jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: S. K. Jameer Basha | E. Ajith Jubilson
Source: IEEE Access (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Regressive Multi-Scale BiGRU Model for Work Function Prediction of MXene Materials With Oppositional Learning-Based Optimization MXenes are two-dimensional transition metal carbides and nitrides with variable compositions and complex chemical properties. MXenes are used in various applications such as electronics, energy storage, and catalysis due to their highly tunable electrical, optical, and physical characteristics and composition adaptability. The applicability of MXenes for these applications can be determined by its work function, which is a crucial fundamental characteristic. Many researchers have recently concentrated on machine learning models to predict the work function, but these models still need improvement. So, this research proposes a deep learning (DL) based accurate 2D MXenes work function prediction model called a Regressive Multi-scale Dynamic Adaptive Convolutional BiGRU Weighting (RMDAC-BiGRUW) ensemble model. To improve the effectiveness, the pre-processing stage is included for normalizing and standardizing the input data. Furthermore, the oppositional gazelle transition algorithm (OGTA) technique is employed for deep feature selection. Finally, the proposed DL-based ensemble model, composed of several layers, is utilized for work function prediction. The proposed model is accessed with various existing models in the performance evaluation scenario, including decision tree, Random Forest (RF), K-Nearest Neighbors (KNN), and Convolutional Neural network (CNN). Furthermore, different kinds of analysis are performed in terms of mean absolute error (MAE), mean squared error (MSE), and coefficient of determination (R2). Finally, the experimental outcomes demonstrate that the proposed model is perfectly suitable for precise MXenes work function prediction as the model selection criterion values are better compared to the existing models. In addition, the proposed work function prediction model obtained 0.1875 as MSE, 0.7662 as R2, and 0.1875 as MAE. MXenes ; Computer science ; Random forest ; Artificial intelligence ; Machine learning ; Mean squared error ; Weighting ; Convolutional neural network ; Ensemble forecasting ; Feature selection ; Piecewise ; Function (biology) ; Algorithm ; Mathematics ; Engineering ; Statistics MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: Tianjun Xie | Gerhard R. Wittreich | Matthew T. Curnan | Geun Ho Gu | Kevin Seals | Justin S. Tolbert
Source: Journal of Chemical Information and Modeling (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Machine-Learning-Enabled Thermochemistry Estimator Modeling adsorbates on single-crystal metals is critical in rational catalyst design and other research that requires detailed thermochemistry. First-principles simulations via density functional theory (DFT) are among the prevalent tools to acquire such information about surface species. While they are highly dependable, DFT calculations often require intensive computational resources and runtime. These limiting factors become particularly pronounced when investigating large sets of complex molecules on heavy noble metals. Consequently, our ability to explore these species and their corresponding energetics is limited. In this work, we establish a novel framework that utilizes techniques including molecular encoding, descriptor synthesis, and machine learning to overcome the limitation of costly DFT simulations. Simultaneously, we estimate thermochemical information efficiently at the DFT accuracy level. More specifically, we translated our training molecules into text-based identifiers through a simplified molecular-input line-entry system. Following that, we parametrize our training matrices with sets of short-range descriptors based on group methods, applying first the nearest neighbors to account for linear contributions. This is coupled with the long-range descriptors characterizing second nearest neighbors to account for nonlinear corrections. Finally, we use linear regression and machine learning techniques, such as Gaussian process regressions to regress over the linear and nonlinear matrix systems, respectively. This is the first work to our knowledge that encompasses both the first and second nearest neighbors based on the group theory throughout the featurization, training, and deployment stages. We trained and validated our models with 459 surface species on Pt(111), Ru(0001), and Ir(111) surfaces. Results exhibit robust performance to reproduce the energetics of interest, such as enthalpies, entropies, and heat capacities, at various temperatures. Notably, the mean absolute errors can be reduced by 48% during training and 19% during prediction at a minimum, when compared to the classical group method. Leveraging the novel framework, our machine-learning-enabled thermochemistry estimator significantly empowers us to research the thermochemistry of complex species on metal catalysts. Thermochemistry ; Density functional theory ; Computer science ; Nonlinear system ; Machine learning ; Range (aeronautics) ; Estimator ; Artificial intelligence ; Chemistry ; Computational chemistry ; Mathematics ; Materials science ; Physics ; Quantum mechanics ; Physical chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; CO2 Reduction Techniques and Catalysts
Authors: Jiali Guo | Songran Yang | Chenghui Wang | Jing Liu | Yanzhi Guo | Zongwei Yang | Xueyan Zhao | Xuemei Pu
Source: Crystal Growth & Design (article, 2024-09-25)
Field: Chemistry — Physical and Theoretical Chemistry

