jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Philippe Schwaller | Benjamin Hoover | Jean‐Louis Reymond | Hendrik Strobelt | Teodoro Laino
Source: Science Advances (article, 2021-04-07)
Field: Materials Science — Materials Chemistry

Extraction of organic chemistry grammar from unsupervised learning of chemical reactions Humans use different domain languages to represent, explore, and communicate scientific concepts. During the last few hundred years, chemists compiled the language of chemical synthesis inferring a series of "reaction rules" from knowing how atoms rearrange during a chemical transformation, a process called atom-mapping. Atom-mapping is a laborious experimental task and, when tackled with computational methods, requires continuous annotation of chemical reactions and the extension of logically consistent directives. Here, we demonstrate that Transformer Neural Networks learn atom-mapping information between products and reactants without supervision or human labeling. Using the Transformer attention weights, we build a chemically agnostic, attention-guided reaction mapper and extract coherent chemical grammar from unannotated sets of reactions. Our method shows remarkable performance in terms of accuracy and speed, even for strongly imbalanced and chemically complex reactions with nontrivial atom-mapping. It provides the missing link between data-driven and rule-based approaches for numerous chemical reaction tasks. Extraction (chemistry) ; Computer science ; Grammar ; Chemistry ; Artificial intelligence ; Organic chemistry ; Linguistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Topic Modeling
Authors: Joshua Meyers | Benedek Fabian | Nathan Brown
Source: Drug Discovery Today (review, 2021-06-01)
Field: Materials Science — Materials Chemistry

De novo molecular design and generative models Molecular design strategies are integral to therapeutic progress in drug discovery. Computational approaches for de novo molecular design have been developed over the past three decades and, recently, thanks in part to advances in machine learning (ML) and artificial intelligence (AI), the drug discovery field has gained practical experience. Here, we review these learnings and present de novo approaches according to the coarseness of their molecular representation: that is, whether molecular design is modeled on an atom-based, fragment-based, or reaction-based paradigm. Furthermore, we emphasize the value of strong benchmarks, describe the main challenges to using these methods in practice, and provide a viewpoint on further opportunities for exploration and challenges to be tackled in the upcoming years. Drug discovery ; Generative grammar ; Computer science ; Artificial intelligence ; Representation (politics) ; Field (mathematics) ; Computational biology ; Data science ; Bioinformatics ; Biology ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Gyorgy Scrinis | Kristen Lyons
Source: Griffith Research Online (Griffith University, Queensland, Australia) (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

The Emerging Nano-Corporate Paradigm: Nanotechnology and the Transformation of Nature, Food and Agri-Food Systems Nanotechnology represents the latest in a line of technological innovations set to transform agriculture and food production. From the farm to the table, nanotechnology research and development is being applied across the entire agri-food system. In this paper we outline the contours of what we call the nano-corporate food paradigm. We examine research and commercial applications of nanotechnology in the agriculture and food sectors, and showcase the ways in which the nano-corporate food paradigm is being applied, and at the same time may shape and transform agri-food systems. Food systems ; Agriculture ; Food engineering ; Nanotechnology ; Food processing ; Food industry ; Applications of nanotechnology ; Paradigm shift ; Business ; Engineering ; Materials science ; Food security ; Physics ; Chemistry ; Geography ; Food science Nanoparticles: synthesis and applications
Authors: Seppe Terryn | Jakob Langenbach | Ellen Roels | Joost Brancart | Camille Bakkali-Hassani | Quentin-Arthur Poutrel | Antonia Georgopoulou | Thomas George Thuruthel | Ali Safaei | Pasquale Ferrentino | Tutu Sebastian | Sophie Norvez | Fumiya Iida | Anton W. Bosman | François Tournilhac | Frank Clemens | Guy Van Assche | Bram Vanderborght
Source: Materials Today (review, 2021-03-20)
Field: Materials Science — Polymers and Plastics

A review on self-healing polymers for soft robotics Soft robotics ; Robotics ; Soft materials ; Artificial intelligence ; Self-healing ; Computer science ; Construct (python library) ; Adaptation (eye) ; Robot ; Polymer ; Nanotechnology ; Materials science ; Medicine ; Psychology ; Composite material Polymer composites and self-healing ; Advanced Sensor and Energy Harvesting Materials ; Electrospun Nanofibers in Biomedical Applications
Authors: Victor Fung | Jiaxin Zhang | Eric Juarez | Bobby G. Sumpter
Source: npj Computational Materials (article, 2021-06-03)
Field: Materials Science — Materials Chemistry

