jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Chandra Chowdhury | Esackraj Karthikraja | V. Subramanian
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

DFT and machine learning guided investigation into the design of new dual-atom catalysts based on α-2 graphyne The realm of atomic catalysts has witnessed notable advancements; yet, the predominant focus remains on single atomic catalysts (SACs). The exploration and successful implementation of dual atomic catalysts (DACs) pose intricate challenges, primarily concerning thermodynamic stability and optimal metallic composition. To address these issues, we present a comprehensive theoretical investigation of α-2 graphyne (GPY)-based DACs, synthesized in-house with a keen focus on formation stability. Density functional theory (DFT) simulations were leveraged to ascertain each DAC structure's stability, considering numerous transition metal permutations totalling about 823 DACs. Furthermore, we developed a machine learning (ML) model that predicts stability based solely on the physical characteristics of the constituent elements in the DACs, thus eliminating the need for extensive DFT calculations. Our findings not only offer detailed insights into atomic interactions but also highlight promising candidates for DACs, pushing beyond traditional trial-and-error synthesis approaches. This study fosters a deeper understanding of DACs and paves new pathways for exploring atomic catalysts for practical applications. Graphyne ; Stability (learning theory) ; Density functional theory ; Dual (grammatical number) ; Atom (system on chip) ; Catalysis ; Nanotechnology ; Focus (optics) ; Computer science ; Chemistry ; Materials science ; Computational chemistry ; Physics ; Machine learning ; Parallel computing Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advancements in Battery Materials
Authors: J P Supriya | Raviraj Shetty | Sawan Shetty | Gururaj Bolar | Adithya Hegde
Source: Materials Research Express (article, 2024-11-01)
Field: Materials Science — Polymers and Plastics

Mechanical and physical characterization of chemically treated Hibiscus Rosa-Sinensis polymer matrix composites using deep learning and statistical approach Abstract The transition to sustainable materials in composite manufacturing is crucial for reducing environmental impact and costs. Natural fibers, particularly from plants like Hibiscus Rosa-Sinensis, offer an eco-friendly and cost-effective alternative to traditional reinforcement materials in polymer composites. This study explores the development and characterization of polymer composites reinforced with chemically treated Hibiscus Rosa-Sinensis (HRS) fibers. HRS fibers, derived from the plant Hibiscus Rosa-Sinensis, are notable for their availability, mechanical properties, and environmental benefits. The research investigates how fiber weight percentage, fiber length, and fiber thickness affect the physical and mechanical properties of the composites, including void content, microhardness, water absorption, tensile strength, flexural strength, and Impact Strength. Composites with a fiber configuration of 15 Wt%, 10 mm length, and 2 mm thickness have exhibited optimal performance, achieving an ultimate tensile strength of 30.76 MPa, flexural strength of 50.8 MPa, Impact Strength of 119 J m −1 , and a peak microhardness of 22.326 Hv. These parameters significantly enhance the composite’s structural integrity and durability. The study also highlights the critical role of fiber dimensions i.e. with greater fiber weight percentages leading to increased void content and water absorption rates, which peaked at 6.19% and 3.45%, respectively. Further, predictive modelling using Feed-Forward Artificial Neural Network (FFANN) and Response Surface Methodology (RSM) revealed that FFANN has outperformed RSM, achieving an average accuracy of 95%–98% compared to the average accuracy of RSM at 85%–90%. Finally, microstructural analysis has corroborated with the experimental results, highlighting the potential of Hibiscus Rosa-Sinensis fibers in enhancing the performance of natural fiber-reinforced composites for various industrial applications. Composite material ; Characterization (materials science) ; Materials science ; Matrix (chemical analysis) ; Polymer ; Nanotechnology Natural Fiber Reinforced Composites ; Polymer Nanocomposites and Properties ; Nanocomposite Films for Food Packaging
Authors: Mei Cheng | Xiya Jia | Zhimin Zhang
Source: Materials (article, 2024-10-10)
Field: Materials Science — Biomaterials

Prediction of Mechanical Properties of Rare-Earth Magnesium Alloys Based on Convolutional Neural Networks Rare-earth magnesium alloys exhibit higher comprehensive mechanical properties compared to other series of magnesium alloys, effectively expanding their applications in aerospace, weapons, and other fields. In this work, the tensile strength, yield strength, and elongation of a Mg-Gd-Y-Zn-Zr rare-earth magnesium alloy under different process conditions were determined, and a large number of microstructure observations and analyses were carried out for the tensile specimens; a prediction model of the corresponding mechanical properties was established by using a convolutional neural network (CNN), in which the metallographic diagram of the rare-earth magnesium alloy was taken as the input, and the corresponding tensile strength, yield strength, elongation, and three mechanical properties were taken as the output. The stochastic gradient descent (SGD) algorithm was used for parameter optimization and experimental validation, and the results showed that the average relative errors of the tensile strength and yield strength prediction results were 1.90% and 3.14%, respectively, which were smaller than the expected error of 5%. Ultimate tensile strength ; Materials science ; Elongation ; Magnesium ; Magnesium alloy ; Microstructure ; Alloy ; Rare earth ; Yield (engineering) ; Metallurgy ; Composite material Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Hydrogen Storage and Materials
Authors: Lei Liu | Jia Zhou | Jingfu Shi | Changqing Miao
Source: Macromolecules (article, 2024-08-01)
Field: Materials Science — Materials Chemistry

The Mechanical Anisotropy and Failure Mechanism of Poly(<i>p</i>-phenylene terephthalamide) Using Machine Learning Molecular Dynamics Simulations As a peculiarly polymeric crystalline material, poly(p-phenylene terephthalamide) (PPTA) presents anisotropy according to the different intermolecular interactions, such as van der Waals (vdW) bonding along its a-axis, hydrogen bonding along its b-axis, and covalent bonding along its c-axis. The high anisotropy leads to the difficulties and challenges for experimental and theoretical studies on the complex mechanical behavior. In this paper, a machine learning potential for PPTA is established and the anisotropic mechanical behavior and failure mechanism under tensile conditions are investigated with molecular dynamics simulations. The results show that the PPTA undergoes a multilevel growth behavior of stress when stretched along the van der Waals interaction direction, which originates from the multiple changes in the molecular structure of PPTA under tensile stress. Furthermore, the hydrogen-bonding network goes through a reconfiguration and increases the elongation of the material upon stretching along the hydrogen-bonding interaction direction. Additionally, it is found that the Cbenzene–C starts to break first and leads to material failure upon stretching along the chain direction, which is different from previous findings. This work shows the applicability of machine learning potentials in complex crystalline polymer materials and provides atomic-scale insights into the design of high-performance polymer crystalline materials. van der Waals force ; Intermolecular force ; Anisotropy ; Materials science ; Hydrogen bond ; Poly(p-phenylene) ; Ultimate tensile strength ; Polymer ; Molecular dynamics ; Chemical physics ; Composite material ; Phenylene ; Computational chemistry ; Chemistry ; Molecule ; Physics ; Optics ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Polymer crystallization and properties
Authors: Chia-Min Lin | Abishek Khatri | Da Yan | Cheng-Chien Chen
Source: Materials (preprint, 2024-11-02)
Field: Materials Science — Materials Chemistry

Machine Learning and First-Principle Predictions of Materials with Low Lattice Thermal Conductivity We performed machine learning (ML) simulations and density functional theory (DFT) calculations to search for materials with low lattice thermal conductivity, κL. Several cadmium (Cd) compounds containing elements from the alkali metal and carbon groups including A<sub>2</sub>CdX (A = Li, Na, and K; X = Pb, Sn, and Ge) are predicted by our ML models to exhibit very low κL values (<1.0 W/mK), rendering these materials suitable for potential thermal management and insulation applications. Further DFT calculations of electronic and transport properties indicate that the figure of merit, ZT, for the thermoelectric performance can exceed 1.0 in compounds such as K<sub>2</sub>CdPb, K<sub>2</sub>CdSn, and K<sub>2</sub>CdGe, which are therefore also promising thermoelectric materials. Thermal conductivity ; Lattice (music) ; Materials science ; Condensed matter physics ; Thermal ; Statistical physics ; Thermodynamics ; Composite material ; Physics ; Acoustics Machine Learning in Materials Science ; Thermal properties of materials
Authors: Alexandre Dézaphie | Clovis Lapointe | Alexandra M. Goryaeva | Jérôme Creuze | Mihai‐Cosmin Marinica
Source: Computational Materials Science (article, 2024-11-05)
Field: Materials Science — Materials Chemistry

Designing hybrid descriptors for improved machine learning models in atomistic materials science simulations The efficiency and performance of machine learning force fields in materials science are significantly influenced by the quality of the embedding of the atomic neighborhood through atomic descriptors and learning algorithms. In this study, we explore various methods for combining two existing descriptors to formulate a hybrid descriptor that enhances the performance of the learned force field. Within the framework of widely used models in materials science, such as linear and quadratic models, we propose several strategies for the design of hybrid descriptors and we perform an extensive analysis to evaluate their performance. Using databases for defects in body-centered cubic (W, Fe) and face-centered cubic (Ni) metals, we assess the models’ effectiveness. Additionally, we explore their potential as surrogate models for challenging yet informative quantities in atomistic materials science, such as the vibrational entropy of defects. By improving the coupling scheme of both descriptors, we propose models that combine the advantages of linear models, which are linear in descriptor dimensionality and easy to train, with the precision of quadratic models, which have quadratic dimensionality and are more challenging to parameterize. The proposed approaches have numerical complexity similar to linear models while maintaining the accuracy of quadratic models, opening new possibilities for machine learning-based molecular dynamics and surrogate modeling in materials science. Computer science ; Molecular dynamics ; Materials science ; Nanotechnology ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: E. V. Fomin
Source: Metals (article, 2024-03-31)
Field: Materials Science — Materials Chemistry

Molecular Dynamics Study of Temperature Dependence of Grain Boundaries (100) in Pure Aluminum with Application of Machine Learning As is known, grain boundary (GB) energy determines the mobility of GBs and their population in metals. In this work, we study the energy of GBs in the (100) crystallographic plane and in the temperature range from 100 to 700 K. The study is carried out using both the molecular dynamic (MD) method and machine learning approach to approximate the MD data in order to obtain functional dependence in the form of a feed-forward neural network (FCNN). We consider the tilt and twist grain boundaries in the range of misorientation angles from 0 to 90°. Also, we calculate the average and minimum energy over the ensemble of GB states, since there are many stable and metastable structures with different energies even at a fixed grain misorientation. The minimum energies decrease with increasing temperature, which is consistent with the results of other studies. The scatter of GB energies in the temperature range from 100 to 700 K is obtained on the basis of MD simulation data. The obtained energy spread is in reasonable agreement with the data from other works on the values of GB energy in pure aluminum. The predictive ability of the trained FCNN as well as its ability to interpolate between the energy and temperature points from MD data are both demonstrated. Misorientation ; Grain boundary ; Metastability ; Atmospheric temperature range ; Range (aeronautics) ; Work (physics) ; Materials science ; Aluminium ; Energy (signal processing) ; Molecular dynamics ; Population ; Condensed matter physics ; Crystallography ; Thermodynamics ; Physics ; Chemistry ; Microstructure ; Computational chemistry ; Metallurgy ; Quantum mechanics ; Composite material Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Aluminum Alloy Microstructure Properties
Authors: Nihang Fu | Sadman Sadeed Omee | Jianjun Hu
Source: Computational Materials Science (article, 2024-12-19)
Field: Materials Science — Materials Chemistry

Physical encoding improves OOD performance in deep learning materials property prediction Property (philosophy) ; Encoding (memory) ; Computer science ; Physical property ; Artificial intelligence ; Materials science ; Composite material Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Semiconductor materials and devices
Authors: Krzysztof Marciniec | Justyna Nowakowska | Elwira Chrobak | Ewa Bębenek | Małgorzata Latocha
Source: Molecules (article, 2024-07-02)
Field: Chemistry — Organic Chemistry

Synthesis, Docking, and Machine Learning Studies of Some Novel Quinolinesulfonamides–Triazole Hybrids with Anticancer Activity In the presented work, a series of 22 hybrids of 8-quinolinesulfonamide and 1,4-disubstituted triazole with antiproliferative activity were designed and synthesised. The title compounds were designed using molecular modelling techniques. For this purpose, machine-learning, molecular docking, and molecular dynamics methods were used. Calculations of the pharmacokinetic parameters (connected with absorption, distribution, metabolism, excretion, and toxicity) of the hybrids were also performed. The new compounds were synthesised via a copper-catalysed azide-alkyne cycloaddition reaction (CuAAC). 8-<i>N</i>-Methyl-<i>N</i>-{[1-(7-chloroquinolin-4-yl)-1<i>H</i>-1,2,3-triazol-4-yl]methyl}quinolinesulfonamide was identified in in silico studies as a potential strong inhibitor of Rho-associated protein kinase and as a compound that has an appropriate pharmacokinetic profile. The results obtained from in vitro experiments confirm the cytotoxicity of derivative <b>9b</b> in four selected cancer cell lines and the lack of cytotoxicity of this derivative towards normal cells. The results obtained from silico and in vitro experiments indicate that the introduction of another quinolinyl fragment into the inhibitor molecule may have a significant impact on increasing the level of cytotoxicity toward cancer cells and indicate a further direction for future research in order to find new substances suitable for clinical applications in cancer treatment. In silico ; Cytotoxicity ; ADME ; Docking (animal) ; Chemistry ; Stereochemistry ; AutoDock ; Cycloaddition ; In vitro ; Click chemistry ; Combinatorial chemistry ; Triazole ; 1,2,3-Triazole ; Biochemistry ; Organic chemistry Click Chemistry and Applications ; Computational Drug Discovery Methods ; Synthesis and Catalytic Reactions
Authors: Julius B. Kleine Büning | Stefan Grimme | Markus Bursch
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Machine learning-based correction for spin–orbit coupling effects in NMR chemical shift calculations As one of the most powerful analytical methods for molecular and solid-state structure elucidation, NMR spectroscopy is an integral part of chemical laboratories associated with a great research interest in its computational simulation. Particularly when heavy atoms are present, a relativistic treatment is essential in the calculations as these influence also the nearby light atoms. In this work, we present a Δ-machine learning method that approximates the contribution to <sup>13</sup>C and <sup>1</sup>H NMR chemical shifts that stems from spin-orbit (SO) coupling effects. It is built on computed reference data at the spin-orbit zeroth-order regular approximation (ZORA) DFT level for a set of 6388 structures with 38 740 <sup>13</sup>C and 64 436 <sup>1</sup>H NMR chemical shifts. The scope of the methods covers the 17 most important heavy p-block elements that exhibit heavy atom on the light atom (HALA) effects to covalently bound carbon or hydrogen atoms. Evaluated on the test data set, the approach is able to recover roughly 85% of the SO contribution for <sup>13</sup>C and 70% for <sup>1</sup>H from a scalar-relativistic PBE0/ZORA-def2-TZVP calculation at virtually no extra computational costs. Moreover, the method is transferable to other baseline DFT methods even without retraining the model and performs well for realistic organotin and -lead compounds. Finally, we show that using a combination of the new approach with our previous Δ-ML method for correlation contributions to NMR chemical shifts, the mean absolute NMR shift deviations from non-relativistic DFT calculations to experimental values can be halved. Spin–orbit interaction ; Coupling (piping) ; Chemical shift ; Spin (aerodynamics) ; Computational chemistry ; Materials science ; Molecular physics ; Nuclear magnetic resonance ; Chemistry ; Chemical physics ; Physics ; Condensed matter physics ; Thermodynamics Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications ; Spectroscopy and Quantum Chemical Studies
Authors: Juanjuan Liu | Rui-Jia Sun | Xuan Bao | Jiefu Yang | Yanling Chen | Bijun Tang | Zheng Liu
Source: Small (review, 2024-07-07)
Field: Materials Science — Materials Chemistry

Machine Learning Driven Atom‐Thin Materials for Fragrance Sensing Fragrance plays a crucial role in the daily lives. Its importance spans various sectors, from therapeutic purposes to personal care, making the understanding and accurate identification of fragrances essential. To fully harness the potential of fragrances, efficient and precise fragrance sensing and identification are necessary. However, current fragrance sensors face several limitations, particularly in detecting and differentiating complex scent profiles with high accuracy. To address these challenges, the use of atom-thin materials in fragrance sensors has emerged as a groundbreaking approach. These atom-thin sensors, characterized by their enhanced sensitivity and selectivity, offer significant improvements over traditional sensing technology. Moreover, the integration of Machine Learning (ML) into fragrance sensing has opened new opportunities in the field. ML algorithms applied to fragrance sensing facilitate advancements in four key domains: accurate fragrance identification, precise discrimination between different fragrances, improved detection thresholds for subtle scents, and prediction of fragrance properties. This comprehensive review delves into the synergistic use of atom-thin materials and ML in fragrance sensing, providing an in-depth analysis of how these technologies are revolutionizing the field, offering insights into their current applications and future potential in enhancing the understanding and utilization of fragrances. Identification (biology) ; Nanotechnology ; Biochemical engineering ; Key (lock) ; Field (mathematics) ; Computer science ; Atom (system on chip) ; Artificial intelligence ; Human–computer interaction ; Materials science ; Embedded system ; Engineering ; Computer security Luminescence and Fluorescent Materials ; Advanced Chemical Sensor Technologies ; Polydiacetylene-based materials and applications
Authors: Hui Xu | Wenhao Lv | Shaojie Yang | Shuna Yang | Yawei Liu | Feng Huo
Source: AIChE Journal (article, 2024-09-30)
Field: Materials Science — Materials Chemistry

Enhancing <scp>HER</scp> catalyst screening of modified <scp>MXenes</scp> through <scp>DFT</scp> and machine learning integration Abstract MXenes doped with non‐metallic and transition metal elements exhibit remarkable potential as catalysts in the hydrogen energy. Nonetheless, efficiently identifying viable materials from a vast array of candidates remains a formidable challenge. Here, we conducted density functional theory (DFT) calculations to obtain the hydrogen adsorption free energy () of 78 types of doped TiVCO 2 MXene catalysts. Then we employed machine learning models to categorize the values of the 78 catalysts, resulted in an accurate model which only uses 7 readily available elemental features but has an impressive accuracy of 93.6%. Our model successfully predicting 5 TiVCO 2 catalysts doped with S with superior performance, subsequently validated through DFT calculations. This classification methodology not only evaluates the range of effectively but also facilitates qualitative prediction and screening of catalysts, presenting a novel approach for catalytic systems with limited available data. MXenes ; Catalysis ; Density functional theory ; Chemistry ; Adsorption ; Materials science ; Computer science ; Combinatorial chemistry ; Nanotechnology ; Computational chemistry ; Physical chemistry ; Organic chemistry MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Machine Learning in Materials Science
Authors: Θεόδωρος Λούτας | Athanasios Oikonomou | Christoforos Rekatsinas
Source: Composite Structures (article, 2024-09-21)
Field: Chemistry — Organic Chemistry

Bio-inspired discontinuous composite materials with a machine learning optimized architecture Composite number ; Architecture ; Materials science ; Artificial intelligence ; Composite material ; Computer science ; Art Polydiacetylene-based materials and applications ; Machine Learning in Materials Science ; Polymer composites and self-healing