jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Andy S. Anker | Emil T. S. Kjær | Mikkel Juelsholt | Kirsten M. Ø. Jensen
Source: Journal of Applied Crystallography (article, 2023-12-04)
Field: Materials Science — Materials Chemistry

<i>POMFinder</i> : identifying polyoxometallate cluster structures from pair distribution function data using explainable machine learning Characterization of a material structure with pair distribution function (PDF) analysis typically involves refining a structure model against an experimental data set, but finding or constructing a suitable atomic model for PDF modelling can be an extremely labour-intensive task, requiring carefully browsing through large numbers of possible models. Presented here is <i>POMFinder</i>, a machine learning (ML) classifier that rapidly screens a database of structures, here polyoxometallate (POM) clusters, to identify candidate structures for PDF data modelling. The approach is shown to identify suitable POMs from experimental data, including <i>in situ</i> data collected with fast acquisition times. This automated approach has significant potential for identifying suitable models for structure refinement to extract quantitative structural parameters in materials chemistry research. <i>POMFinder</i> is open source and user friendly, making it accessible to those without prior ML knowledge. It is also demonstrated that <i>POMFinder</i> offers a promising modelling framework for combined modelling of multiple scattering techniques. Computer science ; Experimental data ; Classifier (UML) ; Data set ; Function (biology) ; Cluster (spacecraft) ; Set (abstract data type) ; Data mining ; Machine learning ; Artificial intelligence ; Mathematics Polyoxometalates: Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Krisztina Regős | Rémy Pawlak | Xing Wang | Ernst Meyer | Silvio Decurtins | G. Domokos | Kostya S. Novoselov | Shi‐Xia Liu | Ulrich Aschauer
Source: Proceedings of the National Academy of Sciences (article, 2023-04-11)
Field: Materials Science — Materials Chemistry

Polygonal tessellations as predictive models of molecular monolayers Molecular self-assembly plays a very important role in various aspects of technology as well as in biological systems. Governed by covalent, hydrogen or van der Waals interactions-self-assembly of alike molecules results in a large variety of complex patterns even in two dimensions (2D). Prediction of pattern formation for 2D molecular networks is extremely important, though very challenging, and so far, relied on computationally involved approaches such as density functional theory, classical molecular dynamics, Monte Carlo, or machine learning. Such methods, however, do not guarantee that all possible patterns will be considered and often rely on intuition. Here, we introduce a much simpler, though rigorous, hierarchical geometric model founded on the mean-field theory of 2D polygonal tessellations to predict extended network patterns based on molecular-level information. Based on graph theory, this approach yields pattern classification and pattern prediction within well-defined ranges. When applied to existing experimental data, our model provides a different view of self-assembled molecular patterns, leading to interesting predictions on admissible patterns and potential additional phases. While developed for hydrogen-bonded systems, an extension to covalently bonded graphene-derived materials or 3D structures such as fullerenes is possible, significantly opening the range of potential future applications. van der Waals force ; Monte Carlo method ; Computer science ; Statistical physics ; Molecular dynamics ; Intuition ; Theoretical computer science ; Molecule ; Computational chemistry ; Chemistry ; Physics ; Mathematics Machine Learning in Materials Science ; Surface Chemistry and Catalysis ; Computational Drug Discovery Methods
Authors: Saurabh Vinod Parmar | P. S. DESHMUKH | Rutuja Sankpal | Siddhika Watharkar | Vidya Avasare
Source: The Journal of Physical Chemistry A (article, 2023-09-27)
Field: Materials Science — Materials Chemistry

Machine Learning-Enabled Predictions of Condensed Fukui Functions and Designing of Metal Pincer Complexes for Catalytic Hydrogenation of CO<sub>2</sub> This research showcases the machine learning (ML)-enabled homogeneous catalyst discovery to be employed in carbon dioxide hydrogenation. To achieve the desired turnover frequency (TOF), the electrophilicity of the central metal atom is a crucial factor in transition metal pincer complexes. The condensed Fukui function is a direct measure of the catalytic performance of these pincer complexes. Herein, we demonstrate that machine learning is a convenient and effiecient method to calculate condensed Fukui functions of the central metal atom. The electrophilicity values of 202 pincer complexes were calculated by using density functional theory (DFT) to train the ML model. The test data of the experimentally established pincer complexes show a direct linkage between calculated electrophilicity and experimental TOF. Further, this data was used to develop an ML protocol to screen 2,84,062 catalyst complexes to get the electrophilicity values of the Mn, Fe, Co, and Ni transition metals encompassing various permutation combinations of PNP, PNN, NNN, and PCP pincer ligands. These findings validate the efficacy of machine learning in the rapid screening of metal pincer catalysts based on condensed Fukui functions. Pincer movement ; Electrophile ; Catalysis ; Density functional theory ; Chemistry ; Fukui function ; Computational chemistry ; Atom (system on chip) ; Metal ; Transition metal ; Combinatorial chemistry ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Carbon dioxide utilization in catalysis ; CO2 Reduction Techniques and Catalysts
Authors: Valerio Briganti | Alessandro Lunghi
Source: Machine Learning Science and Technology (article, 2023-07-04)
Field: Materials Science — Materials Chemistry

Efficient generation of stable linear machine-learning force fields with uncertainty-aware active learning Abstract Machine-learning (ML) force fields (FFs) enable an accurate and universal description of the potential energy surface of molecules and materials on the basis of a training set of ab initio data. However, large-scale applications of these methods rest on the possibility to train accurate ML models with a small number of ab initio data. In this respect, active-learning (AL) strategies, where the training set is self-generated by the model itself, combined with linear ML models are particularly promising. In this work, we explore an AL strategy based on linear regression and able to predict the model’s uncertainty on predictions for molecular configurations not sampled by the training set, thus providing a straightforward recipe for the extension of the latter. We apply this strategy to the spectral neighbor analysis potential and show that only tens of ab initio simulations of atomic forces are required to generate FFs for room-temperature molecular dynamics at or close to chemical accuracy and which stability can be systematically improved by the user at modest computational expenses. Moreover, the method does not necessitate any conformational pre-sampling, thus requiring minimal user intervention and parametrization. Ab initio ; Computer science ; Parametrization (atmospheric modeling) ; Force field (fiction) ; Stability (learning theory) ; Set (abstract data type) ; Machine learning ; Artificial intelligence ; Basis (linear algebra) ; Mathematics ; Chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Wenlong Chen | Kai Chen
Source: Computational Materials Science (article, 2023-09-06)
Field: Materials Science — Materials Chemistry

Nonlinear probabilistic virtual sample generation using Gaussian process latent variable model and fitting for rubber material Gaussian process ; Probabilistic logic ; Computer science ; Nonlinear system ; Gaussian ; Latent variable ; Artificial intelligence ; Kriging ; Sample (material) ; Algorithm ; Machine learning ; Pattern recognition (psychology) Machine Learning in Materials Science ; Machine Learning and Data Classification ; Fault Detection and Control Systems
Authors: Meike N. Leiske | Christopher Kuenneth | Jonas De Breuck | Bruno G. De Geest | Richard Hoogenboom
Source: Macromolecular Chemistry and Physics (article, 2023-08-03)
Field: Chemistry — Organic Chemistry

Amphiphilic Zwitterionic Bioderived Block Copolymers from Glutamic Acid and Cholesterol – Ability to Form Nanoparticles and Serve as Vectors for the Delivery of 6‐Mercaptopurine Abstract In this work, the straightforward synthesis of amphiphilic zwitterionic bioderived block copolymers (BCPs) using glutamic acid (Glu) and cholesterol (Chol) as building blocks are reported. The previously established Glu‐derivative N Boc‐Glu‐O t Bu‐methacrylate ( N Boc‐Glu‐O t Bu‐MA) serves as hydrophobic precursor for the zwitterionic block, while a mostly unexplored cholesteryl‐derived methacrylate monomer (Chol‐MA) with increased side chain flexibility functions as the hydrophobic block. In the first step, N Boc‐Glu‐O t Bu‐MA is polymerized via reversible addition‐fragmentation chain‐transfer (RAFT) polymerization. Afterward, the linear polymer is chain‐extended with Chol‐MA, yielding P( N Boc‐Glu‐O t Bu‐MA) n ‐ b ‐(Chol‐MA) m BCPs with varying block ratios. After deprotection under acidic conditions, polymers with a block weight ratio of 87:13 (Glu‐OH‐MA:Chol‐MA) readily assemble into polymeric nanoparticles (NPs) of a desirable size below 100 nm diameter, making them good candidates for biomedical applications. The experimental results are supported using computations of the partition coefficients and machine learning models for the prediction of the polymer densities of the different BCPs. In addition, high (up to 20 wt.%) loading of the hydrophobic anti‐cancer drug 6‐mercaptopurine (6‐MP) is achieved in these NPs during the assembly process. The cytostatic activity of 6‐MP NPs is demonstrated in vitro on MDA‐MB‐231 breast cancer cells. These results emphasize the potential of amphiphilic zwitterionic bioderived NPs for the delivery of hydrophobic drugs. Amphiphile ; Copolymer ; Chemistry ; Chain transfer ; Raft ; Methacrylate ; Polymerization ; Polymer ; Monomer ; Nanoparticle ; Polymer chemistry ; Combinatorial chemistry ; Radical polymerization ; Materials science ; Organic chemistry ; Nanotechnology Advanced Polymer Synthesis and Characterization ; Nanoparticle-Based Drug Delivery ; RNA Interference and Gene Delivery
Authors: Vasiliy S. Krasnikov | Alexander E. Mayer
Source: Metals (article, 2023-01-03)
Field: Materials Science — Materials Chemistry

Initiation and Mechanisms of Plasticity in Bimetallic Al-Cu Composite We studied the shear deformation of a laminar Al-Cu composite with (100) and (110) interfaces with a shear perpendicular to the lamellae in comparison with pure single crystal Al and Cu at strain rates of 109 s−1 and 108 s−1 and different initial pressures in the range from −3 GPa to +50 GPa. The results of molecular dynamics (MD) for the plasticity initiation are generalized by means of an artificial neural network (ANN) trained by MD data for the (100) interface, and a rate sensitivity parameter identified using MD data for different strain rates. The ANN-based approach allows us to extrapolate MD data to much lower strain rates, which are more relevant for typical dynamic loadings. The considered problem is of interest as an example of the application of the developed ANN-based approach to bimetallic systems, whereas previously it was tested only for pure metals; in addition, Al-Cu composites are of practical interest for technology. The interface between metals reduces the shear strength of the composite in comparison with both pure metals. At an initial pressure below 10 GPa, the plasticity begins in the aluminum part of the composite, while at higher pressures, the plasticity of the copper part starts first. At a pressure above 40 GPa, a phase transition in the aluminum part governs the plasticity development. All this leads to a nonmonotonic pressure dependence of the critical shear stress of the Al-Cu composite in the case of (100) and (110) interfaces without misorientation. Misorientation decreases the critical stress of the nucleation of lattice dislocation and makes the pressure dependence of this stress monotonic. Deformation modes, with a defect-free copper part and a strain-accommodating aluminum part are observed in the MD and can be useful for technological applications related to deformable conducting materials. Misorientation ; Materials science ; Plasticity ; Composite number ; Composite material ; Shear (geology) ; Strain rate ; Bimetallic strip ; Critical resolved shear stress ; Metallurgy ; Microstructure ; Shear rate ; Metal Microstructure and mechanical properties ; Aluminum Alloys Composites Properties ; High-Velocity Impact and Material Behavior
Authors: Roman A. Eremin | Innokentiy S. Humonen | A. A. Kazakov | Vladimir D. Lazarev | Anatoly P. Pushkarev | Semen Budennyy
Source: Computational Materials Science (article, 2023-11-27)
Field: Materials Science — Materials Chemistry

Graph neural networks for predicting structural stability of Cd- and Zn-doped <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si5.svg" display="inline" id="d1e465"><mml:mi>γ</mml:mi></mml:math>-CsPbI3 Density functional theory ; Band gap ; Atom (system on chip) ; Electronic structure ; Stability (learning theory) ; Materials science ; Crystallography ; Algorithm ; Chemistry ; Physics ; Computational chemistry ; Mathematics ; Machine learning ; Computer science ; Optoelectronics Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Sara Sirovec | Maja Benković | Davor Valinger | Tea Sokač Cvetnić | Jasenka Gajdoš Kljusurić | Ana Jurinjak Tušek | Tamara Jurina
Source: Chemosensors (article, 2023-05-05)
Field: Chemistry — Analytical Chemistry

Development of ANN Models for Prediction of Physical and Chemical Characteristics of Oil-in-Aqueous Plant Extract Emulsions Using Near-Infrared Spectroscopy The potential of applying Artificial Neural Network (ANN) models based on near-infrared (NIR) spectra for the characterization of physical and chemical features of oil-in-aqueous oregano/rosemary extract emulsions was explored in this work. Emulsions were prepared using a batch emulsification process, with pea protein as the emulsifier. NIR spectral data were connected to the results of the analysis of physical and chemical properties of the emulsions (zeta potential, Feret droplet diameter, total polyphenolic content, and antioxidant capacity) with the final aim of quantitative prediction of the physical and chemical features. For that purpose, robust non-linear multivariate analysis (Artificial Neural Network modeling) was applied. The spectra themselves were preprocessed using several approaches (raw spectra, Savitzky–Golay smoothing, standard normal variate, and multiplicative scatter corrections) after which the impact of NIR spectral preprocessing on the ANN model’s efficiency was evaluated. The results show that NIR spectroscopy integrated with ANN computation can be employed to quantitatively predict the physical and chemical properties of oil-in-plant extract emulsions (R2 &gt; 0.9). Biological system ; Near-infrared spectroscopy ; Artificial neural network ; Chemometrics ; Materials science ; Chemistry ; Analytical Chemistry (journal) ; Computer science ; Artificial intelligence ; Chromatography ; Physics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Phytochemicals and Antioxidant Activities
Authors: Ziyan Guo | Xuhao Liu | Zehua Pan | Yexin Zhou | Zheng Zhong | Zilin Yan
Source: Engineering Computations (article, 2023-08-08)
Field: Materials Science — Materials Chemistry

Data augmentation and data mining towards microstructure and property relationship for composites Purpose In recent years, the convolutional neural network (CNN) based deep learning approach has succeeded in data-mining the relationship between microstructures and macroscopic properties of materials. However, such CNN models usually rely heavily on a large set of labeled images to ensure the accuracy and generalization ability of the predictive models. Unfortunately, in many fields, acquiring image data is expensive and inconvenient. This study aims to propose a data augmentation technique to enhance the performance of the CNN models for linking microstructural images to the macroscopic properties of composites. Design/methodology/approach Microstructures of composites are synthesized using discrete element simulations and Potts kinetic Monte Carlo simulations. Macroscopic properties such as the elastic modulus, Poisson's ratio, shear modulus, coefficient of thermal expansion, and triple-phase boundary length density are extracted on representative volume elements. The CNN model is trained using the 3D microstructural images as inputs and corresponding macroscopic properties as the labels. The comparison of the predictive performance of the CNN models with and without data augmentation treatment are compared. Findings The comparison between the prediction performance of CNN models with and without data augmentation showed that the former reduced the weighted mean absolute percentage error (WMAPE) for the prediction from 5.1627% to 1.7014%. This significant reduction signifies that the proposed data augmentation method can effectively enhance the generalization ability and robustness of CNN models. Originality/value This study demonstrates that data augmentation is beneficial for solving the problems of model overfitting, data scarcity, and sample imbalance for CNN-based deep learning tasks at a low cost. By developing more and advanced data augmentation techniques, deep learning accelerated homogenization will boost the multi-scale computational mechanics and materials. Overfitting ; Convolutional neural network ; Robustness (evolution) ; Computer science ; Artificial intelligence ; Data set ; Generalization ; Mean absolute percentage error ; Algorithm ; Artificial neural network ; Mathematics Machine Learning in Materials Science ; Composite Material Mechanics ; Injection Molding Process and Properties
Authors: Fatemeh Kenari | Szilárd Molnár | Igor D. Borges | Hamilton B. Napolitano | Pál Perjési
Source: International Journal of Molecular Sciences (article, 2023-05-10)
Field: Chemistry — Organic Chemistry

(E)-2-Benzylidenecyclanones: Part XVIII Study the Possible Link between Glutathione Reactivity and Cancer Cell Cytotoxic Effects of Some Cyclic Chalcone Analogs A Comparison of the Reactivity of the Open-Chain and the Seven-Membered Homologs Non-enzymatic thiol addition into the α,β-unsaturated carbonyl system is associated with several biological effects. In vivo, the reactions can form small-molecule thiol (e.g., glutathione) or protein thiol adducts. The reaction of two synthetic (4'-methyl- and 4'-methoxy substituted) cyclic chalcone analogs with reduced glutathione (GSH) and <i>N</i>-acetylcysteine (NAC) was studied by (high-pressure liquid chromatography-ultraviolet spectroscopy) HPLC-UV method. The selected compounds displayed in vitro cancer cell cytotoxicity (IC<sub>50</sub>) of different orders of magnitude. The structure of the formed adducts was confirmed by (high-pressure liquid chromatography-mass spectrometry) HPLC-MS. The incubations were performed under three different pH conditions (pH 3.2/3.7, 6.3/6.8, and 8.0/7.4). The chalcones intrinsically reacted with both thiols under all incubation conditions. The initial rates and compositions of the final mixtures depended on the substitution and the pH. The frontier molecular orbitals and the Fukui function were carried out to investigate the effects on open-chain and seven-membered cyclic analogs. Furthermore, machine learning protocols were used to provide more insights into physicochemical properties and to support the different thiol-reactivity. HPLC analysis indicated diastereoselectivity of the reactions. The observed reactivities do not directly relate to the different in vitro cancer cell cytotoxicity of the compounds. Chemistry ; Glutathione ; Thiol ; Chalcone ; Reactivity (psychology) ; High-performance liquid chromatography ; Adduct ; Cytotoxicity ; Stereochemistry ; Enzyme ; Organic chemistry ; In vitro ; Biochemistry Synthesis and biological activity ; Synthesis of Organic Compounds ; Sulfur-Based Synthesis Techniques
Authors: Hao Zhang | Qingxin Yang | Changgeng Zhang | Yongjian Li | Yifan Chen
Source: IEEE Transactions on Magnetics (article, 2023-09-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetic Properties Simulation of Electrical Steel Sheet Based on Recurrent Neural Network In this work, a Play model based on recurrent neural network (RNN) is proposed to predict hysteresis characteristics of electrical steel sheets under complex excitation conditions. The proposed model combines the neural network with the Play hysteresis operator, replacing the distribution function in the hysteresis model with the trained neural network parameter structure. An automatic selection method for neural network hyperparameters based on genetic algorithm (GA) is suggested, which improves the prediction accuracy. Comparing the experimental with the model prediction results, the proposed model can accurately predict the hysteresis characteristics of materials under sinusoidal and harmonic conditions. Artificial neural network ; Hysteresis ; Computer science ; Electrical steel ; Hyperparameter ; Magnetic hysteresis ; Genetic algorithm ; Algorithm ; Artificial intelligence ; Materials science ; Machine learning ; Magnetization ; Physics ; Magnetic field Magnetic Properties and Applications ; Grey System Theory Applications
Authors: Nan Mu | Tingting Bo | Yugao Hu | Shiqian Cao | Ruixin Xu | Yanyu Liu | Wei Zhou
Source: Computational Materials Science (article, 2023-04-11)
Field: Materials Science — Materials Chemistry

High-throughput screening of H2 production catalysts from doped In2SSe monolayer: Valence electrons-based descriptor Monolayer ; Doping ; Electron ; Catalysis ; Throughput ; Valence (chemistry) ; Materials science ; Valence electron ; Chemistry ; Nanotechnology ; Optoelectronics ; Computer science ; Physics 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films ; Advanced Thermoelectric Materials and Devices
Authors: V. Villazón-León | Adrián Bonilla‐Petriciolet | Juan Carlos Tapia-Picazo | Gabriel Luna‐Bárcenas
Source: Fluid Phase Equilibria (article, 2023-09-01)
Field: Chemistry — Organic Chemistry

Calculation of liquid Cp of pure compounds using an improved thermodynamic model based on group contributions and artificial neural networks Chemistry ; Group (periodic table) ; Group contribution method ; Artificial neural network ; Thermodynamics ; Organic chemistry ; Phase equilibrium ; Artificial intelligence ; Phase (matter) Chemical Thermodynamics and Molecular Structure ; Crystallization and Solubility Studies ; Thermal and Kinetic Analysis
Authors: Riya Aghara | Hemant Yadav | Sanni Kapatel
Source: Brazilian Journal of Physics (article, 2023-11-23)
Field: Materials Science — Polymers and Plastics

Predicting Optical Band Gap of MoO3 Using Machine Learning Algorithms for Optoelectronic Applications Gradient boosting ; Boosting (machine learning) ; Band gap ; Random forest ; Machine learning ; Artificial intelligence ; Algorithm ; Physics ; Mean squared error ; Regression ; Doping ; Computer science ; Optoelectronics ; Statistics ; Mathematics Transition Metal Oxide Nanomaterials ; Gas Sensing Nanomaterials and Sensors ; ZnO doping and properties