jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sudhansu Sekhar Nath | Poonam Sundriyal
Source:  (article, 2025-06-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optimizing 3D Printed MnO2 Electrode for Enhancing Supercapacitor Performances Through Machine Learning Abstract The next generation of portable and wearable electronics demands the development of flexible and efficient energy storage systems. Supercapacitors are best suitable for portable energy storage because of their high-power density, fast charge/discharge capability, long cycle life, better safety, and lightweight. Extrusion based additive manufacturing is an efficient and ecofriendly method to fabricate 3D energy storage devices as it has better control over electrode, layer thickness and porosity, which enhances electrolyte ion diffusion and mechanical flexibility. Manganese dioxide (MnO2) has drawn much attention as a supercapacitor electrode material because of its high theoretical capacitance, low cost, and environmental safety. In this method, extrusion-based 3D printer (Hyrel Engine Hr) was used to fabricate customizable MnO2 electrode structures. The ink was prepared by mixing active material (MnO2), conductive agent (activated carbon) and binder (Poly(vinylidene fluoride)) with mass ratio of 8:1.5:0.5. The optimized printed supercapacitors exhibited high specific capacitance of 3.31 F/cm2, high energy density of 0.45 mWh/cm2 and a power density of 27.13 mW/cm2 at a current density of 2 mA/cm2 and stability over an extended number of charge-discharge cycles. The random forest (RF) algorithm was the most accurate for predicting the performances. The accuracy of the ML model was also cross-validated with the experimental data, showing strong predictive capability over performance metrics of different architectures of MnO2 electrodes. This work highlights the synergy between machine learning and additive manufacturing for the rapid design and optimization of supercapacitor performance for next-generation electronics. Supercapacitor ; Energy storage ; Power density ; Capacitance ; Electrode ; Electrical conductor ; Inkwell ; 3D printing ; Mixing (physics) Supercapacitor Materials and Fabrication
Authors: Nathan Gil A. Estrada | Rinlee Butch M. Cervera
Source: Applied Sciences (article, 2025-08-27)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Predictive Modeling for Solid Oxide Electrolysis Cell (SOEC) Electrochemical Performance Solid oxide electrolysis cells (SOECs) are emerging as a promising technology for high-efficiency and environmentally friendly hydrogen production. While laboratory-scale experiments and physics-based simulations have significantly advanced SOEC research, there remains a need for faster, scalable, and cost-effective methods to predict electrochemical performance. This study explores the feasibility of using machine learning (ML) techniques to model the performance of SOECs with the material configuration LSM-YSZ/YSZ/Ni-YSZ. A dataset of 593 records (from 31 IV curves) was compiled from 12 peer-reviewed sources and used to train and evaluate four ML algorithms: SVR, ANN, XGBoost, and Random Forest. Among these, XGBoost achieved the highest accuracy, with an R2 of 98.39% for cell voltage prediction and 98.10% for IV curve interpolation test under typical conditions. Extrapolation tests revealed the model’s limitations in generalizing beyond the bounds of the training data, emphasizing the importance of comprehensive data coverage. Overall, the results confirm that ML models, particularly XGBoost, can serve as accurate and efficient tools for predicting SOEC electrochemical behavior when applied with appropriate data coverage and guided by materials science concepts. Electrolysis ; Materials science ; Computer science ; Chemical engineering ; Chemistry ; Engineering ; Electrode ; Electrolyte Advancements in Solid Oxide Fuel Cells ; Machine Learning in Materials Science ; Chemical Looping and Thermochemical Processes
Authors: Arthur Souesme | René Guinebretière | O Castelnau | Alexandre Boulle
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2025-11-19)
Field: Materials Science — Materials Chemistry

High-throughput determination of crystallite size and microstrain from X-ray diffraction data with deep neural networks The development of high-throughput X-ray diffraction (XRD) techniques presents significant challenges to traditional data analysis frameworks. The present work demonstrates that deep convolutional neural networks (CNNs) can reliably quantify key microstructural parameters of polycrystalline materials, specifically crystallite size and heterogeneous strain (microstrain) extracted from powder XRD patterns, within milliseconds. A CNN was trained using synthetic data generated with a dedicated code based on the GSAS-II software. We employ the example of monoclinic ZrO2 to critically examine the theoretical performances of the CNN. The accuracy of the predictions was systematically investigated within a crystallite size range of 5 – 1000 nm and a microstrain range of 0.05-2%. In this context, the role of the resolution of the diffractometer and the XRD peak profile shape is discussed in details. For instance, on a low-resolution laboratory diffractometer it is possible to determine the crystallite size within a 5–100 nm range with 99.7% accuracy in the absence of microstrain, and 97.3% accuracy when microstrains are present. Higher accuracies are obtained with a high-resolution diffractometer and over an extended range of size and microstrain. The potential of the CNN is demonstrated by the analysis of the crystallite size and microstrains in MgAl2O4 formed by a solid-state reaction between MgO and Al₂O₃ using in situ XRD at 1200 °C performed at the ESRF. The CNN can determine the crystallite size and microstrain in 0.004 s per diagram with values that deviate by ~7% and ~8% from a conventional Rietveld refinement, respectively, which is contained within the statistical uncertainties of these parameters. We further demonstrate that the same training dataset can be used for other regression problems, for instance phase fraction quantification, with no additional computational cost and only minor modifications to the network architecture. This work paves the way for real-time data analysis at synchrotron facilities. Crystallite ; Diffractometer ; Diffraction ; Monoclinic crystal system ; Rietveld refinement ; Range (aeronautics) ; X-ray crystallography ; Powder Diffractometer Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; High-pressure geophysics and materials
Authors: Lado Filipovic
Source:  (article, 2025-09-24)
Field: Materials Science — Materials Chemistry

From Atoms to Reactors: Multi-Scale Modeling for Semiconductor Fabrication Accurately predicting surface topography evolution is essential for manufacturing complex three-dimensional (3D) semiconductor devices. To overcome the limitations of slow and costly experimental development cycles, a predictive, physicsbased simulation approach that bridges multiple scales is required. This paper presents comprehensive multi-scale modeling approaches that integrate simulations from the atomistic to the reactor level. At the lowest scale, we use Density Functional Theory (DFT) and Molecular Dynamics (MD), including machine learning-enhanced methods, to derive fundamental parameters like sticking coefficients and sputtering yields. These inform robust feature-scale models implemented in the open-source ViennaPS simulator, which captures complex surface kinetics by tracking local properties such as species coverages, passivation layer thickness, and physical damage. At the highest scale, reactor simulations provide physically-based boundary conditions, while surrogate models are developed to efficiently link equipment settings to process outcomes. The predictive power of the methods is demonstrated through a calibrated model for selective SiGe etching that accurately predicts etch profiles for different device geometries and process conditions. This holistic approach provides an end-to-end simulation capability, enabling predictive process optimization and establishing a foundation for future applications in automated process control and inverse design. Process (computing) ; Semiconductor device fabrication ; Passivation ; Process modeling ; Etching (microfabrication) ; Semiconductor ; Sputtering ; Tracking (education) ; Fabrication ; Semiconductor device Machine Learning in Materials Science ; Advancements in Semiconductor Devices and Circuit Design ; Nanowire Synthesis and Applications
Authors: S. O. Jimoh | Akeem Ganiyu Rabiu | Christian Ugochukwu Ebere | Sofiat Adeleke | Saheed Abayomi Onifade | Akinbode Ayekooto Olonisakin | Ayobami Elias Ajadi
Source: Sahel Journal of Life Sciences FUDMA (review, 2025-03-31)
Field: Materials Science — Biomaterials

The Emerging Role of Saccharomyces cerevisiae in Polyhydroxyalkanoate Biosynthesis: A Review Increasing environmental concerns over plastic pollution and the depletion of fossil resources have driven global interest in biodegradable polymers, such as polyhydroxyalkanoates (PHAs). While bacterial systems have traditionally dominated PHA production, Saccharomyces cerevisiae has recently emerged as a promising alternative due to its Generally Recognized as Safe (GRAS) status, industrial robustness, and compatibility with advanced genetic engineering tools. This review explores the current state and future potential of S. cerevisiae as a cell factory for PHA biosynthesis. It examines metabolic engineering strategies to enhance precursor availability, the expression of heterologous PHA biosynthetic genes, and the integration of dynamic regulatory elements to optimise production. Recent advancements in feedstock utilisation, particularly lignocellulosic and industrial wastes bioreactor design, and downstream processing, are highlighted, along with challenges such as low yield, enzyme misfolding, and extraction inefficiencies. The review also emphasises the importance of omics technologies, synthetic biology, and machine learning in accelerating strain development and scalability. Overall, the article underscores the transformative potential of S. cerevisiae in enabling sustainable bioplastic production and contributing to circular bioeconomy goals. Polyhydroxyalkanoates ; Saccharomyces cerevisiae ; Biosynthesis ; Chemistry ; Business ; Biochemistry ; Biology ; Yeast ; Genetics ; Enzyme ; Bacteria biodegradable polymer synthesis and properties ; Biopolymer Synthesis and Applications ; Carbon dioxide utilization in catalysis
Authors: S. P. Pronin | Anastasia G. Zryumova | Alexander А. Piletsky | V. I. Belyaev
Source: Engineering Technologies and Systems (article, 2025-09-25)
Field: Chemistry — Analytical Chemistry

Intelligent Assessment of Wheat Yield through the Variable Potential of Seeds Introduction. Evaluation of wheat seed quality is an integral part of the technological process of its production since it increases the yield. The yield is affected by many different factors. The evaluation methods are constantly being improved taking into account new factors, physical methods and technical means. Currently seeds and crops sowing quality intelligent evaluation methods are developing very rapidly. The electrophysical method allows evaluating the soil influence on seeds by the variable potential. Aim of the Study. The study is aimed at examining changes in the variable potential of wheat seeds of a known crop yields during seed swelling in solutions with different potassium and sodium ratios and creating a convolutional neural network to estimate potential crop yields through the variable potential and known potassium and sodium ratios. Materials and Methods. The studies were carried out using seeds of two varieties of spring wheat with different yields. To simulate soil quality, there were used solutions with diffe­rent potassium chloride and sodium chloride ratios. The variable potential was measured using a device based on the data acquisition board LA50-USB. The yield was estimated using wavelet transform and deep convolutional neural network with ResNet groups. Results. There have been developed the experimental graphs of the variable potential change depending on the potassium and sodium ratio in a solution simulating soil quality. The neural network was used to classify the potential yield of wheat seeds through wavelet transforms of the variable potential, and potassium and sodium ratios. There has been compiled a table of neural network responses to test variable potentials. Discussion and Conclusion. The developed graphs of the variable potential change depending on potassium change in the external environment were compared with the results of studies by other authors. The results qualitatively coincide. The developed neural network can classify the potential yield of wheat seeds through the variable potential, and potassium and sodium ratios. The conducted study is useful for agricultural enterprises and farmers. The proposed methodology for assessing potential crop yields through variable potential and water extract will allow optimizing the process of potassium application to the soil. Yield (engineering) ; Potassium ; Sowing ; Crop yield ; Sodium ; Variable (mathematics) ; Convolutional neural network ; Crop Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Eun-Suk Jeong | Sang-Wook Han
Source: Current Applied Physics (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Can artificial intelligence techniques replace human labor in EXAFS data analysis? Extended X-ray absorption fine structure ; Artificial neural network ; Absorption (acoustics) ; Reinforcement learning ; Automation ; Data set Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; Fiber-reinforced polymer composites
Authors: Kevin M. Dalton | Doeke R. Hekstra
Source: Structural Dynamics (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Scaling Scaling: Stochastic Variational Inference for X-ray Diffraction Data The systematic errors inherent in crystallographic data introduce a core challenge to structure determination. These errors result from numerous processes including absorption by the sample and surrounding material, source polarization, and imperfections in the crystal lattice. Prior to merging redundant observations, these artifacts are typically corrected by an optimization algorithm known as scaling. Recently, we showed that the two procedures, scaling and merging, could be unified into a single algorithm based on Bayesian (variational) inference and deep learning. While this approach compares favorably to other algorithms across a broad range of experimental designs, it was limited in applicability by its memory requirements. This was particularly prohibitive for serial crystallography wherein the number of diffraction images becomes too large to fit in memory. In this talk, I will introduce a solution to this problem. By adding a pooling mechanism to the neural scaling model from our previous work, it is possible to train the model on small batches of data. Thereby the memory requirements are determined by the batch size rather than the size of the full data set. This innovation opens the door to treating very large datasets with hundreds of thousands or millions of diffraction images. Scaling ; Inference ; Diffraction ; Statistical physics ; Computer science ; Algorithm ; Mathematics ; Physics ; Artificial intelligence ; Optics ; Geometry X-ray Diffraction in Crystallography
Authors: Xiaoqi Zhang | Yutao Li | Xin Jin | Berend Smit
Source: Digital Discovery (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

FFLAME: a fragment-to-framework learning approach for MOF potentials Metal-organic frameworks (MOFs) exhibit immense structural diversity and hold promise for applications ranging from gas storage and separation to energy storage and conversion. However, structural flexibility makes accurate and scalable property prediction difficult. While machine learning potentials (MLPs) offer a compelling balance between accuracy and efficiency, most existing models are system-specific and lack transferability across different MOFs. In this work, we introduce FFLAME - Fragment-to-Framework Learning Approach for MOF Potentials, a fragment-centric strategy for training transferable MLPs. By decomposing MOFs into their constituent metal clusters and organic linkers, FFLAME enables efficient reuse of chemical environments and significantly reduces the need for full-framework training data. We demonstrate that fragment-informed training improves model generalizability, particularly in data-scarce regimes, and accelerates convergence during fine-tuning. FFLAME achieves near-target accuracy on unseen MOFs with minimal additional training. These results establish a robust and data-efficient pathway toward general-purpose MLPs for the simulation of diverse framework materials. Flexibility (engineering) ; Scalability ; Transferability ; Property (philosophy) ; Reuse ; Convergence (economics) ; Ranging Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Weiming Mao | Puyan Li | Yixiong Feng | Qin Xie | Yan Wang | Jincan Zhang | Ziqi Wang | Yubing Chen | Jiayu Fu | Luzhao Sun | Zhongfan Liu | Xiuju Song
Source: npj Computational Materials (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

High-throughput defect detection and coverage quantification of graphene grids via a dual-stage deep learning framework Abstract Graphene grids exhibit exceptional loading capacity for macromolecules, single atoms, and nanoparticles, offering significant potential for exploring the structure and properties of various materials at the nanoscale. However, challenges such as carbon film rupture, contamination, and uneven graphene film coverage frequently occur during grid fabrication. Here we propose a dual-stage deep learning model integrating U-Net and an enhanced YOLO11 architecture, enabling efficient and accurate defect detection and graphene coverage quantification. A tailored data augmentation strategy expanded the initial defect dataset by more than an order of magnitude, which directly contributed to an overall 11.72% improvement across the model’s performance metrics. With the integration of the multi-scale convolutional attention (MSCA) module and the slicing-aided hyper inference (SAHI) method, the model achieved a 0.67% mean absolute percentage error (MAPE), while reducing the average detection time from 26.6 to 0.1 min per image. The proposed model holds strong potential for extension to various material characterization image analysis tasks, providing a scalable strategy for high-throughput image processing that bridges fundamental research with industrial-scale applications. Deep learning ; Scalability ; Graphene ; Grid ; Inference ; Convolutional neural network ; Image (mathematics) ; Characterization (materials science) Graphene research and applications ; Machine Learning in Materials Science ; Big Data and Digital Economy
Authors: Nathan Woodruff | Nooruddin Jarrar | Nicholas Kane | Jeremy Hartvigsen | Meng Li | Micah Casteel | Kevin L. Gering
Source: ECS Meeting Abstracts (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

(<i>Invited</i>) Advanced Analytical Characterization Methods and Tools for Solid Oxide Cells and Systems Increasingly large amounts of data available from the growth in electrolysis experiments has provided an opportunity to leverage additional computational and data analysis tools. A limited amount of time is available to produce quality forecasts of new material sets for the lifetime needed for commercial solid oxide technologies. The tools developed provide greater depth into the potential mechanisms of cell behavior and increase the predictive power of limited data sets into the future. By leveraging tools previously developed at Idaho National Laboratory for battery degradation characterization and prediction, we have developed additional tools to apply these techniques to SOECs. The mechanistic terms of the fitting provide insight into the degradation processes in the cell and can assist in prioritizing post-test cell destructive and non-destructive examination. The tool utilizes sigmoidal rate expressions (SRE) to separate different degradations in the time domain to different mechanisms. By selecting and fitting the parameters, the data from the cells can then be trained via machine learning techniques. The trained model can then be used to predict cell behavior for future lifetime. In tandem with the analysis tool development, we have developed Faraday, an application which integrates electrolysis cell data storage, and provisions visualization, analysis, and reporting tools for cell characterization. Faraday leverages the DeepLynx data warehouse to integrate cell data from a variety of disparate sources and standardizes them using an ontology. Characterization (materials science) ; Materials science ; Computer science ; Nanotechnology ; Process engineering ; Chemistry ; Systems engineering ; Engineering Solid-state spectroscopy and crystallography ; X-ray Diffraction in Crystallography ; Force Microscopy Techniques and Applications
Authors: Raashiq Ishraaq | Siddhartha Das
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

Combined Machine Learning and Atomistic Simulations Reveal Multi-State Hydration in Cationic Brushes in the Presence of Halide Counterions In this communication, we employ a combination of all-atom molecular dynamics simulations and machine learning to establish the effect of different halide screening counterions (fluoride, chloride, bromide, and iodide ions) on the prevalence of two separate hydration states of the {N(CH<sub>3</sub>)<sub>3</sub>}<sup>+</sup> functional group of the PMETA (([poly(2-(methacryloyloxy)ethyl) trimethylammonium) cationic brushes. Counterion ; Halide ; Cationic polymerization ; Iodide ; Molecular dynamics Radioactive element chemistry and processing ; Ionic liquids properties and applications ; Chemical and Physical Properties in Aqueous Solutions
Authors: Fang-Cheng Wang | Qi-Jun Ye | Yucheng Zhu | Xin-Zheng Li
Source: ArXiv.org (preprint, 2025-06-05)
Field: Materials Science — Materials Chemistry

Classification and enumeration of solid-solid phase transition mechanisms Crystal-structure match (CSM), the atom-to-atom correspondence between two crystalline phases, is used extensively to describe solid-solid phase transition (SSPT) mechanisms. However, existing computational methods cannot account for all possible CSMs. Here, we propose a formalism to classify all CSMs into a tree structure, which is independent of the choices of unit cell and supercell. We rigorously proved that only a finite number of noncongruent CSMs are of practical interest. By representing CSMs as integer matrices, we introduce the crystmatch method to exhaustively enumerate them, which uncontroversially solves the CSM optimization problem under any geometric criterion. For most SSPTs, crystmatch can reproduce all known deformation mechanisms and CSMs within 10 CPU minutes, while also revealing thousands of new candidates. The resulting database can be further used for comparing experimental phenomena, high-throughput energy barrier calculations, or machine learning. Enumeration ; Formalism (music) ; Phase transition ; Integer (computer science) ; Tree (set theory) ; Unit circle Machine Learning in Materials Science ; Quasicrystal Structures and Properties ; Advanced Electron Microscopy Techniques and Applications