jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ikenna Chibuzor Emeji | Michael Kumi | Reinout Meijboom
Source: ACS Omega (article, 2024-07-29)
Field: Chemistry — Organic Chemistry

Performance Evaluation of Benzyl Alcohol Oxidation with <i>tert</i>-Butyl Hydroperoxide to Benzaldehyde Using the Response Surface Methodology, Artificial Neural Network, and Adaptive Neuro-Fuzzy Inference System Model The adaptive neuro-fuzzy inference system (ANFIS), central composite experimental design (CCD)-response surface methodology (RSM), and artificial neural network (ANN) are used to model the oxidation of benzyl alcohol using the <i>tert</i>-butyl hydroperoxide (TBHP) oxidant to selectively yield benzaldehyde over a mesoporous ceria-zirconia catalyst. Characterization reveals that the produced catalyst has hysteresis loops, a sponge-like structure, and structurally induced reactivity. Three independent variables were taken into consideration while analyzing the ANN, RSM, and ANFIS models: the amount of catalyst (A), reaction temperature (B), and reaction time (C). With the application of optimum conditions, along with a constant (45 mmol) TBHP oxidant amount, (30 mmol) benzyl alcohol amount, and rigorous refluxing of 450 rpm, a maximum optimal benzaldehyde yield of 98.4% was obtained. To examine the acceptability of the models, further sensitivity studies including statistical error functions, analysis of variance (ANOVA) results, and the lack-of-fit test, among others, were employed. The obtained results show that the ANFIS model is the most suited to predicting benzaldehyde yield, followed by RSM. Green chemistry matrix calculations for the reaction reveal lower values of the <i>E</i>-factor (1.57), mass intensity (MI, 2.57), and mass productivity (MP, 38%), which are highly desirable for green and sustainable reactions. Therefore, utilizing a ceria-zirconia catalyst synthesized via the inverse micelle method for the oxidation of benzyl alcohol provides a green and sustainable methodology for the synthesis of benzaldehyde under mild conditions. Benzaldehyde ; Benzyl alcohol ; Response surface methodology ; Adaptive neuro fuzzy inference system ; Materials science ; Central composite design ; Catalysis ; Chemistry ; Fuzzy logic ; Computer science ; Organic chemistry ; Chromatography ; Artificial intelligence ; Fuzzy control system Chemical Synthesis and Reactions ; Oxidative Organic Chemistry Reactions ; Catalysis and Oxidation Reactions
Authors: Chunhui Li | Steven Farrell | Benjamin Gilbert | Piotr Zarzycki
Source: Computational Materials Science (article, 2024-03-15)
Field: Chemistry — Electrochemistry

Deep neural network surrogate for surface complexation model of metal oxide/electrolyte interface Surface Complexation Models (SCM) are one of the most successful geochemical thermodynamics models of the speciation and charging of the mineral/electrolyte interface. However, finding unique and transferable SCM parameter values by fitting experimental data using existing numerical solvers is often challenging. We hypothesized that SCM surrogates built using Artificial Intelligence algorithms provide an alternative strategy for determining model parameter values and thus offer an ultrafast prediction of electrical double layer characteristics of metal oxide/electrolyte interface. We generated synthetic datasets of surface charge and electrokinetic potential as a function of pH, ionic strength, varying site densities, acidity, and ion-affinities of mineral surfaces using the 2-pK Triple Layer Model - one of the most used generic SCM. We explored a wide range of types of mineral surfaces using random walks in the parameter space. Next, we trained and validated two AI surrogates using our synthetic SCM data. We showed that AI-SCM surrogates for oxide/electrolyte interfaces are possible and cost-effective. They allow ultrafast and accurate SCM parametrization using synthetic data. AI-SCM can predict thermodynamic parameters from just a few titration data points, such as ion affinities and proton binding constants. What is more, the computational strategy we presented here to surrogate geochemical models by pre-trained AI architectures is more universal and can be applied to other geochemical/thermodynamic models – replacing the computationally expensive solvers with trained AI, thus minimizing computing costs and speeding up data analysis. Electrolyte ; Oxide ; Interface (matter) ; Metal ; Materials science ; Artificial neural network ; Chemical engineering ; Surface (topology) ; Inorganic chemistry ; Chemistry ; Adsorption ; Computer science ; Physical chemistry ; Metallurgy ; Electrode ; Artificial intelligence ; Engineering Electrochemical Analysis and Applications ; Analytical Chemistry and Sensors ; Gas Sensing Nanomaterials and Sensors
Authors: Arnold Pradhan | Fei Xu | Daniele Salvato | Indrajit Charit | C. D. Judge | Luca Capriotti | Tiankai Yao
Source: Materials Characterization (article, 2024-11-05)
Field: Materials Science — Materials Chemistry

Characterization of Fuel Cladding Chemical Interaction on a High Burnup U-10Zr Metallic Fuel via Electron Energy Loss Spectroscopy Enhanced by Machine Learning Materials science ; Cladding (metalworking) ; Burnup ; Characterization (materials science) ; Spectroscopy ; Nuclear engineering ; Electron energy loss spectroscopy ; Energy (signal processing) ; Metal ; Metallurgy ; Nanotechnology ; Transmission electron microscopy ; Engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Physics and Applications
Authors: Hakkı Yavuz | Arda Akyıldız | Mehmet Onur Gülbahçe
Source: Turkish Journal of Electrical Power and Energy Systems (review, 2024-06-27)
Field: Chemistry — Physical and Theoretical Chemistry

A Comprehensive Review of Physics-Based Battery Models and Comparing Different Physics-Based Models for Various Chemistries The increasing importance of batteries across diverse sectors, spanning from consumer electronics to electric vehicles, underscores the critical necessity for precise battery models. This review delineates four primary categories of battery models: empirical, equivalent circuit, data-driven, and physics-based models. Empirical models like the Nernst and Shepherd models offer simplicity but lack precision. Equivalent circuit models strike a balance between simplicity and accuracy, albeit with validation constraints. Data-driven methods leverage machine learning to predict battery performance accurately but require high-quality datasets. Physics-based models integrate fundamental electrochemical processes for detailed understanding, albeit with heightened computational complexity. Comparative analyses, with a focus on lithium-ion batteries, reveal trade-offs between computational efficiency and accuracy. The Single Particle Model and its extension single particle model with electrolyte dynamics emerge as efficient options, with single particle model with electrolyte dynamics showing promising accuracy akin to Single Particle Model. Additionally, comparisons across different battery chemistries unveil varying levels of modeling precision. This article compares different electrochemical modeling techniques across chemistries to discern optimal methods. The electrochemical model, which is one of the battery modeling techniques, has been examined and investigated in detail in this study and has contributed to the literature on how the model with which chemistry works with which electrochemical model. In addition, this study contributed to the existing lithium ferro-phosphate chemistry modeling with optimization technique in pybamm. The synthesis offers insights into diverse modeling methodologies and their implications for battery research and development, guiding future investigations toward more tailored modeling strategies for specific applications. Cite this article as: H. Yavuz, A. Akyildiz and M. O. Gulbahce, “A comprehensive review of physics-based battery models and comparing different physicsbased models for various chemistries,” Turk J Electr Power Energy Syst., 2024; 4(2), 108-117. Physics ; Battery (electricity) ; Statistical physics ; Quantum mechanics Various Chemistry Research Topics
Authors: Ivan Zlobin | Nikita Toroptsev | Gleb M. Averochkin | Alexander Pavlov
Source: Chinese Journal of Polymer Science (article, 2024-11-11)
Field: Materials Science — Materials Chemistry

Pre-trained Mol2Vec Embeddings as a Tool for Predicting Polymer Properties Materials science ; Polymer ; Polymer science ; Composite material ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electron and X-Ray Spectroscopy Techniques
Authors: Rasmus Christensen | Morten M. Smedskjær
Source: MRS Bulletin (article, 2024-12-31)
Field: Materials Science — Ceramics and Composites

Predicting dynamics from structure in a sodium silicate glass Abstract Understanding the dynamics of atoms in glasses is crucial for unraveling the origin of relaxation and the glass transition as well as predicting transport properties. However, identifying the structural features controlling atom dynamics in glasses remains challenging. Recently, machine learning models based on graph neural networks (GNNs) have successfully been used to predict future dynamics, but these prior studies focused primarily on model systems such as Kob–Andersen-type Lennard–Jones mixtures. This study investigates the use of local descriptors, GNN models, and molecular dynamics simulations to clarify the atomics dynamics in a realistic glass system (sodium silicate) across varying time scales. By harnessing the capabilities of different structural representations, we develop effective models for predicting the dynamics of sodium ions within the glassy silicate network, based solely on the initial atom positions. We further demonstrate the viability of our approach through comparison to previously proposed methods. Our findings pave the way for designing new glass formulations with tailored dynamical properties (e.g., as glassy electrolytes for batteries). Impact statement Glass science has long grappled with understanding the fundamental nature and origin of glassy dynamics. The governing principles of atomic dynamics in glasses remain elusive as it is not obvious what to look for in the glass structure. While previous studies have focused on simplified model systems, we demonstrate for the first time that machine learning models can be used to accurately predict multi-time scale atomic dynamics in a complex oxide glass (sodium silicate) from the static atomic structure. By comparing different machine learning architectures, we establish that graph neural networks outperform conventional structural descriptors for dynamics prediction, with graph representations being able to effectively capture the complex multibody correlations that govern dynamics. Our findings show that the future dynamics in oxide glasses on time scales up to nanoseconds are at least partially encoded in the initial glassy configuration itself, showing that glassy dynamics is not a completely stochastic process. The capability to predict dynamics from structure has major implications as it could provide new tools for rational design of glassy materials with tailored dynamical properties and functionalities, possibly accelerating development of advanced glasses for applications in areas such as solid-state batteries and nuclear waste immobilization. Graphical abstract Sodium silicate ; Silicate glass ; Silicate ; Materials science ; Dynamics (music) ; Mineralogy ; Chemical physics ; Chemical engineering ; Geology ; Chemistry ; Metallurgy ; Composite material ; Physics ; Engineering Glass properties and applications ; Building materials and conservation ; Cultural Heritage Materials Analysis
Authors: Nikalabh Dihingia | Gabriel A. Vázquez-Lizardi | Ryan J. Wu | Danielle Reifsnyder Hickey
Source: The Journal of Chemical Physics (article, 2024-03-04)
Field: Materials Science — Materials Chemistry

Quantifying the thickness of WTe2 using atomic-resolution STEM simulations and supervised machine learning For two-dimensional (2D) materials, the exact thickness of the material often dictates its physical and chemical properties. The 2D quantum material WTe2 possesses properties that vary significantly from a single layer to multiple layers, yet it has a complicated crystal structure that makes it difficult to differentiate thicknesses in atomic-resolution images. Furthermore, its air sensitivity and susceptibility to electron beam-induced damage heighten the need for direct ways to determine the thickness and atomic structure without acquiring multiple measurements or transferring samples in ambient atmosphere. Here, we demonstrate a new method to identify the thickness up to ten van der Waals layers in Td-WTe2 using atomic-resolution high-angle annular dark-field scanning transmission electron microscopy image simulation. Our approach is based on analyzing the intensity line profiles of overlapping atomic columns and building a standard neural network model from the line profile features. We observe that it is possible to clearly distinguish between even and odd thicknesses (up to seven layers), without using machine learning, by comparing the deconvoluted peak intensity ratios or the area ratios. The standard neural network model trained on the line profile features allows thicknesses to be distinguished up to ten layers and exhibits an accuracy of up to 94% in the presence of Gaussian and Poisson noise. This method efficiently quantifies thicknesses in Td-WTe2, can be extended to related 2D materials, and provides a pathway to characterize precise atomic structures, including local thickness variations and atomic defects, for few-layer 2D materials with overlapping atomic column positions. van der Waals force ; Materials science ; Scanning transmission electron microscopy ; Artificial neural network ; Resolution (logic) ; Characterization (materials science) ; Optics ; Computational physics ; Transmission electron microscopy ; Physics ; Nanotechnology ; Computer science ; Artificial intelligence Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: Weiyi Gong | Jinshan He | Fan Wang | Xitao Wang
Source: Journal of Materials Research and Technology (article, 2024-11-01)
Field: Materials Science — Materials Chemistry

Thermodynamic calculation-assisted design of 500 MPa high performance steel by machine learning In order to rationally design Q500 low-alloy wind power steel with stable microstructure and properties within a large cooling rate range, this study proposed a design system that utilizes thermodynamic database, machine learning (ML), and the multi-objective genetic optimization algorithm (MOGA). Thermodynamic calculation has been used to obtain a large amount of data on phase fractions and mechanical properties for various chemical composition at different cooling rates. By comparing the predictive capabilities of different ML models, the deep neural network (DNN) and MOGA were combined to design Q500 wind power steel. Subsequently, three groups of designed chemical compositions were appropriately selected for verification. The results indicate that the yield strength of the experimental steel was over 500 MPa, and the maximum error in microstructure and mechanical property prediction was 5.0%. The prediction results demonstrated the rationality of the composition design framework. Meanwhile, the method of combining the material database and ML proposed in this study can also be applied to the design of other low-alloy steels. Materials science ; Metallurgy Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Ajnabiul Hoque | Manajit Das | Mayank Baranwal | Raghavan B. Sunoj
Source: arXiv (Cornell University) (preprint, 2024-07-14)
Field: Materials Science — Materials Chemistry

ReactAIvate: A Deep Learning Approach to Predicting Reaction Mechanisms and Unmasking Reactivity Hotspots A chemical reaction mechanism (CRM) is a sequence of molecular-level events involving bond-breaking/forming processes, generating transient intermediates along the reaction pathway as reactants transform into products. Understanding such mechanisms is crucial for designing and discovering new reactions. One of the currently available methods to probe CRMs is quantum mechanical (QM) computations. The resource-intensive nature of QM methods and the scarcity of mechanism-based datasets motivated us to develop reliable ML models for predicting mechanisms. In this study, we created a comprehensive dataset with seven distinct classes, each representing uniquely characterized elementary steps. Subsequently, we developed an interpretable attention-based GNN that achieved near-unity and 96% accuracy, respectively for reaction step classification and the prediction of reactive atoms in each such step, capturing interactions between the broader reaction context and local active regions. The near-perfect classification enables accurate prediction of both individual events and the entire CRM, mitigating potential drawbacks of Seq2Seq approaches, where a wrongly predicted character leads to incoherent CRM identification. In addition to interpretability, our model adeptly identifies key atom(s) even from out-of-distribution classes. This generalizabilty allows for the inclusion of new reaction types in a modular fashion, thus will be of value to experts for understanding the reactivity of new molecules. Reactivity (psychology) ; Computer science ; Artificial intelligence ; Psychology ; Medicine Machine Learning in Materials Science
Authors: Hao He | Kangshu Li | Xujing Ji | Xiaoxu Zhao | Lianming Tong | Jin Zhang
Source: ACS Nano (article, 2024-05-14)
Field: Materials Science — Materials Chemistry

Chiral Stacking Identification of Two-Dimensional Triclinic Crystals Enabled by Machine Learning Chiral materials possess broken inversion and mirror symmetry and show great potential in the application of next-generation optic, electronic, and spintronic devices. Two-dimensional (2D) chiral crystals have planar chirality, which is nonsuperimposable on their 2D enantiomers by any rotation about the axis perpendicular to the substrate. The degree of freedom to construct vertical stacking of 2D monolayer enantiomers offers the possibility of chiral manipulation for designed properties by creating multilayers with either a racemic or enantiomerically pure stacking order. However, the rapid recognition of the relative proportion of two enantiomers becomes demanding due to the complexity of stacking orders of 2D chiral crystals. Here, we report the unambiguous identification of racemic and enantiomerically pure stackings for layered ReSe<sub>2</sub> and ReS<sub>2</sub> using circular polarized Raman spectroscopy. The chiral Raman response is successfully manipulated by the enantiomer proportion, and the stacking orders of multilayer ReSe<sub>2</sub> and ReS<sub>2</sub> can be completely clarified with the help of second harmonic generation and scanning transmission electron microscopy measurements. Finally, we trained an artificial intelligent Spectra Classification Assistant to predict the chirality and the complete crystallographic structures of multilayer ReSe<sub>2</sub> from a single circular polarized Raman spectrum with the accuracy reaching 0.9417 ± 0.0059. Stacking ; Raman spectroscopy ; Triclinic crystal system ; Chirality (physics) ; Materials science ; Enantiomer ; Crystallography ; Monolayer ; Nanotechnology ; Crystal structure ; Chemistry ; Stereochemistry ; Optics ; Chiral symmetry breaking ; Symmetry breaking ; Physics ; Organic chemistry ; Quantum mechanics 2D Materials and Applications ; Spectroscopy and Quantum Chemical Studies ; Surface Chemistry and Catalysis
Authors: Bilal Ahmad Mir | Hilal Tayara | Kil To Chong
Source: Computational Biology and Chemistry (article, 2024-06-15)
Field: Materials Science — Materials Chemistry

SB-Net: Synergizing CNN and LSTM networks for uncovering retrosynthetic pathways in organic synthesis Retrosynthetic analysis ; Computer science ; Net (polyhedron) ; Artificial intelligence ; Chemistry ; Mathematics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Yiyang Sun | Yinian Tang | Jing Zhou | Bingchen Guo | Feiyan Yuan | Bo Yao | Yang Yu | Chun Li
Source: Biochemical and Biophysical Research Communications (article, 2024-05-22)
Field: Chemistry — Organic Chemistry

Computational design of myoglobin-based carbene transferases for monoterpene derivatization Derivatization ; Myoglobin ; Monoterpene ; Carbene ; Chemistry ; Chromatography ; Biochemistry ; Mass spectrometry ; Catalysis Cyclopropane Reaction Mechanisms ; Click Chemistry and Applications ; Biochemical and Molecular Research
Authors: Chengxi Zhao | Honglai Liu | Da‐Hui Qu | Andrew I. Cooper | Linjiang Chen
Source: Chemical Science (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

A machine learned potential for investigating single crystal to single crystal transformations in complex organic molecular systems The packing of organic molecular crystals is often dominated by weak non-covalent interactions, making their <i>in situ</i> rearrangement under external stimuli challenging to understand. We investigate a pressure-induced single-crystal-to-single-crystal (SCSC) transformation between two polymorphs of 2,4,5-triiodo-1<i>H</i>-imidazole using machine learning potentials. This process involves the rearrangement of halogen and hydrogen bonds combined with proton transfer within a complex solid-state system. We developed a strategy to progressively approach the transition state along the phase transition path from both ends by using both the α and β crystal phases as initial structures for active learning. This method allowed us to develop a DFT-based machine learning potential that faithfully describes both of the stable phases and the transition processes. Our results demonstrate that these anisotropic interactions are represented accurately during molecular dynamic simulations. Bond breaking and reforming during proton transfer is observed and analysed in detail. This approach holds promise for simulating SCSC transitions in organic molecular crystals involving anisotropic interactions and chemical bond changes. Single crystal ; Crystal (programming language) ; Materials science ; Chemistry ; Computer science ; Chemical physics ; Crystallography Machine Learning in Materials Science ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography
Authors: M. V. Ganduglia-Pirovano | A. Martı́nez-Arias | Shuang Chen | Yuemin Wang | Pablo G. Lustemberg
Source: Materials Today Sustainability (article, 2024-04-04)
Field: Materials Science — Materials Chemistry

Comment on “surface characterization of cerium oxide catalysts using deep learning with infrared spectroscopy of CO” We demonstrate that the predicted distribution of ceria facets in CeO2 nanorods, derived from the analysis of the measured IR spectrum of CO by fitting it using experimentally or DFT(HSE) calculated CO vibrational frequencies collected on all three low-index ceria surfaces, does not align with that derived using deep learning methods [1]. The latter utilizes a dataset based on extensive DFT(PBE) + U calculations, which are erroneous and have thus been scaled to the experimental values. Cerium oxide ; Characterization (materials science) ; Cerium ; Materials science ; Nanorod ; Infrared ; Infrared spectroscopy ; Spectroscopy ; Complex oxide ; Catalysis ; Analytical Chemistry (journal) ; Physical chemistry ; Oxide ; Nanotechnology ; Chemistry ; Physics ; Optics ; Metallurgy ; Organic chemistry ; Quantum mechanics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advancements in Solid Oxide Fuel Cells