jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Reza Barbaz‐Isfahani | Hooman Dadras | Amir Teimouri | Saeed Saber‐Samandari | Manouchehr Salehi
Source: Materials & Design (article, 2025-11-08)
Field: Materials Science — Polymers and Plastics

Computational and experimental investigation of microcapsule-based self-healing polymers: Macro-mechanical finite element modeling and artificial neural network predictions • Healing in polymers with multi-core microcapsules is evaluated by micro-mechanical FEM. • ANN is utilized to assess volume fraction, microcapsule properties, and healing. • SEM was employed to examine fracture surfaces in microcapsule self-healing polymers. • Despite higher cost, macro-mechanical FEM achieved superior accuracy in predictions. This study presents a comprehensive computational and experimental analysis of self-healing polymers containing multi-core microcapsules. A macro-mechanical finite element model (FEM) was developed to evaluate healing efficiency, incorporating homogenized and impacted regions simulated in Abaqus-Explicit with VUSDFLD subroutines to capture stress–strain responses. An artificial neural network (ANN) was employed to predict the effects of microcapsule volume fraction (VF) (5–10 %) and nanoparticle reinforcement (multi-walled carbon nanotubes (MWCNT), nanoclay, nanosilica) on tensile strength and healing performance. The FEM demonstrated high accuracy (<5% error) compared to experimental data, outperforming multi-scale models despite higher computational costs. The ANN revealed that increasing microcapsule content enhances healing efficiency, while nanoparticle reinforcement reduces it due to restricted healing agent release from mechanically stronger capsules. SEM analysis identified three healing mechanisms: localized healing near ruptured microcapsules, crack-path filling, and wide-crack bridging. Validation against experimental and multi-scale data confirmed the model’s reliability for optimizing self-healing material design. Key findings indicate that 10 vol% microcapsules maximize healing efficiency, though nanoparticle reinforcement trades mechanical strength for reduced self-repair capability. This work advances computational tools for autonomous healing materials, providing a framework for balancing mechanical performance and self-healing functionality in structural applications. Finite element method ; Artificial neural network ; Nanoparticle ; Reinforcement ; Ultimate tensile strength ; Subroutine ; Polymer ; Carbon nanotube Polymer composites and self-healing ; Microbial Applications in Construction Materials ; Grouting, Rheology, and Soil Mechanics
Authors: X. LU | Jiannian Yao | Jingnan Wang | Xi Wang
Source: Chemical Engineering Science (article, 2025-12-14)
Field: Materials Science — Materials Chemistry

High-throughput screening of dual atom catalysts for oxygen reduction reaction based on machine learning Catalysis ; Dual (grammatical number) ; Oxygen atom ; Atom (system on chip) ; Oxygen ; Reduction (mathematics) ; Oxygen reduction reaction Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Nanoporous metals and alloys
Authors: Hao Yi | Lei Deng | Zhao Liu | Jun Huang | Xinyun Wang | Xuefeng Tang | Junsong Jin
Source: Journal of Alloys and Compounds (article, 2025-11-13)
Field: Materials Science — Materials Chemistry

Constitutive modeling of TC18 titanium alloy incorporating dynamic phase transformation and recrystallization with machine learning assistance Dynamic recrystallization ; Microstructure ; Constitutive equation ; Strain rate ; Titanium alloy ; Flow stress ; Softening ; Recrystallization (geology) Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming ; High-Velocity Impact and Material Behavior
Authors: Yubai Shi | Ruoyu Wang | Zhicheng Zhong | Yao Wu | Shi Liu | Liang Si | Ri He
Source: Materials Genome Engineering Advances (article, 2025-04-24)
Field: Materials Science — Materials Chemistry

Domain and switching dynamics in antiferroelectric PbZrO<sub>3</sub>: Machine learning molecular dynamics simulation Abstract Antiferroelectric (AFE) materials have received great attention because of their potential applications in the energy sector. Nevertheless, the properties of AFE materials have not been explored for a long time, especially the atomic‐scale understanding of AFE domain walls. Here, using first‐principles‐based machine learning potentials, we identify the atomic structures, energies, and dynamic properties of the domain walls for AFE lead zirconate. It is found that the domain wall can reduce the critical antiferroelectric‐ferroelectric transition field. During the electric field‐driven polarization switching process, the domain wall is immobile. Importantly, we observe that a distinct domain structure spontaneously forms in bulk lead zirconate upon annealing at 300 K. The domain structure exhibits an alternating array of clockwise–anticlockwise vortexes along radial with continuous polarization rotation. This anomalous AFE vortex is derived from the energy degeneracy in four possible orientations of the polarization order, which can enhance the dielectric response in the terahertz. The current results give an implication for the emergence of AFE vortex in AFE materials as well as ferroelectric materials. Dynamics (music) ; Antiferroelectricity ; Domain (mathematical analysis) ; Molecular dynamics ; Statistical physics ; Computer science ; Physics ; Mathematics ; Quantum mechanics ; Mathematical analysis Machine Learning in Materials Science ; Ferroelectric and Piezoelectric Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Irvin Séchepée | Louis Didon | Lucie Bajic | Hiroaki Matsumoto
Source: Materials Characterization (article, 2025-05-11)
Field: Materials Science — Materials Chemistry

Using machine learning as a supportive tool to systematically study the tensile properties of a Ti-6Al-2Sn-4Zr-2Mo-Si alloy with various microstructures: Effects of texture, phase proportions and grain size Materials science ; Microstructure ; Texture (cosmology) ; Alloy ; Ultimate tensile strength ; Metallurgy ; Grain size ; Phase (matter) ; Titanium alloy ; Artificial intelligence ; Computer science Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Metal and Thin Film Mechanics
Authors: Mukhtar Ahmad | Abdulraheem S. A. Almalki | Wajid Sajjad | Farhan Zafar | Hafiz Muhammad Asif | Muhammad Ali Khan | Naeem Akhtar | Muhammad Sohail | Gaber A. M. Mersal | Mohamed M. Ibrahim
Source: Journal of Electroanalytical Chemistry (article, 2025-06-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Design and fabrication of machine learning optimized Ni and Co-doped CuO nanocomposite as promising material for supercapacitor application Supercapacitor ; Nanocomposite ; Chemistry ; Fabrication ; Doping ; Nanotechnology ; Electrochemistry ; Chemical engineering ; Electrode ; Optoelectronics ; Materials science Supercapacitor Materials and Fabrication ; Copper-based nanomaterials and applications ; ZnO doping and properties
Authors: Saeed Roshani | Salah I. Yahya | Pouya Karami | Muhammad Akmal Chaudhary | Maher Assaad | Fariborz Parandin | Fawwaz Hazzazi | Fawnizu Azmadi Hussin | Sobhan Roshani
Source: Optical Materials Express (article, 2025-05-30)
Field: Chemistry — Analytical Chemistry

Intelligent photonic crystal-based optical sensor for accurate glycerol-water mixture measurement using artificial neural networks This paper proposes an intelligent photonic crystal-based optical sensor designed for the first time to accurately measure glycerol-water concentration and temperature. The proposed sensor features a novel two-dimensional (2D) photonic crystal structure with an optimized waveguide configuration to enhance refractive index sensitivity. The sensor structure does not include defect rods, which simplifies fabrication and enhances stability. By using the unique optical properties of photonic crystals and the artificial neural network (ANN), the proposed design ensures high precision and stability in detecting changes in the glycerol concentration. The performance of the sensor was evaluated based on sensitivity, detection limit (DL), figure of merit (FOM), and quality factor (Q-F) across different temperatures and glycerol concentrations. The optical response of the sensor was numerically analyzed and simulated using the finite-difference time-domain (FDTD) method. Then, a feedforward ANN model was developed and trained to predict glycerol concentration and temperature from the output spectral data, enabling intelligent and real-time analysis. The results demonstrate that the proposed sensor achieves high sensitivity (up to 89.9 nm/RIU), a low detection limit (0.0003–0.0010 RIU −1 ), and an excellent Q-factor (5233), making it a highly effective solution for refractive index sensing. Overall, the findings confirm that the proposed photonic crystal sensor, enhanced with ANN-based intelligent analysis, offers high accuracy, stability, and fast response, making it suitable for biomedical, pharmaceutical, and industrial applications where precise glycerol concentration measurements are required. Artificial neural network ; Materials science ; Photonic crystal ; Refractive index ; Crystal (programming language) ; Optical sensing ; Optics ; Optoelectronics ; Artificial intelligence ; Computer science Spectroscopy and Chemometric Analyses
Authors: Timothy T. Yang | Wissam A. Saidi
Source: ACS Catalysis (article, 2025-08-15)
Field: Materials Science — Materials Chemistry

Machine Learning-Accelerated First-Principles Molecular Dynamics Reveals C–C Coupling Mechanisms toward Ethylene on Cu(100) The Cu(100) termination has been identified as the most effective facet for converting CO and CO2 into ethylene. To enhance both the activity and selectivity of ethylene production, we perform machine-learning-accelerated, first-principles molecular dynamics simulations at 298 K in an explicit solvent at pH 7 to elucidate the C–C coupling mechanism─the critical reaction step in forming C2+ products. Among the six potential C–C coupling pathways, the most feasible are CO* dimerization and CO – CHO* and CHO* – CHO* couplings. Using the computational hydrogen electrode method, we demonstrate that all three pathways are equally accessible at −0.6 V vs RHE. At a potential below −1.0 V vs RHE, the thermodynamic barriers for the CO – CHO* and CHO* – CHO* pathways become negligible. Our computational findings explain the experimental observations, particularly the absence of C2+ products above −0.4 V vs RHE and the peaks in ethylene production near −0.6 and −1.0 V vs RHE. Since CHO* acts as a key intermediate common to both C–C coupling and CH4 formation, we propose that suppressing CHO* hydrogenation would inhibit CH4 pathways, thereby maximizing ethylene selectivity. Ethylene ; Coupling (piping) ; Molecular dynamics ; Catalysis ; Dynamics (music) ; Chemical physics ; Chemistry ; Materials science ; Computational chemistry ; Physics ; Organic chemistry Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Advanced Chemical Physics Studies
Authors: Yubing Qian | Xiang Li | Zhe Li | Weiluo Ren | Ji Chen
Source: Wiley Interdisciplinary Reviews Computational Molecular Science (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Deep Learning Quantum Monte Carlo for Solids ABSTRACT Deep learning has deeply changed the paradigms of many research fields. At the heart of chemical and physical sciences is the accurate ab initio calculation of many‐body wavefunctions, which has become one of the most notable examples to demonstrate the power of deep learning in science. In particular, the introduction of deep learning into quantum Monte Carlo (QMC) has significantly advanced the frontier of ab initio calculation, offering a universal tool to solve the electronic structure of materials and molecules. Deep learning QMC architectures were initially designed and tested on small molecules, focusing on comparisons with other state‐of‐the‐art ab initio methods. Methodological developments, including extensions to real solids and periodic models, have been rapidly progressing, and reported applications are fast expanding. This review covers the theoretical foundation of deep learning QMC for solids, the neural network wavefunction ansatz, and various other methodological developments. Applications on computing energy, electron density, electric polarization, force, and stress of real solids are also reviewed. The methods have also been extended to other periodic systems and finite temperature calculations. The review highlights the potential and existing challenges of deep learning QMC in materials chemistry and condensed matter physics. Monte Carlo method ; Quantum Monte Carlo ; Statistical physics ; Quantum ; Computer science ; Physics ; Quantum mechanics ; Statistics ; Mathematics Machine Learning in Materials Science ; Theoretical and Computational Physics ; Electronic and Structural Properties of Oxides
Authors: Jiemin Zheng | Yuan Gao | Keqing Li | Yang Zheng | Guanqiao Li | Leilei Zhang | Jinghui Wu | Yan Shi | Mingxin Huo | Xianze Wang
Source: Resources Conservation and Recycling (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Machine learning assisted optimization of polyoxometalate catalyzed lignin oxidation and depolymerization through reverse design Depolymerization ; Polyoxometalate ; Lignin ; Catalysis ; Chemistry ; Organic chemistry Polyoxometalates: Synthesis and Applications ; Lignin and Wood Chemistry ; Catalysis and Hydrodesulfurization Studies
Authors: Yan Yuan | Yilin Zhao | Lilin Lu | Junjie Wang | Jingbo Chen | Shubin Liu | Paul W. Ayers | Dongbo Zhao
Source: Journal of Chemical Theory and Computation (article, 2025-06-10)
Field: Materials Science — Materials Chemistry

