jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Roberta Cappabianca | Paolo De Angelis | Annalisa Cardellini | Eliodoro Chiavazzo | Pietro Asinari
Source: ACS Omega (article, 2022-11-10)
Field: Materials Science — Biomaterials

Assembling Biocompatible Polymers on Gold Nanoparticles: Toward a Rational Design of Particle Shape by Molecular Dynamics Gold nanoparticles (AuNPs) have received great attention in a number of fields ranging from the energy sector to biomedical applications. As far as the latter is concerned, due to rapid renal clearance and a short lifetime in blood, AuNPs are often encapsulated in a poly(lactic-<i>co</i>-glycolic acid) (PLGA) matrix owing to its biocompatibility and biodegradability. A better understanding of the PLGA polymers on the AuNP surface is crucial to improve and optimize the above encapsulation process. In this study, we combine a number of computational approaches to explore the adsorption mechanisms of PLGA oligomers on a Au crystalline NP and to rationalize the PLGA coating process toward a more efficient design of the NP shape. Atomistic simulations supported by a recently developed unsupervised machine learning scheme show the temporal evolution and behavior of PLGA clusterization by tuning the oligomer concentration in aqueous solutions. Then, a detailed surface coverage analysis coupled with free energy landscape calculations sheds light on the anisotropic nature of PLGA adsorption onto the AuNP. Our results prove that the NP shape and topology may address and privilege specific sites of adsorption, such as the Au {1 1 1} crystal planes in selected NP samples. The modeling-based investigation suggested in this article offers a solid platform to guide the design of coated NPs. PLGA ; Materials science ; Polymer ; Nanoparticle ; Adsorption ; Nanotechnology ; Biocompatibility ; Molecular dynamics ; Colloidal gold ; Chemical engineering ; Chemistry ; Computational chemistry ; Organic chemistry ; Composite material Nanoparticle-Based Drug Delivery ; Gold and Silver Nanoparticles Synthesis and Applications ; Pickering emulsions and particle stabilization
Authors: Eric Hedlund | Keith G. Hedlund | Adam Green | Ravin Chowdhury | Cheol S. Park | Joseph E. Maclennan | Noel A. Clark
Source: Physics of Fluids (article, 2022-09-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Detection of islands and droplets on smectic films using machine learning Machine learning techniques have been developed to identify inclusions on the surface of freely suspended smectic liquid crystal films imaged by reflected light microscopy. The experimental images are preprocessed using Canny edge detection and then passed to a radial kernel support vector machine (SVM) trained to recognize circular islands and droplets. The SVM is able to identify these objects of interest with an accuracy that far exceeds that of conventional tracking software, especially when the background image is non-uniform or when the target features are in close proximity to one another. This method could be applied to tracking objects in a variety of visually inhomogeneous fluid environments, in order to study growth dynamics, the development of spatial order, and hydrodynamic behavior. Tracking (education) ; Support vector machine ; Kernel (algebra) ; Physics ; Artificial intelligence ; Canny edge detector ; Enhanced Data Rates for GSM Evolution ; Edge detection ; Optics ; Computer vision ; Liquid crystal ; Optical microscope ; Pattern recognition (psychology) ; Image (mathematics) ; Image processing ; Computer science ; Mathematics Liquid Crystal Research Advancements ; Material Dynamics and Properties ; Surfactants and Colloidal Systems
Authors: Xiaohan Zhu | Xiaochen Li | Mingjie Zheng
Source: Metals (article, 2022-04-11)
Field: Materials Science — Materials Chemistry

Predicting the Irradiation Swelling of Austenitic and Ferritic/Martensitic Steels, Based on the Coupled Model of Machine Learning and Rate Theory As nuclear structural materials, austenitic and ferritic/martensitic (F/M) steels will face inevitable irradiation swelling when fulfilling a role in nuclear reactors, especially under high-dose irradiation. For this work, a coupled machine learning rate theory (ML-RT) model for the swelling of austenitic and F/M steels was developed. In this model, ML was introduced to predict the steady-state irradiation swelling onset dose (Donset), while the improved RT was developed to simulate the swelling behavior after the incubation period. More than 200 series of data on the Donset of different structures of steel were collected for the ML prediction. The coefficient of determination (R) of the results in ML is more than 0.9. In the RT, the evolutions of the dislocation loop and void were described and calculated rather than using the fitting parameters. Cascade efficiency was employed to describe the cascade process. The coupled ML-RT model was verified with the swelling data from neutron irradiation experiments for various steels. The theoretical results of the swelling peak temperatures and swelling behavior are more accurate and reasonable, compared with those from the previous RT model. Using the ML-RT model, the swelling performance of CLAM steel under neutron irradiation of up to 180 dpa was predicted. The differences between the swelling performance of austenitic steels and F/M steels were analyzed and the differences were mainly associated with the bias. These results will be helpful for evaluating the neutron irradiation swelling behavior of candidate structural materials. Swelling ; Materials science ; Austenite ; Irradiation ; Martensite ; Metallurgy ; Composite material ; Microstructure ; Nuclear physics Fusion materials and technologies ; Nuclear Materials and Properties ; Nuclear reactor physics and engineering
Authors: Siavash Soltani | Chad W. Sinclair | Jörg Rottler
Source: Physical review. E (article, 2022-08-08)
Field: Materials Science — Materials Chemistry

Exploring glassy dynamics with Markov state models from graph dynamical neural networks Using machine learning techniques, we introduce a Markov state model (MSM) for a model glass former that reveals structural heterogeneities and their slow dynamics by coarse-graining the molecular dynamics into a low-dimensional feature space. The transition timescale between states is larger than the conventional structural relaxation time τ_{α}, but can be obtained from trajectories much shorter than τ_{α}. The learned map of states assigned to the particles corresponds to local excess Voronoi volume. These results resonate with classic free volume theories of the glass transition, singling out local packing fluctuations as one of the dominant slowly relaxing features. Statistical physics ; Markov chain ; Voronoi diagram ; Relaxation (psychology) ; State space ; Granularity ; Feature (linguistics) ; Dynamics (music) ; Graph ; State (computer science) ; Computer science ; Physics ; Mathematics ; Algorithm ; Theoretical computer science ; Machine learning ; Geometry Material Dynamics and Properties ; Theoretical and Computational Physics ; Liquid Crystal Research Advancements
Authors: Janine Lins | Ute Ebeling | Kerstin Wohlgemuth
Source: Crystal Growth & Design (article, 2022-08-05)
Field: Materials Science — Materials Chemistry

Agglomeration Kernel Determination by Combining In-Process Image Analysis and Modeling In solution crystallization processes of organic compounds, agglomeration is often an undesired phenomenon because it influences important particle properties such as size, shape, and purity. However, until now, it is still challenging to measure and quantify agglomeration in the process. Kinetics are often determined by fitting the agglomeration kernel together with other kinetic parameters to a measured particle size distribution (PSD). The approach presented decouples agglomeration from particle sizes and crystal growth. By sophisticated image analysis using a deep learning approach, process monitoring of agglomerates is enabled. Extended by modeling and experimental investigation, this allows for a systematic investigation of the agglomeration behavior of the system depending on operation conditions. Here, l-alanine/water is used as a model system. Based on the developed inline image acquisition and instance segmentation, including detection and classification of single crystals and agglomerates, the agglomeration during crystallization is tracked, and the agglomeration kernel is estimated. Further, the agglomeration degree of the final product can be predicted within the limits of the investigated parameters. It is found that the system only tends to agglomerate moderately and agglomeration is reduced when the crystallization time is short, which is defined by the saturation temperature or the cooling rate. Economies of agglomeration ; Agglomerate ; Crystallization ; Kernel (algebra) ; Process (computing) ; Particle-size distribution ; Materials science ; Particle size ; Biological system ; Mineralogy ; Computer science ; Chemistry ; Thermodynamics ; Mathematics ; Chemical engineering ; Physics ; Engineering ; Composite material Crystallization and Solubility Studies ; Spectroscopy and Chemometric Analyses ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Elizabeth Cobbinah | Oliver B. Generalao | Sathish Kumar Lageshetty | Indra Adrianto | Seema Singh | Gerard G. Dumancas
Source: Chemosensors (article, 2022-10-11)
Field: Chemistry — Analytical Chemistry

