jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kaikai Ma | Yuk Ha Cheung | Kent O. Kirlikovali | Haomiao Xie | Karam B. Idrees | Xiaoliang Wang | Timur İslamoğlu | John H. Xin | Omar K. Farha
Source: Advanced Materials (article, 2023-06-13)
Field: Materials Science — Materials Chemistry

Fibrous Zr‐MOF Nanozyme Aerogels with Macro‐Nanoporous Structure for Enhanced Catalytic Hydrolysis of Organophosphate Toxins Metal-organic frameworks (MOFs) with Lewis acid catalytic sites, such as zirconium-based MOFs (Zr-MOFs), comprise a growing class of phosphatase-like nanozymes that can degrade toxic organophosphate pesticides and nerve agents. Rationally engineering and shaping MOFs from as-synthesized powders into hierarchically porous monoliths is essential for their use in emerging applications, such as filters for air and water purification and personal protection gear. However, several challenges still limit the production of practical MOF composites, including the need for sophisticated reaction conditions, low MOF catalyst loadings in the resulting composites, and poor accessibility to MOF-based active sites. To overcome these limitations, a rapid synthesis method is developed to introduce Zr-MOF nanozyme coating into cellulose nanofibers, resulting in the formation of processable monolithic aerogel composites with high MOF loadings. These composites contain Zr-MOF nanozymes embedded in the structure, and hierarchical macro-micro porosity enables excellent accessibility to catalytic active sites. This multifaceted rational design strategy, including the selection of a MOF with many catalytic sites, fine-tuning the coating morphology, and the fabrication of a hierarchically structured monolithic aerogel, renders synergistic effects toward the efficient continuous hydrolytic detoxification of organophosphorus-based nerve agent simulants and pesticides from contaminated water. Materials science ; Aerogel ; Catalysis ; Coating ; Zirconium ; Metal-organic framework ; Porosity ; Nanoporous ; Nanotechnology ; Chemical engineering ; Composite material ; Organic chemistry ; Adsorption ; Chemistry Advanced Nanomaterials in Catalysis ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Jannis Born | Matteo Manica
Source: Nature Machine Intelligence (article, 2023-04-06)
Field: Materials Science — Materials Chemistry

Regression Transformer enables concurrent sequence regression and generation for molecular language modelling Abstract Despite tremendous progress of generative models in the natural sciences, their controllability remains challenging. One fundamentally missing aspect of molecular or protein generative models is an inductive bias that can reflect continuous properties of interest. To that end, we propose the Regression Transformer (RT), a method that abstracts regression as a conditional sequence modelling problem. This introduces a new direction for multitask language models, seamlessly bridging sequence regression and conditional sequence generation. We demonstrate that, despite using a nominal-scale training objective, the RT matches or surpasses the performance of conventional regression models in property prediction of small molecules, proteins and chemical reactions. Critically, priming the same model with continuous properties yields a competitive conditional generative model that outperforms specialized approaches in a substructure-constrained, property-driven molecule generation benchmark. Our dichotomous approach is facilitated by an alternating training scheme that enables the model to decorate seed sequences on the basis of desired property constraints, for example, to optimize reaction yield. We expect that the RT’s capability to jointly tackle predictive and generative tasks in biochemistry can find applications in property-driven, local exploration of the chemical or protein space. Such multitask approaches will pave the road towards foundation models in materials design. Computer science ; Regression ; Generative grammar ; Artificial intelligence ; Sequence (biology) ; Machine learning ; Property (philosophy) ; Generative model ; Mathematics ; Statistics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Bryan Ronain Smith | Elazer R. Edelman
Source: Nature Cardiovascular Research (review, 2023-04-03)
Field: Materials Science — Biomaterials

Nanomedicines for cardiovascular disease The leading cause of death in the world, cardiovascular disease (CVD), remains a formidable condition for researchers, clinicians and patients alike. CVD comprises a broad collection of diseases spanning the heart, the vasculature and the blood that runs through and interconnects them. Limitations in CVD therapeutic and diagnostic landscapes have generated excitement for advances in nanomedicine, a field focused on improving patient outcomes through transformative therapies, imaging agents and ex vivo diagnostics. CVD nanomedicines are fundamentally shaped by their intended clinical application, including (1) cardiac or heart-related biomaterials, which can be functionally (for example, mechanically, immunologically, electrically) improved by incorporating nanomaterials; (2) the vasculature, involving systemically injected nanotherapeutics and imaging nanodiagnostics, nano-enabled biomaterials or tissue-nanoengineered solutions; and (3) improving the sensitivity and/or specificity of ex vivo diagnostic devices for patient samples. While immunotherapy has developed into a key pillar of oncology in the past dozen years, CVD immunotherapy and immunoimaging are recently emergent and likely to factor substantially in CVD management in the coming decade. The nanomaterials in CVD-related clinical trials and many promising preclinical strategies indicate that nanomedicine is on the cusp of greatly impacting patients with CVD. Here we review these recent advances, highlighting key clinical opportunities in the rapidly emerging field of CVD nanomedicine. Disease ; Medicine ; Intensive care medicine ; Internal medicine Nanoparticle-Based Drug Delivery ; Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications
Authors: Joana Amaral | Ana Karla Moreira Lobo | Elizabete Carmo‐Silva
Source: New Phytologist (review, 2023-12-07)
Field: Chemistry — Analytical Chemistry

Regulation of Rubisco activity in crops Efficient plant acclimation to changing environmental conditions relies on fast adjustments of the transcriptome, proteome, and metabolome. Regulation of enzyme activity depends on the activity of specific chaperones, chemical post-translational modifications (PTMs) of amino acid residues, and changes in the cellular and organellar microenvironment. Central to carbon assimilation, and thus plant growth and yield, Rubisco activity is regulated by its chaperone Rubisco activase (Rca) and by adjustments in the chloroplast stroma environment. Focused on crops, this review highlights the main PTMs and stromal ions and metabolites affecting Rubisco and Rca in response to environmental stimuli. Rca isoforms differ in regulatory properties and heat sensitivity, with expression changing according to the surrounding environment. Much of the physiological relevance of Rubisco and Rca PTMs is still poorly understood, though some PTMs have been associated with Rubisco regulation in response to stress. Ion and metabolite concentrations in the chloroplast change in response to variations in light and temperature. Some of these changes promote Rubisco activation while others inhibit activation, deactivate the enzyme, or change the rates of catalysis. Understanding these regulatory mechanisms will aid the development of strategies to improve carbon fixation by Rubisco under rapidly changing environments as experienced by crop plants. RuBisCO ; Photosynthesis ; Biology ; Agronomy ; Botany Dye analysis and toxicity ; Water Quality Monitoring and Analysis ; Botanical Research and Applications
Authors: Kedar Hippalgaonkar | Qianxiao Li | Xiaonan Wang | John W. Fisher | James Kirkpatrick | Tonio Buonassisi
Source: Nature Reviews Materials (review, 2023-01-24)
Field: Materials Science — Materials Chemistry

Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics Artificial intelligence ; Workflow ; Robotics ; Computer science ; Machine learning ; Software deployment ; Field (mathematics) ; Domain (mathematical analysis) ; Deep learning ; Automation ; Heuristics ; Domain knowledge ; Software engineering ; Data science ; Robot ; Database ; Engineering Machine Learning in Materials Science ; Software Engineering Research ; Computational Drug Discovery Methods
Authors: Qingyao Huang | Damian Szklarczyk | Mingcong Wang | Milan Simonovic | Christian von Mering
Source: Molecular & Cellular Proteomics (article, 2023-08-31)
Field: Chemistry — Spectroscopy

PaxDb 5.0: Curated Protein Quantification Data Suggests Adaptive Proteome Changes in Yeasts The "Protein Abundances Across Organisms" database (PaxDb) is an integrative metaresource dedicated to protein abundance levels, in tissue-specific or whole-organism proteomes. PaxDb focuses on computing best-estimate abundances for proteins in normal/healthy contexts and expresses abundance values for each protein in "parts per million" in relation to all other protein molecules in the cell. The uniform data reprocessing, quality scoring, and integrated orthology relations have made PaxDb one of the preferred tools for comparisons between individual datasets, tissues, or organisms. In describing the latest version 5.0 of PaxDb, we particularly emphasize the data integration from various types of raw data and how we expanded the number of organisms and tissue groups as well as the proteome coverage. The current collection of PaxDb includes 831 original datasets from 170 species, including 22 Archaea, 81 Bacteria, and 67 Eukaryota. Apart from detailing the data update, we also present a comparative analysis of the human proteome subset of PaxDb against the two most widely used human proteome data resources: Human Protein Atlas and Genotype-Tissue Expression. Lastly, through our protein abundance data, we reveal an evolutionary trend in the usage of sulfur-containing amino acids in the proteomes of Fungi. Proteome ; Biology ; Computational biology ; Proteomics ; Protein expression ; Organism ; Human proteome project ; Model organism ; Bioinformatics ; Genetics ; Gene Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Bioinformatics and Genomic Networks
Authors: Gabriel Bradford | Jeffrey Lopez | Jurğis Ruža | Michael A. Stolberg | Richard Osterude | Jeremiah A. Johnson | Rafael Gómez‐Bombarelli | Yang Shao‐Horn
Source: ACS Central Science (article, 2023-01-23)
Field: Materials Science — Materials Chemistry

Chemistry-Informed Machine Learning for Polymer Electrolyte Discovery Solid polymer electrolytes (SPEs) have the potential to improve lithium-ion batteries by enhancing safety and enabling higher energy densities. However, SPEs suffer from significantly lower ionic conductivity than liquid and solid ceramic electrolytes, limiting their adoption in functional batteries. To facilitate more rapid discovery of high ionic conductivity SPEs, we developed a chemistry-informed machine learning model that accurately predicts ionic conductivity of SPEs. The model was trained on SPE ionic conductivity data from hundreds of experimental publications. Our chemistry-informed model encodes the Arrhenius equation, which describes temperature activated processes, into the readout layer of a state-of-the-art message passing neural network and has significantly improved accuracy over models that do not encode temperature dependence. Chemically informed readout layers are compatible with deep learning for other property prediction tasks and are especially useful where limited training data are available. Using the trained model, ionic conductivity values were predicted for several thousand candidate SPE formulations, allowing us to identify promising candidate SPEs. We also generated predictions for several different anions in poly(ethylene oxide) and poly(trimethylene carbonate), demonstrating the utility of our model in identifying descriptors for SPE ionic conductivity. Ionic conductivity ; Conductivity ; Electrolyte ; Ionic bonding ; Ethylene carbonate ; Ceramic ; Arrhenius equation ; Materials science ; Computer science ; Ion ; Chemistry ; Activation energy ; Physical chemistry ; Organic chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Materials and Technologies
Authors: Samantha M. McDonald | Emily K. Augustine | Quinn Lanners | Cynthia Rudin | L. Catherine Brinson | Matthew L. Becker
Source: Nature Communications (review, 2023-08-10)
Field: Materials Science — Materials Chemistry

Applied machine learning as a driver for polymeric biomaterials design Polymers are ubiquitous to almost every aspect of modern society and their use in medical products is similarly pervasive. Despite this, the diversity in commercial polymers used in medicine is stunningly low. Considerable time and resources have been extended over the years towards the development of new polymeric biomaterials which address unmet needs left by the current generation of medical-grade polymers. Machine learning (ML) presents an unprecedented opportunity in this field to bypass the need for trial-and-error synthesis, thus reducing the time and resources invested into new discoveries critical for advancing medical treatments. Current efforts pioneering applied ML in polymer design have employed combinatorial and high throughput experimental design to address data availability concerns. However, the lack of available and standardized characterization of parameters relevant to medicine, including degradation time and biocompatibility, represents a nearly insurmountable obstacle to ML-aided design of biomaterials. Herein, we identify a gap at the intersection of applied ML and biomedical polymer design, highlight current works at this junction more broadly and provide an outlook on challenges and future directions. Computer science ; Obstacle ; Biocompatibility ; Nanotechnology ; Intersection (aeronautics) ; Biochemical engineering ; Risk analysis (engineering) ; Data science ; Materials science ; Medicine ; Engineering Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Cell Image Analysis Techniques
Authors: Bohayra Mortazavi | Xiaoying Zhuang | Timon Rabczuk | Alexander V. Shapeev
Source: Materials Horizons (review, 2023-01-01)
Field: Materials Science — Materials Chemistry

Atomistic modeling of the mechanical properties: the rise of machine learning interatomic potentials Since the birth of the concept of machine learning interatomic potentials (MLIPs) in 2007, a growing interest has been developed in the replacement of empirical interatomic potentials (EIPs) with MLIPs, in order to conduct more accurate and reliable molecular dynamics calculations. As an exciting novel progress, in the last couple of years the applications of MLIPs have been extended towards the analysis of mechanical and failure responses, providing novel opportunities not heretofore efficiently achievable, neither by EIPs nor by density functional theory (DFT) calculations. In this minireview, we first briefly discuss the basic concepts of MLIPs and outline popular strategies for developing a MLIP. Next, by considering several examples of recent studies, the robustness of MLIPs in the analysis of the mechanical properties will be highlighted, and their advantages over EIP and DFT methods will be emphasized. MLIPs furthermore offer astonishing capabilities to combine the robustness of the DFT method with continuum mechanics, enabling the first-principles multiscale modeling of mechanical properties of nanostructures at the continuum level. Last but not least, the common challenges of MLIP-based molecular dynamics simulations of mechanical properties are outlined and suggestions for future investigations are proposed. Interatomic potential ; Density functional theory ; Materials science ; Statistical physics ; Molecular dynamics ; Computational chemistry ; Physics ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Hafiz Saad Khaliq | Asad Nauman | Jae‐Won Lee | Hak‐Rin Kim
Source: Advanced Optical Materials (article, 2023-06-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent Progress on Plasmonic and Dielectric Chiral Metasurfaces: Fundamentals, Design Strategies, and Implementation Abstract Over the years, researchers have been exploring ways to artificially design chiral structures and materials, namely metamaterials and metasurfaces. They exhibit unique optical properties that can be used for various applications. However, metasurfaces comprise symmetry‐breaking structures that provide a more convenient solution for planar chiral optics regardless of whether they are plasmonic or dielectric. In general, plasmonic chiral metasurfaces are more suitable for applications requiring a high confinement level and substantial optical near‐field enhancement. In contrast, dielectric chiral metasurfaces are ideal for wide operating wavelength ranges and low losses. This review summarizes the recent progress on plasmonic and dielectric chiral metasurfaces. It includes the fundamental concepts, design strategies, and their implementation for applications in holographic displays, imaging and sensing, and detection. Moreover, an overview of chiral metasurfaces to generate the nonlinear effects, hosting bound states in the continuum, and the significant role of machine‐learning‐based design approaches are also discussed. Finally, some future developments are highlighted where chiral metasurfaces are expected to play a vital role. Plasmon ; Metamaterial ; Dielectric ; Materials science ; Holography ; Nanotechnology ; Optoelectronics ; Optics ; Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Plasmonic and Surface Plasmon Research
Authors: Ziad Alsudairy | Normanda Brown | Allea Campbell | Abrianna Ambus | Bianca Brown | Kayla Smith-Petty | Xinle Li
Source: Materials Chemistry Frontiers (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Covalent organic frameworks in heterogeneous catalysis: recent advances and future perspective This review provides an overview of the recent advances in heterogeneous catalysis using COFs, primarily from 2020 to 2023, covering the fundamentals, advantages, current challenges, and future perspectives. Perspective (graphical) ; Nanotechnology ; Catalysis ; Covalent bond ; Materials science ; Chemistry ; Computer science ; Organic chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Jean‐François Masson | John S. Biggins | Emilie Ringe
Source: Nature Nanotechnology (review, 2023-01-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning for nanoplasmonics Photothermal therapy ; Computer science ; Nanotechnology ; Big data ; Plasmon ; Transformative learning ; Field (mathematics) ; Nanostructure ; Materials science ; Nanomaterials ; Artificial intelligence ; Data mining ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Machine Learning in Materials Science ; Advanced biosensing and bioanalysis techniques
Authors: Mohammad Javad Hajipour | Reihaneh Safavi‐Sohi | Shahriar Sharifi | Nouf N. Mahmoud | Ali Akbar Ashkarran | Elizabeth Voke | Vahid Serpooshan | Milad Ramezankhani | Abbas S. Milani | Markita P. Landry | Morteza Mahmoudi
Source: Small (review, 2023-04-29)
Field: Materials Science — Biomaterials

An Overview of Nanoparticle Protein Corona Literature The protein corona forms spontaneously on nanoparticle surfaces when nanomaterials are introduced into any biological system/fluid. Reliable characterization of the protein corona is, therefore, a vital step in the development of safe and efficient diagnostic and therapeutic nanomedicine products. 2134 published manuscripts on the protein corona are reviewed and a down-selection of 470 papers spanning 2000-2021, comprising 1702 nanoparticle (NP) systems is analyzed. This analysis reveals: i) most corona studies have been conducted on metal and metal oxide nanoparticles; ii) despite their overwhelming presence in clinical practice, lipid-based NPs are underrepresented in protein corona research, iii) studies use new methods to improve reliability and reproducibility in protein corona research; iv) studies use more specific protein sources toward personalized medicine; and v) careful characterization of nanoparticles after corona formation is imperative to minimize the role of aggregation and protein contamination on corona outcomes. As nanoparticles used in biomedicine become increasingly prevalent and biochemically complex, the field of protein corona research will need to focus on developing analytical approaches and characterization techniques appropriate for each unique nanoparticle formulation. Achieving such characterization of the nano-bio interface of nanobiotechnologies will enable more seamless development and safe implementation of nanoparticles in medicine. Nanomedicine ; Corona (planetary geology) ; Nanotechnology ; Nanoparticle ; Characterization (materials science) ; Materials science ; Nanomaterials ; Biomedicine ; Nanotoxicology ; Computer science ; Bioinformatics ; Physics ; Biology Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Protein Interaction Studies and Fluorescence Analysis
Authors: Manuel Aureliano | Ana Luísa De Sousa‐Coelho | Connor C. Dolan | Deborah A. Roess | Debbie C. Crans
Source: International Journal of Molecular Sciences (review, 2023-03-11)
Field: Chemistry — Inorganic Chemistry

Biological Consequences of Vanadium Effects on Formation of Reactive Oxygen Species and Lipid Peroxidation Lipid peroxidation (LPO), a process that affects human health, can be induced by exposure to vanadium salts and compounds. LPO is often exacerbated by oxidation stress, with some forms of vanadium providing protective effects. The LPO reaction involves the oxidation of the alkene bonds, primarily in polyunsaturated fatty acids, in a chain reaction to form radical and reactive oxygen species (ROS). LPO reactions typically affect cellular membranes through direct effects on membrane structure and function as well as impacting other cellular functions due to increases in ROS. Although LPO effects on mitochondrial function have been studied in detail, other cellular components and organelles are affected. Because vanadium salts and complexes can induce ROS formation both directly and indirectly, the study of LPO arising from increased ROS should include investigations of both processes. This is made more challenging by the range of vanadium species that exist under physiological conditions and the diverse effects of these species. Thus, complex vanadium chemistry requires speciation studies of vanadium to evaluate the direct and indirect effects of the various species that are present during vanadium exposure. Undoubtedly, speciation is important in assessing how vanadium exerts effects in biological systems and is likely the underlying cause for some of the beneficial effects reported in cancerous, diabetic, neurodegenerative conditions and other diseased tissues impacted by LPO processes. Speciation of vanadium, together with investigations of ROS and LPO, should be considered in future biological studies evaluating vanadium effects on the formation of ROS and on LPO in cells, tissues, and organisms as discussed in this review. Vanadium ; Reactive oxygen species ; Chemistry ; Lipid peroxidation ; Oxidative stress ; Antioxidant ; Biochemistry ; Biophysics ; Biology ; Inorganic chemistry Vanadium and Halogenation Chemistry ; Heme Oxygenase-1 and Carbon Monoxide ; Metal-Catalyzed Oxygenation Mechanisms