jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junzhong Xie | Xu-Yuan Zhou | Hong Jiang
Source: The Journal of Chemical Physics (review, 2022-09-14)
Field: Materials Science — Materials Chemistry

Perspective on optimal strategies of building cluster expansion models for configurationally disordered materials Cluster expansion (CE) provides a general framework for first-principles-based theoretical modeling of multicomponent materials with configurational disorder, which has achieved remarkable success in the theoretical study of a variety of material properties and systems of different nature. On the other hand, there remains a lack of consensus regarding what is the optimal strategy to build CE models efficiently that can deliver accurate and robust prediction for both ground state energetic properties and statistical thermodynamic properties at finite temperature. There have been continuous efforts to develop more effective approaches to CE model building, which are further promoted by recent tremendous interest of applying machine learning techniques in materials research. In this Perspective, we present a critical review of recent methodological developments in building CE models for multicomponent materials, with particular focus on different approaches and strategies proposed to address cluster selection and training data generation. We comment on the pros and cons of different methods in a general formalism and present some personal views on the prospects of theoretical approaches to multicomponent materials. Computer science ; Formalism (music) ; Perspective (graphical) ; Variety (cybernetics) ; Cluster (spacecraft) ; Management science ; Cluster expansion ; Artificial intelligence ; Engineering ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Thermoelectric Materials and Devices
Authors: M. Vimal | Stefan Sandfeld | Aruna Prakash
Source: Materialia (article, 2022-01-11)
Field: Materials Science — Materials Chemistry

Grain segmentation in atomistic simulations using orientation-based iterative self-organizing data analysis Atomistic simulations have now established themselves as an indispensable tool in understanding deformation mechanisms of materials at the atomic scale. Large scale simulations are regularly used to study the behavior of polycrystalline materials at the nanoscale. In this work, we propose a method for grain segmentation of an atomistic configuration using an unsupervised machine learning algorithm that clusters atoms into individual grains based on their orientation. The proposed method, called the Orisodata algorithm, is based on the iterative self-organizing data analysis technique and is modified to work in the orientation space. The working of the algorithm is demonstrated on a 122 grain nanocrystalline thin film sample in both undeformed and deformed states. The Orisodata algorithm is also compared with two other grain segmentation algorithms available in the open-source visualization tool Ovito. The results show that the Orisodata algorithm is able to correctly identify deformation twins as well as regions separated by low angle grain boundaries. The model parameters have intuitive physical meaning and relate to similar thresholds used in experiments, which not only helps obtain optimal values but also facilitates easy interpretation and validation of results. Materials science ; Nanocrystalline material ; Grain boundary ; Segmentation ; Orientation (vector space) ; Deformation (meteorology) ; Visualization ; Artificial intelligence ; Computer science ; Algorithm ; Nanotechnology ; Geometry ; Microstructure ; Mathematics Microstructure and mechanical properties ; Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications
Authors: G. Venkatachalam | Velivela Hemanth | M Logesh | Piyush Arora | Siva Kumar Mahalingam | Vignesh Pragasam | T. G. Loganathan
Source: Journal of Natural Fibers (article, 2022-12-05)
Field: Materials Science — Polymers and Plastics

Investigation of Tensile Behavior of Carbon Nanotube/Coir Fiber/Fly Ash Reinforced Epoxy Polymer Matrix Composite Fiber-reinforced composite materials are lightweight and can withstand heavy loading conditions. Reinforcement augments the strength of the composite material, which is assessed by its elastic modulus. An attempt is made to reinforce epoxy with Coir Fiber, Carbon Nanotube (CNT) and Fly-ash. Central Composite Design (CCD), a Response Surface Methodology (RSM) tool, which is a Design of Experiment (DOE) technique, is used to fabricate the experimental samples to study their tensile behavior. Analysis of variance (ANOVA) is employed to investigate the effect of reinforcement percentage of CNT, coir-fiber, and fly-ash on tensile behavior of composite. The ANOVA results follow the trend of the experimental values with a deviation of less than 10% in yield strength, tensile strength, and Young’s modulus. Two models (Artificial Neural Network and Multiple Linear Regression Model) originated resting on the regression equation to speculate the elastic modulus for various reinforcement parameters using the experimental data. The main objective is to optimize reinforcement parameters using both the models having a maximum elastic modulus of 2.602 GPa and 2.682 GPa, respectively, which is achieved by the teaching learning-based optimization technique. Furthermore, confirmation experiments validate the optimization process with an error of less than 4%. Materials science ; Composite material ; Epoxy ; Carbon nanotube ; Ultimate tensile strength ; Composite number ; Coir ; Fly ash ; Polymer ; Fiber ; Matrix (chemical analysis) Natural Fiber Reinforced Composites ; Carbon Nanotubes in Composites ; Fiber-reinforced polymer composites
Authors: Chong Teng | Yan Wang | Daniel Huang | Katherine A. Martin | Jean-Baptiste Tristan | Junwei Lucas Bao
Source: Journal of Chemical Theory and Computation (article, 2022-08-08)
Field: Materials Science — Materials Chemistry

Dual-Level Training of Gaussian Processes with Physically Inspired Priors for Geometry Optimizations Gaussian process (GP) regression has been recently developed as an effective method in molecular geometry optimization. The prior mean function is one of the crucial parts of the GP. We design and validate two types of physically inspired prior mean functions: force-field-based priors and posterior-type priors. In this work, we implement a dual-level training (DLT) optimizer for the posterior-type priors. The DLT optimizers can be considered as a class of optimization algorithms that belong to the delta-machine learning paradigm but with several major differences compared to the previously proposed algorithms in the same paradigm. In the first level of the DLT, we incorporate the classical mechanical descriptions of the equilibrium geometries into the prior function, which enhances the performance of the GP optimizer as compared to the one using a constant (or zero) prior. In the second level, we utilize the surrogate potential energy surfaces (PESs), which incorporate the physics learned in the first-level training, as the prior function to refine the model performance further. We find that the force-field-based priors and posterior-type priors reduce the overall optimization steps by a factor of 2-3 when compared to the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimizer as well as the constant-prior GP optimizer proposed in previous works. We also demonstrate the potential of recovering the real PESs with GP with a force-field prior. This work shows the importance of including domain knowledge as an ingredient in the GP, which offers a potentially robust learning model for molecular geometry optimization and for exploring molecular PESs. Prior probability ; Training (meteorology) ; Computer science ; Dual (grammatical number) ; Gaussian ; Geometry ; Artificial intelligence ; Gaussian process ; Machine learning ; Mathematics ; Physics ; Bayesian probability Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Multi-Objective Optimization Algorithms
Authors: Kavitha Thangavelu | Linda Zou
Source: Environmental Nanotechnology Monitoring & Management (article, 2022-03-25)
Field: Materials Science — Materials Chemistry

Evaluating oil removal by amphiphilic MoS2/cellulose acetate fibrous sponge in a flow-through reactor and by artificial neural network Sponge ; Cellulose ; Cellulose acetate ; Amphiphile ; Artificial neural network ; Chemistry ; Chemical engineering ; Pulp and paper industry ; Polymer science ; Materials science ; Organic chemistry ; Computer science ; Engineering ; Biology ; Botany ; Artificial intelligence ; Polymer ; Copolymer Pickering emulsions and particle stabilization ; Surface Modification and Superhydrophobicity ; Solar-Powered Water Purification Methods
Authors: Saleh S Abdelhady | MM Atta | A. A. Megahed | K. A. Abu‐Hasel | Mohammed Alquraish | Ashraf A. Ali | Said H. Zoalfakar
Source: Journal of Industrial Textiles (article, 2022-06-01)
Field: Materials Science — Biomaterials

Modeling electrospun PLGA nanofibers’ diameter using response surface methodology and artificial neural networks The present work is an attempt to model the diameter of Poly Lactic-co-Glycolic Acid (PLGA) nanofibers by utilizing response surface methodology (RSM) and artificial neural networks (ANNs). Hence, determining the optimal electrospinning process conditions to produce a minimum fiber diameter. For modelling the average diameter of nanofibers, RSM approach based on four parameters (polymer concentration, high voltage and needle tip to collector distance and spinning angle) with five-level was compared to ANN technique. In the RSM approach, central composite design (CCD) was used to determine the individual and interaction impacts of the parameters on the average diameter of nanofibers. Several ANNs of single and double hidden layers with different number of cells for each were tried to obtain the best network structure. The experimental and predicted PLGA fiber diameters using an ANN showed a strong correlation, indicating that the network topology of 4-14-1 has good predictability for analyzing factors impacting PLGA fiber diameter. The average absolute relative error for predicting PLGA nanofibers’ diameter using ANN (2.24%) is slightly less than that obtained from RSM (2.59%). The high regression coefficient between the variables and the response (R 2 = 0.9636) shows a good second-order polynomial regression model for evaluating experimental data. The R 2 value was 0.945, indicating that the ANN model was good fitting with the experimental results. The optimum combinations (PLGA concentration of 26 wt.%, high voltage 22 kV, needle tip to collector distance 20 cm, and spinning angle 60 o ) were developed by RSM model for electrospinning PLGA nanofiber that can produce fine, consistent, and high-quality nanofibers. Response surface methodology ; Materials science ; Nanofiber ; Electrospinning ; PLGA ; Central composite design ; Biological system ; Composite material ; Artificial neural network ; Design of experiments ; Polymer ; Nanotechnology ; Machine learning ; Mathematics ; Statistics ; Computer science ; Nanoparticle Electrospun Nanofibers in Biomedical Applications ; Advanced Sensor and Energy Harvesting Materials ; Conducting polymers and applications
Authors: Jacob G. Lee | Chris J. Pickard | Bingqing Cheng
Source: The Journal of Chemical Physics (article, 2022-01-25)
Field: Materials Science — Materials Chemistry

High-pressure phase behaviors of titanium dioxide revealed by a <b>Δ</b>-learning potential Titanium dioxide has been extensively studied in the rutile or anatase phase, while its high-pressure phases are less well-understood, despite that many are thought to have interesting optical, mechanical, and electrochemical properties. First-principles methods, such as density functional theory (DFT), are often used to compute the enthalpies of TiO<sub>2</sub> phases at 0 K, but they are expensive and, thus, impractical for long time scale and large system-size simulations at finite temperatures. On the other hand, cheap empirical potentials fail to capture the relative stabilities of various polymorphs. To model the thermodynamic behaviors of ambient and high-pressure phases of TiO<sub>2</sub>, we design an empirical model as a baseline and then train a machine learning potential based on the difference between the DFT data and the empirical model. This so-called Δ-learning potential contains long-range electrostatic interactions and predicts the 0 K enthalpies of stable TiO<sub>2</sub> phases that are in good agreement with DFT. We construct a pressure-temperature phase diagram of TiO<sub>2</sub> in the range 0 < P < 70 GPa and 100 < T < 1500 K. We then simulate dynamic phase transition processes by compressing anatase at different temperatures. At 300 K, we predominantly observe an anatase-to-baddeleyite transformation at about 20 GPa via a martensitic two-step mechanism with a highly ordered and collective atomic motion. At 2000 K, anatase can transform into cotunnite around 45-55 GPa in a thermally activated and probabilistic manner, accompanied by diffusive movement of oxygen atoms. The pressures computed for these transitions show good agreement with experiments. Our results shed light on how to synthesize and stabilize high-pressure TiO<sub>2</sub> phases, and our method is generally applicable to other functional materials with multiple polymorphs. Anatase ; Phase diagram ; Rutile ; Materials science ; Phase transition ; Density functional theory ; Titanium dioxide ; Thermodynamics ; Phase (matter) ; Chemical physics ; Chemistry ; Computational chemistry ; Physics ; Metallurgy ; Photocatalysis Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Advanced materials and composites
Authors: Ricardo Jorge Pais | Raminta Zmuidinaite | Jonathan Lacey | Christian Jardine | Ray K. Iles
Source: Applied Sciences (article, 2022-03-16)
Field: Chemistry — Spectroscopy

A Rapid and Affordable Screening Tool for Early-Stage Ovarian Cancer Detection Based on MALDI-ToF MS of Blood Serum Ovarian cancer is a worldwide health issue that grows at a rate of almost 250,000 new cases every year. Its early detection is key for a good prognosis and even curative surgery. However, current medical examination methods and tests have been inefficient in detecting ovarian cancer at the early stage, leading to preventable death. So far, new screening tests based on molecular biomarker analysis techniques have not resulted in any substantial improvement in early-stage diagnosis and increased survival. Thus, whilst there remains clear potential to improve outcomes through early detection, novel approaches are needed. Here, we postulated that MALDI-ToF-mass-spectrometry-based tests can be a solution for effective screening of ovarian cancer. In this retrospective cohort study, we generated and analyzed the mass spectra of 181 serum samples of women with and without ovarian cancer. Using bioinformatics pipelines for analysis, including predictive modeling and machine learning, we found distinct mass spectral patterns composed of 9–20 key combinations of peak intensity or peak enrichment features for each stage of ovarian cancer. Based on a scoring algorithm and obtained patterns, the optimal sensitivity for detecting each stage of cancer was 95–97% with a specificity of 97%. Scoring all algorithms simultaneously could detect all stages of ovarian cancer at 99% sensitivity and 92% specificity. The results further demonstrate that the matrix and mass range analyzed played a key role in improving the mass spectral data quality and diagnostic power. Altogether, with the results reported here and increasing evidence of the MS assay’s diagnostic accuracy and instrument robustness, it has become imminent to consider MS in the clinical application for ovarian cancer screening. Ovarian cancer ; Stage (stratigraphy) ; Medicine ; Oncology ; Cancer ; Cancer screening ; Biomarker ; Internal medicine ; Biology Mass Spectrometry Techniques and Applications ; Bacterial Identification and Susceptibility Testing ; Molecular Biology Techniques and Applications
Authors: Rasha Atwi | Matthew Bliss | Maxim A. Makeev | Nav Nidhi Rajput
Source: Scientific Reports (article, 2022-09-21)
Field: Materials Science — Materials Chemistry

MISPR: an open-source package for high-throughput multiscale molecular simulations Computational tools provide a unique opportunity to study and design optimal materials by enhancing our ability to comprehend the connections between their atomistic structure and functional properties. However, designing materials with tailored functionalities is complicated due to the necessity to integrate various computational-chemistry software (not necessarily compatible with one another), the heterogeneous nature of the generated data, and the need to explore vast chemical and parameter spaces. The latter is especially important to avoid bias in scattered data points-based models and derive statistical trends only accessible by systematic datasets. Here, we introduce a robust high-throughput multi-scale computational infrastructure coined MISPR (Materials Informatics for Structure-Property Relationships) that seamlessly integrates classical molecular dynamics (MD) simulations with density functional theory (DFT). By enabling high-performance data analytics and coupling between different methods and scales, MISPR addresses critical challenges arising from the needs of automated workflow management and data provenance recording. The major features of MISPR include automated DFT and MD simulations, error handling, derivation of molecular and ensemble properties, and creation of output databases that organize results from individual calculations to enable reproducibility and transparency. In this work, we describe fully automated DFT workflows implemented in MISPR to compute various properties such as nuclear magnetic resonance chemical shift, binding energy, bond dissociation energy, and redox potential with support for multiple methods such as electron transfer and proton-coupled electron transfer reactions. The infrastructure also enables the characterization of large-scale ensemble properties by providing MD workflows that calculate a wide range of structural and dynamical properties in liquid solutions. MISPR employs the methodologies of materials informatics to facilitate understanding and prediction of phenomenological structure-property relationships, which are crucial to designing novel optimal materials for numerous scientific applications and engineering technologies. Computer science ; Workflow ; Software ; Computational science ; Data mining ; Database Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Scientific Computing and Data Management
Authors: Kanishka Ghosh | Sergio Vernuccio | Alexander W. Dowling
Source: Frontiers in Chemical Engineering (article, 2022-07-18)
Field: Materials Science — Materials Chemistry

Nonlinear Reactor Design Optimization With Embedded Microkinetic Model Information Despite the success of multiscale modeling in science and engineering, embedding molecular-level information into nonlinear reactor design and control optimization problems remains challenging. In this work, we propose a computationally tractable scale-bridging approach that incorporates information from multi-product microkinetic (MK) models with thousands of rates and chemical species into nonlinear reactor design optimization problems. We demonstrate reduced-order kinetic (ROK) modeling approaches for catalytic oligomerization in shale gas processing. We assemble a library of six candidate ROK models based on literature and MK model structure. We find that three metrics—quality of fit (e.g., mean squared logarithmic error), thermodynamic consistency (e.g., low conversion of exothermic reactions at high temperatures), and model identifiability—are all necessary to train and select ROK models. The ROK models that closely mimic the structure of the MK model offer the best compromise to emulate the product distribution. Using the four best ROK models, we optimize the temperature profiles in staged reactors to maximize conversions to heavier oligomerization products. The optimal temperature starts at 630–900K and monotonically decreases to approximately 560 K in the final stage, depending on the choice of ROK model. For all models, staging increases heavier olefin production by 2.5% and there is minimal benefit to more than four stages. The choice of ROK model, i.e., model-form uncertainty, results in a 22% difference in the objective function, which is twice the impact of parametric uncertainty; we demonstrate sequential eigendecomposition of the Fisher information matrix to identify and fix sloppy model parameters, which allows for more reliable estimation of the covariance of the identifiable calibrated model parameters. First-order uncertainty propagation determines this parametric uncertainty induces less than a 10% variability in the reactor optimization objective function. This result highlights the importance of quantifying model-form uncertainty, in addition to parametric uncertainty, in multi-scale reactor and process design and optimization. Moreover, the fast dynamic optimization solution times suggest the ROK strategy is suitable for incorporating molecular information in sequential modular or equation-oriented process simulation and optimization frameworks. Nonlinear system ; Identifiability ; Computer science ; Parametric statistics ; Mathematical optimization ; Mathematics ; Machine learning ; Physics ; Statistics Crystallization and Solubility Studies ; Field-Flow Fractionation Techniques ; Process Optimization and Integration
Authors: Danielle C. Hiener | Geoffrey Hutchison
Source: The Journal of Physical Chemistry A (article, 2022-04-26)
Field: Materials Science — Polymers and Plastics

Pareto Optimization of Oligomer Polarizability and Dipole Moment Using a Genetic Algorithm High-performance electronic components are highly sought after in order to produce increasingly smaller and cheaper electronic devices. Drawing inspiration from inorganic dielectric materials, in which both polarizability and polarization contribute, organic materials can also maximize both. For a large set of small molecules drawn from PubChem, a Pareto-like front appears between the polarizability and dipole moment, indicating the presence of an apparent trade-off between these two properties. We tested this balance in π-conjugated materials by searching for novel conjugated hexamers with simultaneously large polarizabilities and dipole moments with potential use for dielectric materials. Using a genetic algorithm (GA) screening technique in conjunction with an approximate density functional tight-binding method for property calculations, we were able to efficiently search chemical space for optimal hexamers. Given the scope of chemical space, using the GA technique saves considerable time and resources by speeding up molecular searches compared to a systematic search. We also explored the underlying structure-function relationships, including sequence and monomer properties, that characterize large polarizability and dipole moment regimes. Polarizability ; Dipole ; Dielectric ; Moment (physics) ; Algorithm ; Materials science ; Computer science ; Chemical physics ; Chemistry ; Computational chemistry ; Molecule ; Physics ; Optoelectronics ; Quantum mechanics ; Organic chemistry Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Photochromic and Fluorescence Chemistry