jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Eugen Hruška | Ariel Gale | Fang Liu
Source: Journal of Chemical Theory and Computation (article, 2022-01-07)
Field: Materials Science — Materials Chemistry

Bridging the Experiment-Calculation Divide: Machine Learning Corrections to Redox Potential Calculations in Implicit and Explicit Solvent Models Prediction of redox potentials is essential for catalysis and energy storage. Although density functional theory (DFT) calculations have enabled rapid redox potential predictions for numerous compounds, prominent errors persist compared to experimental measurements. In this work, we develop machine learning (ML) models to reduce the errors of redox potential calculations in both implicit and explicit solvent models. Training and testing of the ML correction models are based on the diverse ROP313 data set with experimental redox potentials measured for organic and organometallic compounds in a variety of solvents. For the implicit solvent approach, our ML models can reduce both the systematic bias and the number of outliers. ML corrected redox potentials also demonstrate less sensitivity to DFT functional choice. For the explicit solvent approach, we significantly reduce the computational costs by embedding the microsolvated cluster in implicit bulk solvent, obtaining converged redox potential results with a smaller solvation shell. This combined implicit-explicit solvent model, together with GPU-accelerated quantum chemistry methods, enabled rapid generation of a large data set of explicit-solvent-calculated redox potentials for 165 organic compounds, allowing detailed investigation of the error sources in explicit solvent redox potential calculations. Redox ; Solvation ; Solvent ; Chemistry ; Solvent models ; Density functional theory ; Implicit solvation ; Solvent effects ; Computational chemistry ; Computer science ; Chemical physics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Free Radicals and Antioxidants
Authors: Zihao Wang | Yageng Zhou | Teng Zhou | Kai Sundmacher
Source: Computers & Chemical Engineering (article, 2022-02-17)
Field: Chemistry — Inorganic Chemistry

Identification of optimal metal-organic frameworks by machine learning: Structure decomposition, feature integration, and predictive modeling Decomposition ; Identification (biology) ; Feature (linguistics) ; Artificial intelligence ; Computer science ; Machine learning ; Biological system ; Pattern recognition (psychology) ; Chemistry ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science
Authors: Zhenze Yang | Markus J. Buehler
Source: npj Computational Materials (article, 2022-09-17)
Field: Materials Science — Materials Chemistry

Linking atomic structural defects to mesoscale properties in crystalline solids using graph neural networks Abstract Structural defects are abundant in solids, and vital to the macroscopic materials properties. However, a defect-property linkage typically requires significant efforts from experiments or simulations, and often contains limited information due to the breadth of nanoscopic design space. Here we report a graph neural network (GNN)-based approach to achieve direct translation between mesoscale crystalline structures and atom-level properties, emphasizing the effects of structural defects. Our end-to-end method offers great performance and generality in predicting both atomic stress and potential energy of multiple systems with different defects. Furthermore, the approach also precisely captures derivative properties which strictly observe physical laws and reproduces evolution of properties with varying boundary conditions. By incorporating a genetic algorithm, we then design de novo atomic structures with optimum global properties and target local patterns. The method would significantly enhance the efficiency of evaluating atomic behaviors given structural imperfections and accelerates the design process at the meso-level. Generality ; Mesoscale meteorology ; Graph ; Translation (biology) ; Materials science ; Computer science ; Biological system ; Statistical physics ; Topology (electrical circuits) ; Nanotechnology ; Theoretical computer science ; Mathematics ; Physics ; Chemistry Machine Learning in Materials Science ; Graphene research and applications ; Ferroelectric and Negative Capacitance Devices
Authors: Fang Ding | Lun-Yang Liu | Ting-Li Liu | Yunqi Li | Junpeng Li | Zhao‐Yan Sun
Source: Chinese Journal of Polymer Science (article, 2022-10-08)
Field: Materials Science — Polymers and Plastics

Predicting the Mechanical Properties of Polyurethane Elastomers Using Machine Learning Benchmark (surveying) ; Ultimate tensile strength ; Elastomer ; Bridging (networking) ; Modulus ; Materials science ; Computer science ; Computation ; Artificial intelligence ; Polyurethane ; Composite material ; Machine learning ; Algorithm Polymer composites and self-healing ; Infrastructure Maintenance and Monitoring ; Asphalt Pavement Performance Evaluation
Authors: Valentina Hernández-Muñoz | Davor Ibarra-Pérez | Johan F. Triana | Bastian Martínez-Soto | Matías Faúndez | Diego A. Vasco | Leonardo Gordillo | Felipe Herrera | Claudio García‐Herrera | Alysia Garmulewicz
Source: Materials (article, 2022-06-01)
Field: Materials Science — Biomaterials

Agar Biopolymer Films for Biodegradable Packaging: A Reference Dataset for Exploring the Limits of Mechanical Performance This article focuses on agar biopolymer films that offer promise for developing biodegradable packaging, an important solution for reducing plastics pollution. At present there is a lack of data on the mechanical performance of agar biopolymer films using a simple plasticizer. This study takes a Design of Experiments approach to analyze how agar-glycerin biopolymer films perform across a range of ingredients concentrations in terms of their strength, elasticity, and ductility. Our results demonstrate that by systematically varying the quantity of agar and glycerin, tensile properties can be achieved that are comparable to agar-based materials with more complex formulations. Not only does our study significantly broaden the amount of data available on the range of mechanical performance that can be achieved with simple agar biopolymer films, but the data can also be used to guide further optimization efforts that start with a basic formulation that performs well on certain property dimensions. We also find that select formulations have similar tensile properties to thermoplastic starch (TPS), acrylonitrile butadiene styrene (ABS), and polypropylene (PP), indicating potential suitability for select packaging applications. We use our experimental dataset to train a neural network regression model that predicts the Young's modulus, ultimate tensile strength, and elongation at break of agar biopolymer films given their composition. Our findings support the development of further data-driven design and fabrication workflows. Biopolymer ; Agar ; Materials science ; Composite material ; Polymer science ; Polymer Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Sustainable Design and Development
Authors: Aminul Islam | Saman Rais
Source: Food Chemistry (article, 2022-10-28)
Field: Chemistry — Analytical Chemistry

A facile approach for grafting ion imprinted polymer onto magnetic multi-walled carbon nanotubes for selective removal and preconcentration of cadmium in food and wastewater samples prior to atomic spectrometric determination Carbon nanotube ; Grafting ; Cadmium ; Wastewater ; Molecularly imprinted polymer ; Polymer ; Materials science ; Molecular imprinting ; Environmental chemistry ; Chemical engineering ; Nanotechnology ; Chemistry ; Environmental science ; Catalysis ; Organic chemistry ; Selectivity ; Environmental engineering ; Metallurgy ; Composite material Analytical chemistry methods development ; Adsorption and biosorption for pollutant removal ; Electrochemical Analysis and Applications
Authors: Huijie Pan | Liuxing Feng | Yanli Lu | Yachen Han | Jinping Xiong | Hongmei Li
Source: TrAC Trends in Analytical Chemistry (review, 2022-06-09)
Field: Chemistry — Analytical Chemistry

Calibration strategies for laser ablation ICP-MS in biological studies: A review Calibration ; Isotope dilution ; Certified reference materials ; Mass spectrometry ; Analytical Chemistry (journal) ; Chemistry ; Chromatography ; Mathematics ; Detection limit Analytical chemistry methods development ; Mass Spectrometry Techniques and Applications ; Ion-surface interactions and analysis
Authors: Hao Jiang | Seyed Mohamad Moosavi | Justyna Czaban‐Jóźwiak | Bruno Torre | Aleksander Shkurenko | Zied Ouled Ameur | Jiangtao Jia | Norah Alsadun | Osama Shekhah | Enzo Di Fabrizio | Berend Smit | Mohamed Eddaoudi
Source: Matter (article, 2022-10-31)
Field: Chemistry — Inorganic Chemistry

Reticular chemistry for the rational design of mechanically robust mesoporous merged-net metal-organic frameworks Access to metal-organic frameworks (MOFs) with enhanced mechanical stability is key to their successful deployment in practical applications. However, the high porosity of the material often affects mechanical stability. In this article, to achieve highly porous MOFs with enhanced mechanical stability, we explored the merged-net approach where two relatively fragile frameworks were merged into a robust MOF structure. We demonstrate the effectiveness of this approach by computationally evaluating mechanical properties of sph-MOFs with varying lengths of linkers. Prominently, we pinpoint the significance of triangular rigidity on the robustness of large-pore MOFs and, subsequently, designed and synthesized a rare earth (RE)-based RE-sph-MOF-5 by the reticulation of hexanuclear RE clusters, tritopic linkers, and unprecedentedly large planar hexatopic linkers containing 19 phenyl rings. The mechanical properties of sph-MOFs were characterized and quantified using amplitude-frequency modulation (AM-FM) bimodal atomic force microscopy (AFM) analyses. Markedly, the mesoporous RE-sph-MOF-5 expresses high mechanical stability despite its large mesoporous cavities. Mesoporous material ; Metal-organic framework ; Materials science ; Porosity ; Nanotechnology ; Robustness (evolution) ; Planar ; Rigidity (electromagnetism) ; Reticular connective tissue ; Composite material ; Computer science ; Chemistry ; Adsorption ; Organic chemistry ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Boron and Carbon Nanomaterials Research ; Magnetism in coordination complexes
Authors: Samantha P. Kelly | Vikram V. Shende | Autumn R. Flynn | Qingyun Dan | Ying Ye | Janet L. Smith | Sachiko Tsukamoto | Matthew S. Sigman | David H. Sherman
Source: Journal of the American Chemical Society (article, 2022-10-12)
Field: Chemistry — Organic Chemistry

Data Science-Driven Analysis of Substrate-Permissive Diketopiperazine Reverse Prenyltransferase NotF: Applications in Protein Engineering and Cascade Biocatalytic Synthesis of (−)-Eurotiumin A Prenyltransfer is an early-stage carbon-hydrogen bond (C-H) functionalization prevalent in the biosynthesis of a diverse array of biologically active bacterial, fungal, plant, and metazoan diketopiperazine (DKP) alkaloids. Toward the development of a unified strategy for biocatalytic construction of prenylated DKP indole alkaloids, we sought to identify and characterize a substrate-permissive C2 reverse prenyltransferase (PT). As the first tailoring event within the biosynthesis of cytotoxic notoamide metabolites, PT NotF catalyzes C2 reverse prenyltransfer of brevianamide F. Solving a crystal structure of NotF (in complex with native substrate and prenyl donor mimic dimethylallyl S-thiolodiphosphate (DMSPP)) revealed a large, solvent-exposed active site, intimating NotF may possess a significantly broad substrate scope. To assess the substrate selectivity of NotF, we synthesized a panel of 30 sterically and electronically differentiated tryptophanyl DKPs, the majority of which were selectively prenylated by NotF in synthetically useful conversions (2 to >99%). Quantitative representation of this substrate library and development of a descriptive statistical model provided insight into the molecular origins of NotF's substrate promiscuity. This approach enabled the identification of key substrate descriptors (electrophilicity, size, and flexibility) that govern the rate of NotF-catalyzed prenyltransfer, and the development of an "induced fit docking (IFD)-guided" engineering strategy for improved turnover of our largest substrates. We further demonstrated the utility of NotF in tandem with oxidative cyclization using flavin monooxygenase, BvnB. This one-pot, <i>in vitro</i> biocatalytic cascade enabled the first chemoenzymatic synthesis of the marine fungal natural product, (-)-eurotiumin A, in three steps and 60% overall yield. Prenyltransferase ; Chemistry ; Permissive ; Cascade ; Substrate (aquarium) ; Substrate specificity ; Combinatorial chemistry ; Prenylation ; Biochemistry ; Enzyme ; Virology ; Chromatography Carbohydrate Chemistry and Synthesis ; Chemical Synthesis and Analysis ; Enzyme Catalysis and Immobilization
Authors: Louis Godeffroy | Jean‐François Lemineur | Viacheslav Shkirskiy | Mathias Miranda Vieira | Jean‐Marc Noël | Frédéric Kanoufi
Source: Small Methods (article, 2022-07-05)
Field: Chemistry — Electrochemistry

Bridging the Gap between Single Nanoparticle Imaging and Global Electrochemical Response by Correlative Microscopy Assisted By Machine Vision The nanostructuration of an electrochemical interface dictates its micro- and macroscopic behavior. It is generally highly complex and often evolves under operating conditions. Electrochemistry at these nanostructurations can be imaged both operando and/or ex situ at the single nanoobject or nanoparticle (NP) level by diverse optical, electron, and local probe microscopy techniques. However, they only probe a tiny random fraction of interfaces that are by essence highly heterogeneous. Given the above background, correlative multimicroscopy strategy coupled to electrochemistry in a droplet cell provides a unique solution to gain mechanistic insights in electrocatalysis. To do so, a general machine-vision methodology is depicted enabling the automated local identification of various physical and chemical descriptors of NPs (size, composition, activity) obtained from multiple complementary operando and ex situ microscopy imaging of the electrode. These multifarious microscopically probed descriptors for each and all individual NPs are used to reconstruct the global electrochemical response. Herein the methodology unveils the competing processes involved in the electrocatalysis of hydrogen evolution reaction at nickel based NPs, showing that Ni metal activity is comparable to that of platinum. Bridging (networking) ; Correlative ; Nanotechnology ; Microscopy ; Nanoparticle ; Materials science ; Computer science ; Optics ; Computer security ; Physics ; Philosophy Electrochemical Analysis and Applications ; Machine Learning in Materials Science ; Force Microscopy Techniques and Applications
Authors: Ryan‐Rhys Griffiths | Jake L. Greenfield | Aditya R. Thawani | Arian R. Jamasb | Henry B. Moss | Anthony Bourached | Penelope Jones | William McCorkindale | Alexander A. Aldrick | Matthew J. Fuchter | Alpha A. Lee
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Data-driven discovery of molecular photoswitches with multioutput Gaussian processes Photoswitchable molecules display two or more isomeric forms that may be accessed using light. Separating the electronic absorption bands of these isomers is key to selectively addressing a specific isomer and achieving high photostationary states whilst overall red-shifting the absorption bands serves to limit material damage due to UV-exposure and increases penetration depth in photopharmacological applications. Engineering these properties into a system through synthetic design however, remains a challenge. Here, we present a data-driven discovery pipeline for molecular photoswitches underpinned by dataset curation and multitask learning with Gaussian processes. In the prediction of electronic transition wavelengths, we demonstrate that a multioutput Gaussian process (MOGP) trained using labels from four photoswitch transition wavelengths yields the strongest predictive performance relative to single-task models as well as operationally outperforming time-dependent density functional theory (TD-DFT) in terms of the wall-clock time for prediction. We validate our proposed approach experimentally by screening a library of commercially available photoswitchable molecules. Through this screen, we identified several motifs that displayed separated electronic absorption bands of their isomers, exhibited red-shifted absorptions, and are suited for information transfer and photopharmacological applications. Our curated dataset, code, as well as all models are made available at https://github.com/Ryan-Rhys/The-Photoswitch-Dataset. Gaussian ; Gaussian process ; Physics ; Computer science ; Statistical physics ; Quantum mechanics Photochromic and Fluorescence Chemistry ; Photoreceptor and optogenetics research ; Computational Drug Discovery Methods
Authors: Soulmaz Sarkari | Mehran Khajehmohammadi | Niyousha Davari | Dejian Li | Baoqing Yu
Source: Frontiers in Bioengineering and Biotechnology (review, 2022-09-12)
Field: Materials Science — Surfaces, Coatings and Films

The effects of process parameters on polydopamine coatings employed in tissue engineering applications The biomaterials' success within the tissue engineering field is hinged on the capability to regulate tissue and cell responses, comprising cellular adhesion, as well as repair and immune processes' induction. In an attempt to enhance and fulfill these biomaterials' functions, scholars have been inspired by nature; in this regard, surface modification via coating the biomaterials with polydopamine is one of the most successful inspirations endowing the biomaterials with surface adhesive properties. By employing this approach, favorable results have been achieved in various tissue engineering-related experiments, a significant one of which is the more rapid cellular growth observed on the polydopamine-coated substrates compared to the untreated ones; nonetheless, some considerations regarding polydopamine-coated surfaces should be taken into account to control the ultimate outcomes. In this mini-review, the importance of coatings in the tissue engineering field, the different types of surfaces requiring coatings, the significance of polydopamine coatings, critical factors affecting the result of the coating procedure, and recent investigations concerning applications of polydopamine-coated biomaterials in tissue engineering are thoroughly discussed. Tissue engineering ; Coating ; Surface engineering ; Materials science ; Surface modification ; Nanotechnology ; Adhesion ; Adhesive ; Cell adhesion ; Biomedical engineering ; Engineering ; Mechanical engineering ; Composite material ; Layer (electronics) Polymer Surface Interaction Studies ; Electrospun Nanofibers in Biomedical Applications ; Dendrimers and Hyperbranched Polymers
Authors: Ilhan Bok | Ido Haber | Xiaofei Qu | Aviad Hai
Source: Scientific Reports (article, 2022-05-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

In silico assessment of electrophysiological neuronal recordings mediated by magnetoelectric nanoparticles Magnetoelectric materials hold untapped potential to revolutionize biomedical technologies. Sensing of biophysical processes in the brain is a particularly attractive application, with the prospect of using magnetoelectric nanoparticles (MENPs) as injectable agents for rapid brain-wide modulation and recording. Recent studies have demonstrated wireless brain stimulation in vivo using MENPs synthesized from cobalt ferrite (CFO) cores coated with piezoelectric barium titanate (BTO) shells. CFO-BTO core-shell MENPs have a relatively high magnetoelectric coefficient and have been proposed for direct magnetic particle imaging (MPI) of brain electrophysiology. However, the feasibility of acquiring such readouts has not been demonstrated or methodically quantified. Here we present the results of implementing a strain-based finite element magnetoelectric model of CFO-BTO core-shell MENPs and apply the model to quantify magnetization in response to neural electric fields. We use the model to determine optimal MENPs-mediated electrophysiological readouts both at the single neuron level and for MENPs diffusing in bulk neural tissue for in vivo scenarios. Our results lay the groundwork for MENP recording of electrophysiological signals and provide a broad analytical infrastructure to validate MENPs for biomedical applications. Electrophysiology ; Materials science ; Barium titanate ; Computer science ; Neuroscience ; Biomedical engineering ; Nanotechnology ; Optoelectronics ; Medicine ; Dielectric ; Biology Multiferroics and related materials ; Magnetic and transport properties of perovskites and related materials ; Characterization and Applications of Magnetic Nanoparticles
Authors: Shu Huang | Jacqueline M. Cole
Source: Chemical Science (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

BatteryDataExtractor: battery-aware text-mining software embedded with BERT models BatteryDataExtractor is the first property-specific text-mining tool for auto-generating databases of materials and their property, device, and associated characteristics. The software has been constructed by embedding the BatteryBERT model. Computer science ; Python (programming language) ; Security token ; Artificial intelligence ; Information extraction ; Software ; Pipeline (software) ; Named-entity recognition ; Machine learning ; Transformer ; Sentiment analysis ; Documentation ; Data mining ; Natural language processing ; Programming language Machine Learning in Materials Science ; Software Engineering Research ; Topic Modeling
Authors: Iliana N. Apostolova | A. Apostolov | Julia M. Wesselinowa
Source: Nanomaterials (article, 2022-12-28)
Field: Materials Science — Materials Chemistry

Band Gap Tuning in Transition Metal and Rare-Earth-Ion-Doped TiO2, CeO2, and SnO2 Nanoparticles The energy gap Eg between the valence and conduction bands is a key characteristic of semiconductors. Semiconductors, such as TiO2, SnO2, and CeO2 have a relatively wide band gap Eg that only allows the material to absorb UV light. Using the s-d microscopic model and the Green's function method, we have shown two possibilities to reduce the band-gap energy Eg-reducing the NP size and/or ion doping with transition metals (Co, Fe, Mn, and Cu) or rare earth (Sm, Tb, and Er) ions. Different strains appear that lead to changes in the exchange-interaction constants, and thus to a decrease in Eg. Moreover, the importance of the s-d interaction, which causes room-temperature ferromagnetism and band-gap energy tuning in dilute magnetic semiconductors, is shown. We tried to clarify some discrepancies in the experimental data. Band gap ; Doping ; Semiconductor ; Materials science ; Magnetic semiconductor ; Valence (chemistry) ; Condensed matter physics ; Ion ; Ferromagnetism ; Semimetal ; Transition metal ; Nanoparticle ; Wide-bandgap semiconductor ; Chemical physics ; Nanotechnology ; Optoelectronics ; Chemistry ; Physics ZnO doping and properties ; Gas Sensing Nanomaterials and Sensors ; Quantum Dots Synthesis And Properties