jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jessica Bowles | Sascha Jähnigen | Federica Agostini | Rodolphe Vuilleumier | Anne Zehnacker | F. Calvo | Carine Clavaguéra
Source: ChemRxiv (preprint, 2023-07-24)
Field: Chemistry — Spectroscopy

Vibrational circular dichroism spectroscopy with a classical polarizable force field: alanine in the gas and condensed phases Polarizable force fields are an essential component for chemically accurate modeling of complex molecular systems with a significant degree of fluxionality, beyond harmonic approximations or perturbative treatments. In this contribution we examine the performance of such an approach for the vibrational spectroscopy of the alanine amino acid, in the gas and condensed phases, from the Fourier transform of appropriate time correlation functions generated along molecular dynamics (MD) trajectories. While the infrared (IR) spectrum only requires the electric dipole moment, the vibrational circular dichroism (VCD) spectrum further requires knowledge of the magnetic dipole moment, for which we provide relevant expressions to be used under polarizable force fields. The AMOEBA force field was used to model alanine in the neutral and zwitterionic isolated forms, solvated by water or nitrogen, and as a crystal. Within this framework, comparison of the electric and magnetic dipole moments to those obtained with nuclear velocity perturbation theory based on density-functional theory for the same MD trajectory are found to agree well with one another. The statistical convergence of both the IR and VCD spectra is examined and found to be more demanding in the latter case. Polarizability ; Dipole ; Chemistry ; Force field (fiction) ; Vibrational circular dichroism ; Molecular physics ; Electric dipole moment ; Molecular dynamics ; Density functional theory ; Two-dimensional infrared spectroscopy ; Circular dichroism ; Spectroscopy ; Infrared spectroscopy ; Physics ; Computational chemistry ; Molecule ; Quantum mechanics ; Crystallography Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Samidha Verma | Burouj Armgaan | Sourav Medya | Sayan Ranu
Source: arXiv (Cornell University) (preprint, 2023-06-07)
Field: Materials Science — Materials Chemistry

Empowering Counterfactual Reasoning over Graph Neural Networks through Inductivity Graph neural networks (GNNs) have various practical applications, such as drug discovery, recommendation engines, and chip design. However, GNNs lack transparency as they cannot provide understandable explanations for their predictions. To address this issue, counterfactual reasoning is used. The main goal is to make minimal changes to the input graph of a GNN in order to alter its prediction. While several algorithms have been proposed for counterfactual explanations of GNNs, most of them have two main drawbacks. Firstly, they only consider edge deletions as perturbations. Secondly, the counterfactual explanation models are transductive, meaning they do not generalize to unseen data. In this study, we introduce an inductive algorithm called INDUCE, which overcomes these limitations. By conducting extensive experiments on several datasets, we demonstrate that incorporating edge additions leads to better counterfactual results compared to the existing methods. Moreover, the inductive modeling approach allows INDUCE to directly predict counterfactual perturbations without requiring instance-specific training. This results in significant computational speed improvements compared to baseline methods and enables scalable counterfactual analysis for GNNs. Counterfactual thinking ; Computer science ; Machine learning ; Graph ; Artificial intelligence ; Scalability ; Artificial neural network ; Enhanced Data Rates for GSM Evolution ; Theoretical computer science ; Psychology Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Advanced Graph Neural Networks
Authors: Ashraf Sadat Ghasemi | Batoul Makiabadi | Mohammad Zakarianezhad | Ф. Ашрафи | Mohammad Bagher Bagheri Poor
Source: Turkish computational and theoretical chemistry/Turkish computational and theoretical chemistry : (article, 2023-07-24)
Field: Materials Science — Materials Chemistry