Using Near-Infrared Spectroscopy and Stacked Regression for the Simultaneous Determination of Fresh Cattle and Poultry Manure Chemical Properties Excessive use of animal manure as fertilizers can lead to pollution through the introduction of nitrogen, phosphorus, and other mineral compounds to the environment. Wet chemical analytical methods are traditionally used to determine the precise chemical composition of manure to manage the application of animal waste to the soil. However, such methods require significant resources to carry out the processes. Affordable, rapid, and accurate methods of analyses of various chemical components present in animal manure, therefore, are valuable in managing soil and mitigating water pollution. In this study, a stacked regression ensemble approach using near-infrared spectroscopy was developed to simultaneously determine the amount of dry matter, total ammonium nitrogen, total nitrogen, phosphorus pentoxide, calcium oxide, magnesium oxide, and potassium oxide contents in both cattle and poultry manure collected from livestock production areas in France and Reunion Island. The performance of the stacked regression, an ensemble learning algorithm that is formed by collating the well-performing models for prediction was then compared with that of various other machine learning techniques, including support vector regression (linear, polynomial, and radial), least absolute shrinkage and selection operator, ridge regression, elastic net, partial least squares, random forests, recursive partitioning and regression trees, and boosted trees. Results show that stack regression performed optimally well in predicting the seven abovementioned chemical constituents in the testing set and may provide an alternative to the traditional partial least squares method for a more accurate and simultaneous method in determining the chemical properties of animal manure. Manure ; Partial least squares regression ; Linear regression ; Environmental science ; Polynomial regression ; Mathematics ; Agronomy ; Statistics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Yuying Chen | Pei‐Ru Lin | Xun Zou | Lina Liu | Sixue Ouyang | Huiting Chen | Qingfan Ren | Ying Zeng | Peng Zhao | Jia Tao
Source: Sensors and Actuators B Chemical (article, 2022-08-06)
Field: Chemistry — Spectroscopy

Machine-learning-aided identification of steroid hormones based on the anisotropic galvanic replacement generated sensor array Galvanic cell ; Steroid ; Steroid use ; Identification (biology) ; Hormone ; Steroid hormone ; Computer science ; Anisotropy ; Computational biology ; Medicine ; Endocrinology ; Biology ; Chemistry ; Internal medicine ; Physics ; Optics ; Botany Molecular Sensors and Ion Detection ; Advanced Chemical Sensor Technologies ; Advanced biosensing and bioanalysis techniques
Authors: Luke Watson | Conor D. Rankine | Thomas J. Penfold
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Beyond structural insight: a deep neural network for the prediction of Pt L<sub>2/3</sub>-edge X-ray absorption spectra X-ray absorption spectroscopy at the L<sub>2/3</sub> edge can be used to obtain detailed information about the local electronic and geometric structure of transition metal complexes. By virtue of the dipole selection rules, the transition metal L<sub>2/3</sub> edge usually exhibits two distinct spectral regions: (i) the "white line", which is dominated by bound electronic transitions from metal-centred 2p orbitals into unoccupied orbitals with d character; the intensity and shape of this band consequently reflects the d density of states (d-DOS), which is strongly modulated by mixing with ligand orbitals involved in chemical bonding, and (ii) the post-edge, where oscillations encode the local geometric structure around the X-ray absorption site. In this Article, we extend our recently-developed XANESNET deep neural network (DNN) beyond the K-edge to predict X-ray absorption spectra at the Pt L<sub>2/3</sub> edge. We demonstrate that XANESNET is able to predict Pt L<sub>2/3</sub> -edge X-ray absorption spectra, including both the parts containing electronic and geometric structural information. The performance of our DNN in practical situations is demonstrated by application to two Pt complexes, and by simulating the transient spectrum of a photoexcited dimeric Pt complex. Our discussion includes an analysis of the feature importance in our DNN which demonstrates the role of key features and assists with interpreting the performance of the network. Atomic orbital ; Absorption spectroscopy ; Electronic structure ; Spectral line ; Absorption (acoustics) ; Chemistry ; Dipole ; Spectroscopy ; Enhanced Data Rates for GSM Evolution ; Molecular electronic transition ; Molecular physics ; Atomic physics ; Physics ; Computational chemistry ; Computer science ; Artificial intelligence ; Optics ; Electron Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Zhuang Li | Zhilin Long | Shan Lei | Xiaowei Liu | Lingming Yang | Wei Zhang | Ting Zhang
Source: Physica Scripta (article, 2022-12-02)
Field: Materials Science — Materials Chemistry

Evaluating the corrosion resistance of marine steels under different exposure environments via machine learning Abstract The corrosion behavior of marine engineering steels in marine environment is an extremely complex process, which poses great challenge to accurately evaluate the corrosion resistance of various stees in different marine environment. Owing to the wide application of machine learning (ML) approaches and the accumulation of corrosion data of different steels in natural marine environment, herein, we reported eXtreme Gradient Boosting (XGBoost) ML models for predicting the corrosion rate in submerged, tidal and splash zones. By taking material composition, environmental factors and exposure time as inputs, the developed prediction models can well predict the corrosion rate with the accuracy of 93%, 96% and 93% for submerged, tidal and splash zones, respectively. In addition, we identified the key factors affecting the corrosion resistance of steels in different marine zones, and analyzed the relationship between these factors and corrosion rate by applying SHapley Additive exPlanations (SHAP) method. This work demonstrates that ML model combined with SHAP method are efficient in evaluating corrosion behavior of various steels in different marine environment. Corrosion ; Splash ; Seawater ; Environmental science ; Materials science ; Marine engineering ; Metallurgy ; Computer science ; Geology ; Oceanography ; Engineering ; Mechanical engineering Corrosion Behavior and Inhibition ; Non-Destructive Testing Techniques ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Namjung Kim | Kyoungmin Min
Source: Physical Chemistry Chemical Physics (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Optimal machine learning feature selection for assessing the mechanical properties of a zeolite framework In this study, 45 and 249 critical features were discovered among 896 zeolite descriptors generated by the matminer package for estimating the shear and bulk moduli of zeolites, respectively. A database containing the mechanical properties of 873 zeolite structures, calculated using density functional theory, was used to train the machine learning regression model. The results of using these critical features with the LightGBM algorithm were rigorously compared with those from other regressors as well as with different sets of features. The comparison results indicate that the surrogate model with critical features increases the prediction accuracy of the bulk and shear moduli of zeolites by 17.3% and 10.6%, respectively, and reduces the prediction uncertainty by one-third of that achieved using previously available features. The suggested features originating from several physical and chemical groups highlight the unveiled relationships between the features and mechanical properties of zeolites. The robustness of the constructed model with 356 features was confirmed by applying a set of different training-test set ratios. We believe that the suggested critical features of zeolites can help to understand the mechanical behavior of a half million unlabeled hypothetical zeolite structures and accelerate the discovery of novel zeolites with unprecedented mechanical properties. Zeolite ; Feature selection ; Selection (genetic algorithm) ; Feature (linguistics) ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Chemistry ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis ; Computational Drug Discovery Methods
Authors: Clovis Lapointe | Thomas D. Swinburne | Laurent Proville | Charlotte Becquart | Normand Mousseau | Mihai‐Cosmin Marinica
Source: Physical Review Materials (article, 2022-11-29)
Field: Materials Science — Materials Chemistry

Machine learning surrogate models for strain-dependent vibrational properties and migration rates of point defects Machine learning surrogate models employing atomic environment descriptors have found wide applicability in materials science. In our previous work, this approach yielded accurate and transferable predictions of the vibrational formation entropy of point defects for $\mathcal{O}(N)$ computational cost. The present study investigates the limits of data driven surrogate models in accuracy and applicability for vibrational properties. We propose an improvement of the accuracy by extending the fitting capacity of the model by increasing the dimension of the descriptor space. This is achieved by using a nonlinear relation between descriptors---target observables and when it is possible by including physical relevant information of the underlying energy landscape. The nonlinear extension is used to learn the formation entropy of defects with or without applied strain while including physical information, such as the minimum-saddle point sequences employed for the migration of point defects, is a key ingredient of transition state theory rate approximations. We find excellent predictive power after augmenting the dimensionality of the descriptor space, as demonstrated on large defect databases in $\ensuremath{\alpha}$-iron and amorphous silicon based on semiempirical force fields. The current linear surrogate models are used to investigate the correlation between migration entropy and energy. Our approaches reproduce the Meyer-Neldel compensation law observed from direct calculations in amorphous Si systems. Moreover, the same abstract descriptor space representation for entropy and energy is then used for the statistical correlation analysis. For linear surrogate models, we show that the energy-entropy statistical correlations can be reinterpreted in descriptor space. This provides a simple statistical criterion for the marginal interpretation of the compensation law. More generally, the present work shows how linear surrogate models can accelerate high-throughput workflows, aid the construction of mesoscale material models, and provide new avenues for correlation analysis. Statistical physics ; Entropy (arrow of time) ; Dimensionality reduction ; Surrogate data ; Curse of dimensionality ; Energy landscape ; Surrogate model ; Nonlinear system ; Saddle point ; Artificial intelligence ; Materials science ; Algorithm ; Computer science ; Machine learning ; Mathematics ; Thermodynamics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Nur Hasshima Hasbi | Abdullah Bade | Fuei Pien Chee | Muhammad Izzuddin Rumaling
Source: Computation (article, 2022-06-14)
Field: Chemistry — Analytical Chemistry

Pattern Recognition for Human Diseases Classification in Spectral Analysis Pattern recognition is a multidisciplinary area that received more scientific attraction during this period of rapid technological innovation. Today, many real issues and scenarios require pattern recognition to aid in the faster resolution of complicated problems, particularly those that cannot be solved using traditional human heuristics. One common problem in pattern recognition is dealing with multidimensional data, which is prominent in studies involving spectral data such as ultraviolet-visible (UV/Vis), infrared (IR), and Raman spectroscopy data. UV/Vis, IR, and Raman spectroscopy are well-known spectroscopic methods that are used to determine the atomic or molecular structure of a sample in various fields. Typically, pattern recognition consists of two components: exploratory data analysis and classification method. Exploratory data analysis is an approach that involves detecting anomalies in data, extracting essential variables, and revealing the data’s underlying structure. On the other hand, classification methods are techniques or algorithms used to group samples into a predetermined category. This article discusses the fundamental assumptions, benefits, and limitations of some well-known pattern recognition algorithms including Principal Component Analysis (PCA), Kernel PCA, Successive Projection Algorithm (SPA), Genetic Algorithm (GA), Partial Least Square Regression (PLS-R), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), Partial Least Square-Discriminant Analysis (PLS-DA) and Artificial Neural Network (ANN). The use of UV/Vis, IR, and Raman spectroscopy for disease classification is also highlighted. To conclude, many pattern recognition algorithms have the potential to overcome each of their distinct limits, and there is also the option of combining all of these algorithms to create an ensemble of methods. Linear discriminant analysis ; Principal component analysis ; Pattern recognition (psychology) ; Artificial intelligence ; Support vector machine ; Computer science ; Decision tree ; Kernel principal component analysis ; Partial least squares regression ; Artificial neural network ; Machine learning ; Kernel method Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Metabolomics and Mass Spectrometry Studies