jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Vishnu Jayaprakash | Jae Bem You | Chiranjeevi Kanike | Jinfeng Liu | Christopher McCallum | Xuehua Zhang
Source: Environmental Science & Technology (article, 2024-01-25)
Field: Chemistry — Analytical Chemistry

Determination of Trace Organic Contaminant Concentration via Machine Classification of Surface-Enhanced Raman Spectra Surface-enhanced Raman spectroscopy (SERS) has been well explored as a highly effective characterization technique that is capable of chemical pollutant detection and identification at very low concentrations. Machine learning has been previously used to identify compounds based on SERS spectral data. However, utilization of SERS to quantify concentrations, with or without machine learning, has been difficult due to the spectral intensity being sensitive to confounding factors such as the substrate parameters, orientation of the analyte, and sample preparation technique. Here, we demonstrate an approach for predicting the concentration of sample pollutants from SERS spectra using machine learning. Frequency domain transform methods, including the Fourier and Walsh-Hadamard transforms, are applied to spectral data sets of three analytes (rhodamine 6G, chlorpyrifos, and triclosan), which are then used to train machine learning algorithms. Using standard machine learning models, the concentration of the sample pollutants is predicted with >80% cross-validation accuracy from raw SERS data. A cross-validation accuracy of 85% was achieved using deep learning for a moderately sized data set (∼100 spectra), and 70-80% was achieved for small data sets (∼50 spectra). Performance can be maintained within this range even when combining various sample preparation techniques and environmental media interference. Additionally, as a spectral pretreatment, the Fourier and Hadamard transforms are shown to consistently improve prediction accuracy across multiple data sets. Finally, standard models were shown to accurately identify characteristic peaks of compounds via analysis of their importance scores, further verifying their predictive value. Raman spectroscopy ; Surface-enhanced Raman spectroscopy ; Pollutant ; Artificial intelligence ; Analytical Chemistry (journal) ; Chemistry ; Environmental chemistry ; Biological system ; Computer science ; Raman scattering ; Optics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Fabian L. Thiemann | Niamh O’Neill | Venkat Kapil | Angelos Michaelides | Christoph Schran
Source: Journal of Physics Condensed Matter (review, 2024-11-22)
Field: Materials Science — Materials Chemistry

Introduction to machine learning potentials for atomistic simulations Machine learning potentials have revolutionised the field of atomistic simulations in recent years and are becoming a mainstay in the toolbox of computational scientists. This paper aims to provide an overview and introduction into machine learning potentials and their practical application to scientific problems. We provide a systematic guide for developing machine learning potentials, reviewing chemical descriptors, regression models, data generation and validation approaches. We begin with an emphasis on the earlier generation of models, such as high-dimensional neural network potentials and Gaussian approximation potentials, to provide historical perspective and guide the reader towards the understanding of recent developments, which are discussed in detail thereafter. Furthermore, we refer to relevant expert reviews, open-source software, and practical examples-further lowering the barrier to exploring these methods. The paper ends with selected showcase examples, highlighting the capabilities of machine learning potentials and how they can be applied to push the boundaries in atomistic simulations. Toolbox ; Computer science ; Machine learning ; Artificial intelligence ; Field (mathematics) ; Data science ; Artificial neural network ; Software Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics
Authors: Jiaxin Xu | Tengfei Luo
Source: npj Computational Materials (article, 2024-04-16)
Field: Materials Science — Materials Chemistry

Unlocking enhanced thermal conductivity in polymer blends through active learning Abstract Polymers play an integral role in various applications, from everyday use to advanced technologies. In the era of machine learning (ML), polymer informatics has become a vital field for efficiently designing and developing polymeric materials. However, the focus of polymer informatics has predominantly centered on single-component polymers, leaving the vast chemical space of polymer blends relatively unexplored. This study employs a high-throughput molecular dynamics (MD) simulation combined with active learning (AL) to uncover polymer blends with enhanced thermal conductivity (TC) compared to the constituent single-component polymers. Initially, the TC of about 600 amorphous single-component polymers and 200 amorphous polymer blends with varying blending ratios are determined through MD simulations. The optimal representation method for polymer blends is identified, which involves a weighted sum approach that extends existing polymer representation from single-component polymers to polymer blends. An AL framework, combining MD simulation and ML, is employed to explore the TC of approximately 550,000 unlabeled polymer blends. The AL framework proves highly effective in accelerating the discovery of high-performance polymer blends for thermal transport. Additionally, we delve into the relationship between TC, radius of gyration ( R g ), and hydrogen bonding, highlighting the roles of inter- and intra-chain interactions in thermal transport in amorphous polymer blends. A significant positive association between TC and R g improvement and an indirect contribution from H-bond interaction to TC enhancement are revealed through a log-linear model and an odds ratio calculation, emphasizing the impact of increasing R g and H-bond interactions on enhancing polymer blend TC. Polymer ; Polymer blend ; Materials science ; Amorphous solid ; Component (thermodynamics) ; Chemical physics ; Polymer chemistry ; Thermodynamics ; Chemical engineering ; Composite material ; Chemistry ; Organic chemistry ; Physics Thermal properties of materials ; Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Gerardo M. Casañola‐Martín | Anas Karuth | Hai Pham‐The | Humberto González‐Díaz | Dean C. Webster | Bakhtiyor Rasulev
Source: Communications Chemistry (article, 2024-10-02)
Field: Materials Science — Materials Chemistry

Machine learning analysis of a large set of homopolymers to predict glass transition temperatures Glass transition temperature of polymers, Tg, is an important thermophysical property, which sometimes can be difficult to measure experimentally. In this regard, data-driven machine learning approaches are important alternatives to assess Tg values, in a high-throughput way. In this study, a large dataset of more than 900 polymers with reported glass transition temperature (T<sub>g</sub>) was assembled from various public sources in order to develop a predictive model depicting the structure-property relationships. The collected dataset was curated, explored via cluster analysis, and then split into training and test sets for validation purposes and then polymer structures characterized by molecular descriptors. To find the models, several machine learning techniques, including multiple linear regression (MLR), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), gaussian processes for regression (GPR), and multi-layer perceptron (MLP) were explored. As result, a model with the subset of 15 descriptors accurately predicting the glass transition temperatures was developed. The electronic effect indices were determined to be important properties that positively contribute to the T<sub>g</sub> values. The SVM-based model showed the best performance with determination coefficients (R<sup>2</sup>) of 0.813 and 0.770, for training and test sets, respectively. Also, the SVM model showed the lowest estimation error, RMSE = 0.062. In addition, the developed structure-property model was implemented as a web app to be used as an online computational tool to design and evaluate new homopolymers with desired glass transition profiles. Glass transition ; Set (abstract data type) ; Transition (genetics) ; Materials science ; Computer science ; Composite material ; Chemistry ; Polymer ; Programming language Machine Learning in Materials Science ; Polymer crystallization and properties ; Thermal and Kinetic Analysis
Authors: Sina Maghsoudy | Pouya Zakerabbasi | Alireza Baghban | Amin Esmaeili | Sajjad Habibzadeh
Source: Scientific Reports (article, 2024-01-17)
Field: Materials Science — Materials Chemistry

Connectionist technique estimates of hydrogen storage capacity on metal hydrides using hybrid GAPSO-LSSVM approach The AB<sub>2</sub> metal hydrides are one of the preferred choices for hydrogen storage. Meanwhile, the estimation of hydrogen storage capacity will accelerate their development procedure. Machine learning algorithms can predict the correlation between the metal hydride chemical composition and its hydrogen storage capacity. With this purpose, a total number of 244 pairs of AB<sub>2</sub> alloys including the elements and their respective hydrogen storage capacity were collected from the literature. In the present study, three machine learning algorithms including GA-LSSVM, PSO-LSSVM, and HGAPSO-LSSVM were employed. These models were able to appropriately predict the hydrogen storage capacity in the AB<sub>2</sub> metal hydrides. So the HGAPSO-LSSVM model had the highest accuracy. In this model, the statistical factors of R<sup>2</sup>, STD, MSE, RMSE, and MRE were 0.980, 0.043, 0.0020, 0.045, and 0.972%, respectively. The sensitivity analysis of the input variables also illustrated that the Sn, Co, and Ni elements had the highest effect on the amount of hydrogen storage capacity in AB<sub>2</sub> metal hydrides. Hydrogen storage ; Hydride ; Metal ; Hydrogen ; Mean squared error ; Computer science ; Materials science ; Process engineering ; Biological system ; Algorithm ; Chemistry ; Statistics ; Mathematics ; Engineering ; Metallurgy Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Catalysis and Hydrodesulfurization Studies
Authors: Son Gyo Jung | Guwon Jung | Jacqueline M. Cole
Source: Journal of Chemical Information and Modeling (article, 2024-02-06)
Field: Materials Science — Materials Chemistry

Automatic Prediction of Band Gaps of Inorganic Materials Using a Gradient Boosted and Statistical Feature Selection Workflow Machine learning (ML) methods can train a model to predict material properties by exploiting patterns in materials databases that arise from structure-property relationships. However, the importance of ML-based feature analysis and selection is often neglected when creating such models. Such analysis and selection are especially important when dealing with multifidelity data because they afford a complex feature space. This work shows how a gradient-boosted statistical feature-selection workflow can be used to train predictive models that classify materials by their metallicity and predict their band gap against experimental measurements, as well as computational data that are derived from electronic-structure calculations. These models are fine-tuned via Bayesian optimization, using solely the features that are derived from chemical compositions of the materials data. We test these models against experimental, computational, and a combination of experimental and computational data. We find that the multifidelity modeling option can reduce the number of features required to train a model. The performance of our workflow is benchmarked against state-of-the-art algorithms, the results of which demonstrate that our approach is either comparable to or superior to them. The classification model realized an accuracy score of 0.943, a macro-averaged F1-score of 0.940, area under the curve of the receiver operating characteristic curve of 0.985, and an average precision of 0.977, while the regression model achieved a mean absolute error of 0.246, a root-mean squared error of 0.402, and <i>R</i><sup>2</sup> of 0.937. This illustrates the efficacy of our modeling approach and highlights the importance of thorough feature analysis and judicious selection over a "black-box" approach to feature engineering in ML-based modeling. Workflow ; Feature selection ; Selection (genetic algorithm) ; Feature (linguistics) ; Computer science ; Artificial intelligence ; Gradient boosting ; Pattern recognition (psychology) ; Data mining ; Random forest ; Database Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Seunghee Han | Yeonghun Kang | Hyunsoo Park | Jeesung Yi | Geunyeong Park | Jihan Kim
Source: ACS Applied Materials & Interfaces (article, 2024-03-19)
Field: Materials Science — Materials Chemistry

Multimodal Transformer for Property Prediction in Polymers In this work, we designed a multimodal transformer that combines both the Simplified Molecular Input Line Entry System (SMILES) and molecular graph representations to enhance the prediction of polymer properties. Three models with different embeddings (SMILES, SMILES + monomer, and SMILES + dimer) were employed to assess the performance of incorporating multimodal features into transformer architectures. Fine-tuning results across five properties (i.e., density, glass-transition temperature (<i>T</i><sub>g</sub>), melting temperature (<i>T</i><sub>m</sub>), volume resistivity, and conductivity) demonstrated that the multimodal transformer with both the SMILES and the dimer configuration as inputs outperformed the transformer using only SMILES across all five properties. Furthermore, our model facilitates in-depth analysis by examining attention scores, providing deeper insights into the relationship between the deep learning model and the polymer attributes. We believe that our work, shedding light on the potential of multimodal transformers in predicting polymer properties, paves a new direction for understanding and refining polymer properties. Transformer ; Materials science ; Polymer ; Conductivity ; Computer science ; Biological system ; Electrical engineering ; Voltage ; Composite material ; Physics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: Abdulaziz Nuhu Jibril | Xubo Zhang | Shuo Wang | Zaharaddeen Aminu Bello | Ibeogu Isaiah Henry | Kunjie Chen
Source: Journal of Food Process Engineering (article, 2024-04-01)
Field: Chemistry — Analytical Chemistry

Far‐infrared drying influence on machine learning algorithms in improving corn drying process with graphene irradiation heating plates Abstract Drying methods often suffer from appropriate drying process modeling, low heating efficiency, and high‐energy consumption. However, graphene may provide significant improvements during the drying process due to its ability to save energy and convert electro‐thermal energy effectively. In this study, random forest (RF), surface vector machine (SVM), artificial neural network (ANN), and k‐nearest neighbor (kNN) were the four machine‐learning algorithms employed to analyze the drying process of two types of corn (ZD88 and RP909) at varying temperatures of 35, 45, and 55°C in a graphene far‐infrared drying. The study found that the drying process of corn showed a decrease in moisture ratio during the falling rate period from 35 to 55°C. The moisture diffusivity increased from 35 to 55°C with the rise in the temperature, ranging from 2.74 to 4.36 × 10 −8 m 2 /s for ZD88 and 2.05 to 3.07 × 10 −8 m 2 /s for RP909. The ZD88 showed a heat efficiency of 81.62% at 55°C. The results of the machine learning algorithms revealed that 1–6 ANN was the best architecture. Normalized PUK was the best for SVM filters. A k = 3 and s‐fold = 5 were the best values for k‐NN and RF, respectively. Among the computed algorithms, the tested data for normalized Pearson universal kernel SVM filters proved to be the most effective in computing the process of drying corn. Overall, a graphene far‐infrared dryer showed improved heating efficiency and less energy consumption, providing a new concept for developing a drying process through machine learning algorithms strategies. Practical applications Drying methods often suffer from appropriate drying process modeling, low heating efficiency, and high‐energy consumption. This study provides significant improvements due to the ability of graphene irradiation heating plates to save energy and convert electro‐thermal energy effectively. The study achieved a heating efficiency of 81.62% at an infrared temperature of 55°C. Machine learning algorithms are intelligent models that minimize the limitations in describing the drying process of agricultural materials. The study found the normalized Pearson universal kernel was the most effective computation SVM filter. Therefore, using a graphene far‐infrared dryer showed a better drying alternative for industrial applications, as it has the potential to improve heating efficiency and consume less energy, providing a new concept for developing drying equipment through machine learning algorithms modeling strategies. Process (computing) ; Infrared ; Graphene ; Irradiation ; Materials science ; Process engineering ; Infrared heater ; Algorithm ; Computer science ; Nanotechnology ; Physics ; Engineering ; Optics ; Nuclear physics Spectroscopy and Chemometric Analyses ; Food Drying and Modeling ; Radiative Heat Transfer Studies
Authors: Xiang‐Xin Xiao | Tong‐Yu Bai | Qin Zhang | Zi‐Xun Chen | Zi‐Ni Wang | Jun‐Hao Bai | Li Chen | Bowen Liu | Yu‐Zhong Wang
Source: Chemical Engineering Journal (article, 2024-02-11)
Field: Materials Science — Polymers and Plastics

Machine learning-assisted design and scalable fabrication of high-performance fire-safe polycarbonate for advanced applications Polycarbonate ; Flammability ; Materials science ; Copolymer ; Fabrication ; Dielectric ; Composite material ; Ignition system ; Chemical engineering ; Polymer ; Optoelectronics ; Aerospace engineering ; Engineering Flame retardant materials and properties ; Polymer Nanocomposites and Properties ; Fire dynamics and safety research
Authors: Dágila Melo Rodrigues | Paulo Carteri Coradi | Larissa Pereira Ribeiro Teodoro | Paulo Eduardo Teodoro | Rosana dos Santos Moraes | Marisa Menezes Leal
Source: Scientific Reports (article, 2024-03-14)
Field: Chemistry — Analytical Chemistry

Monitoring and predicting corn grain quality on the transport and post-harvest operations in storage units using sensors and machine learning models Monitoring the intergranular variables of corn grain mass during the transportation, drying, and storage stages it possible to predict and avoid potential grain quality losses. For monitoring the grain mass along the transport, a probe system with temperature, relative humidity, and carbon dioxide sensors was developed to determine the equilibrium moisture content and the respiration of the grain mass. These same variables were monitored during storage. At drying process, the drying air and grain mass temperatures, as well as the relative humidity, were monitored. For the prediction of the physical and physical-chemical quality of the grains, the results obtained from the monitoring were used as input data for the multiple linear regression, artificial neural networks, decision tree, and random forest models. A Pearson correlation was applied to verify the relationship between the monitored and predicted variables. From the results obtained, we verified that the intergranular relative humidity altered the equilibrium moisture content of the grains, contributing to the increased respiration and hence dry matter losses along the transport. At this stage, the artificial neural network model was the most indicated to predict the electrical conductivity, apparent specific mass, and germination. The random forest model satisfactorily estimated the dry matter loss. During drying, the air temperature caused volumetric contraction and thermal damage to the grains, increasing the electric conductivity index. Artificial neural network and random forest models were the most suitable for predicting the quality of dry grains. During storage, the environmental conditions altered the moisture contents causing a reduction in the apparent specific mass, germination, and crude protein, crude fiber, and fat contents. Artificial neural network and random forest were the best predictors of moisture content and germination. However, the random forest model was the best predictor of apparent specific mass, electrical conductivity, and starch content of stored grains. Relative humidity ; Water content ; Environmental science ; Humidity ; Moisture ; Grain quality ; Mass transfer ; Soil science ; Materials science ; Meteorology ; Chemistry ; Agronomy ; Composite material Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Food Drying and Modeling
Authors: S. Jothi Arunachalam | R. Saravanan | T. Sathish | Jayant Giri | Mohammad Kanan
Source: Results in Engineering (article, 2024-11-05)
Field: Materials Science — Polymers and Plastics

Mechanical assessment for enhancing hybrid composite performance through silane treatment using RSM and ANN • The study employed Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) to determine the most effective combination of three components for producing polymer composites. • The study examined the independent variables of Nanoparticle percentage, silane concentration, and silane dipping duration. • The silane treatment of the fiber was determined to be the main factor that affected the flexural strength of the composite. • The flexural strength and hardness of the material were considerably influenced by all properties related to fiber, with the duration of silane dipping being the most crucial element. • The use of nanoparticles improved the strength of the fiber matrix by promoting effective agglomeration. • The concentration of silane also had a notable impact on the flexural characteristics. • RSM analysis determined that the ideal combination for achieving maximum flexural strength and hardness consisted of 5% weight of nanoparticles, 10% weight of silane treatment, and a 20-minute duration of silane dipping. The study utilized Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) to identify the optimal combination of three key factors for producing polymer composites: nanoparticle percentage, silane concentration, and silane dipping duration. RSM and Analysis of Variance (ANOVA) were applied to explore the relationships between these variables and their effects on composite properties. Additionally, the ANN was used to analyze multiple factors, revealing a strong correlation between predicted and observed results. The findings highlighted that silane treatment was the most influential factor in enhancing the composite's flexural strength. Fiber-related properties, particularly the duration of silane dipping, significantly impacted both flexural strength and hardness. Nanoparticles further strengthened the fiber matrix by encouraging effective agglomeration. The ANN demonstrated 95% accuracy in predicting flexural strength and hardness, and its predictions were validated by comparing them with experimental data and the regression model's outcomes. Silane concentration also notably influenced flexural properties. The RSM analysis identified the optimal combination for maximizing flexural strength and hardness: 5% nanoparticles, 10% silane concentration, and a 20-minute silane dipping time. Silane ; Composite number ; Materials science ; Response surface methodology ; Composite material ; Computer science ; Machine learning Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Tribology and Wear Analysis