jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yong‐Jie Hu | Ge Zhao | Mingfei Zhang | Bin Bin | Tyler Del Rose | Qian Zhao | Qun Zu | Yang Chen | Xuekun Sun | Maarten de Jong | Liang Qi
Source: npj Computational Materials (article, 2020-03-20)
Field: Materials Science — Ceramics and Composites

Predicting densities and elastic moduli of SiO2-based glasses by machine learning Abstract Chemical design of SiO 2 -based glasses with high elastic moduli and low weight is of great interest. However, it is difficult to find a universal expression to predict the elastic moduli according to the glass composition before synthesis since the elastic moduli are a complex function of interatomic bonds and their ordering at different length scales. Here we show that the densities and elastic moduli of SiO 2 -based glasses can be efficiently predicted by machine learning (ML) techniques across a complex compositional space with multiple (&gt;10) types of additive oxides besides SiO 2 . Our machine learning approach relies on a training set generated by high-throughput molecular dynamic (MD) simulations, a set of elaborately constructed descriptors that bridges the empirical statistical modeling with the fundamental physics of interatomic bonding, and a statistical learning/predicting model developed by implementing least absolute shrinkage and selection operator with a gradient boost machine (GBM-LASSO). The predictions of the ML model are comprehensively compared and validated with a large amount of both simulation and experimental data. By just training with a dataset only composed of binary and ternary glass samples, our model shows very promising capabilities to predict the density and elastic moduli for k-nary SiO 2 -based glasses beyond the training set. As an example of its potential applications, our GBM-LASSO model was used to perform a rapid and low-cost screening of many (~10 5 ) compositions of a multicomponent glass system to construct a compositional-property database that allows for a fruitful overview on the glass density and elastic properties. Moduli ; Elastic modulus ; Ternary operation ; Lasso (programming language) ; Binary number ; Interatomic potential ; Set (abstract data type) ; Materials science ; Algorithm ; Computer science ; Machine learning ; Function (biology) ; Artificial intelligence ; Statistical physics ; Mathematics ; Chemistry ; Physics ; Molecular dynamics ; Composite material ; Computational chemistry Glass properties and applications ; Phase-change materials and chalcogenides ; Catalysis and Oxidation Reactions
Authors: Ka Wan Li | Miguel A. Gonzalez‐Lozano | Frank Koopmans | August B. Smit
Source: Frontiers in Molecular Neuroscience (review, 2020-12-23)
Field: Chemistry — Spectroscopy

Recent Developments in Data Independent Acquisition (DIA) Mass Spectrometry: Application of Quantitative Analysis of the Brain Proteome Mass spectrometry is the driving force behind current brain proteome analysis. In a typical proteomics approach, a protein isolate is digested into tryptic peptides and then analyzed by liquid chromatography-mass spectrometry. The recent advancements in data independent acquisition (DIA) mass spectrometry provide higher sensitivity and protein coverage than the classic data dependent acquisition. DIA cycles through a pre-defined set of peptide precursor isolation windows stepping through 400-1,200 m/z across the whole liquid chromatography gradient. All peptides within an isolation window are fragmented simultaneously and detected by tandem mass spectrometry. Peptides are identified by matching the ion peaks in a mass spectrum to a spectral library that contains information of the peptide fragment ions' pattern and its chromatography elution time. Currently, there are several reports on DIA in brain research, in particular the quantitative analysis of cellular and synaptic proteomes to reveal the spatial and/or temporal changes of proteins that underlie neuronal plasticity and disease mechanisms. Protocols in DIA are continuously improving in both acquisition and data analysis. The depth of analysis is currently approaching proteome-wide coverage, while maintaining high reproducibility in a stable and standardisable MS environment. DIA can be positioned as the method of choice for routine proteome analysis in basic brain research and clinical applications. Proteome ; Mass spectrometry ; Tandem mass spectrometry ; Proteomics ; Chemistry ; Chromatography ; Protein mass spectrometry ; Tandem mass tag ; Bottom-up proteomics ; Quantitative proteomics ; Computational biology ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Yirui Huang | Zhenhui Ren | Dongming Li | Xuan Liu
Source: Plant Methods (review, 2020-08-06)
Field: Chemistry — Analytical Chemistry

Phenotypic techniques and applications in fruit trees: a review Phenotypic information is of great significance for irrigation management, disease prevention and yield improvement. Interest in the evaluation of phenotypes has grown with the goal of enhancing the quality of fruit trees. Traditional techniques for monitoring fruit tree phenotypes are destructive and time-consuming. The development of advanced technology is the key to rapid and non-destructive detection. This review describes several techniques applied to fruit tree phenotypic research in the field, including visible and near-infrared (VIS-NIR) spectroscopy, digital photography, multispectral and hyperspectral imaging, thermal imaging, and light detection and ranging (LiDAR). The applications of these technologies are summarized in terms of architecture parameters, pigment and nutrient contents, water stress, biochemical parameters of fruits and disease detection. These techniques have been shown to play important roles in fruit tree phenotypic research. Hyperspectral imaging ; Multispectral image ; Phenotype ; Remote sensing ; Phenomics ; Tree (set theory) ; Computer science ; Biology ; Environmental science ; Geography ; Genomics ; Genome ; Mathematics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Geun Ho Gu | Juhwan Noh | Sungwon Kim | Seoin Back | Zachary W. Ulissi | Yousung Jung
Source: The Journal of Physical Chemistry Letters (article, 2020-03-19)
Field: Materials Science — Materials Chemistry

Practical Deep-Learning Representation for Fast Heterogeneous Catalyst Screening The binding site and energy is an invaluable descriptor in high-throughput screening of catalysts, as it is accessible and correlates with the activity and selectivity. Recently, comprehensive binding energy prediction machine-learning models have been demonstrated and promise to accelerate the catalyst screening. Here, we present a simple and versatile representation, applicable to any deep-learning models, to further accelerate such process. Our approach involves labeling the binding site atoms of the unrelaxed bare surface geometry; hence, for the model application, density functional theory calculations can be completely removed if the optimized bulk structure is available as is the case when using the Materials Project database. In addition, we present ensemble learning, where a set of predictions is used together to form a predictive distribution that reduces the model bias. We apply the labeled site approach and ensemble to crystal graph convolutional neural network and the ∼40 000 data set of alloy catalysts for CO<sub>2</sub> reduction. The proposed model applied to the data set of unrelaxed structures shows 0.116 and 0.085 eV mean absolute error, respectively, for CO and H binding energy, better than the best method (0.13 and 0.13 eV) in the literature that requires costly geometry relaxations. The analysis of the model parameters demonstrates that the model can effectively learn the chemical information related to the binding site. Representation (politics) ; Convolutional neural network ; Computer science ; Density functional theory ; Set (abstract data type) ; Artificial neural network ; Binding energy ; Artificial intelligence ; Ensemble learning ; Machine learning ; Chemistry ; Computational chemistry ; Physics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Carbon dioxide utilization in catalysis
Authors: Mohammadreza Khalaj | Mohammadreza Kamali | Maria Elisabete V. Costa | Isabel Capela
Source: Journal of Cleaner Production (article, 2020-05-10)
Field: Materials Science — Materials Chemistry

Green synthesis of nanomaterials - A scientometric assessment Nanomaterials ; Nanotechnology ; Environmental science ; Materials science Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Nanomaterials for catalytic reactions
Authors: Yun Zhang | Xiaojie Xu
Source: Journal of Superconductivity and Novel Magnetism (article, 2020-09-21)
Field: Materials Science — Materials Chemistry

Machine Learning F-Doped Bi(Pb)–Sr–Ca–Cu–O Superconducting Transition Temperature Superconductivity ; Materials science ; Liquid helium ; Doping ; Condensed matter physics ; Superconducting transition temperature ; Transition temperature ; Refrigeration ; Superconducting magnet ; Magnet ; Engineering physics ; Helium ; Thermodynamics ; Physics ; Optoelectronics ; Quantum mechanics Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Nuclear Physics and Applications
Authors: Shifang Yuan | Yi Yan | Gregory A. Solan | Yanping Ma | Wen‐Hua Sun
Source: Coordination Chemistry Reviews (article, 2020-02-27)
Field: Chemistry — Organic Chemistry

Recent advancements in N-ligated group 4 molecular catalysts for the (co)polymerization of ethylene Chemistry ; Catalysis ; Polymerization ; Ethylene ; Copolymer ; Polymer chemistry ; Imine ; Metallocene ; Olefin polymerization ; Ligand (biochemistry) ; Branching (polymer chemistry) ; Polymer ; Steric effects ; Organic chemistry Organometallic Complex Synthesis and Catalysis ; Carbon dioxide utilization in catalysis ; Synthetic Organic Chemistry Methods
Authors: Mingming Xu | Qiang Chen | Lin‐Hua Xie | Jian‐Rong Li
Source: Coordination Chemistry Reviews (article, 2020-06-29)
Field: Chemistry — Inorganic Chemistry

Exchange reactions in metal-organic frameworks: New advances Chemistry ; Metal-organic framework ; Nanotechnology ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Molecular Sensors and Ion Detection
Authors: Francisco G. Cirujano | Nuria Martín | Lik H. Wee
Source: Chemistry of Materials (article, 2020-12-06)
Field: Chemistry — Inorganic Chemistry

Design of Hierarchical Architectures in Metal–Oganic Frameworks for Catalysis and Adsorption A key factor to improve the performance of metal–organic frameworks (MOFs) is the design and synthesis of hierarchical interconnected porosity at different length scales. The presence of secondary mesopore systems, in addition to the intrinsic framework micropores, avoids inherent mass-transfer constraints that occur in multiple examples of liquid phase chemical processes. Different strategies to introduce ordered void spaces in the MOF materials are reviewed here, based on either bottom-up or top-down approaches. A thorough discussion of the different bottom-up synthesis protocols, based on the interactions of the molecular building blocks with soft or hard templates, is followed by presenting synthesis concepts relating to multidimensional MOF transformations, interfacial and modulated synthesis. The holy grail of defect engineering MOF crystals with controlled porosity is tackled through several top-down approaches in the nanoscale regime. The resultant hierarchical MOFs with interconnected porosity represent the next generation of advanced catalysts and adsorbents, as is presented here by using a vast number of examples where the hierarchical porous structure is key to the MOF performance. A combination of tailor-made pore shape, size, and functionality leads to a synergistic effect between the MOF structures and functions, which offers an improvement on catalysis and adsorption while preserving their porosity, reactivity, and stability. The hierarchical MOFs presented in the different sections of this Review are compared with the purely microporous parent MOFs, used as benchmark in challenging applications where the performance is boosted in the case of hierarchical MOFs. Porosity ; Metal-organic framework ; Materials science ; Microporous material ; Template ; Nanotechnology ; Mesoporous material ; Adsorption ; Catalysis ; Computer science ; Chemistry Metal-Organic Frameworks: Synthesis and Applications ; Enhanced Oil Recovery Techniques ; Zeolite Catalysis and Synthesis
Authors: Kan Hatakeyama‐Sato | Toshiki Tezuka | Momoka Umeki | Kenichi Oyaizu
Source: Journal of the American Chemical Society (article, 2020-01-15)
Field: Materials Science — Materials Chemistry

AI-Assisted Exploration of Superionic Glass-Type Li<sup>+</sup> Conductors with Aromatic Structures It has long remained challenging to predict the properties of complex chemical systems, such as polymer-based materials and their composites. We have constructed the largest database of lithium-conducting solid polymer electrolytes (10<sup>4</sup> entries) and employed a transfer-learned graph neural network to accurately predict their conductivity (mean absolute error of less than 1 on a logarithmic scale). The bias-free prediction by the network helped us to find superionic conductors composed of charge-transfer complexes of aromatic polymers (ionic conductivity of around 10<sup>-3</sup> S/cm at room temperature). The glassy design was contrary to the traditional concept of rubbery polymer electrolytes, but it was found to be appropriate to achieve fast, decoupled motion of ionic species from polymer chains and to enhance thermal and mechanical stability. The unbiased suggestions generated by machine learning models can help researches to discover unexpected chemical phenomena, which could also induce a paradigm shift of energy-related functional materials. Chemistry ; Electrical conductor ; Fast ion conductor ; Lithium (medication) ; Crystallography ; Chemical physics ; Chemical engineering ; Physical chemistry ; Composite material ; Electrolyte Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Rongjun Chen | Kejie Shao | Bina Fu | Dong H. Zhang
Source: The Journal of Chemical Physics (article, 2020-05-27)
Field: Materials Science — Materials Chemistry

Fitting potential energy surfaces with fundamental invariant neural network. II. Generating fundamental invariants for molecular systems with up to ten atoms Symmetry adaptation is crucial in representing a permutationally invariant potential energy surface (PES). Due to the rapid increase in computational time with respect to the molecular size, as well as the reliance on the algebra software, the previous neural network (NN) fitting with inputs of fundamental invariants (FIs) has practical limits. Here, we report an improved and efficient generation scheme of FIs based on the computational invariant theory and parallel program, which can be readily used as the input vector of NNs in fitting high-dimensional PESs with permutation symmetry. The newly developed method significantly reduces the evaluation time of FIs, thereby extending the FI-NN method for constructing highly accurate PESs to larger systems beyond five atoms. Because of the minimum size of invariants used in the inputs of the NN, the NN structure can be very flexible for FI-NN, which leads to small fitting errors. The resulting FI-NN PES is much faster on evaluating than the corresponding permutationally invariant polynomial-NN PES. Invariant (physics) ; Artificial neural network ; Permutation (music) ; Computer science ; Computational complexity theory ; Symmetry (geometry) ; Algorithm ; Mathematics ; Physics ; Artificial intelligence ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Antonio Vassallo | Maria Francesca Silletti | Immacolata Faraone | Luigi Milella
Source: Journal of Nanomaterials (article, 2020-09-12)
Field: Materials Science — Materials Chemistry

Nanoparticulate Antibiotic Systems as Antibacterial Agents and Antibiotic Delivery Platforms to Fight Infections Today’s human society, product of decades of progress in all fields of knowledge, would have been unimaginable without the discovery of antibiotics and more generally of antimicrobials. However, from the beginning, the scientific community was aware that microorganisms through various strategies were able to hinder and render vain antibiotic action. Common examples are the phenomena of persistence, tolerance, and resistance, up to the creation of the feared bacterial biofilms. Antibiotics are a precious but equally labile resource that must be preserved but at the same time reinforced to safeguard their effectiveness. Nanoparticulate systems such as nanobactericides, with their inherent antibacterial activity, and nanocarriers, which operate as drug delivery systems for conventional antibiotics, are innovative therapies made available by nanotechnology. Inorganic nanoparticles are effective both as nanobactericides (AgNPs, ZnONPs, and TiO 2 NPs) and as nanocarriers (AgNPs, AuNPs, ZnONPs, and TiO 2 NPs) against sensitive and multi-drug-resistant bacterial strains. Liposomes are among the most studied and flexible antibiotic delivery platforms: conventional liposomes allow passive targeting at the mononuclear phagocytic system (MPS); “stealth” liposomes prevent macrophage uptake so as to eradicate infections in tissues and organs outside MPS; thanks to their positive charge, cationic liposomes interact preferentially with bacterial and biofilm surfaces, acting as innate antibacterials as well as drug delivery systems (DDS); fusogenic liposomes have fluid bilayers that promote fusion with microbial membranes; and finally, ligand-targeted liposomes provide active targeting at infection sites. Dendrimers are among the most recent and attractive nanoparticulate systems, thanks to their multibranched nanoarchitecture, which equipped them with multiple active sites for loading antibiotics and also interacting with bacteria. Finally, nanoantibiotics represent a new hopeful generation of antibiotic candidates capable of increasing or even restoring the clinical efficacy of “old” antibiotics rendered useless by the resistance phenomena. Nanocarriers ; Liposome ; Antibiotics ; Drug delivery ; Nanotechnology ; Antimicrobial ; Biofilm ; Cationic liposome ; Materials science ; Microbiology ; Chemistry ; Biology ; Bacteria Nanoparticles: synthesis and applications ; Antimicrobial agents and applications ; Pharmaceutical and Antibiotic Environmental Impacts
Authors: Kerri A. Ball | Kristofor J. Webb | S. J. Coleman | Kira Cozzolino | Jeremy Jacobsen | Kevin R. Jones | Michael H. B. Stowell | William M. Old
Source: Communications Biology (article, 2020-02-14)
Field: Chemistry — Spectroscopy

An isothermal shift assay for proteome scale drug-target identification Most small molecule drugs act on living systems by physically interacting with specific proteins and modulating target function. Identification of drug binding targets, within the complex milieu of the human proteome, remains a challenging task of paramount importance in drug discovery. Existing approaches for target identification employ complex workflows with limited throughput. Here, we present the isothermal shift assay (iTSA), a mass spectrometry method for proteome-wide identification of drug targets within lysates or living cells. Compared with prevailing methods, iTSA uses a simplified experimental design with increased statistical power to detect thermal stability shifts that are induced by small molecule binding. Using a pan-kinase inhibitor, staurosporine, we demonstrate improved performance over commonly used thermal proteome profiling methods, identifying known targets in cell lysates and living cells. We also demonstrate the identification of both known targets and additional candidate targets for the kinase inhibitor harmine in cell and tissue lysates. Proteome ; Computational biology ; Staurosporine ; Drug discovery ; Small molecule ; Identification (biology) ; Chemistry ; Biology ; Proteomics ; Cell biology ; Kinase ; Biochemistry ; Protein kinase A Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Computational Drug Discovery Methods
Authors: T. Delord | P. Huillery | L. Nicolas | G. Hétet
Source: Nature (article, 2020-03-23)
Field: Materials Science — Materials Chemistry

Spin-cooling of the motion of a trapped diamond Diamond ; Quantum ; Spins ; Quantum entanglement ; Coherent control ; Quantum sensor ; Field (mathematics) ; Spin (aerodynamics) ; Dissipation ; Macroscopic quantum phenomena Diamond and Carbon-based Materials Research ; Mechanical and Optical Resonators ; Geophysics and Sensor Technology
Authors: Xiaoyong Du | Ye Xiao | Jiaming Huang | Yao Zhang | Yanan Duan | Heng Wang | Chuan Shi | Gen‐Qiang Chen | Xumu Zhang
Source: Nature Communications (article, 2020-06-26)
Field: Chemistry — Inorganic Chemistry

Cobalt-catalyzed highly enantioselective hydrogenation of α,β-unsaturated carboxylic acids Asymmetric hydrogenation of α,β-unsaturated acids catalyzed by noble metals has been well established, whereas, the asymmetric hydrogenation with earth-abundant-metal was rarely reported. Here, we describe a cobalt-catalyzed asymmetric hydrogenation of α,β-unsaturated carboxylic acids. By using chiral cobalt catalyst bearing electron-donating diphosphine ligand, high activity (up to 1860 TON) and excellent enantioselectivity (up to >99% ee) are observed. Furthermore, the cobalt-catalyzed asymmetric hydrogenation is successfully applied to a broad spectrum of α,β-unsaturated carboxylic acids, such as various α-aryl and α-alkyl cinnamic acid derivatives, α-oxy-functionalized α,β-unsaturated acids, α-substituted acrylic acids and heterocyclic α,β-unsaturated acids (30 examples). The synthetic utility of the protocol is highlighted by the synthesis of key intermediates for chiral drugs (6 cases). Preliminary mechanistic studies reveal that the carboxy group may be involved in the control of the reactivity and enantioselectivity through an interaction with the metal centre. Enantioselective synthesis ; Cobalt ; Catalysis ; Chemistry ; Stereoisomerism ; Organic chemistry ; Combinatorial chemistry Asymmetric Hydrogenation and Catalysis ; Catalysis for Biomass Conversion ; Surface Chemistry and Catalysis
Authors: Romain Liénard | Julien De Winter | Olivier Coulembier
Source: Journal of Polymer Science (article, 2020-05-26)
Field: Chemistry — Organic Chemistry

Cyclic polymers: Advances in their synthesis, properties, and biomedical applications Abstract Since the time first synthetic macrocycles were observed as academic curiosities, great advances have been made. Thanks to the development of controlled polymerization processes, new catalytic systems and characterization techniques during the last decades, well‐defined cyclic polymers are now readily accessible. This further permits the determination of their unique set of properties, mainly due to their lack of chain ends, and their use for industrial applications can now be foreshadowed. This review aims to give an overview on the recent progresses in the field of ring polymers to this day. The current state of the art of the preparation of cyclic polymers, the challenges related to it such as the purification of the samples and the scalability of the synthetic processes, the properties arising from the cyclic topology and the potential use of cyclo ‐based polymers for biomedical applications are as many topics covered in this review. Polymer ; Nanotechnology ; Characterization (materials science) ; Materials science ; Polymerization ; Polymer science ; Computer science ; Combinatorial chemistry ; Chemistry Organoboron and organosilicon chemistry ; Organometallic Complex Synthesis and Catalysis ; Synthetic Organic Chemistry Methods