Benchmarking graph neural networks for materials chemistry Abstract Graph neural networks (GNNs) have received intense interest as a rapidly expanding class of machine learning models remarkably well-suited for materials applications. To date, a number of successful GNNs have been proposed and demonstrated for systems ranging from crystal stability to electronic property prediction and to surface chemistry and heterogeneous catalysis. However, a consistent benchmark of these models remains lacking, hindering the development and consistent evaluation of new models in the materials field. Here, we present a workflow and testing platform, MatDeepLearn, for quickly and reproducibly assessing and comparing GNNs and other machine learning models. We use this platform to optimize and evaluate a selection of top performing GNNs on several representative datasets in computational materials chemistry. From our investigations we note the importance of hyperparameter selection and find roughly similar performances for the top models once optimized. We identify several strengths in GNNs over conventional models in cases with compositionally diverse datasets and in its overall flexibility with respect to inputs, due to learned rather than defined representations. Meanwhile several weaknesses of GNNs are also observed including high data requirements, and suggestions for further improvement for applications in materials chemistry are discussed. Benchmarking ; Computer science ; Workflow ; Hyperparameter ; Machine learning ; Artificial intelligence ; Benchmark (surveying) ; Graph ; Flexibility (engineering) ; Strengths and weaknesses ; Artificial neural network ; Data science ; Theoretical computer science ; Database Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Mengjie Liu | Alon Grinberg Dana | Matthew S. Johnson | Mark J. Goldman | Agnes Jocher | A. Mark Payne | Colin A. Grambow | Kehang Han | Nathan W. Yee | Emily Mazeau | Katrín Blöndal | Richard H. West | C. Franklin Goldsmith | William H. Green
Source: Journal of Chemical Information and Modeling (article, 2021-05-28)
Field: Materials Science — Materials Chemistry

Reaction Mechanism Generator v3.0: Advances in Automatic Mechanism Generation In chemical kinetics research, kinetic models containing hundreds of species and tens of thousands of elementary reactions are commonly used to understand and predict the behavior of reactive chemical systems. Reaction Mechanism Generator (RMG) is a software suite developed to automatically generate such models by incorporating and extrapolating from a database of known thermochemical and kinetic parameters. Here, we present the recent version 3 release of RMG and highlight improvements since the previously published description of RMG v1.0. Most notably, RMG can now generate heterogeneous catalysis models in addition to the previously available gas- and liquid-phase capabilities. For model analysis, new methods for local and global uncertainty analysis have been implemented to supplement first-order sensitivity analysis. The RMG database of thermochemical and kinetic parameters has been significantly expanded to cover more types of chemistry. The present release includes parallelization for faster model generation and a new molecule isomorphism approach to improve computational performance. RMG has also been updated to use Python 3, ensuring compatibility with the latest cheminformatics and machine learning packages. Overall, RMG v3.0 includes many changes which improve the accuracy of the generated chemical mechanisms and allow for exploration of a wider range of chemical systems. Cheminformatics ; Python (programming language) ; Computer science ; Suite ; Generator (circuit theory) ; Computational science ; Biological system ; Chemistry ; Computational chemistry ; Thermodynamics ; Programming language ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Process Optimization and Integration
Authors: Junzong Feng | Bao‐Lian Su | Hesheng Xia | Shanyu Zhao | Chao Gao | Lukai Wang | Osarenkhoe Ogbeide | Jian Feng | Tawfique Hasan
Source: Chemical Society Reviews (review, 2021-01-01)
Field: Chemistry — Spectroscopy

Printed aerogels: chemistry, processing, and applications As an extraordinarily lightweight and porous functional nanomaterial family, aerogels have attracted considerable interest in academia and industry in recent decades. Despite the application scopes, the modest mechanical durability of aerogels makes their processing and operation challenging, in particular, for situations demanding intricate physical structures. "Bottom-up" additive manufacturing technology has the potential to address this drawback. Indeed, since the first report of 3D printed aerogels in 2015, a new interdisciplinary research area combining aerogel and printing technology has emerged to push the boundaries of structure and performance, further broadening their application scope. This review summarizes the state-of-the-art of printed aerogels and presents a comprehensive view of their developments in the past 5 years, and highlights the key near- and mid-term challenges. Aerogel ; Nanotechnology ; Materials science ; Computer science Aerogels and thermal insulation ; Advanced Sensor and Energy Harvesting Materials ; Surface Modification and Superhydrophobicity
Authors: Baoyong Zhu | Jianwei Cao | Soumya Mukherjee | Tony Pham | Tao Zhang | Teng Wang | Xue Jiang | Katherine A. Forrest | Michael J. Zaworotko | Kai‐Jie Chen
Source: Journal of the American Chemical Society (article, 2021-01-13)
Field: Chemistry — Inorganic Chemistry

Pore Engineering for One-Step Ethylene Purification from a Three-Component Hydrocarbon Mixture Ethylene production from C2 hydrocarbon mixtures through one separation step is desirable but challenging because of the similar size and physical properties of acetylene, ethylene, and ethane. Herein, we report three new isostructural porous coordination networks (<b>NPU-1</b>, <b>NPU-2</b>, <b>NPU-3</b>; NPU represents Northwestern Polytechnical University) that are sustained by 9-connected nodes based upon a hexanuclear metal cluster of composition [Mn<sub>6</sub>(μ<sub>3</sub>-O)<sub>2</sub>(CH<sub>3</sub>COO)<sub>3</sub>]<sup>6+</sup>. <b>NPU-1/2/3</b> exhibit a dual cage structure that was systematically fine-tuned in terms of cage size to realize selective adsorption of C<sub>2</sub>H<sub>2</sub> and C<sub>2</sub>H<sub>6</sub> over C<sub>2</sub>H<sub>4</sub>. Dynamic breakthrough experiments demonstrated that <b>NPU-1</b> produces ethylene in >99.9% purity from a three-component gas mixture (1:1:1 C<sub>2</sub>H<sub>2</sub>/C<sub>2</sub>H<sub>4</sub>/C<sub>2</sub>H<sub>6</sub>). Molecular modeling studies revealed that the dual adsorption preference for C<sub>2</sub>H<sub>2</sub> and C<sub>2</sub>H<sub>6</sub> over C<sub>2</sub>H<sub>4</sub> originates from (a) strong hydrogen-bonding interactions between electronegative carboxylate O atoms and C<sub>2</sub>H<sub>2</sub> molecules in one cage and (b) multiple non-covalent interactions between the organic linkers of the host network and C<sub>2</sub>H<sub>6</sub> molecules in the second cage. Chemistry ; Isostructural ; Acetylene ; Hydrocarbon ; Adsorption ; Molecule ; Component (thermodynamics) ; Ethylene ; Chemical engineering ; Carboxylate ; Polymer chemistry ; Organic chemistry ; Thermodynamics Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Supramolecular Chemistry and Complexes
Authors: Svitlana Rozanova | Katalin Barkovits | Miroslav Nikolov | Carla Schmidt | Henning Urlaub | Katrin Marcus
Source: Methods in molecular biology (review, 2021-01-01)
Field: Chemistry — Spectroscopy

Quantitative Mass Spectrometry-Based Proteomics: An Overview Abstract In recent decades, mass spectrometry has moved more than ever before into the front line of protein-centered research. After being established at the qualitative level, the more challenging question of quantification of proteins and peptides using mass spectrometry has become a focus for further development. In this chapter, we discuss and review actual strategies and problems of the methods for the quantitative analysis of peptides, proteins, and finally proteomes by mass spectrometry. The common themes, the differences, and the potential pitfalls of the main approaches are presented in order to provide a survey of the emerging field of quantitative, mass spectrometry-based proteomics. Mass spectrometry ; Proteomics ; Chemistry ; Chromatography ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Maximilian Ruf | Noel H. Wan | Hyeongrak Choi | Dirk Englund | Ronald Hanson
Source: Journal of Applied Physics (article, 2021-08-16)
Field: Materials Science — Materials Chemistry

Quantum networks based on color centers in diamond With the ability to transfer and process quantum information, large-scale quantum networks will enable a suite of fundamentally new applications, from quantum communications to distributed sensing, metrology, and computing. This Perspective reviews requirements for quantum network nodes and color centers in diamond as suitable node candidates. We give a brief overview of state-of-the-art quantum network experiments employing color centers in diamond and discuss future research directions, focusing, in particular, on the control and coherence of qubits that distribute and store entangled states, and on efficient spin–photon interfaces. We discuss a route toward large-scale integrated devices combining color centers in diamond with other photonic materials and give an outlook toward realistic future quantum network protocol implementations and applications. Quantum network ; Diamond ; Qubit ; Quantum information science ; Quantum ; Coherence (philosophical gambling strategy) ; Quantum technology ; Quantum information Diamond and Carbon-based Materials Research ; Quantum Information and Cryptography ; Quantum optics and atomic interactions
Authors: Patrizia Bocchetta | Liang‐Yu Chen | Juliana Dias Corpa Tardelli | Andréa Cândido dos Reis | Facundo Almeraya-Calderón | Paola Leo
Source: Coatings (article, 2021-04-21)
Field: Materials Science — Materials Chemistry

Passive Layers and Corrosion Resistance of Biomedical Ti-6Al-4V and β-Ti Alloys The high specific strength, good corrosion resistance, and great biocompatibility make titanium and its alloys the ideal materials for biomedical metallic implants. Ti-6Al-4V alloy is the most employed in practical biomedical applications because of the excellent combination of strength, fracture toughness, and corrosion resistance. However, recent studies have demonstrated some limits in biocompatibility due to the presence of toxic Al and V. Consequently, scientific literature has reported novel biomedical β-Ti alloys containing biocompatible β-stabilizers (such as Mo, Ta, and Zr) studying the possibility to obtain similar performances to the Ti-6Al-4V alloys. The aim of this review is to highlight the corrosion resistance of the passive layers on biomedical Ti-6Al-4V and β-type Ti alloys in the human body environment by reviewing relevant literature research contributions. The discussion is focused on all those factors that influence the performance of the passive layer at the surface of the alloy subjected to electrochemical corrosion, among which the alloy composition, the method selected to grow the oxide coating, and the physicochemical conditions of the body fluid are the most significant. Biocompatibility ; Corrosion ; Materials science ; Metallurgy ; Alloy ; Simulated body fluid ; Titanium alloy ; Coating ; Titanium ; Toughness ; Layer (electronics) ; Oxide ; Crevice corrosion ; Composite material Titanium Alloys Microstructure and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Orthopaedic implants and arthroplasty
Authors: Yixi Zhuang | Dunrong Chen | Wenjing Chen | Wenxing Zhang | Xin Su | Renren Deng | Zhongfu An | Hongmin Chen | Rong‐Jun Xie
Source: Light Science & Applications (article, 2021-06-23)
Field: Materials Science — Materials Chemistry

X-ray-charged bright persistent luminescence in NaYF4:Ln3+@NaYF4 nanoparticles for multidimensional optical information storage NaYF<sub>4</sub>:Ln<sup>3+</sup>, due to its outstanding upconversion characteristics, has become one of the most important luminescent nanomaterials in biological imaging, optical information storage, and anticounterfeiting applications. However, the large specific surface area of NaYF<sub>4</sub>:Ln<sup>3+</sup> nanoparticles generally leads to serious nonradiative transitions, which may greatly hinder the discovery of new optical functionality with promising applications. In this paper, we report that monodispersed nanoscale NaYF<sub>4</sub>:Ln<sup>3+</sup>, unexpectedly, can also be an excellent persistent luminescent (PersL) material. The NaYF<sub>4</sub>:Ln<sup>3+</sup> nanoparticles with surface-passivated core-shell structures exhibit intense X-ray-charged PersL and narrow-band emissions tunable from 480 to 1060 nm. A mechanism for PersL in NaYF<sub>4</sub>:Ln<sup>3+</sup> is proposed by means of thermoluminescence measurements and host-referred binding energy (HRBE) scheme, which suggests that some lanthanide ions (such as Tb) may also act as effective electron traps to achieve intense PersL. The uniform and spherical NaYF<sub>4</sub>:Ln<sup>3+</sup> nanoparticles are dispersible in solvents, thus enabling many applications that are not accessible for traditional PersL phosphors. A new 3-dimensional (2 dimensions of planar space and 1 dimension of wavelength) optical information-storage application is demonstrated by inkjet-printing multicolor PersL nanoparticles. The multicolor persistent luminescence, as an emerging and promising emissive mode in NaYF<sub>4</sub>:Ln<sup>3+</sup>, will provide great opportunities for nanomaterials to be applied to a wider range of fields. Nanomaterials ; Materials science ; Luminescence ; Nanoparticle ; Nanotechnology ; Phosphor ; Information storage ; Lanthanide ; Optoelectronics ; Ion ; Chemistry ; Computer science Luminescence Properties of Advanced Materials ; Luminescence and Fluorescent Materials ; Lanthanide and Transition Metal Complexes
Authors: Zachary J. Baum | Xiang Yu | Philippe Y. Ayala | Yanan Zhao | Steven P. Watkins | Qiongqiong Angela Zhou
Source: Journal of Chemical Information and Modeling (review, 2021-07-15)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Chemistry: Current Trends and Future Directions The application of artificial intelligence (AI) to chemistry has grown tremendously in recent years. In this Review, we studied the growth and distribution of AI-related chemistry publications in the last two decades using the CAS Content Collection. The volume of both journal and patent publications have increased dramatically, especially since 2015. Study of the distribution of publications over various chemistry research areas revealed that analytical chemistry and biochemistry are integrating AI to the greatest extent and with the highest growth rates. We also investigated trends in interdisciplinary research and identified frequently occurring combinations of research areas in publications. Furthermore, topic analyses were conducted for journal and patent publications to illustrate emerging associations of AI with certain chemistry research topics. Notable publications in various chemistry disciplines were then evaluated and presented to highlight emerging use cases. Finally, the occurrence of different classes of substances and their roles in AI-related chemistry research were quantified, further detailing the popularity of AI adoption in the life sciences and analytical chemistry. In summary, this Review offers a broad overview of how AI has progressed in various fields of chemistry and aims to provide an understanding of its future directions. Chemistry ; Popularity ; Computer science ; Data science ; Nanotechnology ; Materials science ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Zhigang Hu | Yuxiang Wang | Dan Zhao
Source: Chemical Society Reviews (review, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

The chemistry and applications of hafnium and cerium(<scp>iv</scp>) metal–organic frameworks The coordination connection of organic linkers to the metal clusters leads to the formation of metal-organic frameworks (MOFs), where the metal clusters and ligands are spatially entangled in a periodic manner. The immense availability of tuneable ligands of different length and functionalities gives rise to robust molecular porosity ranging from several angstroms to nanometres. Among the large family of MOFs, hafnium (Hf) based MOFs have been demonstrated to be highly promising for practical applications due to their unique and outstanding characteristics such as chemical, thermal, and mechanical stability, and acidic nature. Since the report of UiO-66(Hf) and DUT-51(Hf) in 2012, less than 200 Hf-MOFs (ca. 50 types of structures) have been reported. Besides, tetravalent cerium [Ce(iv)] has been proven to be capable of forming similar topological MOF structures to Zr and Hf since its first discovery in 2015. So far, ca. 40 Ce(iv) MOFs with 60% having UiO-66-type structure have been reported. This review will offer a holistic summary of the chemistry, uniqueness, synthesis, and applications of Hf/Ce(iv)-MOFs with a focus on presenting the development in the Hf/Ce(iv)-clusters, topologies, ligand structures, synthetic strategies, and practical applications of Hf/Ce(iv)-MOFs. In the end, we will present the research outlook for the development of Hf/Ce(iv)-MOFs in the future, including fundamental design of Hf/Ce(iv)-clusters, defect engineering, and various applications including membrane development, diversified types of catalytic reactions, irradiation absorption in nuclear waste treatment, water production and wastewater treatment, etc. We will also present the emerging computational approaches coupled with machine-learning algorithms that can be applied in screening Hf and Ce(iv) based MOF structures and identifying the best-performing MOFs for tailor-made applications in future practice. Cerium ; Metal-organic framework ; Ligand (biochemistry) ; Hafnium ; Chemistry ; Metal ; Nanotechnology ; Combinatorial chemistry ; Zirconium ; Materials science ; Inorganic chemistry ; Organic chemistry ; Adsorption ; Receptor Metal-Organic Frameworks: Synthesis and Applications ; Nanocluster Synthesis and Applications ; Magnetism in coordination complexes