jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yuli Anggraini | Nuriadi Manurung | Febby Madona Yuma
Source: Indonesian Journal of Artificial Intelligence and Data Mining (article, 2025-08-06)
Field: Chemistry — Analytical Chemistry

Fresh Fruit Bunches Forecasting with the Double Exponential Smoothing Method PTPN IV Regional 1 Huta Padang Plantation is an oil palm plantation unit in North Sumatra that manages the cultivation, harvesting, and processing of Fresh Fruit Bunches (FFB) to produce palm oil. During the harvest process to produce Crude Palm Oil (CPO), a lot is often wasted due to unstable market demand. This research aims to implement the double exponential smoothing method for the production of fresh fruit bunches to be used as CPO. The problem faced is that production uncertainty often causes an imbalance between crop yields and market demand. If production is excessive, the company risks a spike in storage costs and potential product spoilage. Conversely, if production is too low, the company may miss out on market opportunities. This forecasting uses historical data from the previous year. The results of research at PTPN IV Regional 1 Kebun Huta Padang show that palm oil production forecasting for February 2025 amounted to 982.97 Kg with an error rate of 7.44%. This value is obtained using an alpha constant of 0.8 which gives the smallest forecasting error rate. Exponential smoothing ; Palm oil ; Production (economics) ; Product (mathematics) ; Bunches ; Value (mathematics) ; Crop ; Palm Spectroscopy and Chemometric Analyses
Authors: Tayyab Naveed | Asfandyar Khan | Muhammad Ramzan | Rehana Ilyas | Arooj Shahid | Imran Ahmad Khan | Muhammad Awais | Kashif Javed
Source: Textiles (article, 2025-10-13)
Field: Materials Science — Polymers and Plastics

Fabric Utilization of Women’s Kameez Designs with Different Types of Sleeves in the Apparel Industry The apparel industry is changing dynamically and quickly to manufacturing sustainable fashion products and the development of sustainable design strategies that minimize material consumption at the source. This study addresses a critical research gap by quantitatively evaluating the impact of fusing traditional South Asian garment construction (the kameez) with varied, Western-inspired sleeve geometries on key manufacturing metrics. Thirty-three distinct women’s garment styles, comprising three kameez types (simple, princess-cut, open-front) each paired with eleven different sleeve designs, were developed in the apparel industry to study the effect of fabric efficiency, wastage, and cost-effectiveness. The virtual patterns and markers were drafted and accomplished through Garment Gerber Technology (GGT) software to analyze fabric consumption, fabric efficiency, and cost-effectiveness. The results revealed that paneled kameez styles, such as the princess-cut and open-front, are significantly more material-efficient, achieving average fabric efficiencies of up to 83.95%, compared to the monolithic simple kameez, which averaged only 75.68%. Among sleeve types, multi-constructions like the slit sleeve and cuff sleeve proved most efficient (achieving up to 86.91% efficiency), while voluminous, single-piece designs like the umbrella sleeve consumed the most fabric and were the least efficient. Open-front kameez slit sleeves (OFSL3), simple kameez slit sleeves (SSL3), and princess-cut kameez slit sleeves (PCSL3), were better and more sustainable selections since they were most efficient in fabric efficiency (i.e., 86.91%, 86.17%, and 86.09%). Furthermore, the simple kameez style has the highest fabric wastage (above 22%), while the princess kameez style has the least (below 19%). The simple kameez slit sleeves design (SSL3) has the minimum wastage, while the simple kameez umbrella sleeves design (SSL4) has the maximum wastage. From a cost perspective, the open-front kameez slit sleeve (OFSL1) was identified as the most economical design, whereas the simple kameez with an umbrella sleeve (SSL4) was the most expensive. Statistical analysis confirmed that the differences between kameez styles were significant (p &lt; 0.05). Thus, adoption of specific, sustainable, deliberate design choices and incorporating paneling into the garment body and utilizing multi-piece sleeve constructions offer a quantifiable and strategic approach for manufacturers to reduce material waste, optimize fabric utilization, and improve production cost-effectiveness. Clothing ; Textile industry ; Simple (philosophy) ; Fast fashion ; Fashion industry ; Software ; Fashion design ; Sustainable design Textile materials and evaluations ; Fashion and Cultural Textiles ; Crafts, Textile, and Design
Authors: A Hossain | Abdullah Al Mamun | Md. Faisal Mahmud | A T M Faiz Ahmed
Source: Communications in development and assembling of textile products (article, 2025-12-20)
Field: Materials Science — Polymers and Plastics

Impact of yarn type (fiber content), yarn fineness, and structural variation on width contraction of flat-knitted rib derivatives This study systematically examines width-wise contraction in double jersey knit fabrics produced on a flatbed rib knitting machine, emphasizing the influence of yarn fiber composition, yarn fineness, and structural variations. Fabrics with different structural designs were manufactured using plied yarns composed of 100% cotton, 100% polyester, and polyester-cotton blends (50% for both) across different yarn counts, all knitted under a uniform machine gauge and stitch settings to ensure structural consistency. Subsequently, the wale densities of these samples were measured after dry relaxation, and a modified analytical formula was employed to conveniently calculate the width contraction for rib-based flat-knitted structures. The results revealed that fiber type has a pronounced effect, with cotton-based fabrics exhibiting higher contraction compared to polyester, although the yarn-consisting blend of these fibers showed a statistically non-significant difference in width contraction either with Cotton or Polyester. Yarn fineness (tex) showed a strong negative correlation with width contraction, as validated by a high co-efficient of determination (R² &gt; 0.95), indicating its predictive power. Structural variations, such as jersey type, missing-needle patterns, and rib configurations (such as simple ribs and broad ribs) were found to have notable effects on contraction owing to their impact on wale density and fabric geometry, and statistical analyses confirmed the substantial influence of such structural choices on width contraction. This study provides critical insights into the predictive control of fabric width during the design and manufacturing stages. Yarn ; Fineness ; Contraction (grammar) ; Fiber ; Fiber type ; Hexagonal crystal system Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Mechanical Behavior of Composites
Authors: Johannes K. Krondorfer | Christian W. Binder | Andreas Hauser
Source: arXiv (Cornell University) (preprint, 2025-01-27)
Field: Materials Science — Materials Chemistry

Symmetry- and Gradient-enhanced Gaussian Process Regression for the Active Learning of Potential Energy Surfaces in Porous Materials The theoretical investigation of gas adsorption, storage, separation, diffusion and related transport processes in porous materials relies on a detailed knowledge of the potential energy surface of molecules in a stationary environment. In this article, a new algorithm is presented, specifically developed for gas transport phenomena, which allows for a highly cost-effective determination of molecular potential energy surfaces. It is based on a symmetry-enhanced version of Gaussian Process Regression with embedded gradient information and employs an active learning strategy to keep the number of single point evaluations as low as possible. The performance of the algorithm is tested for a selection of gas sieving scenarios on porous, N-functionalized graphene and for the intermolecular interaction of CH$_4$ and N$_2$. Symmetry (geometry) ; Porosity ; Gaussian process ; Regression ; Process (computing) ; Kriging ; Energy (signal processing) ; Porous medium ; Materials science ; Gaussian ; Statistical physics ; Computer science ; Mathematics ; Physics ; Composite material ; Machine learning ; Statistics ; Geometry ; Quantum mechanics Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference
Authors: Gulru Turgunova | Dildora Nuraliyeva
Source:  (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Bayesian optimization of optical materials with tunable bandgaps Bayesian optimization methods allow for efficient exploration of stable solar cell materials with desired values of bandgap energies. quantum-chemical methods to prospect the best candidate from a pool of thousands compounds can take more than 150 experiments. Conventional global optimization methods require over 2,000 iterations, which renders high-accuracy calculations of candidate materials computationally intractable. Scientific-of-science: Many optimization techniques are very sensitive to properties of the optimization problem, a situation that scientific-of-science study would aim to reveal. Bayesian symbolic regression reveals a procedure for the eigensolver to converge from a random wave function in 94 % of the cases. These human insights on the shape of the band structure are ingrained in the Bayesian optimization approaches, and in our work, the band gaps of the best performing hybrid organic— perovskite materials are found within 40 iterations for a single detail of the band structure and within 80 iterations for discriminating between multiple crystal structures. The methodology is embodied as an interactive design framework that connects atomic and device level features. This approach not only presents the Bayesian optimization on tunable bandgap optical materials as a new and powerful paradigm for materials development but also is predicted to be widely applicable given the progress of materials chemistries in recent decades. Bayesian optimization ; Bayesian probability ; Band gap ; Optimization problem ; Optimization algorithm ; Function (biology) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Organic Electronics and Photovoltaics
Authors: Minhee Park | Min Ki Kim | Yesol Kim | Seung Jae Kwak | Woo‐Bin Jung | Jung Hee-Tae | Won Bo Lee | YongJoo Kim
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

From Prediction to Synthesis: Dft-Active Learning-Guided Design of Multimetallic Catalysts for Hydrogen Evolution Catalysis ; Hydrogen production ; Computer science ; Combinatorial chemistry ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies ; Advanced Materials Characterization Techniques
Authors: Ryota Ito | Takamitsu Ishiyama | Koki Nozawa | Jun Kikkawa | Takashi Suemasu | Kaoru Toko
Source: CrystEngComm (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

High-quality multilayer graphene on insulators <i>via</i> metal catalyst alloying in layer exchange with Bayesian optimization Multilayer graphene was synthesized on glass via metal-induced layer exchange using Ni–Co–Fe alloy catalysts. Bayesian optimization identified the optimal composition, achieving high crystallinity suitable for next-generation electronics. Graphene ; Materials science ; Layer (electronics) ; Bayesian optimization ; Catalysis ; Quality (philosophy) ; Metal ; Nanotechnology ; Chemical engineering ; Metallurgy ; Computer science ; Chemistry ; Machine learning ; Physics ; Engineering ; Organic chemistry Graphene research and applications ; Thermal properties of materials ; Radiation Effects in Electronics
Authors: Kohei Nakamura | Daigo Furuya | Yoshio Miura | Takahiro Yamazaki | Alexandre Lira Foggiatto | Yuma Iwasaki | Masato Kotsugi
Source: Science and Technology of Advanced Materials Methods (article, 2025-04-04)
Field: Materials Science — Materials Chemistry

Efficient discovery of large magnetic anisotropy with thermodynamically stable materials via multi-objective Bayesian optimization This study presents an efficient magnetic material exploring method with large magnetocrystalline anisotropy (MCA) and thermodynamic stability by combining multi-objective Bayesian optimization (MBO) with first-principles calculations. Focusing on six-layer tetragonal ordered alloys of rare-earth-free elements (Fe, Co, Ni, Cu), our approach optimizes material properties and process conditions within high-dimensional spaces. Explanatory variables derived from elemental and structural information were employed to enhance Bayesian optimization. Utilizing Gaussian process regression and dimensionality-reduced descriptors, we achieved efficient and robust exploration for precomputed dataset. As a result, MBO efficiently suggested the following three materials – Fe/Fe/Fe/Ni/Fe/Ni, Fe/Co/Fe/Co/Fe/Ni, and Fe/Co/Co/Fe/Ni/Ni – that exhibit thermodynamic stability while demonstrating an MCA value up to four times larger than that of <i>L</i>1<sub>0</sub>-FeNi. The developed methodology accelerates material discovery and reduces resource demands, offering a scalable materials solution for optimizing functionality and process conditions across diverse material systems. This study accelerates advanced magnetic materials discovery by integrating multi-objective Bayesian optimization and first-principles calculations, uncovering rare-earth-free alloys with exceptional magnetocrystalline anisotropy and thermodynamic stability, enabling scalable materials development. Bayesian optimization ; Anisotropy ; Bayesian probability ; Materials science ; Computer science ; Statistical physics ; Physics ; Artificial intelligence ; Quantum mechanics Machine Learning in Materials Science ; Grey System Theory Applications ; Non-Destructive Testing Techniques
Authors: Daigo Kaneko | Risa Iwatsubo | Hajime Wagata | Hiromasa Kaneko
Source: Industrial & Engineering Chemistry Research (article, 2025-03-15)
Field: Materials Science — Materials Chemistry

Adaptive Design of Experimental Conditions for LaFeO<sub>3</sub> Crystals with Experiments and Bayesian Optimization In this study, Bayesian optimization was combined with experiments to optimize the experimental conditions for the synthesis of LaFeO3 crystals with desired crystal properties using the flux growth method. The first set of candidates for the experimental conditions was determined based on the design of experiments. LaFeO3 crystals were synthesized under specific conditions, and their crystal properties were analyzed. Using the obtained data set, a Gaussian process regression model Y = f(X) was constructed with experimental conditions as explanatory variables X and crystal properties as objective variables Y. Candidate experimental conditions were then input into the model to predict crystal properties, and an acquisition function was calculated. Candidate experimental conditions with large values of the acquisition function were selected. Subsequently, LaFeO3 crystals were synthesized under these conditions, and their crystal properties analyzed. Construction of the Gaussian process regression model, selection of the next candidates for experimental conditions, and synthesis were repeated to synthesize LaFeO3 crystals with the desired crystal properties. Three case studies confirmed that LaFeO3 crystals with small crystallite sizes, calculated using X-ray diffraction (XRD) patterns and Scherrer’s equation, can be synthesized within a small number of experiments. Furthermore, an index was prepared to evaluate the presence of impurities by excluding the LaFeO3-derived peaks from the XRD patterns of the products, which were used as Y in addition to crystallite size. A multiobjective optimization was conducted, and the suppression of impurity formation and reduction of crystallite size were achieved simultaneously. Bayesian optimization ; Bayesian probability ; Computer science ; Materials science ; Biological system ; Process engineering ; Artificial intelligence ; Engineering ; Biology Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors
Authors: Nick Julian | Giacomo Po | Enrique Martínez | Nithin Mathew | Danny Pérez
Source: Modelling and Simulation in Materials Science and Engineering (article, 2025-12-18)
Field: Materials Science — Materials Chemistry

Active learning of a crystal plasticity flow rule from discrete dislocation dynamics simulations Abstract Continuum-scale material deformation models, such as crystal plasticity (CP), can significantly enhance their predictive accuracy by incorporating input from lower-scale (i.e. mesoscale) models. The procedure to generate and extract the relevant information is however typically complex and ad hoc, involving decision and intervention by domain experts, leading to long development times. In this study, we develop a principled approach for calibration of continuum-scale models using lower scale information by representing a CP flow rule as a Gaussian process model. This representation allows for efficient parameter space exploration, guided by the uncertainty embedded in the model through a process known as Bayesian optimization (BO). We demonstrate a semi-autonomous BO loop which instantiates discrete dislocation dynamics simulations whose initial conditions are automatically chosen to optimize the uncertainty of a model CP flow rule. Our self-guided computational pipeline efficiently generated a dataset and corresponding model whose error, uncertainty, and physical feature sensitivities were validated with comparison to an independent dataset four times larger, demonstrating a valuable and efficient active learning implementation readily transferable to similar material systems. Representation (politics) ; Calibration ; Bayesian optimization ; Gaussian process ; Flow (mathematics) ; Dislocation ; Pipeline (software) ; Process (computing) ; Gaussian Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Gaussian Processes and Bayesian Inference
Authors: Bejjipurapu, Akhil | Karumuri, Sharmila | Flanagan, Joseph C. | Tucker, Victoria | Bilionis, Ilias | Strachan, Alejandro | Sandhage, Kenneth H. | Titus, Michael S.
Source: ArXiv.org (preprint, 2025-12-17)
Field: Materials Science — Materials Chemistry

Active Learning Discovery of High Temperature Oxidation Resistant Refractory Complex Concentrated Alloys Refractory complex concentrated alloys (RCCAs) are of significant interest for advanced high-temperature applications, owing to their broad compositional range and potential for attractive mechanical properties and oxidation resistance. However, their compositional complexity poses significant challenges to conventional alloy discovery methodologies. In this study, an active learning framework is introduced that integrates Gaussian process regression with Bayesian global optimization to accelerate identification of oxidation-resistant RCCAs. Focusing on aluminum-containing quaternary systems, alloy and oxide descriptors were used to predict oxidation performance at 1000$^\circ$C. Beginning with a dataset of 81 experimentally validated RCCAs, this framework was used to iteratively select alloy batches (five alloys per batch) with optimization based on a balance between exploration and exploitation to minimize associated experimental costs. After six iterations, two alloys were identified (nominal Al$_{30}$Mo$_5$Ti$_{15}$Cr$_{50}$ and Al$_{40}$Mo$_5$Ti$_{30}$Cr$_{25}$) that exhibited specific mass gains less than 1 mg/cm$^2$ at 1000$^\circ$C in air. Both of these alloys formed adherent external $α$-Al$_2$O$_3$ scales and exhibited parabolic oxidation kinetics consistent with diffusion-limited scale growth. Furthermore, our multiobjective analysis demonstrates that these alloys simultaneously achieve high specific hardness ($&gt;0.12$ HV$_{0.5}$m$^3$/kg) and thermal expansion compatibility with thermal barrier coating systems, positioning them as promising bond coat candidates. This work underscores the efficacy of active learning in traversing complex compositional landscapes, and offers a scalable strategy for the development of advanced materials suitable for extreme environments. Bayesian optimization ; Alloy ; Oxide ; Coating ; Kriging ; Thermal barrier coating ; Gaussian process ; Scalability ; Thermal Machine Learning in Materials Science ; High Entropy Alloys Studies ; High-Temperature Coating Behaviors
Authors: Yue Yin | Jr‐Hau He | Runze Li | Qiu, Yunze | Dingsheng Wang | Li, Jun | Xiao Hai
Source: ArXiv.org (preprint, 2025-03-25)
Field: Materials Science — Materials Chemistry

LOCAL: A Locality-based Active Learning Framework for Predicting the Stability of Dual-Atom Catalysts Dual-atom catalysts supported on nitrogen-doped graphene (DAC/NG) are emerging as a family of promising catalysts that can overcome intrinsic limitations of single-atom catalysts. However, comprehensive assessment of their structural stability is prohibitively demanding due to a vast local configurational space. Here we introduce LOCAL, a locality-based framework that combines graph convolutional networks with active learning to efficiently predict DAC/NG stability by leveraging chemically intuitive locality quantified by crystal orbital Hamilton population analysis. We demonstrate the effectiveness of LOCAL over a comprehensive dataset of 611,648 DAC/NG structures, achieving a test mean absolute error of 0.15~eV while invoking density functional theory calculations for only 16,704 structures (2.7% of the dataset). Thus, LOCAL enables efficient and accurate construction of phase diagrams for DAC/NG across diverse compositions reciprocally validated with experimentally synthesized configurations for representative systems. Our framework composes an essential methodology for accelerating the discovery and optimization of high-performance complex catalysts. Stability (learning theory) ; Locality ; Population ; Graph ; Density functional theory ; Phase (matter) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts