jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yi Je Cho | Kathy Lu
Source: Materials Today Advances (article, 2023-05-31)
Field: Materials Science — Ceramics and Composites

A materials informatics approach for composition and property prediction of polymer-derived silicon oxycarbides Polymer-derived silicon oxycarbide (SiOC) materials enable the formation of homogeneous microstructures and high temperature stable properties. However, the relationships between the processing parameters and microstructures/properties have not been clearly understood. In this study, a materials informatics approach was employed to the SiOC materials to analyze and estimate the relationships. Datasets were constructed from results of previously reported literature about SiOC. The correlation analysis provided processing parameter ranking regarding the corresponding influences on the properties and microstructures. Such an understanding can be utilized for desired material fabrication. Machine learning models with high accuracy were proposed using the ranked features obtained from the correlation analysis. In addition, important points on the data collection, correlation analysis, and machine learning as well as limitations of the current dataset were discussed. The proposed workflow for the SiOC materials can be extended to different types of polymer-derived ceramics by incorporating various features and targets involved in the processing variables, microstructures, and properties. Materials informatics ; Workflow ; Microstructure ; Materials science ; Silicon ; Ranking (information retrieval) ; Polymer ; Ceramic ; Homogeneous ; Computer science ; Artificial intelligence ; Composite material ; Statistical physics ; Physics ; Health informatics ; Optoelectronics Advanced ceramic materials synthesis ; Injection Molding Process and Properties ; Concrete and Cement Materials Research
Authors: Jun Meng | Lane E. Schultz | Ryan Jacobs | Dane Morgan
Source: ECS Meeting Abstracts (article, 2023-08-28)
Field: Materials Science — Materials Chemistry

Discovery of New Fast Oxygen Conductors: Bi<sub>2</sub>MO<sub>4</sub>x (M= rare earth, X= halogen) Via Unsupervised Machine Learning Discovering new fast oxygen conductors is critical for developing many technologies, e.g., solid oxide fuel cells (SOFCs), solid oxide electrolysis cells (SOECs), proton ceramic fuel cells (PCFCs), and solid oxide air batteries, particularly for operation at lower temperatures (below 700 ˚C). By leveraging unsupervised machine learning techniques and large databases of computed materials data (e.g., Materials Project), we are able to rapidly explore and screen for new materials with high oxygen mobility. In this work, we used unsupervised machine learning analysis applied to features extracted from the structure of materials and discovered a new structural class of vacancy-mediated oxygen conductor. To do this, we applied the K-nearest neighbor (k-NN) algorithm on all the approximately 62,000 oxygen-containing compounds in the Materials Project database, using X-ray diffraction (XRD) and pair distribution function (PDF) data to represent the structural features. The neighbors of reference known for good oxygen conductors were screened with criteria relating to material stability, synthesizability, and electronic property dissimilarity (to ensure a novel material) compared to the reference material. Starting with the best-known oxygen conductor, the fluorite-type material, we examined the materials list of its nearest neighbors and identified the Bi 2 MO 4 X (M=Rare-earth element, X= halogen element) structural family as a novel group of fast oxygen conductors. In particular, Bi 2 LaO 4 Cl is a layered bismuth oxyhalide crystallized in the space group P4/mmm, adopting a “triple fluorite” layer [Bi 2 LaO 4 ] + , balanced by the anion Cl - layer. The stability and mobility of oxygen vacancy in Bi 2 LaO 4 Cl were studied using Density Functional Theory (DFT) methods. 2+ oxygen vacancy is the most thermodynamically favorable defect type. Ab initio and machine learning-trained interatomic potential molecular dynamic simulations were performed to study the high temperature oxygen mobility in Bi 2 LaO 4 Cl. The simulation results show that Bi 2 LaO 4 Cl presents an extremely low migration barrier of 0.14 +/- 0.03 eV below 700 ˚C, suggesting 10 to 20 times higher ionic conductivity compared to yttria-stabilized zirconia (YSZ). Bi 2 LaO 4 Cl is an insulator with a wide band gap of 2.65 eV, indicating that Bi 2 LaO 4 Cl may be a promising electrolyte material with high oxygen ion conductivity and low electronic conductivity. This work demonstrates the power of combining unsupervised machine learning approaches with large materials databases for finding new fast oxygen conductors. Materials science ; Oxide ; Oxygen ; Computer science ; Chemistry ; Metallurgy X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells
Authors: Shaohua Chen | Nuo Xu
Source: ACS Applied Materials & Interfaces (article, 2023-02-09)
Field: Materials Science — Materials Chemistry

Detecting Microstructural Criticality/Degeneracy through Hybrid Learning Strategies Trained by Molecular Dynamics Simulations Efficient microstructure design can strongly accelerate the development of materials. However, the complexity of the microstructure-behavior relation renders the criticalities and degeneracies within the microstructure space highly possible. Criticality means that a slight microstructural change can lead to a dramatic transition in material behavior, while degeneracy means that very different microstructures may lead to similar behaviors. To investigate these microstructural characteristics of the fiber/matrix interface within composite materials, we have proposed a hybrid deep-learning-based framework by integrating the supervised feed-forward neural network and the unsupervised autoencoder, which are trained by the molecular dynamics (MD) simulation results. The well-trained model continuously maps the elemental density images within the interfacial area into a low-dimensional latent space. Assisted by the extracted latent features, we can easily detect the criticalities and degeneracies within the original microstructure space of the composite's interface. The predicted microstructural criticalities and degeneracies are validated by investigating their atomistic origins through MD simulations. The proposed framework can be employed for the interfacial microstructure design of composite materials by identifying certain interfacial microstructures that might lead to undesirable behaviors. Materials science ; Criticality ; Molecular dynamics ; Degeneracy (biology) ; Nanotechnology ; Statistical physics ; Chemical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Soohaeng Yoo Willow | Lulu Kang | David D. L. Minh
Source: The Journal of Chemical Physics (article, 2023-09-22)
Field: Materials Science — Materials Chemistry

Learned mappings for targeted free energy perturbation between peptide conformations Targeted free energy perturbation uses an invertible mapping to promote configuration space overlap and the convergence of free energy estimates. However, developing suitable mappings can be challenging. Wirnsberger et al. [J. Chem. Phys. 153, 144112 (2020)] demonstrated the use of machine learning to train deep neural networks that map between Boltzmann distributions for different thermodynamic states. Here, we adapt their approach to the free energy differences of a flexible bonded molecule, deca-alanine, with harmonic biases and different spring centers. When the neural network is trained until "early stopping"-when the loss value of the test set increases-we calculate accurate free energy differences between thermodynamic states with spring centers separated by 1 Å and sometimes 2 Å. For more distant thermodynamic states, the mapping does not produce structures representative of the target state, and the method does not reproduce reference calculations. Free energy perturbation ; Peptide ; Perturbation (astronomy) ; Chemistry ; Physics ; Computational chemistry ; Molecular dynamics ; Biochemistry ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Juhwan Noh | Hyunju Chang
Source: Journal of Chemical Information and Modeling (article, 2023-09-15)
Field: Materials Science — Materials Chemistry

Data-Driven Prediction of Configurational Stability of Molecule-Adsorbed Heterogeneous Catalysts The design of new heterogeneous catalysts that convert small molecules into valuable chemicals is a key challenge for constructing sustainable energy systems. Density functional theory (DFT)-based design frameworks based on the understanding of molecular adsorption on the catalytic surface have been widely proposed to accelerate experimental approaches to develop novel catalysts. In addition, a machine learning (ML)-combined design framework was recently proposed to further reduce the inherent time cost of DFT-based frameworks. However, because of the lack of prior information on chemical interactions between arbitrary surfaces and adsorbates, the efficacy of the computational screening approaches would be reduced by obtaining unexpected structural anomalies (i.e., abnormally converged surface-adsorbate geometries after the DFT calculations) during an exhaustive exploration of chemical space. To overcome this challenge, we propose an ML framework that directly predicts the configurational stability of a given initial surface-adsorbate geometry. Our benchmark experiments with the Open Catalysts 20 (OC20) dataset show promising performance on classifying stable geometry (i.e., <i>F</i>1-score of 0.922, the area under the receiver operating characteristics (AUROC) of 0.906, and Matthews correlation coefficient (MCC) of 0.633) with a high precision of 0.921 by utilizing an ensemble approach. We further interpret the generalizability and domain applicability of the trained model in terms of the chemical space of the OC20 dataset. Furthermore, from an experiment on the training set size dependence of model performance, we found that our ML model could be practically applicable to classify stable configurations even with a relatively small number of training data. Chemical space ; Stability (learning theory) ; Density functional theory ; Generalizability theory ; Benchmark (surveying) ; Computer science ; Adsorption ; Set (abstract data type) ; Molecule ; Catalysis ; Chemical stability ; Computational chemistry ; Chemistry ; Biological system ; Materials science ; Machine learning ; Mathematics ; Thermodynamics ; Physics ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Surface Chemistry and Catalysis
Authors: Junwen Bai | Yuanqi Du | Yingheng Wang | Shufeng Kong | John M. Gregoire | Carla P. Gomes
Source: arXiv (Cornell University) (preprint, 2023-02-03)
Field: Materials Science — Materials Chemistry

Xtal2DoS: Attention-based Crystal to Sequence Learning for Density of States Prediction Modern machine learning techniques have been extensively applied to materials science, especially for property prediction tasks. A majority of these methods address scalar property predictions, while more challenging spectral properties remain less emphasized. We formulate a crystal-to-sequence learning task and propose a novel attention-based learning method, Xtal2DoS, which decodes the sequential representation of the material density of states (DoS) properties by incorporating the learned atomic embeddings through attention networks. Experiments show Xtal2DoS is faster than the existing models, and consistently outperforms other state-of-the-art methods on four metrics for two fundamental spectral properties, phonon and electronic DoS. Computer science ; Sequence (biology) ; Representation (politics) ; Property (philosophy) ; Decodes ; Artificial intelligence ; Multi-task learning ; Task (project management) ; Crystal (programming language) ; Machine learning ; Algorithm ; Decoding methods ; Engineering ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: R. A. Abubakar
Source: American Journal of Mechanics and Applications (article, 2023-11-09)
Field: Materials Science — Materials Chemistry

One-Dimensional Shape Memory Alloys Material Phenomenological Constitutive Model Based on Stress Due to Mechanical and Chemical Energy Change Shape Memory Alloy (SMA) is a material that has the ability to memorize previous shapes after deforming. That is it regains its original shape when temperature increases, converting thermal energy to mechanical energy. This property of the plastic-like deformation which subsequently recovers its original shape is referred to as Shape Memory Effects (SMEs). The history of this material began in the year 1800s. Because of their unique behavior, SMA has great industrial applications. Many constituted models of SMA behavior are formed describing SMA behavior. Most of them are based on experimental phenomenological macroscopic constitutive models consisting of variables that reveal the degree of phase transition that describes the phenomenological macroscopic behavior of SMA. These types of models are very easy and the parameters are also very easy to determine. In this research, a constitutive model is formulated based on the obserbation of experimental data, the SMA behavior is simulated using Artificial Neural Networks (ANN). The phenomenological constitutive model comprises both mechanical and chemical change. In the parameter estimation, the Back-Progation (BP) algorithm and the nonlinear optimization algorithm are used. A numerical simulation is performed, and the phenomenological constitutive model captures well the uniaxial tension and compression experimental data, therefore the constitutive model is verified. Materials science ; Constitutive equation ; Phenomenological model ; Shape-memory alloy ; Stress (linguistics) ; Mechanics ; Psychology ; Thermodynamics ; Metallurgy ; Finite element method ; Physics ; Condensed matter physics ; Philosophy Shape Memory Alloy Transformations
Authors: Veljko Vujčić | Bratislav P. Marinković | Vladimir A. Srécković | Sanja Tošić | Darko Jevremović | Ljubinko M. Ignjatović | M.S. Rabasović | D. Šević | N. S. Simonović | N. J. Mason
Source: Physical Chemistry Chemical Physics (review, 2023-01-01)
Field: Chemistry — Spectroscopy

Current stage and future development of Belgrade collisional and radiative databases/datasets of importance for molecular dynamics Atomic and molecular (A&M) databases that contain information about species, their identities and radiative/collisional processes are essential and helpful tools that are utilized in many fields of physics, chemistry, and chem/phys-informatics. Errors or inconsistencies in the datasets are a serious issue since they can lead to inaccurate predictions and generate problems with the modeling. This demonstrates that data curation efforts around A&M databases are still indispensable and that in the curation process studious attention is required. Therefore, we herein present research activities around Belgrade "nodes" - datasets of collision/radiative cross-sections and rates needed for spectroscopy analysis in various A&M, optical and plasma physics fields. Methodologies of our research and both present and future aspects of the applications are explained. We explored the possibility to extend our nodes towards building a new database on Judd-Ofelt parameters by using machine learning in order to predict optical properties of luminescence materials. In addition, we hope that public availability of our datasets and their graphical representations will also motivate others to investigate the potential of these data. Radiative transfer ; Database ; Informatics ; Process (computing) ; Computer science ; Data curation ; Data science ; Chemistry ; Physics ; Engineering Spectroscopy and Laser Applications ; Luminescence Properties of Advanced Materials ; Atmospheric Ozone and Climate
Authors: Dmitry Kazhdan | Botty Dimanov | Lucie Charlotte Magister | Pietro Barbiero | Mateja Jamnik | Píetro Lió
Source: arXiv (Cornell University) (preprint, 2023-02-09)
Field: Materials Science — Materials Chemistry

GCI: A (G)raph (C)oncept (I)nterpretation Framework Explainable AI (XAI) underwent a recent surge in research on concept extraction, focusing on extracting human-interpretable concepts from Deep Neural Networks. An important challenge facing concept extraction approaches is the difficulty of interpreting and evaluating discovered concepts, especially for complex tasks such as molecular property prediction. We address this challenge by presenting GCI: a (G)raph (C)oncept (I)nterpretation framework, used for quantitatively measuring alignment between concepts discovered from Graph Neural Networks (GNNs) and their corresponding human interpretations. GCI encodes concept interpretations as functions, which can be used to quantitatively measure the alignment between a given interpretation and concept definition. We demonstrate four applications of GCI: (i) quantitatively evaluating concept extractors, (ii) measuring alignment between concept extractors and human interpretations, (iii) measuring the completeness of interpretations with respect to an end task and (iv) a practical application of GCI to molecular property prediction, in which we demonstrate how to use chemical functional groups to explain GNNs trained on molecular property prediction tasks, and implement interpretations with a 0.76 AUCROC completeness score. Completeness (order theory) ; Computer science ; Property (philosophy) ; Artificial intelligence ; Interpretation (philosophy) ; Task (project management) ; Graph ; Natural language processing ; Machine learning ; Theoretical computer science ; Mathematics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Explainable Artificial Intelligence (XAI)
Authors: Neda Kalantari | Ali Farzi | Faez Hamooni | Nagihan Çaylak Delibaş | Ali Tarjomannejad | Aligholi Niaei | Dariush Salari
Source: Research Square (preprint, 2023-08-29)
Field: Chemistry — Inorganic Chemistry

Simultaneous study of different combinations of ZSM- 5 templates and operating conditions in the MTP process; Designing, Modeling and Optimization by RSM-ANN-GA Abstract Process of converting methanol to propylene is influenced by many parameters. The use of smart techniques can be an effective way to investigate variable parameters and finding optimal conditions. In this work, optimal design of ZSM-5 catalysts with different combinations of templates and operating conditions in methanol to propylene process was performed using response surface methodology and hybrid artificial neural network-genetic algorithm method. Objective functions for optimization were methanol conversion and propylene selectivity. Effects of different variables in the dual-responses system, including molar ratios of tetra propyl ammonium bromide (TPABr), Cetyltrimethylammonium bromide (CTAB), and Pluronic F127, as well as weight hourly space velocity of feed and process temperature on the performance of catalysts, were studied both experimentally and theoretically. Modeling results showed that the designed neural network structure for the process had superior accuracy compared to RSM with correlation coefficients of 0.9976, 0.9950 and 0.9946 for training, validation and testing, respectively. By combining optimal templates, optimum operating temperature of 420 °C and WHSV of 1 h -1 were obtained based on the genetic algorithm to achieve maximum selectivity of propylene and the highest possible conversion of methanol. The optimal catalyst had stable performance under the optimal conditions. Response surface methodology ; Ammonium bromide ; Methanol ; Selectivity ; Central composite design ; Bromide ; Materials science ; Catalysis ; Space velocity ; ZSM-5 ; Artificial neural network ; Chemical engineering ; Biological system ; Computer science ; Chemistry ; Chromatography ; Zeolite ; Inorganic chemistry ; Organic chemistry ; Engineering ; Machine learning Zeolite Catalysis and Synthesis ; Catalysis and Oxidation Reactions ; Catalysis and Hydrodesulfurization Studies
Authors: Ilia Kevlishvili | Chenru Duan | Heather J. Kulik
Source: ChemRxiv (preprint, 2023-10-18)
Field: Materials Science — Materials Chemistry

Classification of Hemilabile Ligands Using MachineLearning Discovery of hemilabile ligands that optimally balance reactivity and stability is important for identifying novel catalyst structures. We design a workflow for identifying ligands in the Cambridge Structural Database (CSD) that have been crystalized with distinct denticities and are thus identifiable as hemilabile ligands. To overcome the difficulty of identifying negative example, non-hemilabile ligands in our data set, we implement a semi-supervised learning approach using a label-spreading algorithm together with a set of heuristic rules based on ligand frequency of appearance. We show that a heuristic based on coordinating atom identity alone is not sufficient to identify whether a ligand is hemilabile and our trained machine-learning classification models are instead needed to predict whether a bi-, tri-, or tetradentate ligand is hemilabile with high accuracy and precision. We gain deeper insight into the factors that govern ligand hemilability by conducting feature importance analysis on our models, finding that the second, third, and fourth coordination spheres all play an important role in ligand hemilability. Ligand (biochemistry) ; Set (abstract data type) ; Computer science ; Heuristic ; Feature (linguistics) ; Stability (learning theory) ; Artificial intelligence ; Machine learning ; Chemistry ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions