jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Prashant Chaudhari | Sanjay B. Bari | Sanjay J. Surana | Atul A. Shirkhedkar | Chandrakant Bonde | Saurabh C. Khadse | Vinod G. Ugale | Akhil Nagar | Rameshwar S. Cheke
Source: ACS Omega (article, 2022-05-12)
Field: Chemistry — Organic Chemistry

Discovery and Anticancer Activity of Novel 1,3,4-Thiadiazole- and Aziridine-Based Indolin-2-ones <i>via In Silico</i> Design Followed by Supramolecular Green Synthesis Three crucial anticancer scaffolds, namely indolin-2-one, 1,3,4-thiadiazole, and aziridine, are explored to synthesize virtually screened target molecules based on the c-KIT kinase protein. The stem cell factor receptor c-KIT was selected as target because most U.S. FDA-approved receptor tyrosine kinase inhibitors bearing the indolin-2-one scaffold profoundly inhibit c-KIT. Molecular hybrids of indolin-2-one with 1,3,4-thiadiazole (<b>IIIa</b>-<b>m</b>) and aziridine (<b>VIa</b> and <b>VIc</b>) were afforded through a modified Schiff base green synthesis using β-cyclodextrin-SO<sub>3</sub>H in water as a recyclable proton-donor catalyst. A computational study found that indolin-2,3-dione forms a supramolecular inclusion complex with β-cyclodextrin-SO<sub>3</sub>H through noncovalent interactions. A molecular docking study of all the synthesized compounds was executed on the c-KIT kinase domain, and most compounds displayed binding affinities similar to that of Sunitinib. On the basis of the pharmacokinetic significance of the aryl thioether linkage in small molecules, 1,3,4-thiadiazole hybrids (<b>IIIa</b>-<b>m</b>) were extended to a new series of 3-((5-(phenylthio)-1,3,4-thiadiazol-2-yl)imino)indolin-2-ones (<b>IVa</b>-<b>m</b>) via thioetherification using bis(triphenylphosphine)palladium(II)dichloride as the catalyst for C-S bond formation. Target compounds were tested against NCI-60 human cancer cell lines for a single-dose concentration. Among all three series of indolin-2-ones, the majority of compounds demonstrated broad-spectrum activity toward various cancer cell lines. Compounds <b>IVc</b> and <b>VIc</b> were further evaluated for a five-dose anticancer study. Compound <b>IVc</b> showed a potent activity of IC<sub>50</sub> = 1.47 μM against a panel of breast cancer cell lines, whereas compound <b>VIc</b> exhibited the highest inhibition for a panel of colon cancer cell lines at IC<sub>50</sub> = 1.40 μM. <i>In silico</i> ADME property descriptors of all the target molecules are in an acceptable range. Machine learning algorithms were used to examine the metabolites and phase I and II regioselectivities of compounds <b>IVc</b> and <b>VIc</b>, and the results suggested that these two compounds could be potential leads for the treatment of cancer. Chemistry ; Aziridine ; Stereochemistry ; Quinoxaline ; Docking (animal) ; Supramolecular chemistry ; Combinatorial chemistry ; Molecule ; Ring (chemistry) ; Organic chemistry Click Chemistry and Applications ; Synthesis and Catalytic Reactions ; Quinazolinone synthesis and applications
Authors: Gyu‐Hee Kim | Chihyung Lee | Kyung‐Soo Kim | Doo‐Hyun Ko
Source: Nano Energy (article, 2022-12-14)
Field: Materials Science — Materials Chemistry

Novel structural feature-descriptor platform for machine learning to accelerate the development of organic photovoltaics Organic solar cell ; Ternary operation ; Photovoltaics ; Photovoltaic system ; Materials science ; Feature (linguistics) ; Biological system ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Machine learning ; Engineering Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Yihua Xu | Qiao Shi | Ziyue Zhou | Ke Xu | Yan‐Wen Lin | Li Yang | Zhisen Zhang | Jianyang Wu
Source: 2D Materials (article, 2022-04-02)
Field: Materials Science — Materials Chemistry

Machine learning assisted insights into the mechanical strength of nanocrystalline graphene oxide Abstract The mechanical properties of graphene oxides (GOs) are of great importance for their practical applications. Herein, extensive first-principles-based ReaxFF molecular dynamics (MD) simulations predict the wrinkling morphology and mechanical properties of nanocrystalline GOs (NCGOs), with intricate effects of grain size, oxidation, hydroxylation, epoxidation, grain boundary (GB) hydroxylation, GB epoxidation, GB oxidation being considered. NCGOs show brittle failures initiating at GBs, obeying the weakest link principle. By training the MD data, four machine learning models are developed with capability in estimating the tensile strength of NCGOs, with sorting as eXtreme Gradient Boosting (XGboost) &gt; multilayer perceptron &gt; gradient boosting decision tree &gt; random forest. In the XGboot model, it is revealed that the strength of NCGOs is greatly dictated by oxidation and grain size, and the hydroxyl group plays more critical role in the strength of NCGOs than the epoxy group. These results uncover the pivotal roles of structural signatures in the mechanical strength of NCGOs, and provide critical guidance for mechanical designs of chemically-functionalized nanostructures. Ultimate tensile strength ; Materials science ; Nanocrystalline material ; Graphene ; Oxide ; Mechanical strength ; Brittleness ; Nanotechnology ; Composite material Graphene research and applications ; Graphene and Nanomaterials Applications ; Supercapacitor Materials and Fabrication
Authors: Theodoros E. Karakasidis | Filippos Sofos | Christos Tsonos
Source: Fluids (article, 2022-10-05)
Field: Materials Science — Materials Chemistry

The Electrical Conductivity of Ionic Liquids: Numerical and Analytical Machine Learning Approaches In this paper, we incorporate experimental measurements from high-quality databases to construct a machine learning model that is capable of reproducing and predicting the properties of ionic liquids, such as electrical conductivity. Empirical relations traditionally determine the electrical conductivity with the temperature as the main component, and investigations only focus on specific ionic liquids every time. In addition to this, our proposed method takes into account environmental conditions, such as temperature and pressure, and supports generalization by further considering the liquid atomic weight in the prediction procedure. The electrical conductivity parameter is extracted through both numerical machine learning methods and symbolic regression, which provides an analytical equation with the aid of genetic programming techniques. The suggested platform is capable of providing either a fast, numerical prediction mechanism or an analytical expression, both purely data-driven, that can be generalized and exploited in similar property prediction projects, overcoming expensive experimental procedures and computationally intensive molecular simulations. Generalization ; Computer science ; Symbolic regression ; Genetic programming ; Electrical resistivity and conductivity ; Component (thermodynamics) ; Machine learning ; Focus (optics) ; Ionic liquid ; Artificial intelligence ; Biological system ; Algorithm ; Thermodynamics ; Mathematics ; Chemistry ; Mathematical analysis ; Physics Machine Learning in Materials Science ; Ionic liquids properties and applications ; Advanced Chemical Sensor Technologies
Authors: Qinghang Wang | Haowei Zhai | Zhaoyang Jin | Junjie He | Yang Qin | Wenjun Liu | Yulong Li | D.L. Chen
Source: Journal of Magnesium and Alloys (article, 2022-09-28)
Field: Materials Science — Biomaterials

Hot-deformation kinetics analysis and extrusion parameter optimization of a dilute rare-earth free magnesium alloy The fundamental research on thermo-mechanical conditions provides an experimental basis for high-performance Mg-Al-Ca-Mn alloys. However, there is a lack of systematical investigation for this series alloys on the hot-deformation kinetics and extrusion parameter optimization. Here, the flow behavior, constitutive model, dynamic recrystallization (DRX) kinetic model and processing map of a dilute rare-earth free Mg-1.3Al-0.4Ca-0.4Mn (AXM100, wt.%) alloy were studied under different hot-compressive conditions. In addition, the extrusion parameter optimization of this alloy was performed based on the hot-processing map. The results showed that the conventional Arrhenius-type strain-related constitutive model only worked well for the flow curves at high temperatures and low strain rates. In comparison, using the machine learning assisted model (support vector regression, SVR) could effectively improve the accuracy between the predicted and experimental values. The DRX kinetic model was established, and a typical necklace-shaped structure preferentially occurred at the original grain boundaries and the second phases. The DRX nucleation weakened the texture intensity, and the further growth caused the more scattered basal texture. The hot-processing maps at different strains were also measured and the optimal hot-processing range could be confirmed at the deformation temperatures of 600∼723 K and the strain rates of 0.018∼0.563 s−1. Based on the optimum hot-processing range, a suitable extrusion parameter was considered as 603 K and 0.1 mm/s and the as-extruded alloy in this parameter exhibited a good strength-ductility synergy (yield strength of ∼ 232.1 MPa, ultimate strength of ∼ 278.2 MPa and elongation-to-failure of ∼ 20.1%). Finally, the strengthening-plasticizing mechanisms and the relationships between the DRXed grain size, yield strength and extrusion parameters were analyzed. Materials science ; Dynamic recrystallization ; Extrusion ; Strain rate ; Deformation (meteorology) ; Magnesium alloy ; Nucleation ; Alloy ; Metallurgy ; Hot working ; Composite material ; Thermodynamics Magnesium Alloys: Properties and Applications ; Metallurgy and Material Forming ; Aluminum Alloys Composites Properties
Authors: Majedeh Gheytanzadeh | Fatemeh Rajabhasani | Alireza Baghban | Sajjad Habibzadeh | Otman Abida | Amin Esmaeili | Muhammad Tajammal Munir
Source: Scientific Reports (article, 2022-12-19)
Field: Materials Science — Materials Chemistry

Estimating hydrogen absorption energy on different metal hydrides using Gaussian process regression approach Hydrogen is a promising alternative energy source due to its significantly high energy density. Also, hydrogen can be transformed into electricity in energy systems such as fuel cells. The transition toward hydrogen-consuming applications requires a hydrogen storage method that comes with pack hydrogen with high density. Among diverse methods, absorbing hydrogen on host metal is applicable at room temperature and pressure, which does not provide any safety concerns. In this regard, AB<sub>2</sub> metal hydride with potentially high hydrogen density is selected as an appropriate host. Machine learning techniques have been applied to establish a relationship on the effect of the chemical composition of these hosts on hydrogen storage. For this purpose, a data bank of 314 data point pairs was used. In this assessment, the different A-site and B-site elements were used as the input variables, while the hydrogen absorption energy resulted in the output. A robust Gaussian process regression (GPR) approach with four kernel functions is proposed to predict the hydrogen absorption energy based on the inputs. All the GPR models' performance was quite excellent; notably, GPR with Exponential kernel function showed the highest preciseness with R<sup>2</sup>, MRE, MSE, RMSE, and STD of 0.969, 2.291%, 3.909, 2.501, and 1.878, respectively. Additionally, the sensitivity of analysis indicated that ZR, Ti, and Cr are the most demining elements in this system. Hydrogen storage ; Hydrogen ; Gaussian function ; Absorption (acoustics) ; Hydride ; Gaussian ; Hydrogen fuel ; Kriging ; Kernel (algebra) ; Materials science ; Biological system ; Computer science ; Chemistry ; Mathematics ; Machine learning ; Computational chemistry ; Composite material Hydrogen Storage and Materials ; Ammonia Synthesis and Nitrogen Reduction ; Hybrid Renewable Energy Systems
Authors: Sterling G. Baird | Marianne Liu | Taylor D. Sparks
Source: Computational Materials Science (article, 2022-05-19)
Field: Materials Science — Materials Chemistry

High-dimensional Bayesian optimization of 23 hyperparameters over 100 iterations for an attention-based network to predict materials property: A case study on CrabNet using Ax platform and SAASBO Hyperparameter ; Property (philosophy) ; Bayesian optimization ; Bayesian network ; Computer science ; Bayesian probability ; Artificial intelligence ; Materials science Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Isaac Duah Boateng | Lucas Kuehnel | Christopher R. Daubert | Joseph Agliata | Wenxue Zhang | Ravinder Kumar | Sherry Flint-García | Azlin Mustapha | Pavel Somavat | Caixia Wan
Source: Food & Function (review, 2022-12-13)
Field: Chemistry — Analytical Chemistry

Updating the<i>status quo</i>on the extraction of bioactive compounds in agro-products using a two-pot multivariate design. A comprehensive review Extraction is regarded as the most crucial stage in analyzing bioactive compounds. Nonetheless, due to the intricacy of the matrix, numerous aspects must be optimized during the extraction of bioactive components. Although one variable at a time (OVAT) is mainly used, this is time-consuming and laborious. As a result, using an experimental design in the optimization process is beneficial with few experiments and low costs. This article critically reviewed two-pot multivariate techniques employed in extracting bioactive compounds in food in the last decade. First, a comparison of the parametric screening methods (factorial design, Taguchi, and Plackett-Burman design) was delved into, and its advantages and limitations in helping to select the critical extraction parameters were discussed. This was followed by a discussion of the response surface methodologies (central composite (CCD), Doehlert (DD), orthogonal array (OAD), mixture, D-optimal, and Box-Behnken designs (BBD), <i>etc</i>.), which are used to optimize the most critical variables in the extraction of bioactive compounds in food, providing a sequential comprehension of the linear and complex interactions and multiple responses and robustness tests. Next, the benefits, drawbacks, and possibilities of various response surface methodologies (RSM) and some of their usages were discussed, with food chemistry, analysis, and processing from the literature. Finally, extraction of food bioactive compounds using RSM was compared to artificial neural network modeling with their drawbacks discussed. We recommended that future experiments could compare these designs (BBD <i>vs.</i> CCD <i>vs.</i> DD, <i>etc</i>.) in the extraction of food-bioactive compounds. Besides, more research should be done comparing response surface methodologies and artificial neural networks regarding their practicality and limitations in extracting food-bioactive compounds. Response surface methodology ; Design of experiments ; Orthogonal array ; Taguchi methods ; Computer science ; Fractional factorial design ; Biochemical engineering ; Robustness (evolution) ; Multivariate statistics ; Central composite design ; Factorial experiment ; Extraction (chemistry) ; Plackett–Burman design ; Process (computing) ; Machine learning ; Process engineering ; Mathematics ; Chemistry ; Engineering ; Chromatography ; Statistics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Sensory Analysis and Statistical Methods
Authors: Mhanna Mhanna | Mohamed Sy | Ali Elkhazraji | Aamir Farooq
Source: Optics Express (article, 2022-09-26)
Field: Chemistry — Spectroscopy

Deep neural networks for simultaneous BTEX sensing at high temperatures In the study of chemical reactions, it is desirable to have a diagnostic strategy that can detect multiple species simultaneously with high sensitivity, selectivity, and fast time response. Laser-based selective detection of benzene, toluene, ethylbenzene, and xylenes (BTEX) has been challenging due to the similarly broad absorbance spectra of these species. Here, a mid-infrared laser sensor is presented for selective and simultaneous BTEX detection in high-temperature shock tube experiments using deep neural networks (DNN). A shock tube was coupled with a non-intrusive mid-infrared laser source, scanned over 3038.6-3039.8 cm<sup>-1</sup>, and an off-axis cavity enhanced absorption spectroscopy (OA-CEAS) setup of ∼ 100 gain to enable trace detection. Absorption cross-sections of BTEX species were measured at temperatures of 1000-1250 K and pressures near 1 atm. A DNN model with five hidden layers of 256, 128, 64, 32, and 16 nodes was implemented to split the composite measured spectra into the contributing spectra of each species. Several BTEX mixtures with varying mole fractions (0-600 ppm) of each species were prepared manometrically and shock-heated to 1000-1250 K and 1 atm, and the composite measured absorbance were split into contributions from each BTEX species using the developed DNN model, and thus make selective determinations of BTEX species. Predicted and manometric mole fractions were in good agreement with an absolute relative error of ∼ 11%. We obtained a minimum detection limit of 0.73-1.38 ppm of the target species at 1180 K. To the best of our knowledge, this work reports the first successful implementation of multispecies detection with a single narrow wavelength-tuning laser in a shock tube with laser absorption spectroscopy. BTEX ; Absorbance ; Ethylbenzene ; Analytical Chemistry (journal) ; Detection limit ; Materials science ; Absorption spectroscopy ; Laser ; Infrared spectroscopy ; Spectroscopy ; Absorption (acoustics) ; Spectral line ; Toluene ; Chemistry ; Optics ; Chromatography ; Physics ; Organic chemistry ; Composite material Spectroscopy and Laser Applications ; Laser Design and Applications ; Atmospheric Ozone and Climate
Authors: Jesper Byggmästar | Giorgos Nikoulis | Aslak Fellman | Fredric Granberg | Flyura Djurabekova | K. Nordlund
Source: Journal of Physics Condensed Matter (article, 2022-05-12)
Field: Materials Science — Materials Chemistry

Multiscale machine-learning interatomic potentials for ferromagnetic and liquid iron A large and increasing number of different types of interatomic potentials exist, either based on parametrised analytical functions or machine learning. The choice of potential to be used in a molecular dynamics simulation should be based on the affordable computational cost and required accuracy. We develop and compare four interatomic potentials of different complexity for iron: a simple machine-learned embedded atom method (EAM) potential, a potential with machine-learned two- and three-body-dependent terms, a potential with machine-learned EAM and three-body terms, and a Gaussian approximation potential with the smooth overlap of atomic positions descriptor. All potentials are trained to the same diverse database of body-centred cubic and liquid structures computed with density functional theory. The first three potentials are tabulated and evaluated efficiently using cubic spline interpolations, while the fourth one is implemented without additional optimisation. The four potentials span three orders of magnitude in computational cost. We compare and discuss the advantages of each potential in terms of transferability and the balance between accuracy and computational cost. Interatomic potential ; Transferability ; Computer science ; Atom (system on chip) ; Gaussian ; Density functional theory ; Molecular dynamics ; Potential method ; Artificial intelligence ; Statistical physics ; Algorithm ; Machine learning ; Computational chemistry ; Physics ; Chemistry Machine Learning in Materials Science ; Protein Structure and Dynamics ; X-ray Diffraction in Crystallography
Authors: Bin Jia | Yaoyue Zhang | A. Rusinek | Xinke Xiao | Rugang Chai | Guochao Gu
Source: International Journal of Impact Engineering (article, 2022-02-22)
Field: Materials Science — Materials Chemistry

Thermo-viscoplastic behavior and constitutive relations for 304 austenitic stainless steel over a wide range of strain rates covering quasi-static, medium, high and very high regimes Strain rate ; Materials science ; Viscoplasticity ; Flow stress ; Constitutive equation ; Strain hardening exponent ; Strain (injury) ; Sensitivity (control systems) ; Plasticity ; Austenitic stainless steel ; Hardening (computing) ; Composite material ; Upset ; Atmospheric temperature range ; Thermodynamics ; Mathematics ; Finite element method ; Physics High-Velocity Impact and Material Behavior ; Metallurgy and Material Forming ; Nuclear Materials and Properties
Authors: Lifeng Li | Zenan Shi | Hong Liang | Jie Liu | Zhiwei Qiao
Source: Nanomaterials (article, 2022-01-03)
Field: Chemistry — Inorganic Chemistry

Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting Atmospheric water harvesting by strong adsorbents is a feasible method of solving the shortage of water resources, especially for arid regions. In this study, a machine learning (ML)-assisted high-throughput computational screening is employed to calculate the capture of H<sub>2</sub>O from N<sub>2</sub> and O<sub>2</sub> for 6013 computation-ready, experimental metal-organic frameworks (CoRE-MOFs) and 137,953 hypothetical MOFs (hMOFs). Through the univariate analysis of MOF structure-performance relationships, <i>Q</i><sub>st</sub> is shown to be a key descriptor. Moreover, three ML algorithms (random forest, gradient boosted regression trees, and neighbor component analysis (NCA)) are applied to hunt for the complicated interrelation between six descriptors and performance. After the optimizing strategy of grid search and five-fold cross-validation is performed, three ML can effectively build the predictive model for CoRE-MOFs, and the accuracy <i>R</i><sup>2</sup> of NCA can reach 0.97. In addition, based on the relative importance of the descriptors by ML, it can be quantitatively concluded that the <i>Q</i><sub>st</sub> is dominant in governing the capture of H<sub>2</sub>O. Besides, the NCA model trained by 6013 CoRE-MOFs can predict the selectivity of hMOFs with a <i>R</i><sup>2</sup> of 0.86, which is more universal than other models. Finally, 10 CoRE-MOFs and 10 hMOFs with high performance are identified. The computational screening and prediction of ML could provide guidance and inspiration for the development of materials for water harvesting in the atmosphere. Environmental science ; Computer science ; Artificial intelligence ; Machine learning Metal-Organic Frameworks: Synthesis and Applications ; Enhanced Oil Recovery Techniques ; Solar-Powered Water Purification Methods