Evaluation of absorption behavior of Streptozocin anti-cancer drug on Cr doped Carbon Nanotube (5,5) using DFT theoretical method In this study, the physicochemical characteristics of the adsorption of the anticancer drug Streptozocin (STZ, Zanosar) on the extern surface of Cr-doped carbon nanotube (CNTCr) have been investigated. Optimization all structures were performed using the DFT method at the mpw1pw91/6-311G level of theory. The energies, the highest occupied molecular orbital (HOMO), the lowest unoccupied molecular orbital (LUMO), the density of states (DOS), the distribution of electric charges, and the dipole moments have been calculated to investigate the physical chemistry behaviors of the structures. As well as, the molecular descriptors such as electrophilicity (ω), chemical potential (μ), chemical hardness (h) and chemical softness (S) of compounds were investigated. Examination of the intramolecular and intermolecular bonds indicates that the adsorption of the drug on the nanotube surface has been taking place. Also, the calculated adsorption energy was negative and indicates that the adsorption is thermodynamically possible. All the obtained results of the theoretical calculations have shown that CNTCr is suitable for delivering the anti-cancer drug STZ. HOMO/LUMO ; Adsorption ; Carbon nanotube ; Density functional theory ; Intermolecular force ; Computational chemistry ; Nanotube ; Chemistry ; Intramolecular force ; Materials science ; Physical chemistry ; Organic chemistry ; Molecule ; Nanotechnology Graphene research and applications ; Fullerene Chemistry and Applications ; Carbon Nanotubes in Composites
Authors: Benjamin C. Orsburn
Source:  (preprint, 2023-09-14)
Field: Chemistry — Spectroscopy

Identifying post-translational modifications with single cell proteomics With the rapid expansion of capabilities in the analysis of proteins in single cells, we now can now identify multiple classes of protein post-translational modifications on some of these proteins. Each new technology that has increased our protein coverage has likewise increased our ability to identify and quantify modified peptides. In this chapter I will discuss our current capabilities and challenges specific to this emerging field of study and inevitable service of demand. Posttranslational modification ; Proteomics ; Computational biology ; Computer science ; Protein expression ; Nanotechnology ; Data science ; Chemistry ; Biology ; Biochemistry ; Materials science Advanced Proteomics Techniques and Applications ; Peptidase Inhibition and Analysis ; Click Chemistry and Applications
Authors: Edoardo Filippi-Mazzola | Ernst C. Wit
Source: arXiv (Cornell University) (preprint, 2023-12-19)
Field: Materials Science — Materials Chemistry

Modeling non-linear Effects with Neural Networks in Relational Event Models Dynamic networks offer an insight of how relational systems evolve. However, modeling these networks efficiently remains a challenge, primarily due to computational constraints, especially as the number of observed events grows. This paper addresses this issue by introducing the Deep Relational Event Additive Model (DREAM) as a solution to the computational challenges presented by modeling non-linear effects in Relational Event Models (REMs). DREAM relies on Neural Additive Models to model non-linear effects, allowing each effect to be captured by an independent neural network. By strategically trading computational complexity for improved memory management and leveraging the computational capabilities of Graphic Processor Units (GPUs), DREAM efficiently captures complex non-linear relationships within data. This approach demonstrates the capability of DREAM in modeling dynamic networks and scaling to larger networks. Comparisons with traditional REM approaches showcase DREAM superior computational efficiency. The model potential is further demonstrated by an examination of the patent citation network, which contains nearly 8 million nodes and 100 million events. Computer science ; Artificial neural network ; Dream ; Event (particle physics) ; Computational model ; Artificial intelligence ; Computational complexity theory ; Theoretical computer science ; Machine learning ; Algorithm Machine Learning in Materials Science ; Functional Brain Connectivity Studies
Authors: Robert Schönherr | J. Boger | J. M. Rudolph | Mareike Harms | Jaqueline Kaiser | Sophie Nachtschatt | Marla Wobbe | Rainer Duden | Peter König | Gleb Bourenkov | T. Schneider | Lars Redecke
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-06-16)
Field: Materials Science — Materials Chemistry

InCellCryst - A streamlined approach to structure elucidation using <i>in cellulo</i> crystallized recombinant proteins Abstract With the advent of serial X-ray crystallography on microfocus beamlines at free-electron laser and synchrotron facilities, the demand for protein microcrystals has significantly risen in recent years. However, by conventional methods extensive efforts are usually required to purify proteins and produce sufficiently homogeneous microcrystals. Here, we present InCellCryst, an advanced pipeline for producing homogeneous microcrystals directly within living insect cells. Our baculovirus-based cloning system enables the production of crystals from completely unmodified proteins as well as the screening of different cellular compartments to maximize chances for protein crystallization. By optimizing cloning procedures, recombinant virus production, crystallization and crystal detection, X-ray diffraction data can be collected 24 days after the start of target gene cloning. Furthermore, new strategies for serial synchrotron diffraction data collection directly from crystals within living cells abolish the need to purify the recombinant protein or the associated microcrystals. Protein crystallization ; Recombinant DNA ; Cloning (programming) ; Crystallization ; Synchrotron ; Homogeneous ; Computational biology ; Diffraction ; Gene ; Chemistry ; Crystallography ; Biology ; Materials science ; Computer science ; Optics ; Physics ; Biochemistry Enzyme Structure and Function ; Biochemical and Molecular Research ; Viral Infectious Diseases and Gene Expression in Insects
Authors: Yogesh Kumar | Vishva Sree Venkata | Laxmi Priya | Rotti Srinivasamurthy Swathi | K Priyanka | G. Vyshnavi | N Ria | S Badhon | S Khushbu | S Akter | Hossain | A Wibowo | Y Rahayu | A Riyanto | Hidayatulloh | N Zahan | M Hasan | Reya Malek | G Xue | S Liu | Y Ma
Source: International Journal of Emerging Trends in Engineering Research (article, 2023-12-07)
Field: Chemistry — Analytical Chemistry

Mushroom Decay Detection using Deep Learning This project aims to develop a system for detecting decay in mushrooms using image processing techniques and Deep Learning. Decay in mushrooms is a significant issue in the food industry, as it can lead to quality deterioration and potentially harmful consumption. The proposed system involves capturing images of mushrooms using a camera and processing these images to detect any signs of decay. The image processing techniques will include pre- processing, feature extraction, and classification using deep learning algorithms. The dataset used for training and testing the system will consist of images of both healthy and decayed mushrooms. The system's performance will be evaluated based on accuracy. The outcome of this project will be a tool that can assist in early detection of decay in mushrooms, thus reducing food waste and improving food safety. Artificial intelligence ; Computer science ; Deep learning ; Mushroom ; Feature extraction ; Image processing ; Feature (linguistics) ; Computer vision ; Machine learning ; Pattern recognition (psychology) ; Image (mathematics) ; Food science Spectroscopy and Chemometric Analyses ; Date Palm Research Studies
Authors: Bingyu Zhang | Pengcheng Lu | Roozbeh Tabrizian | Philip X.‐L. Feng | Yingying Wu
Source: arXiv (Cornell University) (preprint, 2023-11-06)
Field: Materials Science — Materials Chemistry

2D Magnetic Heterostructures: Spintronics and Quantum Future The discovery of two-dimensional (2D) magnetism within atomically thin structures derived from layered crystals has opened up a new realm for exploring magnetic heterostructures. This emerging field provides a foundational platform for investigating unique physical properties and exquisite phenomena at the nanometer and molecular/atomic scales. By engineering 2D interfaces using physical methods and selecting interlayer interactions, we unlock the potential for extraordinary exchange dynamics. This potential extends to high-performance and high-density magnetic memory applications, as well as future advancements in neuromorphic and quantum computing. This review delves into recent advances in 2D magnets, elucidates the mechanisms behind 2D interfaces, and highlights the development of 2D devices for spintronics and quantum information. Particular focus is placed on 2D magnetic heterostructures with topological properties, promising for a resilient and low-error information system. Finally, we discuss the trends of 2D heterostructures for future electronics, considering the challenges and opportunities from physics, material synthesis, and technological prospective. Spintronics ; Heterojunction ; Magnetism ; Neuromorphic engineering ; Nanotechnology ; Quantum ; Nanoelectronics ; Materials science ; Physics ; Engineering physics ; Computer science ; Condensed matter physics ; Ferromagnetism ; Quantum mechanics Electronic and Structural Properties of Oxides ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Ke Meng | Enbo Liu
Source: SCIREA Journal of Physics (article, 2023-04-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

MXene-based material optimized shielding effectiveness of chassis The electronic equipment needs a special circuit design during working in complex electromagnetic environmental. Human-made or natural electromagnetic interference has a large effect on electronics. Both the couple of signal cable and the bit error rate during high-speed communication affects the reliability of equipment. Thus, electronic equipment with these problems is bad for the process of industrial production. Electromagnetic shielding is a good method to improve the radiated susceptibility of electronic equipment. The material of the chassis, the structure of chassis, and the thickness of the chassis wall affect the shielding effectiveness of chassis. Thus, in order to investigate the effect of microwave absorption material on the shielding effectiveness of chassis. This article is based on the material of the chassis, combing the simulation of High Frequency Structure Simulation (HFSS) software, to study the effect of MXene-based microwave absorption material on the shielding effectiveness. The simulation results indicate that chassis optimized by MXene-based materials has a better shielding effectiveness. When the thickness of the microwave absorption material is 0.6 mm, it shows a good shielding effectiveness. Chassis ; Electromagnetic shielding ; Automotive engineering ; Materials science ; Composite material ; Computer science ; Mechanical engineering ; Engineering Electromagnetic wave absorption materials ; Electromagnetic Compatibility and Measurements ; RFID technology advancements
Authors: Jacob S. Hirschi | Dayana Bashirova | Tim J. Zuehlsdorff
Source: ChemRxiv (preprint, 2023-06-07)
Field: Chemistry — Physical and Theoretical Chemistry

Opening the Density-Functional Theory Black Box: a Collection of Pedagogic Jupyter Notebooks Density-Functional Theory (DFT) is indubitably the most popular and among the most successful approaches for approximately solving the many-electron Schrödinger equation. The level of understanding on the part of both researchers and students using DFT, however, is lacking given the availability of black box software. The present work addresses this knowledge gap by providing three Jupyter notebooks, easily accessible through the Google Colaboratory (GitHub repository: https://github.com/tjz21/DFT_PIB_Code), that provide a short skirmish with the fundamentals of DFT through a particle in a box-type model system. These notebooks were tested in conjunction with a problem worksheet in a graduate-level quantum chemistry course; pre- and post-activity survey results reveal largely positive reactions to this implementation and sustained enthusiasm for the subject. Worksheet ; Enthusiasm ; Density functional theory ; Black box ; Computer science ; Subject (documents) ; Software ; Code (set theory) ; Theoretical computer science ; Physics ; Mathematics education ; Programming language ; World Wide Web ; Quantum mechanics ; Mathematics ; Artificial intelligence ; Psychology Various Chemistry Research Topics ; Advanced Chemical Physics Studies ; Academic Writing and Publishing
Authors: Alessio Zaccone
Source: PubMed (preprint, 2023-06-09)
Field: Materials Science — Materials Chemistry

General theory of the viscosity of liquids and solids from nonaffine particle motions A microscopic formula for the viscosity of liquids and solids is derived rigorously from a first-principles (microscopically reversible) Hamiltonian for particle-bath atomistic motion. The derivation is done within the framework of nonaffine linear response theory. This formula may lead to a valid alternative to the Green-Kubo approach to describe the viscosity of condensed matter systems from molecular simulations without having to fit long-time tails. Furthermore, it provides a direct link between the viscosity, the vibrational density of states of the system, and the zero-frequency limit of the memory kernel. Finally, it provides a microscopic solution to Maxwell's interpolation problem of viscoelasticity by naturally recovering Newton's law of viscous flow and Hooke's law of elastic solids in two opposite limits. Viscosity ; Viscoelasticity ; Hamiltonian (control theory) ; Classical mechanics ; Physics ; Viscous liquid ; Kernel (algebra) ; Volume viscosity ; Interpolation (computer graphics) ; Limit (mathematics) ; Statistical physics ; Mechanics ; Thermodynamics ; Motion (physics) ; Mathematics ; Mathematical analysis Material Dynamics and Properties ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Zhaoliang Hou | et al.
Source:  (preprint, 2023-04-19)
Field: Materials Science — Materials Chemistry

Supplemental Material: Three-dimensional mineral dendrites reveal a nonclassical crystallization pathway &lt;p&gt;Supplemental methods, Figures S1–S4, and Movies S1 and S2. &lt;/p&gt; Crystallization ; Mineral ; Chemistry ; Materials science ; Metallurgy Solidification and crystal growth phenomena ; Theoretical and Computational Physics ; Nonlinear Dynamics and Pattern Formation
Authors: D. L. Muccignat | G. Boyle | Nathan Garland | Peter W. Stokes | Ronald D. White
Source: arXiv (Cornell University) (preprint, 2023-11-22)
Field: Materials Science — Materials Chemistry

An iterative deep learning procedure for determining electron scattering cross-sections from transport coefficients We propose improvements to the Artificial Neural Network (ANN) method of determining electron scattering cross-sections from swarm data proposed by coauthors. A limitation inherent to this problem, known as the inverse swarm problem, is the non-unique nature of its solutions, particularly when there exists multiple cross-sections that each describe similar scattering processes. Considering this, prior methods leveraged existing knowledge of a particular cross-section set to reduce the solution space of the problem. To reduce the need for prior knowledge, we propose the following modifications to the ANN method. First, we propose a Multi-Branch ANN (MBANN) that assigns an independent branch of hidden layers to each cross-section output. We show that in comparison with an equivalent conventional ANN, the MBANN architecture enables an efficient and physics informed feature map of each cross-section. Additionally, we show that the MBANN solution can be improved upon by successive networks that are each trained using perturbations of the previous regression. Crucially, the method requires much less input data and fewer restrictive assumptions, and only assumes knowledge of energy loss thresholds and the number of cross-sections present. Cross section (physics) ; Computer science ; Artificial neural network ; Scattering ; Set (abstract data type) ; Feature (linguistics) ; Algorithm ; Regression ; Cross-validation ; Electron scattering ; Mathematical optimization ; Supervised learning ; Artificial intelligence ; Mathematics ; Physics ; Optics ; Statistics ; Quantum mechanics Machine Learning in Materials Science ; Machine Learning and ELM ; GaN-based semiconductor devices and materials
Authors: Rita Moussallem | Elissa El Rassy | Jalal Faraj | Jean‐Luc Bailleul
Source:  (article, 2023-01-01)
Field: Materials Science — General Materials Science

IDENTIFICATION OF THE THERMOPHYSICAL PROPERTIES OF POLYMER AND COMPOSITE MATERIALS DURING THEIR TRANSFORMATION International audience Composite number ; Identification (biology) ; Materials science ; Transformation (genetics) ; Polymer ; Composite material ; Chemistry Material Properties and Applications ; Thermal and Kinetic Analysis ; Environmental and Industrial Safety
Authors: Dennis Wilfred | Maria Rajendran | R. Dorothy | Anitha Nilavan | S. Rajendran | Časlav Lačnjevac
Source: Zastita materijala (article, 2023-03-15)
Field: Materials Science — Materials Chemistry

Influence of a candy on the corrosion resistance of orthodontic wire made of Gold 18K in the presence of artificial saliva Corrosion resistance of an orthodontic wire made of Gold 18K alloy immersed in artificial saliva in the absence and presence of 500 ppm of Éclairschocolate candy has been investigated by potentiodynamic polarization study and electrochemical impedance spectra (EIS). It is observed that corrosion resistance of Gold 18K alloy immersed in artificial saliva in the presence of 500 ppm of Éclairs chocolate candy increases. Hence it is concluded that people clipped with orthodontic wire made of Gold 18K alloy need not worry about taking Éclairs chocolate candy orally. When Gold 18K alloy is immersed in artificial saliva in the presence of 500 ppm of Éclairs chocolate candy, Linear Polarisation Resistance (LPR) value increases from 4844947 Ohmcm2 to 35700904 Ohmcm2 ; corrosion current decreases from 7.726x10-9A/cm2 to 1.350x109A/cm2 ; charge transfer resistance value(Rt) increases from 172100 Ohmcm2 to 398800 Ohmcm2 ; impedance value increases from 5.445 to 5.975; double layer capacitance decreases from 2.963x10-11F/cm2 to 1.1279x10-11F/cm2 , and phase angle increases from 44.68° to 48.23°. Corrosion ; Saliva ; Materials science ; Dielectric spectroscopy ; Capacitance ; Metallurgy ; Composite material ; Medicine ; Electrochemistry ; Chemistry ; Electrode ; Internal medicine Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses
Authors: Bo Dong | Yun-Fei Jia | Wei Wang
Source: Research Square (preprint, 2023-06-07)
Field: Materials Science — Biomaterials

Designing load-bearing bio-inspired materials for simultaneous static properties and dynamic damping: multi-objective optimization for micro-structure Toughness ; Stiffness ; Load bearing ; Bearing (navigation) ; Optimal design ; Materials science ; Set (abstract data type) ; Computer science ; Material Design ; Mechanical engineering ; Structural engineering ; Composite material ; Engineering ; Artificial intelligence Calcium Carbonate Crystallization and Inhibition ; Bone Tissue Engineering Materials ; 3D Printing in Biomedical Research