jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Christopher J. Bartel | Amalie Trewartha | Qi Wang | Alexander Dunn | Anubhav Jain | Gerbrand Ceder
Source: npj Computational Materials (article, 2020-07-10)
Field: Materials Science — Materials Chemistry

A critical examination of compound stability predictions from machine-learned formation energies Abstract Machine learning has emerged as a novel tool for the efficient prediction of material properties, and claims have been made that machine-learned models for the formation energy of compounds can approach the accuracy of Density Functional Theory (DFT). The models tested in this work include five recently published compositional models, a baseline model using stoichiometry alone, and a structural model. By testing seven machine learning models for formation energy on stability predictions using the Materials Project database of DFT calculations for 85,014 unique chemical compositions, we show that while formation energies can indeed be predicted well, all compositional models perform poorly on predicting the stability of compounds, making them considerably less useful than DFT for the discovery and design of new solids. Most critically, in sparse chemical spaces where few stoichiometries have stable compounds, only the structural model is capable of efficiently detecting which materials are stable. The nonincremental improvement of structural models compared with compositional models is noteworthy and encourages the use of structural models for materials discovery, with the constraint that for any new composition, the ground-state structure is not known a priori. This work demonstrates that accurate predictions of formation energy do not imply accurate predictions of stability, emphasizing the importance of assessing model performance on stability predictions, for which we provide a set of publicly available tests. Stability (learning theory) ; Constraint (computer-aided design) ; Work (physics) ; Stoichiometry ; Set (abstract data type) ; Structural stability ; Density functional theory ; Experimental data Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Chemistry and Materials
Authors: Sanggyu Chong | Sangwon Lee | Baekjun Kim | Jihan Kim
Source: Coordination Chemistry Reviews (article, 2020-08-09)
Field: Chemistry — Inorganic Chemistry

Applications of machine learning in metal-organic frameworks Workflow ; Variety (cybernetics) ; Field (mathematics) ; Computer science ; Artificial intelligence ; Data science ; Chemistry ; Database Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Umair Qasim | Ahmed I. Osman | Ala’a H. Al‐Muhtaseb | Charlie Farrell | Mohammed Al‐Abri | Muzaffar Ali | Dai‐Viet N. Vo | Farrukh Jamil | David W. Rooney
Source: Environmental Chemistry Letters (review, 2020-09-24)
Field: Materials Science — Biomaterials

Renewable cellulosic nanocomposites for food packaging to avoid fossil fuel plastic pollution: a review Abstract The extensive use of petroleum-based synthetic and non-biodegradable materials for packaging applications has caused severe environmental damage. The rising demand for sustainable packaging materials has encouraged scientists to explore abundant unconventional materials. For instance, cellulose, extracted from lignocellulosic biomass, has gained attention owing to its ecological and biodegradable nature. This article reviews the extraction of cellulose nanoparticles from conventional and non-conventional lignocellulosic biomass, and the preparation of cellulosic nanocomposites for food packaging. Cellulosic nanocomposites exhibit exceptional mechanical, biodegradation, optical and barrier properties, which are attributed to the nanoscale structure and the high specific surface area, of 533 m 2 g −1 , of cellulose. The mechanical properties of composites improve with the content of cellulose nanoparticles, yet an excessive amount induces agglomeration and, in turn, poor mechanical properties. Addition of cellulose nanoparticles increases tensile properties by about 42%. Barrier properties of the composites are reinforced by cellulose nanoparticles; for instance, the water vapor permeability decreased by 28% in the presence of 5 wt% cellulose nanoparticles. Moreover, 1 wt% addition of filler decreased the oxygen transmission rate by 21%. We also discuss the eco-design process, designing principles and challenges. Cellulosic ethanol ; Cellulose ; Materials science ; Nanocomposite ; Food packaging ; Cellulose fiber ; Biomass (ecology) ; Environmental pollution ; Ultimate tensile strength ; Lignocellulosic biomass ; Composite material ; Pulp and paper industry ; Chemistry ; Environmental science ; Fiber ; Organic chemistry Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties
Authors: Carl C. L. Schuurmans | Marko Mihajlovic | Christine Hiemstra | Keita Ito | Wim E. Hennink | Tina Vermonden
Source: Biomaterials (review, 2020-12-22)
Field: Materials Science — Biomaterials

Hyaluronic acid and chondroitin sulfate (meth)acrylate-based hydrogels for tissue engineering: Synthesis, characteristics and pre-clinical evaluation Hydrogels based on photocrosslinkable Hyaluronic Acid Methacrylate (HAMA) and Chondroitin Sulfate Methacrylate (CSMA) are presently under investigation for tissue engineering applications. HAMA and CSMA gels offer tunable characteristics such as tailorable mechanical properties, swelling characteristics, and enzymatic degradability. This review gives an overview of the scientific literature published regarding the pre-clinical development of covalently crosslinked hydrogels that (partially) are based on HAMA and/or CSMA. Throughout the review, recommendations for the next steps in clinical translation of hydrogels based on HAMA or CSMA are made and potential pitfalls are defined. Specifically, a myriad of different synthetic routes to obtain polymerizable hyaluronic acid and chondroitin sulfate derivatives are described. The effects of important parameters such as degree of (meth)acrylation and molecular weight of the synthesized polymers on the formed hydrogels are discussed and useful analytical techniques for their characterization are summarized. Furthermore, the characteristics of the formed hydrogels including their enzymatic degradability are discussed. Finally, a summary of several recent applications of these hydrogels in applied fields such as cartilage and cardiac regeneration and advanced tissue modelling is presented. Self-healing hydrogels ; Hyaluronic acid ; Materials science ; Chondroitin sulfate ; Methacrylate ; Tissue engineering ; Acrylate ; Polymer ; Chondroitin ; Biomedical engineering ; Chemical engineering ; Polymer chemistry ; Chemistry ; Glycosaminoglycan ; Composite material ; Biochemistry ; Polymerization ; Copolymer Electrospun Nanofibers in Biomedical Applications ; 3D Printing in Biomedical Research ; Proteoglycans and glycosaminoglycans research
Authors: Feiyun Cui | Zhiru Zhou | H. Susan Zhou
Source: Sensors (review, 2020-02-13)
Field: Chemistry — Analytical Chemistry

Molecularly Imprinted Polymers and Surface Imprinted Polymers Based Electrochemical Biosensor for Infectious Diseases Owing to their merits of simple, fast, sensitive, and low cost, electrochemical biosensors have been widely used for the diagnosis of infectious diseases. As a critical element, the receptor determines the selectivity, stability, and accuracy of the electrochemical biosensors. Molecularly imprinted polymers (MIPs) and surface imprinted polymers (SIPs) have great potential to be robust artificial receptors. Therefore, extensive studies have been reported to develop MIPs/SIPs for the detection of infectious diseases with high selectivity and reliability. In this review, we discuss mechanisms of recognition events between imprinted polymers with different biomarkers, such as signaling molecules, microbial toxins, viruses, and bacterial and fungal cells. Then, various preparation methods of MIPs/SIPs for electrochemical biosensors are summarized. Especially, the methods of electropolymerization and micro-contact imprinting are emphasized. Furthermore, applications of MIPs/SIPs based electrochemical biosensors for infectious disease detection are highlighted. At last, challenges and perspectives are discussed. Molecularly imprinted polymer ; Biosensor ; Nanotechnology ; Molecular imprinting ; Polymer ; Materials science ; Conductive polymer ; Selectivity ; Chemistry ; Organic chemistry Analytical chemistry methods development ; Biosensors and Analytical Detection ; Advanced biosensing and bioanalysis techniques
Authors: Bahareh Azimi | Homa Maleki | Lorenzo Zavagna | Jose Gustavo De la Ossa | Stefano Linari | Andrea Lazzeri | Serena Danti
Source: Journal of Functional Biomaterials (review, 2020-09-22)
Field: Materials Science — Biomaterials

Bio-Based Electrospun Fibers for Wound Healing Being designated to protect other tissues, skin is the first and largest human body organ to be injured and for this reason, it is accredited with a high capacity for self-repairing. However, in the case of profound lesions or large surface loss, the natural wound healing process may be ineffective or insufficient, leading to detrimental and painful conditions that require repair adjuvants and tissue substitutes. In addition to the conventional wound care options, biodegradable polymers, both synthetic and biologic origin, are gaining increased importance for their high biocompatibility, biodegradation, and bioactive properties, such as antimicrobial, immunomodulatory, cell proliferative, and angiogenic. To create a microenvironment suitable for the healing process, a key property is the ability of a polymer to be spun into submicrometric fibers (e.g., via electrospinning), since they mimic the fibrous extracellular matrix and can support neo- tissue growth. A number of biodegradable polymers used in the biomedical sector comply with the definition of bio-based polymers (known also as biopolymers), which are recently being used in other industrial sectors for reducing the material and energy impact on the environment, as they are derived from renewable biological resources. In this review, after a description of the fundamental concepts of wound healing, with emphasis on advanced wound dressings, the recent developments of bio-based natural and synthetic electrospun structures for efficient wound healing applications are highlighted and discussed. This review aims to improve awareness on the use of bio-based polymers in medical devices. Wound healing ; Biocompatibility ; Materials science ; Electrospinning ; Wound care ; Nanotechnology ; Biomedical engineering ; Extracellular matrix ; Polymer ; Biodegradable polymer ; Tissue repair ; Biochemical engineering ; Surgery ; Medicine ; Composite material ; Chemistry ; Engineering Electrospun Nanofibers in Biomedical Applications ; Wound Healing and Treatments ; Silk-based biomaterials and applications
Authors: Jose R. Cabrero‐Antonino | Rosa Adam | Veronica Papa | Matthias Beller
Source: Nature Communications (review, 2020-08-04)
Field: Chemistry — Inorganic Chemistry

Homogeneous and heterogeneous catalytic reduction of amides and related compounds using molecular hydrogen Catalytic hydrogenation of amides is of great interest for chemists working in organic synthesis, as the resulting amines are widely featured in natural products, drugs, agrochemicals, dyes, etc. Compared to traditional reduction of amides using (over)stoichiometric reductants, the direct hydrogenation of amides using molecular hydrogen represents a greener approach. Furthermore, amide hydrogenation is a highly versatile transformation, since not only higher amines (obtained by C-O cleavage), but also lower amines and alcohols, or amino alcohols (obtained by C-N cleavage) can be selectively accessed by fine tuning of reaction conditions. This review describes the most recent advances in the area of amide hydrogenation using H<sub>2</sub> exclusively and molecularly defined homogeneous as well as nano-structured heterogeneous catalysts, with a special focus on catalyst development and synthetic applications. Amide ; Catalysis ; Homogeneous ; Chemistry ; Combinatorial chemistry ; Transfer hydrogenation ; Organic chemistry ; Cleavage (geology) ; Hydrogen ; Catalytic hydrogenation ; Stoichiometry ; Hydrogen molecule ; Materials science ; Ruthenium Asymmetric Hydrogenation and Catalysis ; Advanced Synthetic Organic Chemistry ; Chemical Synthesis and Analysis
Authors: Yingli Liu | Chen Niu | Zhuo Wang | Yong Gan | Yan Zhu | Shuhong Sun | Tao Shen
Source: Journal of Material Science and Technology (review, 2020-05-03)
Field: Materials Science — Materials Chemistry

Machine learning in materials genome initiative: A review Computer science ; Field (mathematics) ; Artificial intelligence ; Machine learning ; Data science ; Analytics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Emmanuelle Lautié | Olivier Russo | Pierre Ducrot | Jean A. Boutin
Source: Frontiers in Pharmacology (review, 2020-04-07)
Field: Chemistry — Organic Chemistry

Unraveling Plant Natural Chemical Diversity for Drug Discovery Purposes The screening and testing of extracts against a variety of pharmacological targets in order to benefit from the immense natural chemical diversity is a concern in many laboratories worldwide. And several successes have been recorded in finding new actives in natural products, some of which have become new drugs or new sources of inspiration for drugs. But in view of the vast amount of research on the subject, it is surprising that not more drug candidates were found. In our view, it is fundamental to reflect upon the approaches of such drug discovery programs and the technical processes that are used, along with their inherent difficulties and biases. Based on an extensive survey of recent publications, we discuss the origin and the variety of natural chemical diversity as well as the strategies to having the potential to embrace this diversity. It seemed to us that some of the difficulties of the area could be related with the technical approaches that are used, so the present review begins with synthetizing some of the more used discovery strategies, exemplifying some key points, in order to address some of their limitations. It appears that one of the challenges of natural product-based drug discovery programs should be an easier access to renewable sources of plant-derived products. Maximizing the use of the data together with the exploration of chemical diversity while working on reasonable supply of natural product-based entities could be a way to answer this challenge. We suggested alternative ways to access and explore part of this chemical diversity with <i>in vitro</i> cultures. We also reinforced how important it was organizing and making available this worldwide knowledge in an "inventory" of natural products and their sources. And finally, we focused on strategies based on synthetic biology and syntheses that allow reaching industrial scale supply. Approaches based on the opportunities lying in untapped natural plant chemical diversity are also considered. Drug discovery ; Chemical space ; Computational biology ; Drug ; Biology ; Data science ; Traditional medicine ; Medicine ; Pharmacology ; Bioinformatics ; Computer science Chemical synthesis and alkaloids ; Traditional and Medicinal Uses of Annonaceae ; Plant biochemistry and biosynthesis
Authors: Shao‐Ping Zheng | Li‐Bo Huang | Zhanhu Sun | Mihail Bãrboiu
Source: Angewandte Chemie International Edition (review, 2020-03-25)
Field: Chemistry — Spectroscopy

Self‐Assembled Artificial Ion‐Channels toward Natural Selection of Functions Owing to their significant physiological functions, especially as selective relays for translocation of physiological relevant species through cellular membranes, natural ion channels play important role in the living organisms. During the last decades, the field of self-assembled ion channels has been continuously developed. Convergent multidimensional self-assembly strategies have been used for the synthesis of unimolecular channels or non-covalent self-organized channels, designed to mimic natural ion channel proteins and for which a rich array of interconverting or adaptive channel conductance states can be observed. In this review, we give an overview on the development of various self-assembled artificial channels in a bottom-up approach, especially their design, self-assembly behaviour, transport activity in lipid bilayer membranes, mechanism of transport and comparison with natural ion channels. Finally, we discuss their applications, the potential challenges facing in this field as well as future development and perspectives. Ion channel ; Lipid bilayer ; Membrane ; Ion transporter ; Nanotechnology ; Biophysics ; Ion ; Chemistry ; Materials science ; Biology ; Biochemistry Molecular Sensors and Ion Detection ; Supramolecular Chemistry and Complexes ; Supramolecular Self-Assembly in Materials
Authors: Junxiao Zhou | Shi-Kai Liu | Haoliang Qian | Yin‐Hai Li | Hailu Luo | Shuangchun Wen | Zhi‐Yuan Zhou | Guang‐Can Guo | Bao-Sen Shi | Zhaowei Liu
Source: Science Advances (article, 2020-12-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Metasurface enabled quantum edge detection Metasurfaces consisting of engineered dielectric or metallic structures provide unique solutions to realize exotic phenomena including negative refraction, achromatic focusing, electromagnetic cloaking, and so on. The intersection of metasurface and quantum optics may lead to new opportunities but is much less explored. Here, we propose and experimentally demonstrate that a polarization-entangled photon source can be used to switch ON or OFF the optical edge detection mode in an imaging system based on a high-efficiency dielectric metasurface. This experiment enriches both fields of metasurface and quantum optics, representing a promising direction toward quantum edge detection and image processing with remarkable signal-to-noise ratio. Photon ; Polarization (electrochemistry) ; Enhanced Data Rates for GSM Evolution ; Quantum ; Physics ; Photon entanglement ; Optics ; Computer science ; Optoelectronics ; Quantum entanglement ; Telecommunications ; Quantum mechanics ; Chemistry Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Random lasers and scattering media
Authors: Nathan S. Johnson | Praveen S. Vulimiri | Albert C. To | Xin-Ya Zhang | Craig Brice | Branden B. Kappes | Aaron P. Stebner
Source: Additive manufacturing (article, 2020-10-19)
Field: Materials Science — Materials Chemistry

Invited review: Machine learning for materials developments in metals additive manufacturing In metals additive manufacturing (AM), materials and components are concurrently made in a single process as layers of metal are fabricated on top of each other in the near-final topology required for the end-use product. Consequently, tens to hundreds of materials and part design degrees of freedom must be simultaneously controlled and understood; hence, metals AM is a highly interdisciplinary technology that requires synchronized consideration of physics, chemistry, materials science, physical metallurgy, computer science, electrical engineering, and mechanical engineering. The use of modern machine learning approaches to model these degrees of freedom can reduce the time and cost to elucidate the science of metals AM and to optimize the engineering of these complex, multidisciplinary processes. New machine learning techniques are not needed for most metals AM development; those used in other sects of materials science will also work for AM. Most prolifically, the density functional theory (DFT) community has used many of them since the early 2000s for evaluating numerous combinations of elements and crystal structures to discover new materials. This materials technologies-focused review introduces the basic mathematics and terminology of machine learning through the lens of metals AM, and then examines potential uses of machine learning to advance metals AM, highlighting the many parallels to previous efforts in materials science and manufacturing while also discussing new challenges and adaptations specific to metals AM. Terminology ; Parallels ; Nanotechnology ; Process (computing) ; Mechanical engineering ; Multidisciplinary approach ; Materials science ; Computer science ; Artificial intelligence ; Process engineering ; Manufacturing engineering ; Engineering Machine Learning in Materials Science ; Additive Manufacturing Materials and Processes ; Titanium Alloys Microstructure and Properties
Authors: Bing Huang | O. Anatole von Lilienfeld
Source: Nature Chemistry (article, 2020-09-14)
Field: Materials Science — Materials Chemistry

Quantum machine learning using atom-in-molecule-based fragments selected on the fly Chemistry ; Chemical space ; Molecule ; Quantum chemistry ; Transferability ; Atom (system on chip) ; Quantum ; Chemical physics ; Nanotechnology ; Computational chemistry ; Biological system ; Machine learning ; Computer science ; Quantum mechanics ; Drug discovery ; Physics ; Supramolecular chemistry Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods
Authors: Catherine G. Vasilopoulou | Karolina Sulek | Andreas‐David Brunner | Ningombam Sanjib Meitei | U. Schweiger-Hufnagel | Sven Meyer | Aiko Barsch | Matthias Mann | Florian Meier
Source: Nature Communications (article, 2020-01-16)
Field: Chemistry — Spectroscopy

Trapped ion mobility spectrometry and PASEF enable in-depth lipidomics from minimal sample amounts A comprehensive characterization of the lipidome from limited starting material remains very challenging. Here we report a high-sensitivity lipidomics workflow based on nanoflow liquid chromatography and trapped ion mobility spectrometry (TIMS). Taking advantage of parallel accumulation-serial fragmentation (PASEF), we fragment on average 15 precursors in each of 100 ms TIMS scans, while maintaining the full mobility resolution of co-eluting isomers. The acquisition speed of over 100 Hz allows us to obtain MS/MS spectra of the vast majority of isotope patterns. Analyzing 1 µL of human plasma, PASEF increases the number of identified lipids more than three times over standard TIMS-MS/MS, achieving attomole sensitivity. Building on high intra- and inter-laboratory precision and accuracy of TIMS collisional cross sections (CCS), we compile 1856 lipid CCS values from plasma, liver and cancer cells. Our study establishes PASEF in lipid analysis and paves the way for sensitive, ion mobility-enhanced lipidomics in four dimensions. Lipidomics ; Ion-mobility spectrometry ; Mass spectrometry ; Sample (material) ; Ion ; Chemistry ; Analytical Chemistry (journal) ; Chromatography ; Biochemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications