jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: V. G. Filippov | Yu. A. Mikhaǐlov | Andrey Elyshev
Source: Кинетика и катализ (article, 2023-03-01)
Field: Materials Science — Materials Chemistry

Machine Learning and Big Data Analysis in the Catalysis Field Recently, there has been a rapid development of experimental methods in the field of catalytic research, an increase in the amount of data that is difficult to process and objectively interpret. These methods will allow you to obtain the necessary information from experimental data using statistical approaches such as PCA, MCR, ALS. The use of new statistical and computational data processing methods will accelerate the development and implementation of catalytic technologies. At the same time, machine learning algorithms are beginning to be actively used to interpret and build descriptive models. This article will discuss the main methods of machine learning and their successful application for the analysis of infrared and X-ray absorption spectroscopy data. Field (mathematics) ; Big data ; Computer science ; Data science ; Data mining ; Mathematics Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Abhishek S. Bhesania | M. Laviola U. Campora G. Benvenuto | MUNETAKA KUBOTA | Sanjib C. Chowdhury | JOHN W. GILLESPIE
Source:  (article, 2023-09-18)
Field: Materials Science — Ceramics and Composites

EVALUATING RATE DEPENDENT BEHAVIOR OF SILANE MOLECULES WITH MOLECULAR DYNAMICS SIMULATIONS AND MACHINE LEARNING In this study, silane molecules of different functionalities and molecular weights are evaluated for their performance under different strain rates 109, 1010, and 1011 1/s in Mode I. loading. Molecular Dynamics simulations are performed over the systems made of a single silane molecule bound to the glass surface via one or two bonds and are simulated in the extended or relaxed position resting on the fiber surface. The silane molecules are categorized into three classes based on these three bond failure mechanism within the silane molecule: (1) Silicon-Oxygen/Silicon-Carbon, (2) Nitrogen-Carbon, and (3) Oxygen-Carbon. For all the configurations of silane molecules simulated, the peak strength demonstrated by the silanes which failed with the 1st bond failure mechanism was higher than those failing with the 2nd and 3rd mechanisms. For each failure mechanism, it is observed that the energy absorbed at failure increases with the chain length of the molecule. The non-bonded interactions between silane molecules and the glass surface contributed to the overall energy absorption. 16 to 29 % of the total energy absorbed was due to non-bonded interactions by different silane molecules. In the end, a methodology to develop and implement a Machine Learning model of Variational Auto-encoder is presented to utilize the MD simulations performed at different strain rates and configurations to predict new silanes with improved rate-dependent strength and energy absorption. Silane ; Silanes ; Molecule ; Materials science ; Chemical physics ; Molecular dynamics ; Silicon ; Computational chemistry ; Nanotechnology ; Chemistry ; Composite material ; Organic chemistry ; Optoelectronics Advanced ceramic materials synthesis ; Dental materials and restorations ; Metal and Thin Film Mechanics
Authors: 亮 高橋 | Akira Takahashi | 悠 熊谷 | Yu Kumagai | 亮 田村 | Ryo Tamura | 史康 大場 | Fumiyasu Oba
Source: Institutional Repositories DataBase (IRDB) (article, 2023-10-01)
Field: Materials Science — Materials Chemistry

Fully autonomous materials screening methodology combining first-principles calculations, machine learning and high-performance computing system Materials screening by high-throughput first-principles calculations is a powerful tool for exploring novel materials with preferable properties. Machine learning techniques are expected to accelerate materials screening by constructing surrogate models and making fast predictions. Especially, black-box optimization methods such as Bayesian optimization, repeating the construction of a prediction model and the selection of data points, have attracted much attention. In this study, we constructed an autonomous materials screening system using first-principles calculations and machine learning working on high-performance computing systems. The performance of the system was evaluated by applying the system to the exploration of high-k dielectrics using band gaps by hybrid functional calculations and dielectric constants by density functional perturbation theory calculations, respectively. The developed system identified materials with anomalous properties, as well as materials with both wide band gaps and high dielectric constants by utilizing appropriate black-box optimization methods, much faster than random exploration. The code for the developed system is published on an open repository. Bayesian optimization ; Dielectric ; Code (set theory) ; Selection (genetic algorithm) ; Bayesian probability ; Experimental data ; Feature selection Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Ferroelectric and Negative Capacitance Devices
Authors: Zeyan Li
Source: Journal of Physics Conference Series (article, 2023-03-01)
Field: Materials Science — Polymers and Plastics

Research on the control of Interlayer Material Properties based on a Neural Network Model Abstract There are many process parameters for the preparation of intercalated melt-blown nonwoven materials, and there are interactions between the parameters, which are further complicated by the intercalated airflow. Therefore, the study between process parameters, structural variables, and product performance becomes more complicated. In this paper, the relationship between the three parameters is modeled separately to provide a certain theoretical basis for the establishment of the product performance regulation mechanism. To investigate the changes in structural variables and product performance after intercalation, this paper determines the correlation between the variables and uses the idea of ANOVA to infer whether the control variables (whether to intercalate or not) bring significant effects on the observed variables. To explore the relationship between process parameters and structural variables. In this paper, the soft Sigmoid function is introduced and the root means the square error is used as the Loss function to reflect the goodness of fit, and finally, the Adam optimization process is chosen to update the parameters. Based on the above structure, BP neural network Model I is established. To explore the relationship between structural variables and product performance. This paper eliminates the correlation between structural variables based on factor analysis. Add neurons to Model I and use the factors to predict the filtration efficiency of the product. Finally, an optimization model to maximize filtration efficiency is established. Sigmoid function ; Function (biology) ; Process (computing) ; Variables ; Product (mathematics) ; Artificial neural network ; Control variable ; Computer science ; Mathematics ; Statistics ; Mathematical optimization ; Biological system ; Artificial intelligence Textile materials and evaluations
Authors: Yiran Wang | Riming Hu | Jiaxiang Shang
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Enhanced Oxygen Reduction of Bi-Atom Catalysts on G-Cn with Heteroatom Doped: A Theoretical Investigation by Density Functional Theory and Machine Learning Heteroatom ; Density functional theory ; Doping ; Catalysis ; Reduction (mathematics) ; Atom (system on chip) ; Oxygen atom ; Oxygen ; Oxygen reduction ; Materials science ; Chemistry ; Computational chemistry ; Computer science ; Optoelectronics ; Physical chemistry ; Organic chemistry ; Mathematics ; Electrochemistry ; Embedded system ; Molecule Machine Learning in Materials Science ; Semiconductor materials and devices ; Electrocatalysts for Energy Conversion
Authors: Narges Ansari | Atieh Sohrabi | Kimia Mirbaghestan | Mahdieh Hashemi
Source: Scientific Reports (article, 2023-12-07)
Field: Materials Science — Materials Chemistry

Data analysis on the three defect wavelengths of a MoS2-based defective photonic crystal using machine learning To reduce the dimension of optoelectronic devices, recently, Molybdenum disulfide (MoS<sub>2</sub>) monolayers with direct bandgap in the visible range are widely used in designing a variety of photonic devices. In these applications, adjustability of the working wavelength and bandwidth with optimum absorption value plays an important role. This work proposes a symmetric defective photonic crystal with three defects containing MoS<sub>2</sub> monolayer to achieve triple narrowband defect modes with wavelength adjustability throughout the Photonic Band Gap (PBG) region, 560 to 680 nm. Within one of our designs remarkable FWHM approximately equal to 5 nm with absorption values higher than 90% for the first and third defect modes are achieved. The impacts of varying structural parameters on absorption value and wavelength of defect modes are investigated. Due to the multiplicity of structural parameters which results in data plurality, the optical properties of the structure are also predicted by machine learning techniques to assort the achieved data. Multiple Linear Regression (MLR) modeling is used to predict the absorption and wavelength of defect modes for four datasets based on various permutations of structural variables. The machine learning modeling results are highly accurate due to the obtained R<sup>2</sup>-score and cross-validation score values higher than 90%. Photonic crystal ; Wavelength ; Photonics ; Materials science ; Optoelectronics ; Absorption (acoustics) ; Monolayer ; Band gap ; Bandwidth (computing) ; Optics ; Computer science ; Physics ; Telecommunications ; Nanotechnology 2D Materials and Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Fengrui Wang | Shiyi Qin | Claribel Acevedo-Vélez | Reid C. Van Lehn | Ví­ctor M. Zavala | David M. Lynn
Source: ChemRxiv (preprint, 2023-02-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Decoding Optical Responses of Contact-Printed Arrays of Thermotropic Liquid Crystals Using Machine Learning: Detection and Reporting of Aqueous Amphiphiles with Enhanced Sensitivity and Selectivity Surfactants and other amphiphilic molecules are used extensively in household products, industrial processes, and biological applications, and are also common environmental contaminants; as such, methods that can detect, sense, or quantify them are of great practical relevance. Aqueous emulsions of thermotropic liquid crystals (LCs) can exhibit distinctive optical responses in the presence of surfactants and have thus emerged as sensitive, rapid, and inexpensive sensors or reporters of environmental amphiphiles. However, many existing LC-in-water emulsions require the use of complicated or expensive instrumentation for quantitative characterization, owing to variations in optical responses among individual LC droplets. In many cases, the responses of LC droplets are also analyzed by human inspection, which can miss subtle color or topological changes encoded in LC birefringence patterns. Here, we report an LC-based surfactant sensing platform that takes a step toward addressing several of these issues and can reliably predict concentrations and types of model surfactants in aqueous solutions. Our approach uses surface-immobilized, microcontact printed arrays of micrometer-scale droplets of thermotropic LCs and hierarchical convolutional neural networks (CNNs) to automatically extract and decode rich information about topological defects and color patterns available in optical micrographs of LC droplets to classify and quantify adsorbed surfactants. In addition, we report computational capabilities to determine relevant optical features extracted by the CNN from LC micrographs, which can provide insights on surfactant adsorption phenomena at LC-water interfaces. Overall, the combination of microcontact-printed LC arrays and machine learning provides a convenient and robust platform that could prove useful for developing high-throughput sensors for on-site testing of environmentally or biologically relevant amphiphiles. Thermotropic crystal ; Materials science ; Amphiphile ; Nanotechnology ; Aqueous solution ; Convolutional neural network ; Characterization (materials science) ; Liquid crystal ; Microcontact printing ; Computer science ; Biological system ; Artificial intelligence ; Chemistry ; Optoelectronics ; Polymer ; Organic chemistry ; Liquid crystalline Liquid Crystal Research Advancements ; Ocular Surface and Contact Lens ; Optical Polarization and Ellipsometry
Authors: Xian Shi | Zongya Wu | Jingxing Yang | Xueting Wang | Wei Zheng | Xiujuan Wang
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Analysis of Phenolic Antioxidant Interactions in Olive Oil Using Molecular Simulation and Artificial Neural Network Artificial neural network ; Olive oil ; Antioxidant ; Biological system ; Chemistry ; Artificial intelligence ; Computer science ; Biochemical engineering ; Food science ; Biology ; Organic chemistry ; Engineering Spectroscopy and Chemometric Analyses
Authors: Yining He | Qian Chen | Wei Lai
Source: SSRN Electronic Journal (preprint, 2023-01-01)
Field: Materials Science — Polymers and Plastics

Computational Study of Na Diffusion and Conduction in P2- and O3-Na2x[Nixti1–X]O2 Materials with Machine-Learning Interatomic Potentials Thermal conduction ; Diffusion ; Interatomic potential ; Materials science ; Condensed matter physics ; Chemical physics ; Engineering physics ; Statistical physics ; Thermodynamics ; Chemistry ; Physics ; Molecular dynamics ; Computational chemistry ; Composite material Conducting polymers and applications ; Solid-state spectroscopy and crystallography ; Electrochemical Analysis and Applications
Authors: Vishal Midya | Cecilia S. Alcala | Elza Rechtman | Jill Gregory | Kurunthachalam Kannan | Irva Hertz Picciotto | Susan L. Teitelbaum | Chris Gennings | María José Rosa | Damaskini Valvi
Source: ISEE Conference Abstracts (article, 2023-09-17)
Field: Chemistry — Electrochemistry

Weighted quantile sum regression and machine-learning assisted discovery of interactions between environmental chemicals in child neurodevelopment BACKGROUND AND AIM: A growing body of literature suggests that developmental exposure to individual or mixtures of environmental chemicals (ECs) is associated with autism spectrum disorder (ASD). However, evaluating interactive effects within environmental chemical mixtures on ASD diagnosis and other health outcomes can be challenging. METHOD: We introduced a combination of the classical exposure-mixture Weighted Quantile Sum (WQS) regression, and a machine-learning method termed signed iterative random forest (SiRF) to discover synergistic interactions between ECs that are (1) associated with higher odds of ASD diagnosis, (2) mimic toxicological interactions, and (3) are present only in a subset of the sample whose chemical concentrations are higher than certain thresholds. In a case-control Childhood Autism Risks from Genetics and Environment (CHARGE) study, we evaluated multi-ordered synergistic interactions among 62 ECs (30 phenols and parabens, 20 phthalates, 6 pesticides, and 6 trace elements) measured in the urine samples of 479 children in association with increased odds for ASD diagnosis (yes vs. no). RESULTS: WQS-SiRF identified two synergistic two-ordered interactions between (1) trace-element cadmium (Cd) and the organophosphate pesticide metabolite - diethyl-phosphate (DEP); and (2) 2,4,6-trichlorophenol (TCP-246) and DEP. Both interactions were suggestively associated with increased odds of ASD diagnosis in the subset of children with urinary concentrations of Cd, DEP, and TCP-246 above the 75th percentile. CONCLUSIONS: This study demonstrates a novel method that combines the inferential power of WQS and the predictive accuracy of machine-learning algorithms to discover potentially biologically relevant chemical-chemical interactions associated with ASD. Quantile regression ; Regression ; Regression analysis ; Computer science ; Machine learning ; Medicine ; Environmental health ; Statistics ; Artificial intelligence ; Mathematics Electrochemical Analysis and Applications ; Advanced Chemical Sensor Technologies
Source: C&EN Global Enterprise (article, 2023-12-18)
Field: Materials Science — Materials Chemistry

What to watch for in 2024 Athina Anastasaki, materials chemist, Swiss Federal Institute of Technology (ETH), Zurich “With increased focus from academic and industry researchers on developing a sustainable and circular polymer economy, many of the highly selective strategies used in organic chemistry may find applications in chemical recycling. Developing new methods to selectively break down waste polymeric materials into desirable raw materials could realize the true potential of plastic waste as a feedstock. Advances in the fields of mechanochemistry, photochemistry, and organometallic catalysis show particular promise in achieving this objective. Effectively utilizing the large amounts of polyethylene and polypropylene in waste streams in an economically viable manner is the ultimate goal of these efforts.” Connor W. Coley, computational chemist, Massachusetts Institute of Technology “We’ll continue to see increased accessibility and routine adoption of [artificial intelligence] techniques in chemistry, in particular Bayesian optimization for identifying reaction conditions and the use of neural network potentials as surrogates Computer science Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Microplastics and Plastic Pollution
Authors: Alex M. Maldonado | Igor Poltavsky | Valentín Vassilev-Galindo | Alexandre Tkatchenko | John A. Keith
Source: ChemRxiv (preprint, 2023-05-03)
Field: Materials Science — Materials Chemistry

Modeling molecular ensembles with gradient-domain machine learning force fields Gradient-domain machine learning (GDML) force fields have shown excellent accuracy, data efficiency, and applicability for molecules with hundreds of atoms, but the employed global descriptor limits transferability to ensembles of molecules. Many-body expansions (MBEs) should provide a rigorous procedure for size-transferable GDML by training models on fundamental n-body interactions. We developed many-body GDML (mbGDML) force fields for water, acetonitrile, and methanol by training 1-, 2-, and 3-body models on only 1000 MP2/def2-TZVP calculations each. Our mbGDML force field includes intramolecular flexibility and intermolecular interactions, providing that the reference data adequately describe these effects. Energy and force predictions of clusters containing up to 20 molecules are within 0.38 kcal/mol per monomer and 0.06 kcal/(mol Å) per atom of reference supersystem calculations. This deviation partially arises from the restriction of the mbGDML model to 3-body interactions. GAP and SchNet in this MBE framework achieved similar accuracies but occasionally had abnormally high errors up to 17 kcal/mol. NequIP trained on total energies and forces of trimers experienced much larger energy errors (at least 15 kcal/mol) as the number of monomers increased—demonstrating the effectiveness of size transferability with MBEs. Given these approximations, our automated mbGDML training schemes also resulted in fair agreement with reference radial distribution functions (RDFs) of bulk solvents. These results highlight mbGDML as valuable for modeling explicitly solvated systems with quantum-mechanical accuracy. Force field (fiction) ; Intramolecular force ; Transferability ; Intermolecular force ; Domain (mathematical analysis) ; Flexibility (engineering) ; Statistical physics ; Atom (system on chip) ; Quantum ; Molecule ; Field (mathematics) ; Computer science ; Chemistry ; Chemical physics ; Physics ; Artificial intelligence ; Mathematics ; Machine learning ; Quantum mechanics ; Statistics ; Mathematical analysis Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Changzhi Ai | Shuang Han | Xin Yang | Tejs Vegge | Heine Anton Hansen
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-07-28)
Field: Materials Science — Materials Chemistry

Deep learning assisted multi-tasking genetic algorithm for screening PdTiH surface with multiple adsorbates in different CO2 reduction reaction conditions The data and figures are used for supporting the paper of "Deep learning assisted multi-tasking genetic algorithm for screening PdTiH surface with multiple adsorbates in different CO2 reduction reaction conditions". Reduction (mathematics) ; Computer science ; Genetic algorithm ; Algorithm ; Deep learning ; Artificial intelligence ; Machine learning ; Materials science ; Mathematics Catalytic Processes in Materials Science ; Machine Learning in Materials Science
Authors: Shiyu Chen | Hazlee Azil Illias | Nurulafiqah Nadzirah Mansor | Wong Jee Keen Raymond
Source:  (article, 2023-12-08)
Field: Materials Science — Materials Chemistry

Partial Discharge Classification in Insulation Material via Discrete Wavelet Transform Noise Reduction and Artificial Neural Network Insulation material degradation stemming from partial discharge (PD) is a major factor in power equipment failure. External electromagnetic interference introduces noise, compromising the accuracy of PD diagnosis, thus, addressing partial discharge noise suppression is crucial. This study examines four PD types which include corona, floating electrode, surface, and void discharge measured from insulation materials. PD signals were combined with Additive White Gaussian Noise (AWGN) and treated with Discrete Wavelet Transform (DWT) for noise reduction. Six features including root mean square error, variance, standard deviation, waveform factor, kurtosis, and skewness were extracted from denoised signals to classify PD types using Artificial Neural Network (ANN) with 10-fold cross-validation. Classification accuracy was assessed and compared. Results indicate effective AWGN removal by DWT, achieving up to 90% classification accuracy for PD defects post-noise reduction using ANN. Noise reduction ; Artificial neural network ; Partial discharge ; Wavelet transform ; Discrete wavelet transform ; Noise (video) ; Computer science ; Reduction (mathematics) ; Wavelet ; Artificial intelligence ; Pattern recognition (psychology) ; Speech recognition ; Materials science ; Engineering ; Mathematics ; Electrical engineering ; Voltage High voltage insulation and dielectric phenomena ; Image and Signal Denoising Methods ; Power Transformer Diagnostics and Insulation