jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yi Dong | Mingchu Ran | Xiao Zhang | Saisai Lin | Haitao Zhao | Yang Yang | Shaojun Liu | Chenghang Zheng | Xiang Gao
Source: ACS ES&T Engineering (article, 2024-02-08)
Field: Materials Science — Materials Chemistry

Machine Learning-Accelerated Disentanglement of Activity–Selectivity Trade-off of Multielement Oxide Denitration Catalysts Developing rapid screening methods for high-performance catalysts is one of the research priorities in environmental science and engineering. However, the activity–selectivity trade-off is generally observed in developing catalysts. Herein, a machine learning workflow to accelerate disentangling the activity–selectivity trade-off of multielement oxide selective catalytic reduction (SCR) denitration catalysts was proposed. An acidity descriptor and a redox descriptor were defined through the systematic investigation of 12 experimentally synthesized MnCe-based catalysts. Assisted with the above-constructed acidity and redox descriptors, a clustering analysis method was successfully applied for rapid screening of the high-performance multielement oxide catalysts. Combining the results of clustering analysis and the performance of the 12 measured catalysts, a group of catalysts with potentially high activity and high N2 selectivity were identified. Three candidate catalysts in this group were evaluated experimentally and exhibited far superior activity and selectivity to those of MnCeOx, indicating the high efficiency of the machine learning strategy. This work provides new ideas for future green catalyst design and leads to significant progress in environmental protection and sustainable resource utilization. Catalysis ; Selectivity ; Oxide ; Redox ; Workflow ; Chemistry ; Cluster analysis ; Computer science ; Inorganic chemistry ; Machine learning ; Database ; Organic chemistry Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques ; Machine Learning in Materials Science
Authors: Javad Shirani | Julio J. Valdés | Alain Tchagang | Kirk H. Bevan
Source: The Journal of Physical Chemistry C (article, 2024-03-08)
Field: Materials Science — Materials Chemistry

Adsorbate-Dependent Electronic Structure Descriptors for Machine Learning-Driven Binding Energy Predictions in Diverse Single Atom Alloys: A Reductionist Approach A long-standing challenge in the design of single atom alloys (SAAs), for catalytic applications, is the determination of a feature space that maximally correlates to molecular binding energies per the Sabatier principle. The more representative a feature space is of the underlying binding properties, the greater the predictive capability of a given machine learning (ML) algorithm. Moreover, the greater the diversity and range of SAA impurities/sites examined, the greater the difficulty in arriving at such a predictive feature. In this work, we undertake to examine the degree to which adsorbate electronic structure properties might address this challenge, in a distinct departure from the traditional substrate electronic structure feature construction found in the catalysis literature. Specifically, as a model system, we explore the predictive capacity of the p-orbital projected density of states (PDOS) pertaining to the adsorption of CO molecules on a wide range of SAA substrates, impurity embeddings, and vicinal cuts. This analysis is executed in two parts. First, we explore the degree to which the entire PDOS distribution, in the form of an energy-dependent vector, can predict binding energies. Subsequently, guided by a rigorous intrinsic dimensionality analysis, uniform manifold approximation and projection visualization, and chemical intuition, we are able to reduce the predictive feature space to just three physical quantities based on semicore level properties and charge filling of the adsorbate–as embedded with the PDOS distribution. This near-intrinsic feature space and the PDOS distribution are both shown to provide significant improvements in predictive accuracy when coupled with regression-based ML methods, even when tackling highly diverse chemical datasets. The results of this analysis both further substantiate the transferability characteristics of SAAs and indicate that adsorbate-based electronic structure features (from either relaxed or unrelaxed chemical datasets) are powerful tools in the prediction of catalytic binding energies in such systems. They also underscore the predictive benefit of finding a feature space with a dimension equal to the intrinsic dimensionality of the data that can maximally correlate with the physical property under investigation when employing ML methods in catalysis studies. Curse of dimensionality ; Binding energy ; Electronic structure ; Atom (system on chip) ; Feature vector ; Feature (linguistics) ; Chemistry ; Artificial intelligence ; Computational chemistry ; Machine learning ; Chemical physics ; Statistical physics ; Materials science ; Physics ; Computer science ; Quantum mechanics Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Erika Paola Fonseca Parra | Jihad Oumerri | Ana‐Andreea Arteni | Jean‐Luc Six | Steven P. Armes | Khalid Ferji
Source: Macromolecules (article, 2024-12-31)
Field: Materials Science — Materials Chemistry

Neural Network-Driven Exploration in Polymerization-Induced Self-Assembly: From 2D to 3D Pseudo-Phase Diagram We explore the application of machine learning (ML) to predict the morphology of poly(glycerol monomethacrylate)-poly(2-hydroxypropyl methacrylate) (PGMA–PHPMA) diblock copolymer nano-objects prepared via polymerization-induced self-assembly (PISA) in aqueous media. Traditional studies typically map copolymer morphology using two-dimensional (2D) pseudo-phase diagrams, plotting variables such as the mean degree of polymerization (Xn) of the solvophobic block against the copolymer concentration (also known as the solids content). In contrast, our approach utilizes deep neural networks (DNNs) trained on literature data to generate detailed three-dimensional (3D) morphology maps. These maps include the molecular weight of the solvophilic block, providing a comprehensive volumetric view that reveals more complex relationships and transitional morphologies that are difficult to capture in 2D representations. This 3D modeling approach enriches our understanding by highlighting critical thresholds and nuanced transitions within the morphology landscape. Such advanced modeling not only deepens our understanding of how PGMA molecular weight influences copolymer morphology but also significantly reduces the need for extensive experimental trials. Consequently, it simplifies the creation of accurate pseudo-phase diagrams across a broad range of aqueous PISA formulations. Experimental validation confirms the accuracy of our models, demonstrating the potential of ML to make predictive modeling more accessible to chemists and paving the way for future research on other PISA formulations. The data set, along with all codes for model training and evaluation, is publicly accessible via both Zenodo and GitHub platforms. Copolymer ; Solvophobic ; Polymerization ; Computer science ; Phase diagram ; Materials science ; Morphology (biology) ; Phase (matter) ; Aqueous solution ; Biological system ; Polymer chemistry ; Polymer ; Chemistry ; Geology ; Organic chemistry ; Molecule Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Tomoko Yokaichiya | Tatsushi Ikeda | Koki Muraoka | Akira Nakayama
Source: The Journal of Chemical Physics (article, 2024-05-24)
Field: Materials Science — Materials Chemistry

On-the-fly kinetic Monte Carlo simulations with neural network potentials for surface diffusion and reaction We develop an adaptive scheme in the kinetic Monte Carlo simulations, where the adsorption and activation energies of all elementary steps, including the effects of other adsorbates, are evaluated "on-the-fly" by employing the neural network potentials. The configurations and energies evaluated during the simulations are stored for reuse when the same configurations are sampled in a later step. The present scheme is applied to hydrogen adsorption and diffusion on the Pd(111) and Pt(111) surfaces and the CO oxidation reaction on the Pt(111) surface. The effects of interactions between adsorbates, i.e., adsorbate-adsorbate lateral interactions, are examined in detail by comparing the simulations without considering lateral interactions. This study demonstrates the importance of lateral interactions in surface diffusion and reactions and the potential of our scheme for applications in a wide variety of heterogeneous catalytic reactions. Kinetic Monte Carlo ; Monte Carlo method ; On the fly ; Statistical physics ; Diffusion ; Kinetic energy ; Artificial neural network ; Dynamic Monte Carlo method ; Physics ; Computer science ; Thermodynamics ; Mathematics ; Artificial intelligence ; Classical mechanics ; Statistics Machine Learning in Materials Science ; Thermal properties of materials ; nanoparticles nucleation surface interactions
Authors: Hossein Abedi | Mohammad Abdollahzadeh | Thomas D. Bush | Othmane Benafan | Ala Qattawi | Mohammad Elahinia
Source: Computational Materials Science (article, 2024-09-11)
Field: Materials Science — Materials Chemistry

Predicting actuation strain in quaternary shape memory alloy NiTiHfX using machine learning Data-driven techniques are used to predict the actuation strain (AS) of NiTiHfX shape memory alloy (SMA). A Machine Learning (ML) approach is used to overcome the high dimensional dependency of NiTiHfX AS on numerous factors, as well as the lack of fully known governing physics. Detailed data extraction on available experimental studies is performed to gather any related information about the actuation strain. The elemental composition, manufacturing approaches, thermal treatments, applied stress, and post-processing steps that are commonly used to process NiTiHfX and have an impact on the material AS are used as input parameters of the ML models. Since a broad data collection is performed the information for each input factor was sufficient for the use of the majority of the accessible information in the literature on NiTiHfX AS. Considering most of the regular NiTiHfX processing factors also enables the option of tuning additional characteristics of NiTiHfX in addition to the ASs. The work is unique as is the first to fully investigate the NiTiHfX actuation strain prediction. To forecast the NiTiHfX AS, a total of 901 data sets or 17,119 data points for eighteen inputs and one output were gathered, verified, and selected. Several machine-learning approaches were applied and joined to gather to guarantee robust modeling. The global model’s overall determination factor (R 2 ) was 0.96, suggesting the viability of the proposed NN model. Such a model opens the possibility of intelligent material selection and processing to maximize the AS or shape memory effect of NiTiHf SMA. Shape-memory alloy ; Materials science ; Quaternary ; Strain (injury) ; Alloy ; Computer science ; Metallurgy ; Geology Shape Memory Alloy Transformations ; Advanced Memory and Neural Computing ; Magnetic and transport properties of perovskites and related materials
Authors: Marinella de Giovanetti | Sondre H. Hopen Eliasson | Sigbjørn Løland Bore | Odile Eisenstein | Michele Cascella
Source: Chemical Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Morphology of lithium halides in tetrahydrofuran from molecular dynamics with machine learning potentials The preferred structures of lithium halides (LiX, with X = Cl, Br, I) in organic solvents have been the subject of a wide scientific debate, and a large variety of forms has been isolated and characterized by X-ray diffraction. The identified molecular scaffolds for LiX are diverse, often built on (LiX) <sub><i>n</i></sub> rings with a prevalence of rhomboidal arrangements and an appropriate number of solvent or Lewis base molecules coordinating the lithium ions. Much less is known about the structures of LiX in solution, limiting the understanding of the synergistic role of LiX in reactions with various organometallic complexes, as prominently represented by the turbo Grignard reaction. Here, we trained a machine learning potential on <i>ab initio</i> data to explore the complex conformational landscape for systems comprising four LiX moieties in tetrahydrofuran (THF). For all the considered halogens a large number of scaffolds were found at thermally accessible free energy values, indicating that LiX in solution are a diverse ensemble constituted of (LiX) <sub><i>n</i></sub> moieties of various sizes, completed by the appropriate number of coordinating THF. LiCl shows a preference for compact, pseudo-cubane Li<sub>4</sub>Cl<sub>4</sub>(THF)<sub>4</sub> structures, coexisting with open rings. At concentrations close to the solubility limit, LiCl forms hexagonal structures, in analogy with literature observations on pre-nucleating NaCl. LiBr tends to favour less compact, more solvated aggregates. LiI significantly differs from the two other cases, producing highly solvated, monomeric, dimeric, or linear structures. This study provides a comprehensive view of LiX in organic solvent, revealing dynamical polymorphism that is not easily observable experimentally. Tetrahydrofuran ; Halide ; Lithium (medication) ; Morphology (biology) ; Molecular dynamics ; Dynamics (music) ; Chemical physics ; Chemistry ; Computational chemistry ; Materials science ; Physics ; Organic chemistry ; Geology ; Psychology ; Paleontology Machine Learning in Materials Science ; Crystallography and molecular interactions ; Crystallization and Solubility Studies
Authors: Waldemar Rączka | Marek Sibielak
Source: Materials (article, 2024-06-25)
Field: Materials Science — Materials Chemistry

Model of Shape Memory Alloy Actuator with the Usage of LSTM Neural Network Shape Memory Alloys (SMAs) are used to design actuators, which are one of the most fascinating applications of SMA. Usually, they are on-off actuators because, in the case of continuous actuators, the nonlinearity of their characteristics is the problem. The main problem, especially in control systems in these actuators, is a hysteretic loop. There are many models of hysteresis, but from a control theory point of view, they are not helpful. This study used an artificial neural network (ANN) to model the SMA actuator hysteresis. The ANN structure and training method are presented in the paper. Data were generated from the Preisach model for training. This approach allowed for quick and controllable data generation, making experiments thoroughly planned and repeatable. The advantage and disadvantage of this approach is the lack of disturbances. The paper's main goal is to model an SMA actuator. Additionally, it explores whether and how an ANN can describe and model the hysteresis loop. A literature review shows that ANNs are used to model hysteresis, but to a limited extent; this means that the hysteresis loop was modelled with a hysteretic element. SMA* ; Shape-memory alloy ; Actuator ; Hysteresis ; Artificial neural network ; Control theory (sociology) ; Nonlinear system ; Computer science ; Control engineering ; Point (geometry) ; Artificial muscle ; Artificial intelligence ; Control (management) ; Engineering ; Mathematics ; Algorithm ; Physics Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Topology Optimization in Engineering
Authors: Weimin Chen | Jinfeng Ling | Kewu Bai | Kaihong Zheng | Fuxing Yin | Lijun Zhang | Yong Du
Source: Transactions of Nonferrous Metals Society of China (article, 2024-10-01)
Field: Materials Science — Materials Chemistry

High-throughput studies and machine learning for design of β titanium alloys with optimum properties Based on experimental data, machine learning (ML) models for Young’s modulus, hardness, and hot-working ability of Ti-based alloys were constructed. In the models, the interdiffusion and mechanical property data were high-throughput re-evaluated from composition variations and nanoindentation data of diffusion couples. Then, the Ti−(22±0.5)at.%Nb−(30±0.5)at.%Zr−(4±0.5)at.%Cr (TNZC) alloy with a single body-centered cubic (BCC) phase was screened in an interactive loop. The experimental results exhibited a relatively low Young’s modulus of (58±4) GPa, high nanohardness of (3.4±0.2) GPa, high microhardness of HV (520±5), high compressive yield strength of (1220±18) MPa, large plastic strain greater than 30%, and superior dry- and wet-wear resistance. This work demonstrates that ML combined with high-throughput analytic approaches can offer a powerful tool to accelerate the design of multicomponent Ti alloys with desired properties. Moreover, it is indicated that TNZC alloy is an attractive candidate for biomedical applications. Throughput ; Materials science ; Titanium ; Titanium alloy ; Computer science ; Metallurgy ; Alloy ; Operating system Titanium Alloys Microstructure and Properties ; Metal and Thin Film Mechanics ; Additive Manufacturing Materials and Processes
Authors: Yuki Yamaguchi | Katsuhiro Nomura | Hiroyuki Shimada | Yasunobu Mizutani | Woosuck Shin | Hirofumi Sumi
Source: Ceramics International (article, 2024-05-11)
Field: Materials Science — Materials Chemistry

Machine learning optimization for a novel process of acid-base chemical densification of BaZrO3 ceramics Materials science ; Ceramic ; Extrapolation ; Relative density ; Process (computing) ; Base (topology) ; Bulk density ; Fabrication ; Cubic zirconia ; Analytical Chemistry (journal) ; Chemical engineering ; Composite material ; Sintering ; Computer science ; Chromatography ; Soil science ; Chemistry Catalytic Processes in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Advanced ceramic materials synthesis
Authors: Kishansinh Rathod | Amit Kumar Choudhary | Andreas Jansche | G. Ketzer‐Raichle | Timo Bernthaler | Gerhard Schneider
Source: Materials Characterization (article, 2024-09-14)
Field: Materials Science — Materials Chemistry

GeGra: Approaching a generic model for quantitative grain size analysis from materials microscopy data using deep learning Grain size has a significant impact on the properties of materials, and is crucial for predicting material properties. Traditional grain size measurement relies heavily on human operators, leading to subjective results, and existing machine learning methods are typically material-specific, requiring significant labeling and training efforts for each new material. This paper provides insight into developing a deep learning-based generic grain boundary detection model (GeGra) from different material micrographs. The model is trained on 1006 images from various microscopy techniques such as light optical, Kerr, and scanning electron microscopy, acquired at different magnifications for different materials such as copper, austenite, brass, sintered hard magnet, hard metal, bronze, nickel silver, and aluminum. The developed GeGra model effectively handles visual artifacts and substructures such as twin grains, which often pose challenges for material-specific, state-of-the-art grain boundary segmentation models. The developed model achieved an IoU score of 69 % on a diverse test set and enables accurate grain size analysis using external image analysis software in less than one minute, according to ASTM standards, which is more than 5 times faster than the manual method. The developed model prioritizes generality with objective that it can have broader applicability for various materials instead of high-precision grain boundary detection. Additionally, the model has the potential to be a foundational tool for generalized grain size analysis in material microscopy, reducing the effort required for such analysis and assisting both material science experts and machine learning users. • GeGra is a generic grain boundary detection model valid across diverse materials. • It overcomes material-specific limitations in ML-assisted automated grain size analysis. • Robust to visual artifacts in microscopy images and substructures within the grains. • Enables automated grain size analysis using third-party software, more than 5× faster than manual methods. • Potential to be a foundational model for materials microscopy tasks specifically for grain boundary detection tasks. Materials science ; Microscopy ; Grain size ; Nanotechnology ; Metallurgy ; Optics Machine Learning in Materials Science ; Advanced Materials Characterization Techniques
Authors: Waleed H. El-Garaihy | A. I. Alateyah | Majed O. Alawad | Fahad N. Alsunaydih | Samar El‐Sanabary | Mohamed S. El‐Asfoury | Haitham S. Alhumud | Hanan Kouta
Source: Materials Today Communications (article, 2024-06-13)
Field: Materials Science — Metals and Alloys

Optimization of ECAP parameters of ZX30 alloy using feature engineering assisted machine learning and response surface approaches Materials science ; Alloy ; Feature (linguistics) ; Feature engineering ; Response surface methodology ; Metallurgy ; Mechanical engineering ; Composite material ; Artificial intelligence ; Machine learning ; Computer science ; Deep learning Hydrogen embrittlement and corrosion behaviors in metals ; Aluminum Alloy Microstructure Properties ; Metal and Thin Film Mechanics
Authors: Hui Zhan | Min Wang | Jiacheng Wang | Jialu Chen | Xiang Yin | Yunliang Yue
Source: Journal of Physics D Applied Physics (article, 2024-05-01)
Field: Materials Science — Materials Chemistry

Predicting the properties of perovskite materials by improved compositionally restricted attention-based networks and explainable machine learning Abstract Understanding the unique properties of perovskite materials is crucial in advancing solar energy technologies. Factors like heat of formation and bandgap significantly influence the light absorption capability and stability of perovskite solar cells. However, it is time-consuming and labor-intensive to obtain the properties of perovskites using traditional experimental or high-throughput computational methods. As a prospective method, machine learning can find regularities in the given training data and give accurate prediction results. In this article, we use deep learning models based on attention mechanisms and elemental features to predict the heat of formation and bandgap of perovskite materials. Random Forest and Gradient Boosted Regression Tree models have also been used for interpretable predictions of properties. The compositionally restricted attention-based network was improved by introducing a densely connected network and optimizing the network structure to increase data processing capabilities. The experiment results show that the mean absolute errors of the heat of formation and bandgap on the test sets are decreased by 5.77% and 3.37% respectively. The optimized model also shows better performance when used for classification tasks. In addition, we use the gradient boosting regression tree model and the shapley additive explanations tool to conduct an interpretable analysis, explaining the impact of different features on the predictions of the properties. Perovskite (structure) ; Materials science ; Computer science ; Artificial intelligence ; Psychology ; Chemical engineering ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides