jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Junkil Park | Wonseok Lee | Jihan Kim
Source: ACS Applied Materials & Interfaces (article, 2024-10-10)
Field: Materials Science — Materials Chemistry

Large-Scale Construction and Analysis of Amorphous Porous Polymer Network Materials In recent decades, data-driven methodologies have emerged as irreplaceable tools in materials science, particularly for elucidating structure-property relationships and facilitating the discovery of novel materials. However, despite the rapid development witnessed in other domains, amorphous materials have received relatively less attention in this context. The disordered atomic structure of amorphous materials resulting from irreversible reactions between building blocks has posed a difficulty in structural modeling, leading to a lack of databases that accurately reflect the amorphous nature of these materials. In this work, a database composed of 10,237 porous polymer networks (PPNs) was constructed from self-assembly simulations, resulting in the largest database of PPNs considering their amorphous characteristics. Through the distinct differences observed in comparison with existing databases, we emphasize that carefully considering the structural disorder of PPNs is essential for accurately characterizing their chemical behaviors. Machine learning models trained on the constructed database have confirmed that the macroscopic properties of amorphous PPNs can be predicted solely from the atomic structures of their monomers, implying that the characteristics of previously unseen PPNs can be assessed without the need for additional self-assembly simulations. Amorphous solid ; Materials science ; Context (archaeology) ; Nanotechnology ; Porosity ; Polymer ; Atomic units ; Scale (ratio) ; Computer science ; Physics ; Chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science
Authors: Warda Fella Belaid | Azeddine Dekhira | Philippe Lesot | Ouassila Ferroukhi
Source: Journal of Chromatography A (article, 2024-10-28)
Field: Materials Science — Materials Chemistry

Development of deep learning software to improve HPLC and GC predictions using a new crown-ether based mesogenic stationary phase and beyond Chemistry ; Crown ether ; Mesogen ; Chromatography ; Stationary phase ; High-performance liquid chromatography ; Software ; Phase (matter) ; Ether ; Organic chemistry ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Juan M. Maffi | J.M. Sosa | Diana A. Estenoz
Source: Chemical Engineering Science (article, 2024-02-13)
Field: Materials Science — Materials Chemistry

Development of hybrid (AI/first principle) models for the optimization of styrene polymerization processes Styrene ; Polymerization ; Materials science ; Polymer science ; Computer science ; Chemical engineering ; Engineering ; Copolymer ; Polymer ; Composite material Machine Learning in Materials Science ; Process Optimization and Integration ; Computational Drug Discovery Methods
Authors: D. Vignesh | Ela Rout
Source: The Journal of Physical Chemistry C (article, 2024-09-24)
Field: Materials Science — Materials Chemistry

Custom Hybrid Activation Function over Delocalization Error for Gap State Predictions of A<sub>2</sub>CeZrO<sub>6</sub> (A = Ba<sup>2+</sup>, Sr<sup>2+</sup>, Ca<sup>2+</sup>, Mg<sup>2+</sup>) Proton Conductors: A First-Principles and Machine Learning Approach The crystal symmetry and band structure are two essential identifiers for a desirable proton-conducting electrolyte in fuel cell technology. However, structural and electrophysical inconsistency among cerium-based polymorphs originates due to multifacet issues ranging from cationic disorder (Ce4+ → Ce3+) to proton–polaron interaction. As a result, the distorted symmetry of Ca2CeZrO6 and Mg2CeZrO6 compounds displays an ambiguous trend in the forbidding gap due to differences in the spatial arrangement of atoms, chemical bonding, and hybridization. Besides material-driven limitations, the widely used density functional theory (DFT) simulations also face the band gap problem due to the lack of matrix element effects, final state effects, finite temperature offsets, and delocalization error by lower generation functionals (local density approximation (LDA) and generalized gradient approximation (GGA)). However, accurate estimations with higher rung functionals (hybrid (HSE06)) are often accompanied by critical limitations, including material-dependent response and exhaustive computations. Meanwhile, the sensitivity of an effective Hubbard parameter (Ueff) within the semiempirical DFT+U approach also limits their practical utility among complex oxides. As a result, we illustrate a mapping between the chemo-structural fingerprints and electronic band energy of distinct proton conductors using a state-of-the-art machine learning (ML) approach as a statistical learning and regression technique to combat the empirical approximations and derivative discontinuity of DFT simulations. In this study, we showcase the performance efficiency and limitations (training instability and generalization loss) of the designed artificial neural network (ANN) using a single activation function with a plausible solution via the custom hybrid activation function. According to our outcomes, the feature engineering of the hybrid activation function assists in overcoming numerical instability, ill conditionality, and dying gradient via adaptive neuro-controllers (tunable parameters) and nonlinear optimization of the loss landscape. As a result, the customized network was further used to model the potential energy surface (PES) of distinct proton conductors under their operating regimes (300–700 °C) to examine the proton landscape and transport characteristics with promising accuracy. Function (biology) ; Physics ; State (computer science) ; Atomic physics ; Crystallography ; Materials science ; Chemistry ; Mathematics ; Algorithm Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Amir Omranpour | Jörg Behler
Source: Journal of Physics Condensed Matter (article, 2024-12-13)
Field: Materials Science — Materials Chemistry

A high-dimensional neural network potential for Co<sub>3</sub>O<sub>4</sub> The Co<sub>3</sub>O<sub>4</sub>spinel is an important material in oxidation catalysis. Its properties under catalytic conditions, i.e. at finite temperatures, can be studied by molecular dynamics simulations, which critically depend on an accurate description of the atomic interactions. Due to the high complexity of Co<sub>3</sub>O<sub>4</sub>, which is related to the presence of multiple oxidation states of the cobalt ions, to date<i>ab initio</i>methods have been essentially the only way to reliably capture the underlying potential energy surface, while more efficient atomistic potentials are very challenging to construct. Consequently, the accessible length and time scales of computer simulations of systems containing Co<sub>3</sub>O<sub>4</sub>are still severely limited. Rapid advances in the development of modern machine learning potentials (MLPs) trained on electronic structure data now make it possible to bridge this gap. In this work, we employ a high-dimensional neural network potential (HDNNP) to construct a MLP for bulk Co<sub>3</sub>O<sub>4</sub>spinel based on density functional theory calculations. After a careful validation of the potential, we compute various structural, vibrational, and dynamical properties of the Co<sub>3</sub>O<sub>4</sub>spinel with a particular focus on its temperature-dependent behavior, including the thermal expansion coefficient. Spinel ; Construct (python library) ; Ab initio ; Artificial neural network ; Statistical physics ; Molecular dynamics ; Electronic structure ; Density functional theory ; Chemistry ; Computer science ; Chemical physics ; Materials science ; Physics ; Computational chemistry ; Machine learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electrocatalysts for Energy Conversion
Authors: Sameera Nalin Venkat | Thomas G. Ciardi | Mingjian Lu | Preston C. DeLeo | Jube Augustino | Adam Goodman | Jayvic C. Jimenez | Anirban Mondal | F. Ernst | Christine A. Orme | Yinghui Wu | Roger H. French | Laura S. Bruckman
Source: Integrating materials and manufacturing innovation (article, 2024-02-20)
Field: Materials Science — Materials Chemistry

A General Materials Data Science Framework for Quantitative 2D Analysis of Particle Growth from Image Sequences Abstract Phase transformations are a challenging problem in materials science, which lead to changes in properties and may impact performance of material systems in various applications. We introduce a general framework for the analysis of particle growth kinetics by utilizing concepts from machine learning and graph theory. As a model system, we use image sequences of atomic force microscopy showing the crystallization of an amorphous fluoroelastomer film. To identify crystalline particles in an amorphous matrix and track the temporal evolution of the particle dispersion, we have developed quantitative methods of 2D analysis. 700 image sequences were analyzed using a neural network architecture, achieving 0.97 pixel-wise classification accuracy as a measure of the correctly classified pixels. The growth kinetics of isolated and impinged particles were tracked throughout time using these image sequences. The relationship between image sequences and spatiotemporal graph representations was explored to identify the proximity of crystallites from each other. The framework enables the analysis of all image sequences without the requirement of sampling for specific particles or timesteps for various materials systems. Pixel ; Amorphous solid ; Graph ; Biological system ; Computer science ; Crystallite ; Particle (ecology) ; Materials science ; Artificial intelligence ; Theoretical computer science ; Chemistry ; Crystallography Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; Electron and X-Ray Spectroscopy Techniques
Authors: Dharavathu Thanusha | Praveen Kumar Balguri
Source: AIP conference proceedings (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Optimizing wear and mechanical characteristics of jute fiber composites reinforced with silicon carbide and prediction using neural network technique This research article focuses on optimizing the strength and wear characteristics of nanocomposites strengthened with silicon carbide and jute fiber using neural network techniques. In this study, three different percentages of nanoparticles of SiC (3%, 6%, and 9%) were prepared and added to the jute fiber and epoxy resin as a base matrix to the composite. The hardness of the composites with and without reinforcement was measured, and it was observed that the upsurge in the percentage of nanoparticles of SiC led to an increase in composite hardness. The wear test was conducted based on L27 array of experiments, where the composition, load, speed, and sliding distance were taken as input parameters, and the specific rate of wear (Srw) was the response studied in this research. Taguchi analysis was performed to determine the optimal combination for minimizing the response. The findings indicated that the larger in the proportion of nanocomponents of SiC and reduction in load reduced the wear of the composites. Moreover, an machine learning based model was developed to forecast the outcomes. The established ANN model was highly accurate in predicting the responses, which demonstrates the potential of neural network techniques in optimizing the mechanical properties of nanocomposites. Overall, this study provides a comprehensive understanding of the consequence of nano SiC on the wear and mechanical characteristics of jute fiber material. The established ANN model can be used as a tool for forecasting the wear and mechanical properties of nanocomposites and can be further used for developing improved composites. The findings of this research can be beneficial in various industries, such as automotive, aerospace, and manufacturing, where the wear and mechanical properties of materials are critical factors. Materials science ; Composite material ; Silicon carbide ; Taguchi methods ; Composite number ; Artificial neural network ; Nanocomposite ; Fiber ; Orthogonal array ; Epoxy ; Advanced composite materials ; Computer science ; Machine learning Natural Fiber Reinforced Composites ; Tribology and Wear Analysis
Authors: Yuxuan Zhang | Zhenjie Li | Zhongkang Han | Runhai Ouyang
Source: Journal of Chemical Theory and Computation (article, 2024-08-01)
Field: Materials Science — Materials Chemistry

Global Optimization of Cation Ordering in Perovskites by Recommendation-Based Basin-Hopping Cation ordering in multication perovskites is related to many important material properties and performances, but computational determination of the cation ordering remains a major challenge. Here, we propose a new computational approach by introducing a machine learning recommender system into the basin-hopping framework (RBH) for optimizing cation ordering. Taking the electrocatalyst Ba<sub>0.5</sub>Sr<sub>0.5</sub>Co<sub>0.8</sub>Fe<sub>0.2</sub>O<sub>3</sub> (BSCF5582) as a showcase example, we found that the efficiency of RBH in identifying low-energy configurations outperforms the methods of cluster expansion and conventional basin-hopping. The RBH results revealed that the BSCF5582 catalyst tended to have a layered ordering of A-site cations and disordered B-site cations both in bulk and on the surfaces. Further, on the A-site-terminated surface, we found the segregation of large Ba atoms. Similarly, on the A-site- terminated surface of the recently developed Cs<sub>0.2</sub>Sr<sub>0.8</sub>Co<sub>0.4</sub>Fe<sub>0.6</sub>O<sub>3</sub> (CSCF2846) catalyst, layered ordering at the A-site and surface enrichment of large Cs atoms were also found. The layered ordering was robust against thermal effects, as found from molecular dynamics simulations at 800 K. This work provides a new approach for thermodynamic global optimization of chemical ordering in multicomponent materials. Computer science ; Recommender system ; Structural basin ; Electrocatalyst ; Materials science ; Chemical physics ; Chemistry ; Geology ; World Wide Web ; Physical chemistry ; Electrochemistry Machine Learning in Materials Science ; Perovskite Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Yan Qi | Lifeng Zhao | Haiqiu Tang | Lei Zhang | Rafiqul Gani
Source: Computers & Chemical Engineering (article, 2024-11-09)
Field: Chemistry — Spectroscopy

Computer aided formulation design based on molecular dynamics simulation: Detergents with fragrance Molecular dynamics ; Computer science ; Dynamics (music) ; Biochemical engineering ; Biological system ; Engineering ; Chemistry ; Computational chemistry ; Physics ; Biology Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Xuan Zhang | Cheng Chen | Xiaoting Wang | Haitao Jiang | Wei Zhao | Xuefeng Cui
Source: Frontiers of Computer Science (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

MetaGIN: a lightweight framework for molecular property prediction Computer science ; Benchmark (surveying) ; Molecular graph ; Property (philosophy) ; Graph ; Quantum chemical ; Theoretical computer science ; Data mining ; Algorithm ; Artificial intelligence ; Molecule ; Machine learning ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Haowei Feng | Tian Hua
Source: Journal of Chemical Information and Modeling (article, 2024-10-04)
Field: Materials Science — Materials Chemistry

Improving Crystal Property Prediction from a Multiplex Graph Perspective Graph neural networks (GNNs) have proven to be effective tools for the rapid and accurate prediction of crystal properties. While most existing methods focus on enriching representations of crystal structures, they do not deeply explore the characteristics of crystal graphs and leverage their intrinsic information from a data science perspective. In this work, we propose the potential multiplex crystal graph neural network (PMCGNN) for crystal property prediction. Based on the characteristics of crystal graphs, we reconstruct the crystal graph into a multiplex graph that includes two views: a global crystal graph embodying infinite potentials and a local crystal graph capturing local atomic interactions. We employ graph transformers (GTs) and message passing neural networks (MPNNs) architectures to learn the atomic representations of these two perspectives. Specifically, we augment the GT by incorporating positional encodings and structural encodings from the local crystal graph. This approach promotes interaction between the two perspectives, enabling the model to learn both node positional and graph structural information from different viewpoints through an attention mechanism. As a result, it enhances the model's ability to learn crystal representations. We conduct comprehensive experiments on the JARVIS and the Materials Project data sets for evaluation. Results show that PMCGNN presents superior performance in 9 crystal prediction tasks while maintaining reasonable computational expense. Multiplex ; Perspective (graphical) ; Property (philosophy) ; Graph ; Computer science ; Theoretical computer science ; Mathematics ; Artificial intelligence ; Bioinformatics ; Epistemology ; Philosophy ; Biology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Yang Hu | Kai Wang | Robert Spatschek
Source: npj Materials Degradation (article, 2024-10-30)
Field: Materials Science — Materials Chemistry

A data-driven strategy for phase field nucleation modeling Abstract We propose a data-driven strategy for parameter selection in phase field nucleation models using machine learning and apply it to oxide nucleation in Fe-Cr alloys. A grand potential-based phase field model, incorporating Langevin noise, is employed to simulate oxide nucleation and benchmarked against the Johnson-Mehl-Avrami-Kolmogorov model. Three independent parameters in the phase field simulations (Langevin noise strength, numerical grid discretization and critical nucleation radius) are identified as essential for accurately modeling the nucleation behavior. These parameters serve as input features for machine learning classification and regression models. The classification model categorizes nucleation behavior into three nucleation density regimes, preventing invalid nucleation attempts in simulations, while the regression model estimates the appropriate Langevin noise strength, significantly reducing the need for time-consuming trial-and-error simulations. This data-driven approach improves the efficiency of parameter selection in phase field models and provides a generalizable method for simulating nucleation-driven microstructural evolution processes in various materials. Nucleation ; Field (mathematics) ; Phase (matter) ; Computer science ; Physics ; Mathematics ; Thermodynamics Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Metallurgical Processes and Thermodynamics
Authors: Hans‐Beat Bürgi
Source: Structural Dynamics (article, 2024-03-01)
Field: Chemistry — Physical and Theoretical Chemistry

The Cambridge Structural Database and structural dynamics With the availability of the computer readable information in the Cambridge Structural Database (CSD), wide ranging, largely automated comparisons of fragment, molecular, and crystal structures have become possible. They show that the distributions of interatomic distances, angles, and torsion angles for a given structural fragment occurring in different environments are highly correlated among themselves and with other observables such as spectroscopic signals, reaction and activation energies. The correlations often extend continuously over large ranges of parameter values. They are reminiscent of bond breaking and forming reactions, polyhedral rearrangements, and conformational changes. They map-qualitatively-the regions of the structural parameter space in which molecular dynamics take place, namely, the low energy regions of the respective (free) energy surfaces. The extension and continuous nature of the correlations provides an organizing principle of large groups of structural data and suggests a reconsideration of traditional definitions and descriptions of bonds, "nonbonded" and "noncovalent" interactions in terms of Lewis acids interacting with Lewis bases. These aspects are illustrated with selected examples of historic importance and with some later developments. It seems that the amount of information in the CSD (and other structural databases) and the knowledge on the nature of, and the correlations within, this body of information should allow one-in the near future-to make credible interpolations and possibly predictions of structures and their properties with machine learning methods. Observable ; Molecular dynamics ; Computer science ; Space (punctuation) ; Torsion (gastropod) ; Statistical physics ; Database ; Chemistry ; Chemical physics ; Computational chemistry ; Physics ; Quantum mechanics Crystallography and molecular interactions ; Molecular spectroscopy and chirality ; Computational Drug Discovery Methods
Authors: Hossein Jalaei Salmani
Source: Journal of Thermal Analysis and Calorimetry (article, 2024-10-14)
Field: Materials Science — Materials Chemistry

Molecular interactions consideration using Hansen solubility parameters in a multilayer perceptron artificial neural network for flash point prediction of organic liquid mixtures Flash point ; Artificial neural network ; Solubility ; Flash (photography) ; Multilayer perceptron ; Hildebrand solubility parameter ; Perceptron ; Biological system ; Materials science ; Chemistry ; Artificial intelligence ; Thermodynamics ; Chromatography ; Computer science ; Organic chemistry ; Physics ; Optics Crystallization and Solubility Studies ; Analytical Chemistry and Chromatography ; Chemical Thermodynamics and Molecular Structure
Authors: Emilio García Méndez | Rocío Semino
Source: Journal of Materials Chemistry A (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Microscopic mechanism of thermal amorphization of ZIF-4 and melting of ZIF-zni revealed <i>via</i> molecular dynamics and machine learning techniques The microscopic mechanism of ZIF-4 amorphization and ZIF-zni melting are elucidated via a combination of data science and computer simulation approaches. Gradual density changes and the breaking and formation of Zn–N bonds are key to both processes. Molecular dynamics ; Mechanism (biology) ; Chemical physics ; Materials science ; Thermal ; Melting point ; Nanotechnology ; Chemistry ; Computational chemistry ; Thermodynamics ; Physics Machine Learning in Materials Science ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography