jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hao‐Hsiang Ku | Ching-Fu Lung | Ching-Ho Chi
Source: Foods (article, 2023-11-03)
Field: Chemistry — Analytical Chemistry

Design of an Artificial Intelligence of Things-Based Sesame Oil Evaluator for Quality Assessment Using Gas Sensors and Deep Learning Mechanisms Traditional oil quality measurement is mostly based on chemical indicators such as acid value, peroxide value, and <i>p</i>-anisidine value. This process requires specialized knowledge and involves complex steps. Hence, this study designs and proposes a Sesame Oil Quality Assessment Service Platform, which is composed of an Intelligent Sesame Oil Evaluator (ISO Evaluator) and a Cloud Service Platform. Users can quickly assess the quality of sesame oil using this platform. The ISO Evaluator employs Artificial Intelligence of Things (AIoT) sensors to detect changes in volatile gases and the color of the oil during storage. It utilizes deep learning mechanisms, including Artificial Neural Network (ANN), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM) to determine and evaluate the quality of the sesame oil. Evaluation results demonstrate that the linear discriminant analysis (LDA) value is 95.13. The MQ2, MQ3, MQ4, MQ7, and MQ8 sensors have a positive correlation. The CNN combined with an ANN model achieves a Mean Absolute Percentage Error (MAPE) of 8.1820% for predicting oil quality, while the LSTM model predicts future variations in oil quality indicators with a MAPE of 0.44%. Finally, the designed Sesame Oil Quality Assessment Service Platform effectively addresses issues related to digitization, quality measurement, supply quality observation, and scalability. Artificial intelligence ; Artificial neural network ; Computer science ; Convolutional neural network ; Deep learning ; Mean absolute percentage error ; Scalability ; Sesame oil ; Machine learning ; Quality (philosophy) ; Database Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Edible Oils Quality and Analysis
Authors: Indranil Roy | Bojun Feng | Subhrajit Roychowdhury | Sandipp Krishnan Ravi | Rajnikant V. Umretiya | Christopher Reynolds | Sayan Ghosh | Raúl B. Rebak | Andrew Hoffman
Source: MRS Communications (article, 2023-01-09)
Field: Materials Science — Materials Chemistry

Understanding oxidation of Fe-Cr-Al alloys through explainable artificial intelligence Abstract The oxidation resistance of FeCrAl based on alloying composition and oxidizing conditions is predicted using a combinatorial experimental and artificial intelligence approach. A neural network (NN) classification model was trained on the experimental FeCrAl dataset produced at GE Research. Furthermore, using the SHapley Additive exPlanations (SHAP) explainable artificial intelligence (XAI) tool, we explore how the NN can showcase further material insights that are unavailable directly from a black-box model. We report that high Al and Cr content forms protective oxide layer, while Mo in FeCrAl creates thick unprotective oxide scale that is vulnerable to spallation due to thermal expansion. Graphical abstract Oxidizing agent ; Spallation ; Materials science ; Oxide ; Artificial neural network ; Layer (electronics) ; Scale (ratio) ; Thermal oxidation ; Artificial intelligence ; Metallurgy ; Computer science ; Nanotechnology ; Physics ; Chemistry Nuclear Materials and Properties ; High-Temperature Coating Behaviors ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Piotr Cysewski | Maciej Przybyłek | Tomasz Jeliński
Source: Materials (article, 2023-09-21)
Field: Chemistry — Spectroscopy

Intermolecular Interactions as a Measure of Dapsone Solubility in Neat Solvents and Binary Solvent Mixtures Dapsone is an effective antibacterial drug used to treat a variety of conditions. However, the aqueous solubility of this drug is limited, as is its permeability. This study expands the available solubility data pool for dapsone by measuring its solubility in several pure organic solvents: N-methyl-2-pyrrolidone (CAS: 872-50-4), dimethyl sulfoxide (CAS: 67-68-5), 4-formylmorpholine (CAS: 4394-85-8), tetraethylene pentamine (CAS: 112-57-2), and diethylene glycol bis(3-aminopropyl) ether (CAS: 4246-51-9). Furthermore, the study proposes the use of intermolecular interactions as molecular descriptors to predict the solubility of dapsone in neat solvents and binary mixtures using machine learning models. An ensemble of regressors was used, including support vector machines, random forests, gradient boosting, and neural networks. Affinities of dapsone to solvent molecules were calculated using COSMO-RS and used as input for model training. Due to the polymorphic nature of dapsone, fusion data are not available, which prohibits the direct use of COSMO-RS for solubility calculations. Therefore, a consonance solvent approach was tested, which allows an indirect estimation of the fusion properties. Unfortunately, the resulting accuracy is unsatisfactory. In contrast, the developed regressors showed high predictive potential. This work documents that intermolecular interactions characterized by solute-solvent contacts can be considered valuable molecular descriptors for solubility modeling and that the wealth of encoded information is sufficient for solubility predictions for new systems, including those for which experimental measurements of thermodynamic properties are unavailable. Solubility ; Solvent ; Intermolecular force ; Chemistry ; Thermodynamics ; Computational chemistry ; Organic chemistry ; Molecule Analytical Chemistry and Chromatography ; Crystallization and Solubility Studies ; Computational Drug Discovery Methods
Authors: M. Gökhan Albayrak | Ertan Evi̇n | Oktay Yi̇ği̇t | Mesut Toğaçar | Burhan Ergen
Source: Engineering Applications of Artificial Intelligence (article, 2023-01-30)
Field: Materials Science — Materials Chemistry

Experimental and artificial intelligence approaches to measuring the wear behavior of DIN St28 steel boronized by the box boronizing method using a mechanically alloyed powder source Feature selection ; Boride ; Materials science ; Computer science ; Context (archaeology) ; Artificial neural network ; Artificial intelligence ; Hardening (computing) ; Feature (linguistics) ; Pattern recognition (psychology) ; Layer (electronics) ; Metallurgy ; Composite material Metal Alloys Wear and Properties ; Tribology and Wear Analysis ; Lubricants and Their Additives
Authors: Alexandre Lira Foggiatto | Yuta Mizutori | Takahiro Yamazaki | Shunsuke Sato | Ken Masuzawa | Ryunosuke Nagaoka | Michiki Taniwaki | Shun Fujieda | Shigeru Suzuki | K. Ishiyama | Tsuguo Fukuda | Yasuhiko Igarashi | Chiharu Mitsumata | Masato Kotsugi
Source: IEEE Transactions on Magnetics (article, 2023-09-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Visualization of the Magnetostriction Mechanism in Fe-Ga Alloy Single Crystal Using Dimensionality Reduction Techniques Iron-gallium (Fe-Ga) alloys have emerged as promising materials due to their notable magnetostriction and ductility properties, making them potential candidates for use in advanced devices. To better understand the mechanism involved in the magnetostriction of Fe-Ga alloys, we have employed a combination of fast Fourier transform (FFT) and unsupervised machine learning techniques. Specifically, we have analyzed the features obtained from the principal component analysis (PCA) decomposition and found a strong correlation between these features and the physical components of the material. By separating the contribution of the magnetostriction in the extracted features, we were able to identify the region where the magnetostriction exhibits a significant increase. Our results show the complex relationship between the magnetostriction and the underlying physical structure of text Fe-Ga alloys and have the potential to inform the development of new materials with improved magnetostrictive properties. Magnetostriction ; Materials science ; Gallium ; Dimensionality reduction ; Fast Fourier transform ; Alloy ; Principal component analysis ; Reduction (mathematics) ; Ductility (Earth science) ; Condensed matter physics ; Computer science ; Metallurgy ; Artificial intelligence ; Physics ; Magnetic field ; Algorithm Magnetic Properties and Applications ; Microstructure and Mechanical Properties of Steels ; Magnetic properties of thin films
Authors: Q. Wang | Chenhui Zheng | M. X. Li | Liang Hu | Haipeng Wang | B. Wei
Source: Applied Physics Letters (article, 2023-06-05)
Field: Materials Science — Materials Chemistry

A genome dependence of metastable phase selection on atomic structure for undercooled liquid Nb90Si10 hypoeutectic alloy The phase selection mechanism within undercooled liquid Nb90Si10 hypoeutectic alloy was investigated by electrostatic levitation technique combined with deep neural network molecular dynamics. A stepwise-solidification procedure was conducted, where the primary phase and eutectic microstructure successively solidified from undercooled liquid alloy and undercooled residual liquid, respectively. The intermetallic phase of the eutectic structure transfers from Nb3Si to βNb5Si3 and finally into αNb5Si3 compound with the increase in liquid undercooling. The deep neural network molecular dynamic simulations have shown that the phase selection between Nb3Si and Nb5Si3 is mainly controlled by the short-range order of residual liquid, considering that the predominant short-range configuration transforms from Nb3Si-like to Nb5Si3-like structures. The αNb5Si3-like medium-range order, which is characterized by vertex-connected ⟨0,2,8,4⟩ clusters, is shown to significantly influence the competitive nucleation of the αNb5Si3 and βNb5Si3 phases. The residual liquid favors the αNb5Si3-like medium-range order rather than βNb5Si3 at large undercoolings, which explains the transformation from βNb5Si3 to αNb5Si3. Supercooling ; Eutectic system ; Materials science ; Alloy ; Intermetallic ; Nucleation ; Phase (matter) ; Metastability ; Composite material ; Thermodynamics ; Chemistry ; Physics Machine Learning in Materials Science ; Solidification and crystal growth phenomena ; Crystallization and Solubility Studies
Authors: Yuanqing Wang | Iván Pulido | Kenichiro Takaba | Benjamin Kaminow | Jenke Scheen | Lily Wang | John D. Chodera
Source: arXiv (Cornell University) (preprint, 2023-02-14)
Field: Materials Science — Materials Chemistry

EspalomaCharge: Machine learning-enabled ultra-fast partial charge assignment Atomic partial charges are crucial parameters in molecular dynamics (MD) simulation, dictating the electrostatic contributions to intermolecular energies, and thereby the potential energy landscape. Traditionally, the assignment of partial charges has relied on surrogates of \textit{ab initio} semiempirical quantum chemical methods such as AM1-BCC, and is expensive for large systems or large numbers of molecules. We propose a hybrid physical / graph neural network-based approximation to the widely popular AM1-BCC charge model that is orders of magnitude faster while maintaining accuracy comparable to differences in AM1-BCC implementations. Our hybrid approach couples a graph neural network to a streamlined charge equilibration approach in order to predict molecule-specific atomic electronegativity and hardness parameters, followed by analytical determination of optimal charge-equilibrated parameters that preserves total molecular charge. This hybrid approach scales linearly with the number of atoms, enabling, for the first time, the use of fully consistent charge models for small molecules and biopolymers for the construction of next-generation self-consistent biomolecular force fields. Implemented in the free and open source package \texttt{espaloma\_charge}, this approach provides drop-in replacements for both AmberTools \texttt{antechamber} and the Open Force Field Toolkit charging workflows, in addition to stand-alone charge generation interfaces. Source code is available at \url{https://github.com/choderalab/espaloma_charge}. Partial charge ; Charge (physics) ; Ab initio ; Molecule ; Intermolecular force ; Molecular dynamics ; Chemical physics ; Physics ; Electrostatics ; Electronegativity ; Statistical physics ; Computational science ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Molecular Junctions and Nanostructures
Authors: Qian Zhao | Kaili Yan | Zhe Cui | Bingyao Wen | Xue Feng | Jintong Li | Junnan Guo | Ao Xu | Kaiming Qiao | Rongchang Ye | Long Yi | Dawei Zhang | Hong Luo | Sergey Taskaev | Hu Zhang
Source: Corrosion Science (article, 2023-03-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data driven accelerated design of high-efficiency corrosion inhibitor for magnetic refrigeration materials Corrosion ; Corrosion inhibitor ; Refrigeration ; Materials science ; Immersion (mathematics) ; Heat exchanger ; Magnetic refrigeration ; Alloy ; Metallurgy ; Magnetic field ; Mechanical engineering ; Engineering ; Magnetization ; Physics Magnetic and transport properties of perovskites and related materials
Authors: Chu Viet Ha | Lê Tiến Hà | P.T. Tho | Duc‐Long Nguyen
Source: RSC Advances (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Phonon transport in Janus monolayer siblings: a comparison of 1T and 2H-ISbTe In the last decade, two-dimension materials with reduced symmetry have attracted a lot of attention due to the emerging quantum features induced by their structural asymmetry. Two-dimensional Janus materials, named after the Roman deity of beginnings and endings who has two faces, have a structure with broken mirror symmetry because the two sides of the material have distinct chemical compositions. Extensive study has been undertaken on phonon transport for Janus monolayers for their strong applicability in thermoelectrics compared to their parent material, while Janus materials with the same space group but a distinct crystal protype have received very little attention. Using first-principles calculations and the Boltzmann transport equation accelerated by a machine learning interatomic potential, we explore the phonon transport of 1T and 2H-ISbTe. ISbTe possesses significant intrinsic phonon-phonon interactions, resulting in a low lattice thermal conductivity, as a result of its covalent bonding and low elastic constants. A thorough examination of phonon group velocity, phonon lifetime, and heat carrier identification reveals that 2H has a low lattice thermal conductivity of 1.5 W mK<sup>-1</sup>, which is 2.3 times lower than its 1T sibling. This study demonstrates Janus ISbTe monolayers have extensive physical phenomena in their thermal transport characteristics, which might provide a new degree of control over their thermal conductivity for applications such as thermal management and thermoelectric devices. Phonon ; Janus ; Condensed matter physics ; Thermal conductivity ; Monolayer ; Thermoelectric materials ; Boltzmann equation ; Materials science ; Thermoelectric effect ; Nanotechnology ; Physics ; Thermodynamics ; Composite material Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; 2D Materials and Applications
Authors: Tom Vermeyen | Ana V. Cunha | Patrick Bultinck | Wouter Herrebout
Source: Communications Chemistry (article, 2023-07-12)
Field: Chemistry — Spectroscopy

Impact of conformation and intramolecular interactions on vibrational circular dichroism spectra identified with machine learning Vibrational Circular Dichroism (VCD) spectra often differ strongly from one conformer to another, even within the same absolute configuration of a molecule. Simulated molecular VCD spectra typically require expensive quantum chemical calculations for all conformers to generate a Boltzmann averaged total spectrum. This paper reports whether machine learning (ML) can partly replace these quantum chemical calculations by capturing the intricate connection between a conformer geometry and its VCD spectrum. Three hypotheses concerning the added value of ML are tested. First, it is shown that for a single stereoisomer, ML can predict the VCD spectrum of a conformer from solely the conformer geometry. Second, it is found that the ML approach results in important time savings. Third, the ML model produced is unfortunately hardly transferable from one stereoisomer to another. Conformational isomerism ; Vibrational circular dichroism ; Intramolecular force ; Spectral line ; Circular dichroism ; Chemistry ; Molecule ; Quantum chemical ; Spectrum (functional analysis) ; Connection (principal bundle) ; Computational chemistry ; Geometry ; Stereochemistry ; Physics ; Mathematics ; Quantum mechanics Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies ; Protein Structure and Dynamics
Authors: Xushi Zhang | Guodong Ye | Chuanxue Wen | Zhisheng Bi
Source: Computational Materials Science (article, 2023-07-04)
Field: Materials Science — Materials Chemistry

Transfer learning for predicting reorganization energy Expediting ; Computer science ; Energy (signal processing) ; Process (computing) ; Density functional theory ; Transfer of learning ; Field (mathematics) ; Efficient energy use ; Artificial intelligence ; Chemistry ; Physics ; Electrical engineering ; Engineering Machine Learning in Materials Science ; Molecular Junctions and Nanostructures ; CO2 Reduction Techniques and Catalysts
Authors: Nicolas P. D. Sawaya | Daniel Marti-Dafcik | Yang Ho | Daniel P. Tabor | David E. Bernal | Alicia Magann | Shavindra Premaratne | Pradeep Dubey | A. Y. Matsuura | N. C. Bishop | Wibe A. de Jong | Simon C. Benjamin | Ojas Parekh | Norm M. Tubman | Katherine Klymko | Daan Camps
Source:  (article, 2023-09-17)
Field: Materials Science — Materials Chemistry

HamLib: A Library of Hamiltonians for Benchmarking Quantum Algorithms and Hardware For a considerable time, large datasets containing problem instances have proven valuable for analyzing computer hardware, software, and algorithms. One notable example of the value of large datasets is ImageNet [1], a vast repository of images that has been instrumental in testing numerous deep learning packages. Similarly, in the domain of computational chemistry and materials science, the availability of extensive datasets such as the Protein Data Bank [2], the Materials Project [3], and QM9 [4] has greatly facilitated the evaluation of new algorithms and software approaches, while also promoting standardization within the field. These well-defined datasets and problem instances, in turn, serve as the foundation for creating benchmarking suites like MLPerf [5] and LINPACK [6], [7]. These suites enable fair and rigorous comparisons of different methodologies and solutions, fostering continuous advancements in various areas of computer science and beyond. Benchmarking ; Computer science ; Standardization ; Domain (mathematical analysis) ; Field (mathematics) ; Software ; Algorithm ; Software engineering ; Computer engineering ; Data science ; Programming language ; Operating system ; Mathematics Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Parallel Computing and Optimization Techniques
Authors: Colton C. Seegmiller | Sterling G. Baird | Hasan M. Sayeed | Taylor D. Sparks
Source: Computational Materials Science (article, 2023-07-14)
Field: Materials Science — Materials Chemistry

Discovering chemically novel, high-temperature superconductors Superconductivity ; Cuprate ; High-temperature superconductivity ; Curse of dimensionality ; Cluster analysis ; Chemical space ; Set (abstract data type) ; Computer science ; Materials science ; Physics ; Machine learning ; Condensed matter physics ; Chemistry ; Drug discovery Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Inorganic Fluorides and Related Compounds
Authors: Hatim Raji | Milad Ghiasi Rad | Emre Acar | H.E. Karaca | Soheil Saedi
Source: Materials Today Communications (article, 2023-12-14)
Field: Materials Science — Materials Chemistry

A machine learning approach to predict austenite finish temperature in quaternary NiTiHfPd SMAs Austenite ; Materials science ; Shape-memory alloy ; Transformation (genetics) ; Quaternary ; Machine learning ; Computer science ; Metallurgy ; Microstructure Shape Memory Alloy Transformations ; Magnetic and transport properties of perovskites and related materials ; High Entropy Alloys Studies
Authors: Barry K. Carpenter
Source: The Journal of Physical Chemistry A (article, 2023-01-03)
Field: Materials Science — Materials Chemistry

Prediction of Kinetic Product Ratios: Investigation of a Dynamically Controlled Case Of the various factors influencing kinetically controlled product ratios, the role of nonstatistical dynamics is arguably the least well understood. In this paper, reactions were chosen in which dynamics played a dominant role in product selection, by design. Specifically, the reactions studied were the ring openings of cyclopropylidene to allene and tetramethylcyclopropylidene to tetramethylallene (2,4-dimethylpenta-2,3-diene). Both reactions have intrinsic reaction coordinates that bifurcate symmetrically, leading to products that are enantiomeric once the atoms are uniquely labeled. The question addressed in the study was whether the outcomes─that is, which product well on the potential energy surface was selected─could be predicted from their initial conditions for individual trajectories in quasiclassical dynamics simulations. Hybrid potentials were developed based on cooperative interaction between molecular mechanics and artificial neural networks, trained against data from electronic structure calculations. These potentials allowed simulations of both gas-phase and condensed-phase reactions. The outcome was that, for both reactions, prediction of initial selection of product wells could be made with >95% success from initial conditions of the trajectories in the gas phase. However, when trajectories were run for longer, looking for "final" products for each trajectory, the predictability dropped off dramatically. In the gas-phase simulations, this drop off was caused by trajectories hopping between product wells on the potential energy surface. That behavior could be suppressed in condensed phases, but then new uncertainty was introduced because the intermolecular interactions between solute and bath, necessary to permit intermolecular energy transfer and cooling of the hot initial products, often led to perturbations of the initial directions of trajectories on the potential energy surface. It would consequently appear that a general ability to predict outcomes for reactions in which nonstatistical dynamics dominate remains a challenge even in the age of sophisticated machine-learning capabilities. Intermolecular force ; Chemistry ; Molecular dynamics ; Potential energy surface ; Kinetic energy ; Chemical physics ; Product (mathematics) ; Potential energy ; Predictability ; Phase space ; Thermodynamics ; Computational chemistry ; Statistical physics ; Molecule ; Physics ; Atomic physics ; Classical mechanics ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods