jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tao Du | Xuan Ge | Fengming Cao | Han Liu | Caijuan Shi | Junwei Ding | Daming Sun | Qiangqiang Zhang | Yuanzheng Yue | Morten M. Smedskjær
Source: Chemistry of Materials (article, 2024-06-03)
Field: Materials Science — Ceramics and Composites

Structural Origin of the Deformation Propensity of Zeolitic Imidazolate Framework Glasses Zeolitic imidazolate framework (ZIF) glasses featuring nanoscale porosity have attracted significant attention due to their potential applications in catalysis, energy storage, gas sorption, and separation. However, their mechanical properties may limit some of these applications. In this work, we investigate the structural origins of the variation of mechanical properties of zinc-based ZIF-62 (ZnIm2–xbImx) glasses with different benzimidazolate (bIm) to imidazolate (Im) ratios. This is achieved using large-scale molecular dynamics simulations with a recent machine learning force field. We find that the simulated ZIF glass structures match those determined using X-ray total scattering. The relatively large bIm group is found to hinder the reconstruction of the coordination network during quenching of the ZIF melts, leading to more disordered Zn tetrahedra. Both Young’s modulus and fracture toughness decrease with an increase in bIm content. Upon fracturing, all of the organic linkers remain intact, while Zn–N bond switching dissipates the strain energy. By correlating the atomic dynamics with the static structure, we find that the deformation propensity of ZIF glass is correlated with Zn mobility, which is in turn determined by the initial atomic volume before deformation across a variety of glass compositions and strain values. These findings could be helpful for designing more fracture-resistant metal–organic framework glasses in the future. Zeolitic imidazolate framework ; Imidazolate ; Materials science ; Deformation (meteorology) ; Chemistry ; Crystallography ; Nanotechnology ; Chemical engineering ; Composite material ; Inorganic chemistry ; Physical chemistry ; Metal-organic framework ; Adsorption Glass properties and applications ; Clay minerals and soil interactions ; Zeolite Catalysis and Synthesis
Authors: Buyu Zhang | Enze Chen | Mark Asta
Source: Computational Materials Science (article, 2024-12-10)
Field: Materials Science — Materials Chemistry

Oxygen grain-boundary segregation in HCP Ti — Computational investigations using an atomic cluster expansion potential The segregation energy of oxygen interstitial solutes to grain boundaries (GBs) in hexagonal close-packed (HCP) titanium is investigated through atomistic simulations based on a machine-learning interatomic potential (MLIP). The Ti–O MLIP is developed for titanium with interstitial oxygen solutes up to a concentration of 20 at.%. It is based on the formalism of the atomic cluster expansion (ACE), trained on an extensive dataset of density functional theory calculations exploring over 200,000 atomic environments for Ti and O interstitials. The ACE MLIP is used to compute oxygen GB segregation energies in 4685 different symmetric tilt GBs with [ 0001 ] , [ 1 1 ̄ 00 ] and [ 1 2 ̄ 10 ] tilt axes. The segregation energies span a range of -0.2 eV to 2.0 eV, and over 90 % of the 4685 GBs display one or more sites with negative (attractive) segregation energies. The lowest-energy sites are found to be those with coordination numbers and Voronoi indices most similar to that of the equilibrium octahedral site in the bulk HCP Ti structure. We further explore the efficacy of crystal-graph convolutional neural network ML models for predicting segregation energies based solely on information about the local atomic environment. • The Ti–O MLIP is developed for titanium with interstitial oxygen solutes up to a concentration of 20 at.% based on atomic cluster expansion (ACE) format. • The MLIP is shown to reproduce with reasonable accuracy properties including elastic constants, twin boundary energies, and grain-boundary segregation energies of oxygen solutes. • Important features and CGCNN prediction models are investigated for oxygen GB segregation energies in 4685 different symmetric tilt GBs. Grain boundary ; Cluster (spacecraft) ; Oxygen ; Cluster expansion ; Materials science ; Chemical physics ; Crystallography ; Metallurgy ; Chemistry ; Thermodynamics ; Physics ; Microstructure ; Computer science Nuclear Materials and Properties ; Machine Learning in Materials Science ; Metal and Thin Film Mechanics
Authors: Fuzuo Wu | Zhixiao Liu | Yangchun Chen | Xun Guo | Jianming Xue | Yuhao Li | Haoxuan Huang | Hong-Bo Zhou | Huiqiu Deng
Source: Computational Materials Science (article, 2024-10-17)
Field: Materials Science — Materials Chemistry

Deep learning-based neural network potential for investigating the synergistic effect of H and He in BCC-Fe Artificial neural network ; Artificial intelligence ; Nanotechnology ; Psychology ; Materials science ; Computer science Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Physics and Applications
Authors: Luca Brugnoli | Maxime Ducamp | François‐Xavier Coudert
Source: The Journal of Physical Chemistry C (article, 2024-11-13)
Field: Materials Science — Materials Chemistry

Neural Network-Based Interatomic Potential for the Study of Thermal and Mechanical Properties of Siliceous Zeolites Molecular dynamics simulations of zeolites are commonly employed for the characterization of their framework dynamics and response to the application of temperature and pressure. While classical interatomic potentials are commonly used for this task, they offer a description of the interactions in the system with limited accuracy. Density functional theory, meanwhile, is accurate, but its high computational expense limits its scalability for large systems or long dynamics. Recent advances in machine learning interatomic potentials, trained on computational data obtained at the quantum chemical level, offer a promising alternative combining high accuracy with computational efficiency. In this study, we developed an MLIP specifically for pure silica zeolites, trained on data from high-temperature ab initio MD simulations across various zeolitic topologies. This MLIP was then applied to predict structural properties, thermal expansion, and pressure response of different zeolites, demonstrating its potential for accuracy and generalizability in simulations of topologies beyond its initial training set. Interatomic potential ; Thermal ; Materials science ; Artificial neural network ; Nanotechnology ; Molecular dynamics ; Chemistry ; Computer science ; Thermodynamics ; Physics ; Computational chemistry ; Artificial intelligence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Zeolite Catalysis and Synthesis
Authors: Heng Hu | Miaomiao Huang | Bin Wang | Didi Zhang | Tao Tan | Kang Yan | Dawei Wu
Source: Ceramics International (article, 2024-10-22)
Field: Materials Science — Materials Chemistry

Multi-iteration active learning for the composition design of potassium–sodium niobate ceramics with enhanced piezoelectric coefficient Materials science ; Piezoelectricity ; Potassium ; Potassium niobate ; Ceramic ; Composition (language) ; Sodium ; Composite material ; Optoelectronics ; Ferroelectricity ; Metallurgy ; Dielectric Ferroelectric and Piezoelectric Materials ; Piezoelectric Actuators and Control ; Acoustic Wave Resonator Technologies
Authors: Jie Zhang | Yi Kong | Li Chen | Nikola Koutná | P.H. Mayrhofer
Source: Acta Materialia (article, 2024-06-25)
Field: Materials Science — Materials Chemistry

Predicting the formation enthalpy and phase stability of (Ti,Al,TM)N (TM = III-VIB group transition metals) by high-throughput ab initio calculations and machine learning Materials science ; Enthalpy ; Ab initio ; Group (periodic table) ; Ab initio quantum chemistry methods ; Transition metal ; Stability (learning theory) ; Phase transition ; Thermodynamics ; Phase (matter) ; Chemical stability ; Throughput ; Physical chemistry ; Crystallography ; Catalysis ; Machine learning ; Chemistry ; Physics ; Organic chemistry ; Computer science Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Thermal and Kinetic Analysis
Authors: Ondrej Vacek | Tomáš Gergeľ | Tomáš Bucha | Radovan Gracovský | Miloš Gejdoš
Source: Forests (article, 2024-12-15)
Field: Chemistry — Organic Chemistry

Automatic Wood Species Classification and Pith Detection in Log CT Images This article focuses on the need for digitalization in the forestry and timber sector using information from CT scans of logs. The National Forest Centre (Slovak Republic) operates a unique 3D CT scanner for wooden logs at the Stráž Biotechnology Park. This real-time scanner generates a 3D model of a log, displaying the wood’s internal features/defects. To optimize log-cutting plans effectively, it is necessary to automatically detect and classify these features and defects in real time, leveraging computer vision principles. Artificial intelligence, specifically neural networks, addresses this need by enabling solutions for tasks of this nature. Building a highly efficient neural network for detecting wood features and defects requires creating a database of log scans and training the network on these data. This is a time-intensive process, as it involves manually marking internal features and defects on hundreds of CT scans of various wood types. A functional neural network for detecting internal wood defects represents a significant advancement in sector digitalization, paving the way for further automation and robotization in wood processing. For the forestry sector to remain competitive, efficiently process raw materials, and improve product quality, the effective application of CT scanning technology is essential. This technological innovation aligns the sector more closely with leaders in other fields, such as the automotive, engineering, and metalworking industries. Pith ; Pattern recognition (psychology) ; Artificial intelligence ; Environmental science ; Computer science ; Forestry ; Mathematics ; Botany ; Biology ; Geography Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Microbial Metabolism and Applications
Authors: Al Mamun | Md Rasel Hossain | Md Ashik Iqbal | Md. Ziaul Haque | Mst Mahfuza Sharmin
Source: Material Science & Engineering International Journal (article, 2024-10-08)
Field: Materials Science — General Materials Science

Recent developments in the synthesis of composite materials for aerospace: case study Composite materials have emerged as a cornerstone of aerospace engineering due to their exceptional properties, such as high strength-to-weight ratios, corrosion resistance, and superior mechanical performance. This research paper provides an in-depth analysis of recent advancements in synthesizing composite materials tailored for aerospace applications. We review cutting-edge manufacturing techniques, explore the integration of nanomaterials and sustainable biocomposites, and highlight five recent case studies illustrating practical applications. This paper also addresses current challenges in developing and manufacturing composites. We also discuss the insight into future research directions, including the potential of AI and machine learning to revolutionize composite material design. Aerospace ; Composite number ; Materials science ; Engineering ; Aerospace engineering ; Composite material Material Properties and Applications ; Additive Manufacturing and 3D Printing Technologies ; Polymer Nanocomposites and Properties
Authors: Özgül Çimen Mesutoğlu
Source: Environmental Monitoring and Assessment (article, 2024-06-19)
Field: Chemistry — Analytical Chemistry

The use of artificial neural network for modelling adsorption of Congo red onto activated hazelnut shell Adsorption ; Congo red ; Activated carbon ; Freundlich equation ; Aqueous solution ; Shell (structure) ; Chemistry ; Kinetic energy ; Fourier transform infrared spectroscopy ; Artificial neural network ; Nuclear chemistry ; Materials science ; Analytical Chemistry (journal) ; Chromatography ; Chemical engineering ; Organic chemistry ; Composite material ; Computer science ; Physics Dye analysis and toxicity ; Adsorption and biosorption for pollutant removal
Authors: J. Kumaraswamy | K Thirumalesh | Avinash Ashok | N B Shankar | S R Praveen | Sukhateertha
Source: Materials Today Communications (article, 2024-11-01)
Field: Materials Science — Ceramics and Composites

Research article on thermal evaluation of Al-Si3N4 composites using Machine Learning Materials science ; Composite material ; Thermal ; Thermodynamics Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Additive Manufacturing and 3D Printing Technologies
Authors: Sercan Yalçın | Muhammed Yıldırım | Bilal Alataş
Source: PeerJ Computer Science (article, 2024-05-31)
Field: Materials Science — Materials Chemistry

Advanced convolutional neural network modeling for fuel cell system optimization and efficiency in methane, methanol, and diesel reforming Fuel cell systems (FCSs) have been widely used for niche applications in the market. Furthermore, the research community has worked on using FCSs for different sectors, such as transportation, stationary power generation, marine and maritime, aerospace, military and defense, telecommunications, and material handling. The reformation of various fuels, such as methanol, methane, and diesel can be utilized to generate hydrogen for FCSs. This study introduces an advanced convolutional neural network (CNN) model designed to accurately forecast hydrogen yield and carbon monoxide volume percentages during the reformation processes of methane, methanol, and diesel. Moreover, the CNN model has been tailored to accurately estimate methane conversion rates in methane reforming processes. The proposed CNN models are created by combining the 3D-CNN and 2D-CNN models. The Keras Tuner approach in Python is employed in this study to find the ideal values for different hyperparameters such as batch size, learning rate, time steps, and optimization method selection. The accuracy of the proposed CNN model is evaluated by using the root mean square error (RMSE), mean absolute percentage error (MAE), mean absolute error (MAE), and R2. The results indicate that the proposed CNN model is better than other artificial intelligence (AI) techniques and standard CNN for performance estimation of reforming processes of methane, diesel, and methanol. The results also show that the suggested CNN model can be used to accurately estimate critical output parameters for reforming various fuels. The proposed method performs better in CO prediction than the support vector machine (SVM), with an R2 of 0.9989 against 0.9827. This novel methodology not only improves performance estimation for reforming processes but also provides a valuable tool for accurately estimating output parameters across various fuel types. Methane ; Mean absolute percentage error ; Diesel fuel ; Convolutional neural network ; Mean squared error ; Computer science ; Methane reformer ; Artificial neural network ; Process engineering ; Artificial intelligence ; Steam reforming ; Engineering ; Mathematics ; Chemistry ; Hydrogen ; Automotive engineering ; Hydrogen production ; Statistics Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Catalysts for Methane Reforming
Authors: Aqib Fawwaz Mohd Amidon | Zakiah Mohd Yusoff | Nurlaila Ismail | Mohd Nasir Taib
Source: Journal of Advanced Research in Applied Sciences and Engineering Technology (article, 2024-07-18)
Field: Chemistry — Organic Chemistry

KNN Euclidean Distance Model Performance on Aquilaria Malaccensis Oil Qualities Agarwood is a highly prized and useful forest product. In Southeast Asia, Aquilaria Malaccensis species are typically the most prevalent. This agarwood is usually used in the manufacture of medicine, the production of high-quality perfumes, and is also used in religious and ethnic ceremonies. According to the study, the agarwood grading process entirely relies on human senses. The graders will evaluate the agarwood oil's color concentration with their unaided eyes and evaluate the amount of scent emitted with their noses. This approach has been proven to have several limitations, including that the graders' health will suffer, the grading procedure will take a very long time, and will consume high operating expenses. Therefore, an established standard grading model that is faster, easier, and more accurate needs to be introduced. Previous researchers found that chemical compounds contained in agarwood oil can be used to grade the quality of agarwood oil. Therefore, this study has used the data obtained that contains significant chemical compounds as input to develop a grading model with the support of machine learning and artificial intelligence, which is the k-Nearest Neighbor (KNN) technique. The output of this grading model is the classification of agarwood oil according to its quality, which includes four different grades. The results of the implementation of KNN grading of this model found that this model has very excellent performance by obtaining 100% for each measurement for the performance evaluator of the classifier. Euclidean distance ; Mathematics ; Computer science ; Artificial intelligence Wood and Agarwood Research ; Advanced Image and Video Retrieval Techniques ; Tea Polyphenols and Effects
Authors: Tiago S. Domingues | Sarwar Hussain | Amir Haji‐Akbari
Source: The Journal of Physical Chemistry Letters (article, 2024-01-29)
Field: Materials Science — Materials Chemistry

Divergence among Local Structure, Dynamics, and Nucleation Outcome in Heterogeneous Nucleation of Close-Packed Crystals Heterogeneous crystal nucleation is the dominant mechanism of crystallization in most systems, yet its underlying physics remains an enigma. While emergent interfacial crystalline order precedes heterogeneous nucleation, its importance in the nucleation mechanism is unclear. Here, we use path sampling simulations of two model systems to demonstrate that crystalline order in its traditional sense is not predictive of the outcome of the heterogeneous nucleation of close-packed crystals. Consequently, structure-based collective variables (CVs) that reliably describe homogeneous nucleation can be poor descriptors of heterogeneous nucleation. This divergence between structure and nucleation outcome is accompanied by an intriguing dynamical anomaly, wherein low-coordinated crystalline particles outpace their liquid-like counterparts. We use committor analysis, high-throughput screening, and machine learning to devise CV optimization strategies and present suitable structural heuristics within the metastable fluid for CV prescreening. Employing such optimized CVs is pivotal for properly characterizing the mechanism of heterogeneous nucleation in metallic and colloidal systems. Nucleation ; Metastability ; Crystallization ; Materials science ; Chemical physics ; Statistical physics ; Chemistry ; Physics ; Thermodynamics Material Dynamics and Properties ; Crystallization and Solubility Studies ; Theoretical and Computational Physics
Authors: Qingyang Fan | Wenjiao Li | Nan Wu | Yingbo Zhao | Yanxing Song | Xinhai Yu | Sining Yun
Source: Chinese Journal of Physics (article, 2024-04-24)
Field: Materials Science — Materials Chemistry

Study of the novel boron nitride polymorphs: First- principles calculations and machine learning Boron nitride ; Materials science ; Shear modulus ; Anisotropy ; Boron ; Stability (learning theory) ; Elastic modulus ; Modulus ; Bulk modulus ; Nitride ; Thermodynamics ; Machine learning ; Nanotechnology ; Composite material ; Physics ; Computer science ; Optics Boron and Carbon Nanomaterials Research ; Machine Learning in Materials Science ; Metal and Thin Film Mechanics
Authors: Ailin Li | Chengwen Bin | Xu Hou | Luocheng Liao | Yunya Liu | Bo Wang | Jie Wang
Source: Ceramics International (article, 2024-11-27)
Field: Materials Science — Materials Chemistry

Machine learning-assisted design of CaBi2Nb2O9-based ceramics with high piezoelectricity and thermal stability Materials science ; Piezoelectricity ; Thermal stability ; Ceramic ; Stability (learning theory) ; Thermal ; Composite material ; Engineering physics ; Mechanical engineering ; Chemical engineering ; Computer science ; Machine learning ; Thermodynamics ; Engineering Ferroelectric and Piezoelectric Materials ; Acoustic Wave Resonator Technologies ; Microwave Dielectric Ceramics Synthesis
Authors: Ashish Kumar | Prashant Singh | Manoj K. Harbola
Source: Oxford Open Materials Science (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Density functional theory of material design: fundamentals and applications—II Abstract This is the second and the final part of the review on density functional theory (DFT), referred to as DFT-II. In the first review, DFT-I, we have discussed wavefunction-based methods, their complexity, and basics of density functional theory. In DFT-II, we focus on fundamentals of DFT and their implications for the betterment of the theory. We start our presentation with the exact DFT results followed by the concept of exchange-correlation (xc) or Fermi-Coulomb hole and its relationship with xc energy functional. We also provide the exact conditions for the xc-hole, xc-energy and xc-potential along with their physical interpretation. Next, we describe the extension of DFT for non-integer number of electrons, the piecewise linearity of total energy and discontinuity of chemical potential at integer particle numbers, and derivative discontinuity of the xc potential, which has consequences on fundamental gap of solids. After that, we present how one obtains more accurate xc energy functionals by going beyond the LDA. We discuss the gradient expansion approximation (GEA), generalized gradient approximation (GGA), and hybrid functional approaches to designing better xc energy functionals that give accurate total energies. However, these functionals fail to predict properties like the ionization potential and the band gap. Thus, we next describe different methods of modelling these potentials and results of their application for calculation of the band gaps of different solids to highlight accuracy of different xc potentials. Finally, we conclude with a glimpse on orbital-free density functional theory and the machine learning approach. Density functional theory ; Orbital-free density functional theory ; Hybrid functional ; Physics ; Coulomb ; Piecewise ; Wave function ; Discontinuity (linguistics) ; Thomas–Fermi model ; Local-density approximation ; Time-dependent density functional theory ; Statistical physics ; Quantum mechanics ; Energy functional ; Electron ; Mathematics ; Mathematical analysis Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Advanced Chemical Physics Studies