Multiproperty Deep Learning of the Correlation Energy of Electrons and the Physicochemical Properties of Molecules The density-based descriptors from the information-theoretic approach (ITA) are used as features for multiproperty deep learning (DL), predicting the correlation energy and physicochemical properties of molecules. In addition to response properties (molecular polarizability α<sub>iso</sub> and NMR shielding constant σ<sub>iso</sub>) where ITA has been shown to work well before, we consider four conceptually distinct but practically related concepts: electron correlation, redox potential, octanol-water partition coefficient (log<i>K</i><sub>ow</sub>), and the wavelength of maximum absorption (λ<sub>max</sub>). The DL-predicted results are in good agreement with either the calculated or experimental counterparts, indicative of the model's robustness. We verified the transferability of redox potentials of phenazine derivatives. Generalizability is observed for the λ<sub>max</sub> data: small chromophores are used for training/validation but the test set has sizable molecules. The trained DL model outperforms the conventional TD-DFT method in terms of accuracy and efficiency. We also showcase that the isotropic quadrupole moment (Θ<sub>iso</sub>) is a good predictor of log<i>K</i><sub>ow</sub>. This establishes that versatile density-based ITA quantities can be used to make accurate, low-cost predictions of both extensive and intensive properties, suggesting that this ITA-DL protocol has the potential for closed-loop chemistry automation. Implication of this work is straightforward, that a universal framework should be possible based on the ITA-based DL models. Chemical physics ; Correlation ; Electron ; Molecule ; Energy (signal processing) ; Computer science ; Data science ; Nanotechnology ; Chemistry ; Materials science ; Physics ; Nuclear physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Prince Kumar Rai | Ankur Gupta
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Predictive modeling of pulse-electrodeposited Cu–Zn alloy and dealloying for porous electrode fabrication Porous metals (PMs) have attracted significant attention in recent years due to their unique structural and functional properties, holding potential for a wide range of applications in catalysis, sensing, energy storage, and filtration. Among these, porous copper (PC), which is produced by dealloying copper-zinc (Cu-Zn) alloys has evolved as a particularly valuable material. In this study, a Cu-Zn alloy is electrochemically deposited onto a Cu wire in a sulphate-based electrolyte containing tri-sodium citrate as a complexing agent. To produce PC, the alloy has been subjected to chemical dealloying to dissolve the less noble element. We have implemented machine learning algorithms such as adaptive neuro-fuzzy inference systems (ANFIS), artificial neural networks (ANN), and response surface methodology (RSM) to model the interaction of process parameters and responses. Statistical modeling has been carried out to investigate the influence of operating parameters, including precursor reagent quantities (0.002-0.2 M), electrodeposition time (15-45 min), and dealloying time (16-24 h), on Zn content, dealloyed weight, and change in grain size. The test results confirm that both models fit the experimental data well, with the ANN model achieving high accuracy (<i>R</i> <sup>2</sup> = 0.98, 0.96, and 0.96 for Zn content, dealloyed weight, and grain size change, respectively); however, the ANFIS model demonstrates superior performance with the highest <i>R</i> <sup>2</sup> value (0.99) and the lowest MAPE (0.003, 0.002, and 0.001 for the respective responses). The RSM-BBD model is best suited for analyzing parameter interactions on responses, as it systematically evaluates the combined effects of multiple variables. By using potentiodynamic polarization curves to compare the corrosion resistance of Cu-Zn electrodes to bare Cu and PC electrodes, it was found that Cu-Zn electrodes have better corrosion resistance. Additionally, dealloying has resulted in a transition from a hydrophobic (110 ± 1°) to a hydrophilic (59 ± 0.5°) surface. Porosity ; Fabrication ; Alloy ; Materials science ; Filtration (mathematics) ; Electrode ; Chemical engineering ; Energy storage ; Range (aeronautics) ; Catalysis ; Nanotechnology ; Metallurgy ; Chemistry ; Composite material ; Organic chemistry ; Physical chemistry ; Thermodynamics Nanoporous metals and alloys ; Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion
Authors: E. A. Ulanov | Ghulām Qādir | Kai Riedmiller | Pascal Friederich | Frauke Gräter
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Predicting hydrogen atom transfer energy barriers using Gaussian process regression Predicting reaction barriers for arbitrary configurations based on only a limited set of density functional theory (DFT) calculations would render the design of catalysts or the simulation of reactions within complex materials highly efficient. We here propose Gaussian process regression (GPR) as a method of choice if DFT calculations are limited to hundreds or thousands of barrier calculations. For the case of hydrogen atom transfer in proteins, an important reaction in chemistry and biology, we obtain a mean absolute error of 3.23 kcal mol<sup>-1</sup> for the range of barriers in the data set using SOAP descriptors and similar values using the marginalized graph kernel. Thus, the two GPR models can robustly estimate reaction barriers within the large chemical and conformational space of proteins. Their predictive power is comparable to a graph neural network-based model, and GPR even outcompetes the latter in the low data regime. We propose GPR as a valuable tool for an approximate but data-efficient model of chemical reactivity in a complex and highly variable environment. Hydrogen atom ; Energy (signal processing) ; Gaussian process ; Process (computing) ; Kriging ; Energy transfer ; Atom (system on chip) ; Atomic physics ; Gaussian ; Materials science ; Statistical physics ; Physics ; Computer science ; Chemistry ; Statistics ; Computational chemistry ; Machine learning ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: S Jiang | Michael A. Webb
Source: Macromolecules (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

Physics-Guided Neural Networks for Transferable Property Prediction in Architecturally Diverse Copolymers The architectural, compositional, and chemical complexities of polymers are fundamentally important to their properties; however, these same factors obfuscate effective predictions. Machine learning offers a promising approach for predicting polymer properties, but model transferability remains a major challenge, particularly when data is scarce due to high acquisition costs or the growth of the parameter space. Here, we examine whether integration with polymer physics theory effectively enhances the transferability of machine learning models to predict properties of architecturally and compositionally diverse polymers. To do so, we first generate ToPoRg-18k─a data set reporting the moments of the distribution of squared radius of gyration for 18,450 polymers with diverse architectures, molecular weights, compositions, and chemical patterns. We then systematically assess the performance of several different models on a series of transferability tasks, such as predicting properties of high-molecular-weight systems from smaller ones or predicting properties of copolymers from homopolymers. We find that a tandem model, GC-GNN, which combines a graph neural network with a fittable model based on ideal Gaussian chain theory, surpasses both standalone polymer physics and graph neural network models in predictive accuracy and transferability. We also demonstrate that predictive transferability varies with polymer architecture due to deviations from the ideal Gaussian chain assumption. Furthermore, the integration with theory endows GC-GNN with additional interpretability, as its learned coefficients correlate strongly with polymer solvophobicity. Overall, this study illustrates the utility of combining polymer physics with data-driven models to improve predictive transferability for architecturally diverse copolymers, showcasing an extension of physics-informed machine learning for macromolecules. Copolymer ; Property (philosophy) ; Artificial neural network ; Polymer science ; Computer science ; Materials science ; Chemistry ; Artificial intelligence ; Polymer ; Organic chemistry Machine Learning in Materials Science ; Polymer composites and self-healing