Cocrystal Prediction Tool (CCPT): A Web Server for Deep Learning-Assisted Cocrystal Screening and Density Evaluation Cocrystallization, as a molecular modification strategy, has exhibited great success in the chemistry and material fields. To reduce time and labor costs in experiments, machine learning of artificial intelligence (AI) has been introduced to accelerate cocrystal development. Despite the theoretical success of the exploits, their usages suffer from professional programmed operation and substantial human intervention, thus hindering their application in practice. To fill up the gap between theory and practical application, we explore a one-step and user-friendly cocrystal prediction web server (CCPT), which integrates two state-of-the-art deep learning models with high generalization and accuracy, including cocrystal screening and density evaluation. Users only need to upload molecule pair files and select the job type. CCPT can automatically perform feature descriptor calculation and prediction tasks (cocrystallization formation and their densities). All prediction results will be packaged into a file format that can be visualized and downloaded on the web page by users. The entire process under the ergonomic graphical interface is code-free operation and a streamlined workflow with minimum human intervention and thus very simple and convenient for the end-user, especially for experimental investigators. CCPT is free and accessible at http://www.scuccpt.cn. We expect that it will be a useful design tool for diverse cocrystal fields. Cocrystal ; Computer science ; Artificial intelligence ; Chemistry Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Machine Learning in Materials Science
Authors: Jake E. Barker | Gareth W. Richings | Yujie Xie | Julia Y. Rho | Calum T. J. Ferguson | Rachel K. O’Reilly | Scott Habershon
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Integrated computational and experimental design of fluorescent heteroatom-functionalised maleimide derivatives The bottom-up design and synthesis of organic molecular species with tailored photophysical properties stands as an important challenge to both computational and experimental chemical science. Overcoming this challenge presents the potential to usher in new tools and approaches to improve our ability to develop new technologies, such as molecular sensors and attuned molecular switches. Here, we report the bottom-up design and characterisation of new fluorescent maleimide derivatives using coupled computational and experimental insights. Using an extensive set of experimentally-measured UV/visible spectra for different functionalized maleimides in different solvents, we train an artificial neural network (ANN) to rapidly correlate maleimide structure (and solvent) with emission spectra characteristics. We subsequently use this computational predictor to identify design principles for novel functionalised maleimide structures with targeted photophysical properties; synthesis and characterisation of several new maleimides demonstrates how this combined strategy can offer new directions for tuning photochemistry, for example opening new routes to designing tailor-made fluorescent probes. Maleimide ; Heteroatom ; Fluorescence ; Chemistry ; Combinatorial chemistry ; Organic chemistry ; Ring (chemistry) ; Physics Luminescence and Fluorescent Materials ; Photochromic and Fluorescence Chemistry ; Click Chemistry and Applications
Authors: Lasse Kreimendahl | Mikhail Karnaukh | Merle I. S. Röhr
Source: The Journal of Physical Chemistry A (article, 2024-12-30)
Field: Materials Science — Materials Chemistry

Diffusion Generative Models for Designing Efficient Singlet Fission Dimers Diffusion generative models, a class of machine learning techniques, have shown remarkable promise in materials science and chemistry by enabling the precise generation of complex molecular structures. In this article, we propose a novel application of diffusion generative models for stabilizing reactive molecular structures identified through quantum mechanical screening. Specifically, we focus on the design challenge presented by singlet fission (SF), a phenomenon crucial for advancing solar cell efficiency beyond theoretical limits. While theoretical chemistry has been successful in predicting intermolecular arrangements with enhanced SF coupling, the practical implementation of these configurations faces challenges due to discrepancies between favorable and stabilized structures. To address this gap, we introduce a three-step strategy combining quantum mechanical screening for identifying optimal molecular arrangements and diffusion generative models for predicting stabilizing linkers. Through a case study of cibalackrot dimers, a promising SF material, we demonstrate the efficacy of our approach in enhancing SF efficiency by stabilizing the desired molecular arrangements. Generative grammar ; Intermolecular force ; Generative model ; Computer science ; Diffusion ; Coupling (piping) ; Nanotechnology ; Chemistry ; Biochemical engineering ; Materials science ; Chemical physics ; Biological system ; Molecule ; Physics ; Artificial intelligence ; Engineering ; Quantum mechanics ; Organic chemistry ; Biology Machine Learning in Materials Science ; Nuclear Materials and Properties ; Electrocatalysts for Energy Conversion
Authors: Vishwesh Venkatraman | P.A. Carvalho
Source: Journal of Applied Crystallography (article, 2024-06-14)
Field: Materials Science — Materials Chemistry

Accurate space-group prediction from composition Predicting crystal symmetry simply from chemical composition has remained challenging. Several machine-learning approaches can be employed, but the predictive value of popular crystallographic databases is relatively modest due to the paucity of data and uneven distribution across the 230 space groups. In this work, virtually all crystallographic information available to science has been compiled and used to train and test multiple machine-learning models. Composition-driven random-forest classification relying on a large set of descriptors showed the best performance. The predictive models for crystal system, Bravais lattice, point group and space group of inorganic compounds are made publicly available as easy-to-use software downloadable from https://gitlab.com/vishsoft/cosy. Bravais lattice ; Group (periodic table) ; Computer science ; Random forest ; Composition (language) ; Crystal structure prediction ; Space (punctuation) ; Chemical space ; Lattice (music) ; Artificial intelligence ; Crystal (programming language) ; Crystal structure ; Machine learning ; Algorithm ; Crystallography ; Chemistry ; Physics ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Rituparna Chowdhury | Rudra Das | Fowzia Binta Faruk Ananna | Arnob Saha | Sadia Nawar | Md. Hamid Hosen
Source:  (article, 2024-05-02)
Field: Chemistry — Analytical Chemistry

Unveiling Predictive Factors in Apple Quality: Leveraging LIME, SHAP, and the Synergy of Machine Learning Models and Artificial Neural Networks In the realm of fruit quality assessment, the demand for accurate identification methods has grown significantly. Traditional measures, such as physical and chemical analyses, prove impractical due to their time-consuming nature and potential sample damage. To address this imperative need for precise and timely apple quality detection, this research explores the application of machine learning algorithms. Recognizing the drawbacks of traditional methods reliant on physical and chemical measures for predicting fruit maturity, non-destructive techniques, such as hyperspectral imaging and machine learning algorithms, are meticulously investigated. The study goes on to evaluate and compare the performance of various machine learning approaches, including Random Forest Classifier (RF), K-Nearest Neighbors Classifier (KNN), Support Vector Classifier (SVC), Extreme Gradient Boosting Classifier (XGB), Light Gradient Boosting Machine Classifier (LGBM), Artificial Neural Network (ANN), and Voting Classifier. Among these, the Artificial Neural Network emerges as the top performer, achieving an impressive accuracy rate of 92.87%. Following the thorough evaluation of the models, Explainable Artificial Intelligence (XAI) techniques, specifically LIME and SHAP, are employed. These techniques playa crucial role in interpreting and visualizing the intricate decision- making process of the models. The insights gained underscore the models' robustness while providing valuable information on the decision-making processes, contributing significantly to advancements in apple quality detection methodologies. Artificial intelligence ; Machine learning ; Computer science ; Artificial neural network ; Random forest ; Classifier (UML) ; Decision tree ; Gradient boosting ; Support vector machine Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Smart Agriculture and AI
Authors: Hongtao Xue | Zhen Chang | Juan Li | Yan-Hong Yang | Junqiang Ren | Yong Zhang | Fuling Tang | Xuefeng Lu | Junchen Li
Source: Materials Today Communications (article, 2024-09-19)
Field: Materials Science — Materials Chemistry

Molecular dynamics simulations of the shear and tensile mechanical properties of rare-earth metal erbium based on deep-learning potential Materials science ; Molecular dynamics ; Shear (geology) ; Erbium ; Earth (classical element) ; Ultimate tensile strength ; Rare earth ; Metal ; Composite material ; Metallurgy ; Optoelectronics ; Computational chemistry ; Doping Machine Learning in Materials Science ; Nuclear Materials and Properties ; Microstructure and mechanical properties
Authors: Florian Rothenhäusler | Rodrigo Q. Albuquerque | Marcel Sticher | Christopher Künneth | Holger Ruckdäschel
Source: Machine Learning with Applications (article, 2024-12-02)
Field: Materials Science — Polymers and Plastics

Application of convolutional neural networks and ensemble methods in the fiber volume content analysis of natural fiber composites The incorporation of natural fibers into fiber-reinforced polymer composites (FRPC) has the potential to bolster their sustainability. A critical attribute of FRPC is the fiber volume content ( FVC ), a parameter that profoundly influences their thermo-mechanical characteristics. However, the determination of FVC in natural fiber composites (NFC) through manual analysis of light microscopy images is a labor-intensive process. In this work, it is demonstrated that the pixels from light microscopy images of NFC can be utilized to predict FVC using machine learning (ML) models. In this proof-of-concept investigation, it is shown that convolutional neural network-based models predict FVC with an accuracy required in polymer engineering applications, with a mean average error of 2.72 % and an R 2 coefficient of 0.85. Finally, it is shown that much simpler ML models, non-specialized in image recognition, besides being much easier and more efficient to optimize and train, can also deliver good accuracies required for FVC characterization, which not only contributes to the sustainability, but also facilitates the access of such models by researchers in regions with little computational resources. This study marks a substantial advancement in the area of automated characterization of NFC, and democratization of knowledge, offering a promising avenue for the enhancement of sustainable materials. • The fiber volume content was predicted using microscopy images and machine learning. • The optimized ML model exhibited an MAE of 2.89% and R 2 of 0.81. • These metrics were enhanced to 2.72% and 0.85 by employing ensemble techniques. Fiber ; Composite material ; Volume (thermodynamics) ; Convolutional neural network ; Natural fiber ; Materials science ; Computer science ; Artificial intelligence ; Physics Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Natural Fiber Reinforced Composites
Authors: Adam M. Krajewski | Jonathan W. Siegel | Zi‐Kui Liu
Source: Computational Materials Science (article, 2024-11-07)
Field: Materials Science — Materials Chemistry

Efficient structure-informed featurization and property prediction of ordered, dilute, and random atomic structures Property (philosophy) ; Materials science ; Statistical physics ; Physics Machine Learning in Materials Science ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography
Authors: Lizandra Barrios-Herrera | Maicon Pierre Lourenço | Jiří Hostaš | Patrizia Calaminici | Andreas M. Köster | Alain Tchagang | Dennis R. Salahub
Source: Journal of Computational Chemistry (article, 2024-03-29)
Field: Materials Science — Materials Chemistry

<scp>Active‐learning</scp> for global optimization of <scp>Ni‐Ceria</scp> nanoparticles: The case of <scp>Ce<sub>4−x</sub>Ni<sub>x</sub>O<sub>8−</sub></scp><sub>x</sub> (x = 1, 2, 3) Ni-CeO<sub>2</sub> nanoparticles (NPs) are promising nanocatalysts for water splitting and water gas shift reactions due to the ability of ceria to temporarily donate oxygen to the catalytic reaction and accept oxygen after the reaction is completed. Therefore, elucidating how different properties of the Ni-Ceria NPs relate to the activity and selectivity of the catalytic reaction, is of crucial importance for the development of novel catalysts. In this work the active learning (AL) method based on machine learning regression and its uncertainty is used for the global optimization of Ce<sub>(4-x)</sub>Ni<sub>x</sub>O<sub>(8-x)</sub> (x = 1, 2, 3) nanoparticles, employing density functional theory calculations. Additionally, further investigation of the NPs by mass-scaled parallel-tempering Born-Oppenheimer molecular dynamics resulted in the same putative global minimum structures found by AL, demonstrating the robustness of our AL search to learn from small datasets and assist in the global optimization of complex electronic structure systems. Nanoparticle ; Computer science ; Chemical engineering ; Engineering Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Catalysis and Oxidation Reactions
Authors: Rasmus M. Borup | Nicolai Ree | Jan H. Jensen
Source: Beilstein Journal of Organic Chemistry (article, 2024-07-16)
Field: Chemistry — Organic Chemistry

pKalculator: A p <i>K</i> <sub>a</sub> predictor for C–H bonds Determining the p<i>K</i> <sub>a</sub> values of various C-H sites in organic molecules offers valuable insights for synthetic chemists in predicting reaction sites. As molecular complexity increases, this task becomes more challenging. This paper introduces pKalculator, a quantum chemistry (QM)-based workflow for automatic computations of C-H p<i>K</i> <sub>a</sub> values, which is used to generate a training dataset for a machine learning (ML) model. The QM workflow is benchmarked against 695 experimentally determined C-H p<i>K</i> <sub>a</sub> values in DMSO. The ML model is trained on a diverse dataset of 775 molecules with 3910 C-H sites. Our ML model predicts C-H p<i>K</i> <sub>a</sub> values with a mean absolute error (MAE) and a root mean squared error (RMSE) of 1.24 and 2.15 p<i>K</i> <sub>a</sub> units, respectively. Furthermore, we employ our model on 1043 p<i>K</i> <sub>a</sub>-dependent reactions (aldol, Claisen, and Michael) and successfully indicate the reaction sites with a Matthew's correlation coefficient (MCC) of 0.82. Chemistry ; Medicinal chemistry Free Radicals and Antioxidants ; Organic Chemistry Cycloaddition Reactions ; Chemical Reaction Mechanisms
Authors: Abhishek Chandramohan | Nadimpalli Raghukiran
Source: Engineering Applications of Artificial Intelligence (article, 2024-07-26)
Field: Materials Science — Polymers and Plastics

Detection and comparison of reversible shape transformations in responsive polymers using deep learning and knowledge transfer by identifying stimulus-triggering characteristic points Computer science ; Artificial intelligence ; Deep learning ; Transfer of learning ; Stimulus (psychology) ; Knowledge transfer ; Pattern recognition (psychology) ; Machine learning ; Cognitive psychology ; Knowledge management ; Psychology Polymer composites and self-healing ; Additive Manufacturing and 3D Printing Technologies ; Machine Learning in Materials Science
Authors: Jiazhen Liu | Siwei Yang | Yuhao Xie | Yue‐Jiao Zhang | Jing‐Hua Tian | Shisheng Zheng | Pei Liang | Jian‐Feng Li
Source: Journal of Computational Biophysics and Chemistry (article, 2024-03-09)
Field: Chemistry — Analytical Chemistry

Classification of Fats and Oils Based on Raman Spectroscopy and Deep Learning Raman spectroscopy is a popular technology for material identification, but it encounters difficulties when distinguishing components with closely related substances by intuition and experience. The distinction of fats and oils is a typical example, which is significant in the food industry. In this work, the Raman spectroscopy and deep learning algorithm are combined to analyze closely related animal fats (lard, butter, mutton fat and chicken fat) and vegetable oils (soybean oil and peanut oil) in a dataset. A deep neural network founded on the VGG architecture with attention mechanism is developed, reaching an accuracy of 100% for fats and oils classification. By combining Raman spectroscopy with deep learning, this research provides a potent technique for tackling the identification of similar substances. Raman spectroscopy ; Spectroscopy ; Artificial intelligence ; Materials science ; Computer science ; Physics ; Optics Spectroscopy and Chemometric Analyses