This review will provide an overview of the synthesis strategies which have been designed to achieve the crystal phase control in TMDs, and the chemical mechanisms that can drive the synthesis of metastable phases. We will provide a critical comparison between growth pathways in vapour- and liquid-phase synthesis techniques. Morphological and chemical characteristics of synthesized materials will be described along with their ability to act as electrocatalysts for the hydrogen evolution reaction from water. Phase stability and reversibility will be discussed and new potential applications will be introduced. This review aims at providing insights into the fundamental understanding of the favourable synthetic conditions for the stabilization of metastable TMD crystals and at stimulating future advancements in the field of large-scale synthesis of materials with crystal phase control. Metastability ; Materials science ; Chemical physics ; Phase (matter) ; Chemical vapor deposition ; Nanotechnology ; Band gap ; Phase transition ; Transition metal ; Crystal (programming language) ; Water splitting ; Chemistry ; Optoelectronics ; Catalysis ; Condensed matter physics ; Physics 2D Materials and Applications ; Perovskite Materials and Applications ; MXene and MAX Phase Materials
Authors: Lihua Chen | Ghanshyam Pilania | Rohit Batra | Tran Doan Huan | Chiho Kim | Christopher Kuenneth | Rampi Ramprasad
Source: Materials Science and Engineering R Reports (article, 2020-12-26)
Field: Materials Science — Materials Chemistry

Polymer informatics: Current status and critical next steps Informatics ; Materials informatics ; Property (philosophy) ; Domain (mathematical analysis) ; Big data ; Productivity Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Yongzheng Cong | Khatereh Motamedchaboki | Santosh A. Misal | Yiran Liang | Amanda J. Guise | Thy Truong | Romain Huguet | Edward D. Plowey | Ying Zhu | Daniel López‐Ferrer | Ryan Kelly
Source: Chemical Science (article, 2020-11-17)
Field: Chemistry — Spectroscopy

Ultrasensitive single-cell proteomics workflow identifies &gt;1000 protein groups per mammalian cell We report on the combination of nanodroplet sample preparation, ultra-low-flow nanoLC, high-field asymmetric ion mobility spectrometry (FAIMS), and the latest-generation Orbitrap Eclipse Tribrid mass spectrometer for greatly improved single-cell proteome profiling. FAIMS effectively filtered out singly charged ions for more effective MS analysis of multiply charged peptides, resulting in an average of 1056 protein groups identified from single HeLa cells without MS1-level feature matching. This is 2.3 times more identifications than without FAIMS and a far greater level of proteome coverage for single mammalian cells than has been previously reported for a label-free study. Differential analysis of single microdissected motor neurons and interneurons from human spinal tissue indicated a similar level of proteome coverage, and the two subpopulations of cells were readily differentiated based on single-cell label-free quantification. Ion-mobility spectrometry ; Proteome ; Proteomics ; Mass spectrometry ; Chemistry ; Cell ; Workflow ; Profiling (computer programming) ; Chromatography ; Instrumentation (computer programming) ; Computational biology ; Computer science ; Biochemistry ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Yichi Zhang | Daniel W. Apley | Wei Chen
Source: Scientific Reports (article, 2020-03-18)
Field: Materials Science — Materials Chemistry

Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables Abstract Although Bayesian Optimization (BO) has been employed for accelerating materials design in computational materials engineering, existing works are restricted to problems with quantitative variables. However, real designs of materials systems involve both qualitative and quantitative design variables representing material compositions, microstructure morphology, and processing conditions. For mixed-variable problems, existing Bayesian Optimization (BO) approaches represent qualitative factors by dummy variables first and then fit a standard Gaussian process (GP) model with numerical variables as the surrogate model. This approach is restrictive theoretically and fails to capture complex correlations between qualitative levels. We present in this paper the integration of a novel latent-variable (LV) approach for mixed-variable GP modeling with the BO framework for materials design. LVGP is a fundamentally different approach that maps qualitative design variables to underlying numerical LV in GP, which has strong physical justification. It provides flexible parameterization and representation of qualitative factors and shows superior modeling accuracy compared to the existing methods. We demonstrate our approach through testing with numerical examples and materials design examples. The chosen materials design examples represent two different scenarios, one on concurrent materials selection and microstructure optimization for optimizing the light absorption of a quasi-random solar cell, and another on combinatorial search of material constitutes for optimal Hybrid Organic-Inorganic Perovskite (HOIP) design. It is found that in all test examples the mapped LVs provide intuitive visualization and substantial insight into the nature and effects of the qualitative factors. Though materials designs are used as examples, the method presented is generic and can be utilized for other mixed variable design optimization problems that involve expensive physics-based simulations. Computer science ; Representation (politics) ; Variable (mathematics) ; Mathematical optimization ; Bayesian probability ; Engineering design process ; Optimal design ; Process (computing) ; Latent variable ; Artificial intelligence ; Machine learning ; Mathematics ; Mechanical engineering ; Engineering Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Optimal Experimental Design Methods
Authors: Hélène Olivier‐Bourbigou | Pierre‐Alain R. Breuil | Lionel Magna | T. Michel | Marta Pastor | Damien Delcroix
Source: Chemical Reviews (review, 2020-07-29)
Field: Chemistry — Organic Chemistry

Nickel Catalyzed Olefin Oligomerization and Dimerization Brought to life more than half a century ago and successfully applied for high-value petrochemical intermediates production, nickel-catalyzed olefin oligomerization is still a very dynamic topic, with many fundamental questions to address and industrial challenges to overcome. The unique and versatile reactivity of nickel enables the oligomerization of ethylene, propylene, and butenes into a wide range of oligomers that are highly sought-after in numerous fields to be controlled. Interestingly, both homogeneous and heterogeneous nickel catalysts have been scrutinized and employed to do this. This rare specificity encouraged us to interlink them in this review so as to open up opportunities for further catalyst development and innovation. An in-depth understanding of the reaction mechanisms in play is essential to being able to fine-tune the selectivity and achieve efficiency in the rational design of novel catalytic systems. This review thus provides a complete overview of the subject, compiling the main fundamental/industrial milestones and remaining challenges facing homogeneous/heterogeneous approaches as well as emerging catalytic concepts, with a focus on the last 10 years. Catalysis ; Homogeneous ; Chemistry ; Petrochemical ; Olefin fiber ; Nickel ; Nanotechnology ; Combinatorial chemistry ; Homogeneous catalysis ; Biochemical engineering ; Organic chemistry ; Materials science ; Engineering ; Physics Organometallic Complex Synthesis and Catalysis ; Asymmetric Hydrogenation and Catalysis ; Catalytic Alkyne Reactions
Authors: Yabo Dan | Yong Zhao | Xiang Li | Shaobo Li | Ming Hu | Jianjun Hu
Source: npj Computational Materials (article, 2020-06-26)
Field: Materials Science — Materials Chemistry

Generative adversarial networks (GAN) based efficient sampling of chemical composition space for inverse design of inorganic materials Abstract A major challenge in materials design is how to efficiently search the vast chemical design space to find the materials with desired properties. One effective strategy is to develop sampling algorithms that can exploit both explicit chemical knowledge and implicit composition rules embodied in the large materials database. Here, we propose a generative machine learning model (MatGAN) based on a generative adversarial network (GAN) for efficient generation of new hypothetical inorganic materials. Trained with materials from the ICSD database, our GAN model can generate hypothetical materials not existing in the training dataset, reaching a novelty of 92.53% when generating 2 million samples. The percentage of chemically valid (charge-neutral and electronegativity-balanced) samples out of all generated ones reaches 84.5% when generated by our GAN trained with such samples screened from ICSD, even though no such chemical rules are explicitly enforced in our GAN model, indicating its capability to learn implicit chemical composition rules to form compounds. Our algorithm is expected to be used to greatly expand the range of the design space for inverse design and large-scale computational screening of inorganic materials. Chemical space ; Inverse ; Range (aeronautics) ; Sampling (signal processing) ; Space (punctuation) ; Generative grammar ; Adversarial system ; Novelty Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Martin Fitzgerald | Michael Heinrich | Anthony Booker
Source: Frontiers in Pharmacology (review, 2020-01-09)
Field: Chemistry — Analytical Chemistry

Medicinal Plant Analysis: A Historical and Regional Discussion of Emergent Complex Techniques The analysis of medicinal plants has had a long history, and especially with regard to assessing a plant's quality. The first techniques were organoleptic using the physical senses of taste, smell, and appearance. Then gradually these led on to more advanced instrumental techniques. Though different countries have their own traditional medicines China currently leads the way in terms of the number of publications focused on medicinal plant analysis and number of inclusions in their Pharmacopoeia. The monographs contained within these publications give directions on the type of analysis that should be performed, and for manufacturers, this typically means that they need access to more and more advanced instrumentation. We have seen developments in many areas of analytical analysis and particularly the development of chromatographic and spectroscopic methods and the hyphenation of these techniques. The ability to process data using multivariate analysis software has opened the door to metabolomics giving us greater capacity to understand the many variations of chemical compounds occurring within medicinal plants, allowing us to have greater certainty of not only the quality of the plants and medicines but also of their suitability for clinical research. Refinements in technology have resulted in the ability to analyze and categorize plants effectively and be able to detect contaminants and adulterants occurring at very low levels. However, advances in technology cannot provide us with all the answers we need in order to deliver high-quality herbal medicines and the more traditional techniques of assessing quality remain as important today. Data science ; Traditional medicine ; Computer science ; Medicine Chromatography in Natural Products ; Metabolomics and Mass Spectrometry Studies ; Phytochemistry and Biological Activities
Authors: Libei Huang | Jianjun Su | Yun Mi Song | Ruquan Ye
Source: Nano-Micro Letters (review, 2020-08-03)
Field: Materials Science — Materials Chemistry

Laser-Induced Graphene: En Route to Smart Sensing The discovery of laser-induced graphene (LIG) from polymers in 2014 has aroused much attention in recent years. A broad range of applications, including batteries, catalysis, sterilization, and separation, have been explored. The advantages of LIG technology over conventional graphene synthesis methods are conspicuous, which include designable patterning, environmental friendliness, tunable compositions, and controllable morphologies. In addition, LIG possesses high porosity, great flexibility, and mechanical robustness, and excellent electric and thermal conductivity. The patternable and printable manufacturing process and the advantageous properties of LIG illuminate a new pathway for developing miniaturized graphene devices. Its use in sensing applications has grown swiftly from a single detection component to an integrated smart detection system. In this minireview, we start with the introduction of synthetic efforts related to the fabrication of LIG sensors. Then, we highlight the achievement of LIG sensors for the detection of a diversity of stimuli with a focus on the design principle and working mechanism. Future development of the techniques toward in situ and smart detection of multiple stimuli in widespread applications will be discussed. Graphene ; Nanotechnology ; Materials science ; Flexibility (engineering) ; Fabrication ; Robustness (evolution) ; Chemistry Graphene research and applications ; Gas Sensing Nanomaterials and Sensors ; Graphene and Nanomaterials Applications
Authors: Yihui Yuan | Tingting Liu | Juanxiu Xiao | Qiuhan Yu | Lijuan Feng | Biye Niu | Shi‐wei Feng | Jiacheng Zhang | Ning Wang
Source: Nature Communications (article, 2020-11-11)
Field: Chemistry — Inorganic Chemistry

DNA nano-pocket for ultra-selective uranyl extraction from seawater Extraction of uranium from seawater is critical for the sustainable development of nuclear energy. However, the currently available uranium adsorbents are hampered by co-existing metal ion interference. DNAzymes exhibit high selectivity to specific metal ions, yet there is no DNA-based adsorbent for extraction of soluble minerals from seawater. Herein, the uranyl-binding DNA strand from the DNAzyme is polymerized into DNA-based uranium extraction hydrogel (DNA-UEH) that exhibits a high uranium adsorption capacity of 6.06 mg g<sup>-1</sup> with 18.95 times high selectivity for uranium against vanadium in natural seawater. The uranium is found to be bound by oxygen atoms from the phosphate groups and the carbonyl groups, which formed the specific nano-pocket that empowers DNA-UEH with high selectivity and high binding affinity. This study both provides an adsorbent for uranium extraction from seawater and broadens the application of DNA for being used in recovery of high-value soluble minerals from seawater. Uranyl ; Uranium ; Seawater ; Adsorption ; Chemistry ; DNA ; Extraction (chemistry) ; Selectivity ; Metal ions in aqueous solution ; Inorganic chemistry ; Vanadium ; Metal ; Nuclear chemistry ; Chromatography ; Materials science ; Biochemistry ; Organic chemistry ; Metallurgy ; Biology Radioactive element chemistry and processing ; Chemical Synthesis and Characterization ; Analytical chemistry methods development
Authors: Kathleen Maleski | Christopher E. Shuck | Aaron T. Fafarman | Yury Gogotsi
Source: Advanced Optical Materials (article, 2020-12-09)
Field: Materials Science — Materials Chemistry

The Broad Chromatic Range of Two‐Dimensional Transition Metal Carbides Abstract Transition metal carbides and nitrides (MXenes) are a relatively new class of 2D materials, which include metallic and semiconducting examples. The chemical compositions of the vast MXene family span a broad range of the periodic table, yet the optical spectra of only a handful of MXene compositions have been reported, and in only one example are the spectroscopic features assigned. The optical properties of nine carbide MXenes, representing a systematic variation of chemical composition and atomic structure are presented here. The observed optical phenomena span the UV to IR and include interband transitions and plasma excitations. The spectral features involving excitation of the plasma provide an optical readout of the composition‐dependent carrier concentration, revealing even subtle changes due to modification of the surface chemistry. The high carrier concentration found in many MXenes differentiates them from other known 2D materials and the authors present evidence in favor of the hypothesis that MXenes host optically active plasmon resonances that naturally span the UV to near‐IR, as a function of composition. This study introduces optical properties of novel MXenes, benefits the interpretation of 2D materials spectroscopy, and suggests the tremendous potential for this class of optoelectronic building blocks. MXenes ; Materials science ; Carbide ; Photonics ; Chemical physics ; Characterization (materials science) ; Nitride ; Plasmon ; Spectroscopy ; Transition metal ; Spectral line ; Nanotechnology ; Optoelectronics ; Chemistry ; Physics ; Layer (electronics) ; Composite material MXene and MAX Phase Materials ; 2D Materials and Applications ; Graphene research and applications