jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Patrycja Kwiek | Małgorzata Jakubowska
Source: Preprints.org (preprint, 2024-07-18)
Field: Chemistry — Analytical Chemistry

Color Standardization of Chemical Solution Images Using Template-Based Histogram Matching in Deep Learning Regression Color distortion in an image poses a challenge for classification and regression when the input data consists of pictures. Because of that, a new algorithm for color standardization of photos is proposed, which serves as a base for a deep neural network regression model. This approach utilizes a custom color template, which was developed based on an initial series of studies and digital imaging. Using the equalized histogram of R, G, B channels from the digital template and its photo, a color mapping strategy is computed. By applying this approach, the histograms are adjusted, and the colors of photos taken with a smartphone are standardized. The proposed algorithm has been developed for a series of photos where the entire surface roughly maintains a uniform color, and the differences in color between the photographs of individual objects are minor. The optimized approach was validated in the colorimetric determination procedure of vitamin C. The dataset for deep neural network in the regression variant was formed from photos of samples under two separate lighting conditions. For the concentration range of vitamin C from 0 to 87.72 µg·mL-1, the RMSE for the test set ranged between 0.75 and 1.95 µg·mL-1, in comparison to the non-standardized variant, where this indicator was at the level of 1.48-2.29 µg·mL-1. The consistency of the predicted concentration results with actual data expressed using R2 was between 0.9956 and 0.9999, for each of the standardized variants. This approach allows for the removal of light reflections on the shiny surface of solutions, which is a common problem in liquid samples. The color matching algorithm has a universal character, and its application scope is not limited. Artificial intelligence ; Histogram ; Consistency (knowledge bases) ; Computer science ; Pattern recognition (psychology) ; Color histogram ; Matching (statistics) ; Artificial neural network ; Template matching ; Regression ; Color normalization ; Computer vision ; Mathematics ; Color image ; Image (mathematics) ; Statistics ; Image processing Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Zhenwei Liang | Jiazhun Huang | Biqi Zhong | Yujia Liu | Tiejun Wang
Source: Industrial & Engineering Chemistry Research (article, 2024-08-02)
Field: Materials Science — Materials Chemistry

Unveiling Feature Importance in Methanol Reforming Systems through the Machine Learning There is a common challenge in the research of methanol reforming, as the development of new catalysts requires a significant amount of time and material costs. This work leverages machine learning (ML) to explore new elements for catalysts, overcoming traditional trial-and-error methods. Utilizes machine learning to conduct in-depth feature analysis on different aspects of published methanol reforming catalyst data, including reaction conditions, elements, and descriptors. We evaluated nine ML models using four input methods to predict catalyst performance, identifying key elements and their descriptors impacting methanol reforming. The prediction reveals the importance of some previously overlooked physical properties, as well as seven potential elements that were not previously studied. And in the final preparation of the catalyst, to confirm the feasibility and accuracy of this prediction method. This novel approach sheds light on designing non-noble metal catalysts beyond conventional frameworks, providing valuable insights for methanol reforming systems. Methanol ; Catalysis ; Feature (linguistics) ; Work (physics) ; Computer science ; Process engineering ; Noble metal ; Artificial intelligence ; Machine learning ; Biochemical engineering ; Chemistry ; Engineering ; Mechanical engineering ; Organic chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalytic Processes in Materials Science
Authors: Shunya Minami | Yasuhiko Hayashi | Stephen Wu | Kenji Fukumizu | Hiroki Sugisawa | Masashi Ishii | Isao Kuwajima | Kazuya Shiratori | Ryo Yoshida
Source: arXiv (Cornell University) (preprint, 2024-08-07)
Field: Materials Science — Materials Chemistry

Scaling Law of Sim2Real Transfer Learning in Expanding Computational Materials Databases for Real-World Predictions To address the challenge of limited experimental materials data, extensive physical property databases are being developed based on high-throughput computational experiments, such as molecular dynamics simulations. Previous studies have shown that fine-tuning a predictor pretrained on a computational database to a real system can result in models with outstanding generalization capabilities compared to learning from scratch. This study demonstrates the scaling law of simulation-to-real (Sim2Real) transfer learning for several machine learning tasks in materials science. Case studies of three prediction tasks for polymers and inorganic materials reveal that the prediction error on real systems decreases according to a power-law as the size of the computational data increases. Observing the scaling behavior offers various insights for database development, such as determining the sample size necessary to achieve a desired performance, identifying equivalent sample sizes for physical and computational experiments, and guiding the design of data production protocols for downstream real-world tasks. Scaling law ; Scaling ; Transfer of learning ; Computer science ; Data science ; Database ; Artificial intelligence ; Mathematics Machine Learning in Materials Science
Authors: Alfonso Gijón | Miguel Molina-Solana | Juan Gómez‐Romero
Source: Journal of Computational Science (article, 2024-07-22)
Field: Materials Science — Materials Chemistry

Graph-neural-network potential energy surface to speed up Monte Carlo simulations of water cluster anions Monte Carlo method ; Computer science ; Cluster (spacecraft) ; Graph ; Artificial neural network ; Computational science ; Artificial intelligence ; Theoretical computer science ; Mathematics ; Statistics ; Computer network Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Bowen Li | Xiao Jin | Ya Gao | John Z. H. Zhang | Tong Zhu
Source: ChemRxiv (preprint, 2024-07-30)
Field: Materials Science — Materials Chemistry

Transition State Searching Accelerated by Deep Learning Potential The exploration and construction of organic chemical reaction networks hold the potential to clarify complex reaction mechanisms. However, this endeavor encounters challenges, particularly in cases requiring computationally intensive quantum mechanical calculations. In recent years, the remarkable advancement of machine learning (ML) technologies has provided a novel approach to address this issue. ML can effectively utilize vast datasets to build and train complex models, enabling prediction and simulation. In this study, we conducted a performance comparison of deep potential for molecular dynamics (DeePMD), recursively embedded atom neural network (REANN), and neural equivariant interatomic potentials (NequIP) based on the Transition1x dataset. The most efficient model, NequIP, was selected for further analysis. Combined with reaction path search methods such as nudged elastic band (NEB) and growing string method (GSM), this model was employed for the identification and exploration of transition states. The results demonstrate that the success ratio of NequIP combined with the NEB method can reach 96.6%, with a mean absolute error (MAE) of 0.32 kcal/mol for barrier prediction. By adding a modest number of data points, NequIP achieves a MAE of 4.01 kcal/mol for barrier prediction in unexplored chemical reaction space. In the future, by combining different reaction network search methods, we anticipate applying this model to swiftly explore reaction pathways and construct more comprehensive reaction networks. We believe that ML will play a significant role in accelerating the elucidation of unknown chemical reaction mechanisms and in revealing complex reaction networks. Computer science ; Artificial neural network ; Transition state ; Deep learning ; Atom (system on chip) ; Path (computing) ; Artificial intelligence ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Mass Spectrometry Techniques and Applications
Authors: Rolf David | Miguel de la Puente | Axel Gomez | Olaia Anton | Guillaume Stirnemann | Damien Laage
Source: arXiv (Cornell University) (preprint, 2024-07-10)
Field: Materials Science — Materials Chemistry

ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials The emergence of artificial intelligence has profoundly impacted computational chemistry, particularly through machine-learned potentials (MLPs), which offer a balance of accuracy and efficiency in calculating atomic energies and forces to be used in molecular dynamics simulations. These MLPs have significantly advanced molecular dynamics simulations across various applications, including large-scale simulations of materials, interfaces, and chemical reactions. Despite these advances, the construction of training datasets - a critical component for the accuracy of MLPs - has not received proportional attention. This is particularly critical for chemical reactivity which depends on rare barrier-crossing events. Here we address this gap by introducing ArcaNN, a comprehensive framework designed for generating training datasets for reactive MLPs. ArcaNN employs a concurrent learning approach combined with advanced sampling techniques to ensure accurate representation of high-energy geometries. The framework integrates automated processes for iterative training, exploration, new configuration selection, and energy and force labeling, while ensuring reproducibility and documentation. We demonstrate ArcaNN's capabilities through a paradigm nucleophilic substitution reaction in solution, showcasing its effectiveness, the uniformly low error of the resulting MLP everywhere along the chemical reaction coordinate, and its potential for broad applications in reactive molecular dynamics. We also provide guidelines on how to assess the quality of a NNP for a reactive system. Training set ; Training (meteorology) ; Sampling (signal processing) ; Computer science ; Artificial intelligence ; Machine learning ; Biological system ; Physics ; Computer vision ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Karine Cappuccio de Castro | Tahmasb Hatami | Roniérik Pioli Vieira | Ivanei Ferreira Pinheiro | Filipe V. Ferreira | Lúcia Helena Innocentini Mei
Source: Journal of Materials Science (article, 2024-10-01)
Field: Materials Science — Biomaterials

Functionalized polymeric biosensors via electrospinning assisted by controlled radical polymerization Electrospinning ; Materials science ; Biosensor ; Polymerization ; Polymer ; Radical polymerization ; Polymer science ; Nanotechnology ; Polymer chemistry ; Chemical engineering ; Composite material Electrospun Nanofibers in Biomedical Applications ; Conducting polymers and applications ; Polymer Surface Interaction Studies
Authors: Amanda S. Barnard
Source: Machine Learning Science and Technology (article, 2024-11-21)
Field: Materials Science — Materials Chemistry

Inverse prediction of Al alloy post-processing conditions using classification with guided oversampling Abstract Machine learning is proving to be an ideal tool for materials design, capable of predicting forward structure-property relationships, and inverse property-structure relationships. However, it has yet to be used extensively for materials engineering challenges, predicting post-processing/structure relationships, and has yet to be used for to predict structure/post-processing relationships for inverse engineering. This is often due to the lack of sufficient metadata, and the overall scarcity and imbalance of processing data in many domains. This topic is explored in the current study using binary and multi-class classification to predict the appropriate post-synthesis processing conditions for aluminium alloys, based entirely on the alloying composition. The data imbalance was addressed using a new guided oversampling strategy that improves model performance by simultaneously balancing the classes and avoiding noise that contributes to over-fitting. This is achieved by through the deliberate but strategic introduction of not-a-numbers (NaNs) and the use of algorithms that naturally avoid them during learning. The outcome is the successful training of highly accurate binary classifiers, with significant reductions in false negatives and/or false positives with respect to the classifiers trained on the original data alone. Superior results were obtained for models predicting whether alloys should be solutionised or aged, post-synthesis, by guiding the re-balancing of the classes based on features (metals) that are highly ranked by the classifier, and then doubling the size of the data set via interpolation. Overall, this strategy has the greatest impact on tasks with a Shannon Diversity Index greater than 1 or less than 0.5, but can be applied to any prediction of post-processing conditions as part of an inverse engineering workflow. Oversampling ; Computer science ; Machine learning ; Binary classification ; Artificial intelligence ; Inverse ; Classifier (UML) ; Data mining ; Naive Bayes classifier ; Algorithm ; Mathematics ; Support vector machine ; Bandwidth (computing) Machine Learning in Materials Science ; Non-Destructive Testing Techniques ; Aluminum Alloy Microstructure Properties
Authors: Yang Yang | Wenming Zhang | Shengbin Chen | Xiaogang Wang | Yuan‐gu Xia | Ji Liu | Bin Hu | Qiang Lü | Bing Zhang
Source: ACS Omega (article, 2024-01-07)
Field: Materials Science — Materials Chemistry

Structure–Energy Relationship Prediction of the HZSM-5 Zeolite with Different Acid Site Distributions by the Neural Network Model Zeolites are a very important family of catalysts. The catalytic activity of zeolites depends on the distribution of acid sites, which has been extensively studied. However, the relationship between the acid site distribution and catalytic efficiency remains unestablished. An onerous computational burden can be imposed when static calculations are applied to analyze the relationship between a catalyst structure and its energy. To resolve this issue, the current work uses neural network (NN) models to evaluate the relationship. By taking the typical HZSM-5 zeolite as an example, we applied the provided atomic coordinates to predict the energy. The network performances of the artificial neural network (ANN) and high-dimensional neural network (HDNN) are compared using the trained results from a dataset containing the identical number of acid sites. Furthermore, the importance of the feature is examined with the aid of a random forest model to identify the pivotal variables influencing the energy. In addition, the HDNN is employed to forecast the energy of an HZSM-5 system with varying numbers of acid sites. This study emphasizes that the energy of zeolites can be rapidly and accurately predicted through the NN, which can assist our understanding of the relationship between the structure and properties, thereby providing more accurate and efficient methods for the application of zeolite materials. Artificial neural network ; Zeolite ; Catalysis ; Feature (linguistics) ; Biological system ; Work (physics) ; Computer science ; Energy (signal processing) ; Brønsted–Lowry acid–base theory ; Artificial intelligence ; Chemistry ; Mathematics ; Engineering ; Organic chemistry ; Mechanical engineering Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Catalysis and Hydrodesulfurization Studies
Authors: Helen Uchenna Modekwe | Ayooluwa Tomiwa Akintola | Olusola Olaitan Ayeleru | Messai A. Mamo | Michael O. Daramola | Kapil Moothi
Source: International Journal of Environmental Science and Technology (article, 2024-07-08)
Field: Materials Science — Materials Chemistry

Artificial neural network for predicting the performance of waste polypropylene plastic-derived carbon nanotubes Abstract In this study, an artificial neural network model using function fitting neural networks was developed to describe the yield and quality of multi-walled carbon nanotubes deposited over NiMo/CaTiO 3 catalyst using waste polypropylene plastics as cheap hydrocarbon feedstock using a single-stage chemical vapour deposition technique. The experimental dataset was developed using a user-specific design with four numeric factors (input variable): synthesis temperature, furnace heating rate, residence time, and carrier gas (nitrogen) flow rate to control the performance (yield and quality) of produced carbon nanotubes. Levenberg–Marquardt algorithm was utilized in training, validating, and testing the experimental dataset. The predicted model gave a considerable correlation coefficient (R) value close to 1. The presented model would be of remarkable benefit to successfully describe and predict the performance of polypropylene-derived carbon nanotubes and show how the predictive variables could affect the response variables (quality and yield) of carbon nanotubes. Polypropylene ; Carbon nanotube ; Artificial neural network ; Plastic waste ; Materials science ; Carbon fibers ; Waste management ; Environmental science ; Composite material ; Process engineering ; Computer science ; Engineering ; Artificial intelligence ; Composite number Carbon Nanotubes in Composites ; Nanotechnology research and applications ; Polymer Nanocomposite Synthesis and Irradiation
Authors: Stephen Taller | Luke Scime | Ty Austin
Source: Materials Today Advances (article, 2024-02-07)
Field: Materials Science — Materials Chemistry

A new paradigm in electron microscopy: Automated microstructure analysis utilizing a dynamic segmentation convolutional neutral network Over the past half century, the transmission electron microscope enabled insight into the fundamental arrangements and structures of materials. State-of-the-art electron microscopes can acquire large image datasets across multiple imaging modalities. However, the manual annotation process for feature or defect quantification may not be feasible with the modern microscope. Convolutional neural networks emerged to characterize individual microstructural features from an image in a cost-effective, consistent manner. However, many of these neural network approaches rely on thousands to hundreds of thousands of manual annotations of each feature type across hundreds of images to train the network for adequate performance. This work focused on the development and application of a pixel-wise defect detection machine-learning dynamic segmentation convolutional neural network with associated automated acquisition and postprocessing to identify microstructural features rapidly and quantitatively from a small initial dataset incorporating multiple imaging modes. The approach was demonstrated for characterization of superalloy 718 from both single image acquisition on multiple detectors to in-situ evolution captured with a single detector on a standard desktop computer to demonstrate the low barrier to entry required for widespread adoption. Pixel-by-pixel class identification was excellent with strong identification of chemically distinct phases, structurally distinct phases, and defect structures, thus demonstrating the new paradigm of machine learning-assisted characterization. Convolutional neural network ; Computer science ; Artificial intelligence ; Segmentation ; Pixel ; Pattern recognition (psychology) ; Feature (linguistics) ; Detector ; Identification (biology) ; Process (computing) ; Computer vision Machine Learning in Materials Science ; Electron and X-Ray Spectroscopy Techniques ; Nuclear Physics and Applications
Authors: Jinrong Huo | Kai Zhang | Pengfei Liu | Haocong Wei | Chaozheng He
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Crystal structure prediction and property calculation of copper–oxygen compounds using innovative search software from first principles A Bayesian optimisation algorithm for deep learning crystal structure prediction software (CBD-GM) is used to predict the structures of Cu(I) and Cu(II) oxides of 2D and 3D materials. Two known 2D structures and two known 3D structures were anticipated, in addition to the prediction of 5 novel structures. All nine structures were optimised and analysed using density-functional theory (DFT). Firstly, DFT calculations using the PBE functional indicate that the structures should be thermodynamically and dynamically stable. Secondly, we calculated the elastic constants using the "stress-strain" method, and the predicted Young's modulus and Poisson's ratios of the materials suggest that they all should have excellent ductile mechanical properties. Calculations of the band structure of the materials performed using the Heyd-Scuseria-Ernzerhof (HSE) hybrid functional indicate that some of the materials should be semiconductors with useful bandgaps. The results therefore provide inspiration for the synthesis of new copper oxides for industrial applications. Density functional theory ; Crystal structure ; Materials science ; Crystal structure prediction ; Hybrid functional ; Semiconductor ; Bulk modulus ; Copper ; Band gap ; Modulus ; Computational chemistry ; Crystallography ; Chemistry ; Composite material ; Metallurgy ; Optoelectronics Copper-based nanomaterials and applications ; ZnO doping and properties ; Electronic and Structural Properties of Oxides
Authors: Irea Mosquera‐Lois | Seán R. Kavanagh | Alex M. Ganose | Aron Walsh
Source: arXiv (Cornell University) (preprint, 2024-01-22)
Field: Materials Science — Materials Chemistry

Machine-learning structural reconstructions for accelerated point defect calculations Defects dictate the properties of many functional materials. To understand the behaviour of defects and their impact on physical properties, it is necessary to identify the most stable defect geometries. However, global structure searching is computationally challenging for high-throughput defect studies or materials with complex defect landscapes, like alloys or disordered solids. Here, we tackle this limitation by harnessing a machine-learning surrogate model to qualitatively explore the defect structural landscape. By learning defect motifs in a family of related metal chalcogenide and mixed anion crystals, the model successfully predicts favourable reconstructions for unseen defects in unseen compositions for 90% of cases, thereby reducing the number of first-principles calculations by 73%. Using CdSe$_x$Te$_{1-x}$ alloys as an exemplar, we train a model on the end member compositions and apply it to find the stable geometries of all inequivalent vacancies for a range of mixing concentrations, thus enabling more accurate and faster defect studies for configurational complex systems. Chalcogenide ; Crystallographic defect ; Materials science ; Point (geometry) ; Computer science ; Artificial intelligence ; Throughput ; Statistical physics ; Machine learning ; Chemical physics ; Algorithm ; Crystallography ; Physics ; Chemistry ; Geometry ; Mathematics ; Optoelectronics Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials ; Chalcogenide Semiconductor Thin Films