jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Subba Rao Chalasani | T. Geetha | C. Chellaswamy | Srinivasan Arul
Source: Optical Engineering (article, 2021-12-16)
Field: Chemistry — Spectroscopy

Optimized convolutional neural network-based multigas detection using fiber optic sensor We developed a convolutional neural network-based high-sensitivity gas optical sensor (CNN-HGS) using photonic crystals for detecting different gases (N2, He, and CO2). Square-shaped dielectric rods with a cubic lattice index were used for the maintenance of the exact distance and area between the rods. The flower pollination optimization algorithm was used for the improvement of the hyperparameters of the CNN. The proposed CNN-HGS consists of three resonance nanocavities: one nanocavity with a refractive index of 2.6 located at the center and the other two subnanocavities with a refractive index of 2.1 placed in the path of the input and output waveguides, respectively. The experiment for the proposed CNN-HGS was conducted with three certified sample gas cylinders and a precise mass flow controller. A clean air generator was linked to the same pipeline for the dilution of the flow of hydrocarbon. During the experiment, a typical gas admixture with concentrations of 60 to 900 ppm was used. Training and testing were conducted using a dataset with 80% and 20% of the total, respectively. The result shows that the proposed optimized CNN delivers a 7.5% improvement in the training accuracy. The CNN-HGS was tested and compared with three other optimization techniques, and the result shows the superiority of the proposed method. Computer science ; Hyperparameter ; Convolutional neural network ; Materials science ; Refractive index ; Sensitivity (control systems) ; Algorithm ; Artificial intelligence ; Optoelectronics ; Electronic engineering Spectroscopy and Laser Applications ; Photonic and Optical Devices ; Gas Sensing Nanomaterials and Sensors
Authors: Marica Grossegesse | Andreas Nitsche | Lars Schaade | Joerg Doellinger
Source: PROTEOMICS (article, 2021-02-22)
Field: Chemistry — Spectroscopy

Application of spectral library prediction for parallel reaction monitoring of viral peptides A major part of the analysis of parallel reaction monitoring (PRM) data is the comparison of observed fragment ion intensities to a library spectrum. Classically, these libraries are generated by data-dependent acquisition (DDA). Here, we test Prosit, a published deep neural network algorithm, for its applicability in predicting spectral libraries for PRM. For this purpose, we targeted 1529 precursors derived from synthetic viral peptides and analyzed the data with Prosit and DDA-derived libraries. Viral peptides were chosen as an example, because virology is an area where in silico library generation could significantly improve PRM assay design. With both libraries a total of 1174 precursors were identified. Notably, compared to the DDA-derived library, we could identify 101 more precursors by using the Prosit-derived library. Additionally, we show that Prosit can be applied to predict tandem mass spectra of synthetic viral peptides with different collision energies. Finally, we used a spectral library predicted by Prosit and a DDA library to identify SARS-CoV-2 peptides from a simulated oropharyngeal swab demonstrating that both libraries are suited for peptide identification by PRM. Summarized, Prosit-derived viral spectral libraries predicted in silico can be used for PRM data analysis, making DDA analysis for library generation partially redundant in the future. In silico ; Peptide library ; Computational biology ; Computer science ; Identification (biology) ; Peptide ; Tandem mass spectrometry ; Combinatorial chemistry ; Chemistry ; Biological system ; Mass spectrometry ; Biology ; Peptide sequence ; Chromatography ; Biochemistry Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; SARS-CoV-2 detection and testing
Authors: Evgeny Posenitskiy | Fernand Spiegelman | Didier Lemoine
Source: Machine Learning Science and Technology (article, 2021-07-13)
Field: Materials Science — Materials Chemistry

On application of deep learning to simplified quantum-classical dynamics in electronically excited states Abstract Deep learning (DL) is applied to simulate non-adiabatic molecular dynamics of phenanthrene, using the time-dependent density functional based tight binding (TD-DFTB) approach for excited states combined with mixed quantum–classical propagation. Reference calculations rely on Tully’s fewest-switches surface hopping (FSSH) algorithm coupled to TD-DFTB, which provides electronic relaxation dynamics in fair agreement with various available experimental results. Aiming at describing the coupled electron-nuclei dynamics in large molecular systems, we then examine the combination of DL for excited-state potential energy surfaces (PESs) with a simplified trajectory surface hopping propagation based on the Belyaev–Lebedev (BL) scheme. We start to assess the accuracy of the TD-DFTB approach upon comparison of the optical spectrum with experimental and higher-level theoretical results. Using the recently developed SchNetPack (Schütt et al 2019 J. Chem. Theory Comput. 15 448–55) for DL applications, we train several models and evaluate their performance in predicting excited-state energies and forces. Then, the main focus is given to the analysis of the electronic population of low-lying excited states computed with the aforementioned methods. We determine the relaxation timescales and compare them with experimental data. Our results show that DL demonstrates its ability to describe the excited-state PESs. When coupled to the simplified BL scheme considered in this study, it provides reliable description of the electronic relaxation in phenanthrene as compared with either the experimental data or the higher-level FSSH/TD-DFTB theoretical results. Furthermore, the DL performance allows high-throughput analysis at a negligible cost. Excited state ; Surface hopping ; Relaxation (psychology) ; Statistical physics ; Adiabatic process ; Potential energy surface ; Molecular dynamics ; Population ; Physics ; Quantum ; Computational physics ; Atomic physics ; Chemistry ; Quantum mechanics ; Ab initio Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Sibo Lin | Jenna C. Fromer | Yagnaseni Ghosh | Brian S. Hanna | Mohamed Elanany | Wei Xu
Source: Scientific Reports (article, 2021-02-25)
Field: Chemistry — Inorganic Chemistry

Computer-assisted catalyst development via automated modelling of conformationally complex molecules: application to diphosphinoamine ligands Simulation of conformationally complicated molecules requires multiple levels of theory to obtain accurate thermodynamics, requiring significant researcher time to implement. We automate this workflow using all open-source code (XTBDFT) and apply it toward a practical challenge: diphosphinoamine (PNP) ligands used for ethylene tetramerization catalysis may isomerize (with deleterious effects) to iminobisphosphines (PPNs), and a computational method to evaluate PNP ligand candidates would save significant experimental effort. We use XTBDFT to calculate the thermodynamic stability of a wide range of conformationally complex PNP ligands against isomeriation to PPN (ΔG<sub>PPN</sub>), and establish a strong correlation between ΔG<sub>PPN</sub> and catalyst performance. Finally, we apply our method to screen novel PNP candidates, saving significant time by ruling out candidates with non-trivial synthetic routes and poor expected catalytic performance. Workflow ; Catalysis ; Ligand (biochemistry) ; Molecule ; Ethylene ; Computer science ; Stability (learning theory) ; Combinatorial chemistry ; Chemistry ; Range (aeronautics) ; Computational chemistry ; Materials science ; Database ; Organic chemistry ; Machine learning ; Biochemistry Asymmetric Hydrogenation and Catalysis ; Synthetic Organic Chemistry Methods ; Organometallic Complex Synthesis and Catalysis
Authors: Kyle Groden | Fernando D. Vila | Li Li | Simon R. Bare | Susannah L. Scott | Jean‐Sabin McEwen
Source: The Journal of Physical Chemistry C (article, 2021-11-24)
Field: Materials Science — Materials Chemistry

First-Principles Approach to Extracting Chemical Information from X-ray Absorption Near-Edge Spectra of Ga-Containing Materials The X-ray absorption near-edge structure (XANES) can provide uniquely detailed information on the coordination environments of important Ga-containing materials with unknown structures, including catalytically active materials. In this study, the Ga K-edge XANES was simulated using first-principles-based methods for seven molecular Ga complexes as well β-Ga2O3, in order to explore the chemical origins of the experimentally observed features. The theoretical spectra were computed using FEFF, CASTEP, and StoBe, to assess the sensitivity of the results to the computational approach. While the XANES features depend on the Ga coordination environment, they are also sensitive to the electronegativity of the ligands and the symmetry at Ga. The white line position responds to changes in both the core state (due to differential screening) and the valence “p” states (arising from differences in ligand coordination). Enhanced Data Rates for GSM Evolution ; Spectral line ; Absorption edge ; X-ray ; Absorption spectroscopy ; Absorption (acoustics) ; Materials science ; XANES ; Crystallography ; Optics ; Physics ; Chemistry ; Optoelectronics ; Computer science ; Artificial intelligence ; Astronomy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: Christoph Schran | Fabian L. Thiemann | P.N. Rowe | Erich A. Müller | Ondřej Maršálek | Angelos Michaelides
Source: Proceedings of the National Academy of Sciences (preprint, 2021-09-13)
Field: Materials Science — Materials Chemistry

Machine learning potentials for complex aqueous systems made simple Simulation techniques based on accurate and efficient representations of potential energy surfaces are urgently needed for the understanding of complex systems such as solid-liquid interfaces. Here we present a machine learning framework that enables the efficient development and validation of models for complex aqueous systems. Instead of trying to deliver a globally optimal machine learning potential, we propose to develop models applicable to specific thermodynamic state points in a simple and user-friendly process. After an initial ab initio simulation, a machine learning potential is constructed with minimum human effort through a data-driven active learning protocol. Such models can afterward be applied in exhaustive simulations to provide reliable answers for the scientific question at hand or to systematically explore the thermal performance of ab initio methods. We showcase this methodology on a diverse set of aqueous systems comprising bulk water with different ions in solution, water on a titanium dioxide surface, and water confined in nanotubes and between molybdenum disulfide sheets. Highlighting the accuracy of our approach with respect to the underlying ab initio reference, the resulting models are evaluated in detail with an automated validation protocol that includes structural and dynamical properties and the precision of the force prediction of the models. Finally, we demonstrate the capabilities of our approach for the description of water on the rutile titanium dioxide (110) surface to analyze the structure and mobility of water on this surface. Such machine learning models provide a straightforward and uncomplicated but accurate extension of simulation time and length scales for complex systems. Computer science ; Machine learning ; Artificial intelligence ; Water model ; Ab initio ; Aqueous solution ; Molecular dynamics ; Chemistry ; Computational chemistry ; Physical chemistry Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum many-body systems
Authors: Roslyn S. Massey | Ravi Prakash
Source: Micromachines (article, 2021-12-17)
Field: Materials Science — Polymers and Plastics

Modeling the Double Layer Capacitance Effect in Electrolyte Gated FETs with Gel and Aqueous Electrolytes Potential implementation of bio-gel Electrolyte Double Layer capacitors (bio-gel EDLCs) and electrolyte-gated FET biosensors, two commonly reported configurations of bio-electrolytic electronic devices, requires a robust analysis of their complex internal capacitive behavior. Presently there is neither enough of the parameter extraction literature, nor an effective simulation model to represent the transient behavior of these systems. Our work aims to supplement present transient thin film transistor modelling techniques with the reported parameter extraction method, to accurately model both bio-gel EDLC and the aqueous electrolyte gated FET devices. Our parameter extraction method was tested with capacitors analogous to polymer-electrolyte gated FETs, electrolyte gated Field effect transistor (EGOFET) and Organic Electrolyte Gated Field Effect Transistor (OEGFET) capacitance stacks. Our method predicts the input/output electrical behavior of bio-gel EDLC and EGOFET devices far more accurately than conventional DLC techniques, with less than 5% error. It is also more effective in capturing the characteristic aqueous electrolyte charging behavior and maximum charging capability which are unique to these systems, than the conventional DLC Zubieta and the Two branch models. We believe this significant improvement in device simulation is a pivotal step towards further integration and commercial implementation of organic bio-electrolyte devices. The effective reproduction of the transient response of the OEGFET equivalent system also predicts the transient capacitive effects observed in our previously reported label-free OEGFET biosensor devices. This is the first parameter extraction method specifically designed for electrical parameter-based modelling of organic bio-electrolytic capacitor devices. Electrolyte ; Capacitance ; Materials science ; Capacitor ; Transistor ; Field-effect transistor ; Optoelectronics ; Nanotechnology ; Chemistry ; Electrode ; Electrical engineering ; Voltage ; Engineering Conducting polymers and applications ; Analytical Chemistry and Sensors ; Neuroscience and Neural Engineering
Authors: Zhifen Luo | Stephen A. Burrows | Xiaoli Fan | Stoyan K. Smoukov | Edo S. Boek
Source: Carbon (article, 2021-07-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Virtual voids method to generate low-density microporous carbon structures using quenched molecular dynamics simulation Microporous material ; Materials science ; Porosity ; Carbon fibers ; Amorphous solid ; Void (composites) ; Molecular dynamics ; Amorphous carbon ; Supercapacitor ; Mesoporous material ; Nanotechnology ; Chemical physics ; Composite material ; Electrode ; Crystallography ; Computational chemistry ; Capacitance ; Chemistry ; Physical chemistry Supercapacitor Materials and Fabrication ; Mesoporous Materials and Catalysis ; Hydrogen Storage and Materials
Authors: Tong‐Yi Zhang | Xingjun Liu
Source: Journal of Materials Informatics (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Informatics is fueling new materials discovery Journal of Materials Informatics (JMI) is an international peer-reviewed, open access journal. The journal provides a platform for presentation, publication, and exchange of materials informatics research, seeking to break down barriers among materials science and engineering, data science and engineering, and artificial intelligence. The journal also provides a venue for presenting innovative approaches to overcome the difficulties in unraveling the complexity of data associated with numerous factors including noise, uncertainty, and small amount, to achieve the quantum jump from data to knowledge. Informatics ; Data science ; Presentation (obstetrics) ; Materials informatics ; Computer science ; Engineering informatics ; Health informatics ; Engineering ; Political science ; Medicine Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Anders Brakestad | Peter Wind | Stig Rune Jensen | Luca Frediani | Kathrin H. Hopmann
Source: The Journal of Chemical Physics (article, 2021-06-02)
Field: Materials Science — Materials Chemistry

Multiwavelets applied to metal–ligand interactions: Energies free from basis set errors Transition metal-catalyzed reactions invariably include steps where ligands associate or dissociate. In order to obtain reliable energies for such reactions, sufficiently large basis sets need to be employed. In this paper, we have used high-precision multiwavelet calculations to compute the metal-ligand association energies for 27 transition metal complexes with common ligands, such as H<sub>2</sub>, CO, olefins, and solvent molecules. By comparing our multiwavelet results to a variety of frequently used Gaussian-type basis sets, we show that counterpoise corrections, which are widely employed to correct for basis set superposition errors, often lead to underbinding. Additionally, counterpoise corrections are difficult to employ when the association step also involves a chemical transformation. Multiwavelets, which can be conveniently applied to all types of reactions, provide a promising alternative for computing electronic interaction energies free from any basis set errors. Counterpoise ; Basis (linear algebra) ; Superposition principle ; Transformation (genetics) ; Basis set ; Gaussian ; Set (abstract data type) ; Dimension (graph theory) ; Computer science ; Ligand (biochemistry) ; Statistical physics ; Computational chemistry ; Algorithm ; Chemistry ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electrocatalysts for Energy Conversion
Authors: Francesca D’Acunzo | Giancarlo Masci
Source: Journal of Polymer Science (article, 2021-10-08)
Field: Chemistry — Organic Chemistry

Playing construction with the monomer toy box for the synthesis of multi‐stimuli responsive copolymers by reversible deactivation radical polymerization protocols Abstract In this review article, we survey the 2016–June 2021 scientific literature on the synthesis of multi‐stimuli responsive (MSR) polymers, the main focus being on reversible deactivation radical polymerization techniques (RDRPs, also known as controlled radical polymerizations). In fact, along more than 40 years of extensive research, RDRPs have boosted the synthesis of stimuli‐responsive polymers. RDRPs are now robust, versatile, relatively user‐friendly and even interconvertible, thus allowing control over composition, sequence, and topology of polymers. Such control can afford materials with well‐defined responses to physical, chemical, and biological external stimuli. Furthermore, “click” reactions are used to combine macromolecular precursors or to introduce specific functional groups in the target structure. As a result, MSR polymers are obtained from diverse combinations of commercial or specially synthesized building blocks arranged at will into desired sequences and architectures. Thanks to this versatility, self‐assembling polymeric structures are designed either to respond to triggers and perform specific applicative tasks, or to investigate the influence of structural variables on the responsivity of polymers. The “green” trend emerging in the field of responsive polymers and RDRPs is also briefly discussed. Polymer ; Monomer ; Polymerization ; Combinatorial chemistry ; Copolymer ; Nanotechnology ; Materials science ; Radical polymerization ; Chemistry ; Computer science ; Organic chemistry Advanced Polymer Synthesis and Characterization ; Polymer Surface Interaction Studies ; biodegradable polymer synthesis and properties
Authors: Huilong Hou | Suxin Qian | Ichiro Takeuchi
Source: arXiv (Cornell University) (preprint, 2021-11-23)
Field: Materials Science — Materials Chemistry

Materials, physics, and systems for multicaloric cooling The calls to minimize greenhouse gas emissions and the demands for higher energy efficiency continue to drive the research worldwide in alternative cooling and refrigeration technologies. The field of caloric cooling has undergone a series of transformations in the past several decades bolstered by the advent of new materials and devices, and these developments have helped fuel the advent of multicalorics in the past decade. Multicaloric materials systems inherently display one or more types of ferroic orders that can give rise to multiple field-induced phase transitions responsible for the caloric effects. Just as multiferroic materials possessing co-existing order parameters have unveiled a plethora of physical phenomena and applications over the years, multicaloric cooling materials have the potential to open up entirely new avenues for extracting heat and spearhead hitherto unknown technological applications. In this Perspective, we survey the emerging field of multicaloric cooling and explore the state-of-the-art caloric materials systems responsive to multiple fields. We present our visions of future applications of multicaloric and caloric cooling in general including key factors governing the overall system efficiency of the cooling devices. Refrigeration ; Vision ; Field (mathematics) ; Engineering physics ; Mechanical engineering ; Systems engineering ; Nanotechnology ; Computer science ; Engineering ; Materials science ; Sociology Advanced Thermoelectric Materials and Devices ; Multiferroics and related materials ; Ferroelectric and Piezoelectric Materials
Authors: Divine Mbom Yufanyi‬ | Hubert Jean Nono | Amah Colette Benedicta Yuoh | Che Dieudonné Tabong | Wirsiy Judith | Agwara Moïse Ondoh
Source: Open Journal of Inorganic Chemistry (article, 2021-01-01)
Field: Chemistry — Inorganic Chemistry

Crystal Packing Studies, Thermal Properties and Hirshfeld Surface Analysis in the Zn(II) Complex of 3-Aminopyridine with Thiocyanate as Co-Ligand Reaction of zinc acetate, potassium thiocyanate and the ligand 3-ampy gave the discrete tetrahedral complex [Zn(NCS)2(3-ampy)2] in which 3-ampy chelates in a monodentate fashion through its pyridine-N atom. It was characterized by single crystal X-ray diffraction, infrared, and elemental analysis. Density Functional Theory calculations were performed in order to gain insights into the role of weak molecular interactions in the complex that influence the self-assembly process and crystal packing. X---H (X = H, C, N and S) inter-actions. S-H interactions (30.2%) were found to be the main interactions that hold the molecules in the crystal structure. Furthermore, the thermolysis of the complex was studied in order to evaluate whether it was suitable as a precursor for zinc sulphide. 2-Aminopyridine ; Crystallography ; Thiocyanate ; Denticity ; Pyridine ; Zinc ; Ligand (biochemistry) ; Chemistry ; Molecule ; Density functional theory ; Crystal structure ; Potassium thiocyanate ; Crystal (programming language) ; Single crystal ; Thermal decomposition ; Thermal analysis ; Inorganic chemistry ; Computational chemistry ; Thermal ; Medicinal chemistry ; Organic chemistry Crystal structures of chemical compounds ; Crystallography and molecular interactions ; Nonlinear Optical Materials Research