jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Felix Full | Quentin Wölflick | Krzysztof Radacki | Holger Braunschweig | Agnieszka Nowak‐Król
Source: Chemistry - A European Journal (article, 2022-07-25)
Field: Chemistry — Organic Chemistry

Enhanced Optical Properties of Azaborole Helicenes by Lateral and Helical Extension The synthesis and characterization of laterally extended azabora[5]-, -[6]- and -[7]helicenes, assembled from N-heteroaromatic and dibenzo[g,p]chrysene building blocks is described. Formally, the π-conjugated systems of the pristine azaborole helicenes were enlarged with a phenanthrene unit leading to compounds with large Stokes shifts, significantly enhanced luminescence quantum yields (Φ) and dissymmetry factors (g<sub>lum</sub> ). The beneficial effect on optical properties was also observed for helical elongation. The combined contributions of lateral and helical extensions resulted in a compound showing green emission with Φ of 0.31 and |g<sub>lum</sub> | of 2.2×10<sup>-3</sup> , highest within the series of π-extended azaborahelicenes and superior to emission intensity and chiroptical response of its non-extended congener. This study shows that helical and lateral extensions of π-conjugated systems are viable strategies to improve features of azaborole helicenes. In addition, single crystal X-ray analysis of configurationally stable [6]- and -[7]helicenes was used to provide insight into their packing arrangements. Luminescence ; Helicene ; Conjugated system ; Phenanthrene ; Chemistry ; Crystallography ; Chrysene ; Stereochemistry ; Elongation ; Materials science ; Polymer ; Molecule ; Organic chemistry ; Optoelectronics ; Ultimate tensile strength Synthesis and Properties of Aromatic Compounds ; Photochromic and Fluorescence Chemistry ; DNA and Nucleic Acid Chemistry
Authors: Lorena Mendive‐Tapia | David Mendive‐Tapia | Can Zhao | Doireann Gordon | Sam Benson | Michael Bromley | Wei Wang | Jun Wu | Adelina Kopp | Lutz Ackermann | Marc Vendrell
Source: Angewandte Chemie International Edition (article, 2022-01-25)
Field: Materials Science — Materials Chemistry

Rational Design of Phe‐BODIPY Amino Acids as Fluorogenic Building Blocks for Peptide‐Based Detection of Urinary Tract <i>Candida</i> Infections Fungal infections caused by Candida species are among the most prevalent in hospitalized patients. However, current methods for the detection of Candida fungal cells in clinical samples rely on time-consuming assays that hamper rapid and reliable diagnosis. Herein, we describe the rational development of new Phe-BODIPY amino acids as small fluorogenic building blocks and their application to generate fluorescent antimicrobial peptides for rapid labelling of Candida cells in urine. We have used computational methods to analyse the fluorogenic behaviour of BODIPY-substituted aromatic amino acids and performed bioactivity and confocal microscopy experiments in different strains to confirm the utility and versatility of peptides incorporating Phe-BODIPYs. Finally, we have designed a simple and sensitive fluorescence-based assay for the detection of Candida albicans in human urine samples. BODIPY ; Candida albicans ; Peptide ; Amino acid ; Rational design ; Fluorescence ; Antimicrobial ; Chemistry ; Confocal microscopy ; Fluorescence microscope ; Biochemistry ; Antimicrobial peptides ; Urinary system ; Antifungal ; Microbiology ; Biology ; Combinatorial chemistry ; Cell biology Luminescence and Fluorescent Materials ; Nanoplatforms for cancer theranostics ; Antimicrobial Peptides and Activities
Authors: Sanggil Park | Herim Han | Hyungjun Kim | Sunghwan Choi
Source: Chemistry - An Asian Journal (review, 2022-04-26)
Field: Materials Science — Materials Chemistry

Machine Learning Applications for Chemical Reactions Machine learning (ML) approaches have enabled rapid and efficient molecular property predictions as well as the design of new novel materials. In addition to great success for molecular problems, ML techniques are applied to various chemical reaction problems that require huge costs to solve with the existing experimental and simulation methods. In this review, starting with basic representations of chemical reactions, we summarized recent achievements of ML studies on two different problems; predicting reaction properties and synthetic routes. The various ML models are used to predict physical properties related to chemical reaction properties (e. g. thermodynamic changes, activation barriers, and reaction rates). Furthermore, the predictions of reactivity, self-optimization of reaction, and designing retrosynthetic reaction paths are also tackled by ML approaches. Herein we illustrate various ML strategies utilized in the various context of chemical reaction studies. Computer science ; Chemical reaction ; Context (archaeology) ; Biochemical engineering ; Property (philosophy) ; Reaction conditions ; Retrosynthetic analysis ; Artificial intelligence ; Chemistry ; Organic chemistry ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Hocine Khelifa | Éric Vagnon | Abderrahmane Béroual
Source: IEEE Access (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

AC Breakdown Voltage and Partial Discharge Activity in Synthetic Ester-Based Fullerene and Graphene Nanofluids This paper deals with the study of the AC breakdown voltage (BDV) and partial discharges (PDs) activity of synthetic ester-based nanofluids (NF) with two kinds of carbonic nanoparticles (NPs), namely graphene (Gr) and fullerene (C<sub>60</sub>); the synthetic ester (SE) being Midel 7131. The BDV measurement was achieved at various concentrations of NPs and different electrodes gap distances, while the partial discharges activity was studied only at the optimal concentration that gave the best improvements of BDV. First, a detailed improved procedure for preparing our NFs is presented. Then, the zeta potential measurements are performed on these NFs, and their stability is checked. After the preparation and characterization of samples, the BDVs and PDs parameters are measured according to IEC 60156 (modified gap distance) and IEC 60270 standards, respectively. Finally, conformity of the experimental data with Normal and Weibull distributions is examined, and the BDV at cumulative probabilities of 1&#x0025; and 50&#x0025; are then deduced. It is shown that BDV outcomes for all studied liquids obey both Normal and Weibull distributions, and the BDVs at cumulative probabilities of 1&#x0025; and 50&#x0025; are improved. Moreover, adding Gr and C<sub>60</sub> nanoparticles at different concentrations enhances the BDV values for investigated electrode gaps (0.1 to 2.0mm). The best improvement is obtained with a concentration of 0.4 g/L for 0.5mm and 0.7mm electrode gap with fullerene and Graphene nanoparticles, respectively. For a 2mm gap distance, the best improvements are of about 12.67&#x0025; and 16.64&#x0025; with 0.4g/L of C<sub>60</sub> and 0.3 g/L of Gr, respectively. It is also shown that the addition of C<sub>60</sub> significantly reduces the activity of partial discharges compared to pure SE, while the addition of Gr destroys the partial discharges resistance of pure SE. Graphene ; Materials science ; Nanofluid ; Fullerene ; Electrode ; Zeta potential ; Nanoparticle ; Partial discharge ; Weibull distribution ; Nanotechnology ; Voltage ; Chemical engineering ; Analytical Chemistry (journal) ; Organic chemistry ; Chemistry ; Physical chemistry ; Electrical engineering ; Mathematics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Lightning and Electromagnetic Phenomena
Authors: Shuo‐Qing Zhang | Li‐Cheng Xu | Shu‐Wen Li | João C. A. Oliveira | Xin Li | Lutz Ackermann | Xin Hong
Source: Chemistry - A European Journal (review, 2022-10-07)
Field: Materials Science — Materials Chemistry

Bridging Chemical Knowledge and Machine Learning for Performance Prediction of Organic Synthesis Recent years have witnessed a boom of machine learning (ML) applications in chemistry, which reveals the potential of data-driven prediction of synthesis performance. Digitalization and ML modelling are the key strategies to fully exploit the unique potential within the synergistic interplay between experimental data and the robust prediction of performance and selectivity. A series of exciting studies have demonstrated the importance of chemical knowledge implementation in ML, which improves the model's capability for making predictions that are challenging and often go beyond the abilities of human beings. This Minireview summarizes the cutting-edge embedding techniques and model designs in synthetic performance prediction, elaborating how chemical knowledge can be incorporated into machine learning until June 2022. By merging organic synthesis tactics and chemical informatics, we hope this Review can provide a guide map and intrigue chemists to revisit the digitalization and computerization of organic chemistry principles. Computer science ; Artificial intelligence ; Bridging (networking) ; Exploit ; Embedding ; Machine learning ; Informatics ; Boom ; Deep learning ; Data science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Arun Mannodi‐Kanakkithodi | Xiaofeng Xiang | Laura M. Jacoby | Robert Biegaj | Scott T. Dunham | Daniel R. Gamelin | Maria K. Y. Chan
Source: Patterns (article, 2022-02-14)
Field: Materials Science — Materials Chemistry

Universal machine learning framework for defect predictions in zinc blende semiconductors We develop a framework powered by machine learning (ML) and high-throughput density functional theory (DFT) computations for the prediction and screening of functional impurities in groups IV, III-V, and II-VI zinc blende semiconductors. Elements spanning the length and breadth of the periodic table are considered as impurity atoms at the cation, anion, or interstitial sites in supercells of 34 candidate semiconductors, leading to a chemical space of approximately 12,000 points, 10% of which are used to generate a DFT dataset of charge dependent defect formation energies. Descriptors based on tabulated elemental properties, defect coordination environment, and relevant semiconductor properties are used to train ML regression models for the DFT computed neutral state formation energies and charge transition levels of impurities. Optimized kernel ridge, Gaussian process, random forest, and neural network regression models are applied to screen impurities with lower formation energy than dominant native defects in all compounds. Density functional theory ; Semiconductor ; Impurity ; Kernel (algebra) ; Materials science ; Hybrid functional ; Zinc ; Machine learning ; Computational chemistry ; Chemical physics ; Chemistry ; Computer science ; Optoelectronics ; Mathematics ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Nguyen Vu Ha Anh | Alessandro Lunghi
Source: Physical review. B./Physical review. B (article, 2022-04-18)
Field: Materials Science — Materials Chemistry

Predicting tensorial molecular properties with equivariant machine learning models Embedding molecular symmetries into machine learning models is key for efficient learning of chemico-physical scalar properties, but little evidence on how to extend the same strategy to tensorial quantities exists. Here we formulate a scalable equivariant machine learning model based on local atomic environment descriptors. We apply it to a series of molecules and show that accurate predictions can be achieved for a comprehensive list of dielectric and magnetic tensorial properties of different ranks. These results show that equivariant models are a promising platform to extend the scope of machine learning in materials modeling. Equivariant map ; Homogeneous space ; Embedding ; Key (lock) ; Computer science ; Scope (computer science) ; Scalar (mathematics) ; Scalability ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Mathematics ; Pure mathematics ; Programming language ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Younes Dehmani | Juliette Lainé | Ayoub Daouli | Lotfi Sellaoui | Adrián Bonilla‐Petriciolet | Taibi Lamhasni | Sadik Abouarnadasse | Michaël Badawi
Source: Chemical Engineering Journal (article, 2022-09-14)
Field: Chemistry — Physical and Theoretical Chemistry

Unravelling the adsorption mechanism of phenol on zinc oxide at various coverages via statistical physics, artificial neural network modeling and ab initio molecular dynamics Adsorption ; Ab initio ; Zinc ; Phenol ; Computational chemistry ; Molecular dynamics ; Chemistry ; Molecule ; Chemical physics ; Hydrogen bond ; Materials science ; Physical chemistry ; Thermodynamics ; Organic chemistry ; Physics Electrostatics and Colloid Interactions ; Water Quality Monitoring and Analysis
Authors: Chen Zhou | Hio Tong Ngan | Jin Soo Lim | Zubin Darbari | Adrián Leandro Lewandowski | Darı́o Stacchiola | Boris Kozinsky | Philippe Sautet | J. Anibal Boscoboinik
Source: Journal of the American Chemical Society (article, 2022-08-11)
Field: Materials Science — Materials Chemistry

Dynamical Study of Adsorbate-Induced Restructuring Kinetics in Bimetallic Catalysts Using the PdAu(111) Model System Dynamic restructuring of bimetallic catalysts plays a crucial role in their catalytic activity and selectivity. In particular, catalyst pretreatment with species such as carbon monoxide and oxygen has been shown to be an effective strategy for tuning the surface composition and morphology. Mechanistic and kinetic understanding of such restructuring is fundamental to the chemistry and engineering of surface active sites but has remained challenging due to the large structural, chemical, and temporal degrees of freedom. Here, we combine time-resolved temperature-programmed infrared reflection absorption spectroscopy, <i>ab initio</i> thermodynamics, and machine-learning molecular dynamics to uncover previously unidentified timescale and kinetic parameters of <i>in situ</i> restructuring in Pd/Au(111), a highly relevant model system for dilute Pd-in-Au nanoparticle catalysts. The key innovation lies in utilizing CO not only as a chemically sensitive probe of surface Pd but also as an agent that induces restructuring of the surface. Upon annealing in vacuum, as-deposited Pd islands became encapsulated by Au and partially dissolved into the subsurface, leaving behind isolated Pd monomers on the surface. Subsequent exposure to 0.1 mbar CO enabled Pd monomers to repopulate the surface up to 373 K, above which complete Pd dissolution occurred by 473 K, with apparent activation energies of 0.14 and 0.48 eV, respectively. These restructuring processes occurred over the span of ∼1000 s at a given temperature. Such a minute-timescale dynamics not only elucidates the fluxional nature of alloy catalysts but also presents an opportunity to fine-tune the surface under moderate temperature and pressure conditions. Bimetallic strip ; Chemistry ; Catalysis ; Carbon monoxide ; Dissolution ; Chemical physics ; Selectivity ; Nanoparticle ; Kinetics ; Chemical engineering ; Physical chemistry ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; nanoparticles nucleation surface interactions
Authors: Syed Aizaz Ali Shah | Ramsha Idrees | Shaukat Saeed
Source: Journal of Energy Storage (review, 2022-09-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A critical review on polyimide derived carbon materials for high-performance supercapacitor electrodes Supercapacitor ; Carbonization ; Materials science ; Carbon fibers ; Heteroatom ; Polyimide ; Nanotechnology ; Electrode ; Nanocomposite ; Chemical engineering ; Carbon nanofiber ; Electrochemistry ; Composite material ; Composite number ; Organic chemistry ; Scanning electron microscope ; Carbon nanotube ; Chemistry ; Layer (electronics) Supercapacitor Materials and Fabrication ; Electrospun Nanofibers in Biomedical Applications ; Electrocatalysts for Energy Conversion
Authors: Jaber Rezaei Mianroodi | Shahed Rezaei | Nima H. Siboni | Bai‐Xiang Xu | Dierk Raabe
Source: npj Computational Materials (article, 2022-04-12)
Field: Materials Science — Materials Chemistry

Lossless multi-scale constitutive elastic relations with artificial intelligence Abstract A seamless and lossless transition of the constitutive description of the elastic response of materials between atomic and continuum scales has been so far elusive. Here we show how this problem can be overcome by using artificial intelligence (AI). A convolutional neural network (CNN) model is trained, by taking the structure image of a nanoporous material as input and the corresponding elasticity tensor, calculated from molecular statics (MS), as output. Trained with the atomistic data, the CNN model captures the size- and pore-dependency of the material’s elastic properties which, on the physics side, derive from its intrinsic stiffness as well as from surface relaxation and non-local effects. To demonstrate the accuracy and the efficiency of the trained CNN model, a finite element method (FEM)-based result of an elastically deformed nanoporous beam equipped with the CNN as constitutive law is compared with that obtained by a full atomistic simulation. The trained CNN model predicts the elasticity tensor in the test dataset with a root-mean-square error of 2.4 GPa (3.0% of the bulk modulus) when compared to atomistic calculations. On the other hand, the CNN model is about 230 times faster than the MS calculation and does not require changing simulation methods between different scales. The efficiency of the CNN evaluation together with the preservation of important atomistic effects makes the trained model an effective atomistically informed constitutive model for macroscopic simulations of nanoporous materials, optimization of nanostructures, and the solution of inverse problems. Constitutive equation ; Convolutional neural network ; Finite element method ; Nanoporous ; Stiffness ; Computer science ; Elasticity (physics) ; Lossless compression ; Artificial neural network ; Continuum mechanics ; Elastic modulus ; Tensor (intrinsic definition) ; Statistical physics ; Materials science ; Algorithm ; Artificial intelligence ; Mathematics ; Physics ; Mechanics ; Data compression ; Geometry ; Composite material ; Nanotechnology ; Thermodynamics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Microstructure and mechanical properties
Authors: Jiahong Shen | Sean D. Griesemer | Abhijith Gopakumar | Bianca Baldassarri | James E. Saal | Muratahan Aykol | Vinay I. Hegde | Chris Wolverton
Source: Journal of Physics Materials (article, 2022-06-23)
Field: Materials Science — Materials Chemistry

Reflections on one million compounds in the open quantum materials database (OQMD) Abstract Density functional theory (DFT) has been widely applied in modern materials discovery and many materials databases, including the open quantum materials database (OQMD), contain large collections of calculated DFT properties of experimentally known crystal structures and hypothetical predicted compounds. Since the beginning of the OQMD in late 2010, over one million compounds have now been calculated and stored in the database, which is constantly used by worldwide researchers in advancing materials studies. The growth of the OQMD depends on project-based high-throughput DFT calculations, including structure-based projects, property-based projects, and most recently, machine-learning-based projects. Another major goal of the OQMD is to ensure the openness of its materials data to the public and the OQMD developers are constantly working with other materials databases to reach a universal querying protocol in support of the FAIR data principles. Database ; Openness to experience ; Protocol (science) ; Computer science ; Density functional theory ; Property (philosophy) ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Rahma Jabbar | Rateb Jabbar | S. Kamoun
Source: Computational Materials Science (review, 2022-07-01)
Field: Materials Science — Materials Chemistry

Recent progress in generative adversarial networks applied to inversely designing inorganic materials: A brief review Generative grammar ; Computer science ; Adversarial system ; Process (computing) ; Generative adversarial network ; Artificial intelligence ; Data science ; Machine learning ; Systems engineering ; Deep learning ; Engineering ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Markus J. Buehler
Source: Materials Today (article, 2022-06-09)
Field: Materials Science — Materials Chemistry

FieldPerceiver: Domain agnostic transformer model to predict multiscale physical fields and nonlinear material properties through neural ologs Computer science ; Nonlinear system ; Artificial neural network ; Convolutional neural network ; Transformer ; Physical system ; Field (mathematics) ; Artificial intelligence ; Engineering ; Mathematics ; Physics ; Voltage Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yixin Yu | Yujie Jiang | Chaohui Zhang | Qiang Bai | Fengyue Fu | Siheng Li | Lina Wang | William W. Yu | Ning Sui | Zhiling Zhu
Source: ACS Materials Letters (article, 2022-09-29)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Graphdiyne-Based Nanozyme Discovery Improving the catalytic activity and broadening the scope of nanozymes are prerequisites to supplement or even supersede natural enzymes. However, the discovery of nanozymes is mostly relied on serendipity with limited fine-tunings of chemical composition, which is often incomprehensive and fragmented. Machine learning (ML) is a promising solution to predict the nanozyme performance and thus accelerate the nanozyme development. Herein, a thorough investigation of the peroxidase (POD) mimic reaction catalyzed by nonmetal atom doped graphdiyne (GDY) is presented and two doped GDYs (B-GDY and N-GDY) with best performance were screened out. Specifically, the extreme gradient boosting (XGB) algorithm can mine the connection between the model parameters and maximum energy barrier (R2 > 78%) or maximum energy consuming step (accuracy > 65%) from the data set constructed by all the nonmetal atom doped GDYs, which provides a method to effectively reduce 20% calculations. In addition, six nonmetal atom doped GDYs with different expected properties were synthesized, and their activity trends in three related experiments were consistent with the predicted results. This study demonstrates that ML can be a useful tool to assist density functional theory (DFT) computational screening and can serve as a guide for optimizing nanozyme performance, depending on doping strategy. Nonmetal ; Doping ; Catalysis ; Computer science ; Density functional theory ; Atom (system on chip) ; Nanotechnology ; Biochemical engineering ; Artificial intelligence ; Materials science ; Machine learning ; Chemistry ; Computational chemistry ; Organic chemistry ; Engineering Advanced Nanomaterials in Catalysis ; Nanocluster Synthesis and Applications ; Advanced biosensing and bioanalysis techniques