jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sharmi Mazumder | Mohammad Hossein Golbabaei | Ning Zhang
Source: Biomimetics (article, 2025-12-01)
Field: Materials Science — Biomaterials

Advances in Computational Modeling and Machine Learning of Cellulosic Biopolymers: A Comprehensive Review The hierarchical structure and multifunctional properties of bio-based cellular materials, particularly cellulose, hemicellulose, and lignin, have attracted increasing attention and interest due to their sustainability and versatility. Recent advances in computational modeling and machine learning strategies have provided transformative insights into the molecular, mechanical, thermal, and electronic behaviors of these biopolymers. This review categorizes the conducted studies based on key material properties and discusses the computational methods utilized, including quantum mechanical approaches, atomistic and coarse-grained molecular dynamics, finite element modeling, and machine learning techniques. For each property, such as structural, mechanical, thermal, and electronic, we have analyzed the progress made in understanding inter- and intra-molecular interactions, deformation mechanisms, phase behavior, and functional performance. For instance, atomistic simulations have shown that cellulose nanocrystals exhibit a highly anisotropic elastic response, with axial elastic moduli ranging from approximately 100 to 200 GPa. Similarly, thermal transport studies have shown that the thermal conductivity along the chain axis (≈5.7 W m<sup>-1</sup> K<sup>-1</sup>) is nearly an order of magnitude higher than that in the transverse direction (≈0.7 W m<sup>-1</sup> K<sup>-1</sup>). In recent years, this research area has also experienced rapid advancement in data-driven methodologies, with the number of machine learning applications for biopolymer systems increasing more than fourfold over the past five years. By bridging multiscale modeling and data-driven approaches, this review aims to illustrate how these techniques can be integrated into a unified framework to accelerate the design and discovery of high-performance bioinspired materials. Eventually, we have discussed emerging opportunities in multiscale modeling and data-driven discovery to outline future directions for the design and application of high-performance bioinspired materials. This review aims to bridge the gap between molecular-level understanding and macroscopic functionality, thereby supporting the rational design of next-generation sustainable materials. Computational model ; Multiscale modeling ; Finite element method ; Bridging (networking) ; Thermal conductivity ; Transformative learning ; Anisotropy ; Elastic modulus Advanced Cellulose Research Studies ; Natural Fiber Reinforced Composites ; Composite Material Mechanics
Authors: Gaoxiang He | Huihui Yan | Rongli Fan | Mingyue Zhao | Jianming Liu | Yong Zhou | Zhigang Zou | Zhaosheng Li
Source: Small Methods (article, 2025-06-12)
Field: Materials Science — Materials Chemistry

Machine Learning on a Synergistic Transition Metal Dual‐Atom Surface for Efficient Decomposition of Ammonia The technology of H<sub>2</sub> production through NH<sub>3</sub> decomposition is of great importance for finding clean energy alternatives to fossil fuels. Here, a framework for screening dual-atom catalysis by integrating machine learning (ML) with high-throughput (HT) calculations to predict the catalytic performance of dual-atom systems for NH<sub>3</sub> decomposition is designed. First-principles-based HT calculations are conducted on 62 randomly selected systems for intermediate steps. Then, feature engineering is employed to obtain features with high importance and low correlation. The results of the HT calculations are subsequently used as a training set to train the ML model. The well-trained model is subsequently used to predict the catalytic performance of 2187 structures. Several potentially good dual-atom catalysts (RuMo─O─C, ScOs─N─C, and OsV─N─C) for NH<sub>3</sub> decomposition are obtained. Finally, the density of states and differential charge analysis show the presence of the synergistic catalytic process in these dual-atom catalysts. Dual (grammatical number) ; Decomposition ; Ammonia ; Atom (system on chip) ; Transition metal ; Metal ; Materials science ; Chemistry ; Chemical physics ; Nanotechnology ; Metallurgy ; Computer science ; Parallel computing ; Organic chemistry ; Catalysis ; Philosophy Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Catalytic Processes in Materials Science
Authors: Mohammad Gheibi | Eriona Canga | Mohamed Eldessouki
Source: Results in Engineering (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Life cycle assessment of nanowires for sustainable material production: A systematic review • Integration of LCA with AI for predictive sustainability assessment. • Development of online dashboard for real-time LCA data visualization. • Application of ex-ante and process-based LCA for emerging nanomaterial systems. • Coupling SSbD principles with LCA to guide safe, eco-efficient material design. • Implementation of ML algorithms for sustainability clustering and optimization. Nanowires (NWs) are groundbreaking materials in the domains of energy storage, photovoltaics, electronics, and biomedicine due to their exceptional electrical, optical, and structural properties. However, because their synthesis often involves energy-intensive processes and hazardous chemicals, there are serious sustainability issues. This analysis integrates bibliometric and content-based evaluations carried out over the past decade to evaluate the environmental performance of silicon, alumina, gallium-based, and silicon-carbide NWs using Life Cycle Assessment (LCA). Comparative results from multiple applications, such as CO₂ photoreduction, lithium-ion batteries, tandem solar cells, and polymer reinforcement, demonstrate that silicon NWs are among the most environmentally hazardous, primarily contributing to human toxicity, mineral resource depletion, and the potential for global warming. Alumina and SiC NWs, on the other hand, have better environmental profiles, underscoring the significance of choosing sustainable materials. Important methodological flaws are noted, such as insufficient end-of-life modelling, the omission of Life Cycle Cost (LCC) and Social LCA (S-LCA), and the failure to adequately account for regional energy mixes. The paper suggests using artificial intelligence and machine learning to improve data-driven modelling, scenario prediction, and sustainability forecasting in order to address these problems. It also suggests combining Safe-and-Sustainable-by-Design (SSbD) principles with LCA. The novelty of this study lies in integrating SSbD and AI/ML into the LCA framework through MATLAB and online dashboards for predictive, data-driven NW sustainability design. It provides a policy-oriented roadmap to steer nanowire technologies toward socially equitable, economically feasible, and environmentally sustainable innovation, consistent with circular economy and SSbD principles. Life-cycle assessment ; Sustainability ; Resource (disambiguation) ; Sustainable development ; Efficient energy use ; Big data ; Novelty Anodic Oxide Films and Nanostructures ; Photovoltaic Systems and Sustainability ; Machine Learning in Materials Science
Authors: Jiacheng Li | Jiaxuan Wang | Tiwei He | Zenan Li | Haojie Xu | Zhenglong Fan | Fan Liao | Yang Liu | Zhenhui Kang
Source: ChemCatChem (article, 2025-05-21)
Field: Materials Science — Materials Chemistry

Dynamic Machine Learning‐Driven Optimization of Microwave‐Synthesized Photocatalysts for Enhanced Hydrogen Peroxide Production Abstract Photocatalysis offers an energy‐efficient and sustainable solution to environmental pollution and energy shortages. The core of this process lies in photocatalysis. However, establishing a clear relationship between their structure and performance through traditional experimental methods is often time‐intensive and labor‐intensive. Machine learning (ML) has recently gained traction in guiding photocatalyst synthesis, though it is often challenged by limited data availability. This study introduces a dynamic ML‐guided approach that iteratively optimizes experimental parameters through successive cycles of ML analysis and experimentation, effectively circumventing the need for large datasets. Applied to the synthesis of photocatalysts via microwave heating of quercetin, this method yielded optimal performance after three iterations, achieving a high hydrogen peroxide production rate. This ML approach demonstrates an effective few‐shot ML optimization strategy for catalyst synthesis. Hydrogen peroxide ; Microwave ; Hydrogen production ; Photocatalysis ; Production (economics) ; Microwave irradiation ; Chemistry ; Photochemistry ; Microwave chemistry ; Catalysis ; Materials science ; Nanotechnology ; Computer science ; Organic chemistry ; Telecommunications Machine Learning in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Advanced Chemical Sensor Technologies
Authors: Philippa L. Jacob | Marie Claire Parker | Daniel J. Keddie | Vincenzo Taresco | Steven M. Howdle | Jonathan D. Hirst
Source: Chemical Science (article, 2025-11-18)
Field: Materials Science — Biomaterials

Explainable random forest predictions of polyester biodegradability using high-throughput biodegradation data The development of new, biodegradable polyesters is becoming increasingly important as legislative and consumer drivers push society towards more sustainable polymers. One key bottleneck in the development of biodegradable polyesters is the slow nature of biodegradability testing, which can often take weeks to months. High-throughput screening assays serve as a rapid tool to determine the biodegradability of materials quickly, without the need for lengthy, expensive testing. When combined with machine learning, the data generated by high-throughput assays can be exploited to predict the properties of other similar materials. Here, we report the development of a high-throughput enzymatic biodegradation assay, which has been used to determine the biodegradability of 48 polyesters. Using data generated from the assay to train a predictive model, we can predict the biodegradability of polyesters using an explainable random forest model with 71% accuracy. Transfer learning and model chaining were investigated as routes to improve the model predictions by exploiting existing literature data. SHAP analysis gives insight into the beneficial structural features of biodegradable polyesters. This understanding can be applied in the development of future biodegradable polyesters. Biodegradation ; Polyester ; Bottleneck ; Chaining ; Random forest biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Polymer crystallization and properties
Authors: Junsong Jin | Fangtao Chai | Jinchuan Long | Chang Gao | Shaolei Wang | Pan Zeng | Xuefeng Tang | Pan Gong | Mao Zhang | Lei Deng | Xinyun Wang
Source: Journal of Materials Research and Technology (article, 2025-07-01)
Field: Materials Science — Biomaterials

Constitutive modeling and workability characterization of pre-deformed AZ31 magnesium alloy during hot shear-compression deformation Pre-deformation is a cost-efficient method to control the microstructure and mechanical properties of wrought magnesium (Mg) alloys. It introduces twins that promote dynamic recrystallization during hot deformation, thereby improving hot workability and weakening texture. However, uniaxial hot compression tests fail to reflect the deformation behavior of Mg alloys under hot shear-compression process. In this study, shear-compression samples (SCS) were employed, and finite element simulations were combined with physical experiments to determine the Mises coefficients in the equivalent stress-strain conversion equation. The deformation characteristics, flow behavior and microstructure/texture evolution mechanisms of pre-deformed AZ31 alloy were systematically investigated under varying process parameters. A genetic algorithm-optimized artificial neural network (GA-ANN) constitutive model was developed using machine learning methods, and hot processing maps were established based on this model. The results show that the proposed method can accurately determine the Mises coefficients, describing the mechanical response of SCS at high temperatures. The shear-compression state causes the material's flow behavior to exhibit a distinct single-peak characteristic, with peak stress and peak strain increasing and then decreasing with the amount of pre-deformation.The GA-ANN model demonstrates good prediction accuracy and generalization ability for the hot shear-compression behavior of pre-deformed AZ31 alloy. The developed hot processing map can precisely predict microstructure evolution. The optimal pre-deformation amount is determined to be 2%, with a recommended hot processing window defined as 300-400 °C and 0.07-7 s −1 . • A method was proposed to determine coefficients in stress-strain conversion equation. • The GA-ANN model considers pre-deformation amount and hot deformation conditions. • A hot processing map considering different pre-deformation amounts was established. • A pre-deformation amount of 2% can improve the hot workability of AZ31 alloy. Materials science ; Magnesium alloy ; Deformation (meteorology) ; Metallurgy ; Alloy ; Magnesium ; Compressive strength ; Constitutive equation ; Shear (geology) ; Characterization (materials science) ; Composite material ; Structural engineering ; Finite element method Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Yajie Wang | Xinran Wang | Liyan Shan
Source: Fuel (article, 2025-04-14)
Field: Chemistry — Analytical Chemistry

Asphalt oil source determination model based on machine learning Asphalt ; Petroleum engineering ; Environmental science ; Computer science ; Artificial intelligence ; Materials science ; Geology ; Composite material Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Advanced Algorithms and Applications
Authors: Pandu Wisesa | Terumasa Tadano | Wissam A. Saidi
Source: Computational Materials Science (article, 2025-01-29)
Field: Materials Science — Materials Chemistry

Deep-learning neural network potentials for titanate perovskites Materials science ; Titanate ; Artificial neural network ; Nanotechnology ; Computer science ; Artificial intelligence ; Metallurgy ; Ceramic Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Memory and Neural Computing
Authors: H. H. Hung | Ying‐Chieh Hung
Source: Journal of the Taiwan Institute of Chemical Engineers (article, 2025-03-05)
Field: Materials Science — Materials Chemistry

Leveraging spatial charge descriptor in deep learning models: Toward highly accurate prediction of vapor-liquid equilibrium Charge (physics) ; Deep learning ; Computer science ; Statistical physics ; Artificial intelligence ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Kan Tang | Shengxian Liu | Jian Zhou | Zhimei Sun
Source: Nanoscale (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Deep-learning-enabled high-throughput Screening of MXene photocatalysts for hydrogen production The challenge of photocatalytic hydrogen production has motivated a targeted search for MXenes as a promising class of materials for this transformation because of their high mobility and high light absorption. High-throughput screening has been widely used to discover new materials, but the relatively high cost limits the chemical space for searching MXenes. We developed a deep-learning-enabled high-throughput screening approach that identified 14 stable candidates with suitable band alignment for water splitting from 23 857 MXenes. Through the deep learning framework utilizing the layered structure of two-dimensional materials trained on the C2DB, we predicted the properties, including formation energy, convex hull energy, and bandgap with mean absolute errors of 0.06 eV per atom, 0.06 eV per atom, and 0.14 eV, respectively. Through further density functional theory and non-adiabatic molecular dynamics calculations, we identified a series of descriptors that demonstrate the photocatalytic potential of the candidate MXenes in solar-to-hydrogen efficiency, light absorption, and carrier separation. Using symbolic regression, we proposed a descriptor that captures the relationship between the built-in electric field and the nonadiabatic electron-hole coupling. Notably, the presence of built-in electric fields in Janus MXenes can suppress recombination rates due to spontaneous separation of photogenerated carriers. Our work demonstrates an efficient computational strategy for inversely designing 2D photocatalysts and provides a strategy for designing high-performance photocatalysts for hydrogen production. Hydrogen production ; Throughput ; Photocatalysis ; Materials science ; High-throughput screening ; Production (economics) ; Nanotechnology ; Hydrogen ; Environmental science ; Computer science ; Catalysis ; Chemistry ; Telecommunications ; Wireless ; Organic chemistry MXene and MAX Phase Materials ; Advanced Photocatalysis Techniques ; Ammonia Synthesis and Nitrogen Reduction
Authors: Bogdan Protsenko | Yuya Kakiuchi | С. А. Гуда | D. Trummer | Anna Zabilska | Світлана Ігорівна Шаповалова | А. В. Солдатов | Оlga V. Safonova | Christophe Copéret | Alexander A. Guda
Source: The Journal of Physical Chemistry C (article, 2025-01-29)
Field: Materials Science — Materials Chemistry

Fingerprint Analysis of X-ray Absorption Spectra with the Machine-Learning Method Trained on the Multielement Experimental Library X-ray absorption near edge structure (XANES) spectroscopy is a powerful method to probe the oxidation state and local structure of metals in catalytic materials. However, it suffers from a lack of unbiased data analysis protocols. Machine learning (ML) overcomes human-related factors by uncovering relevant spectrum-structure relationships and resulting cross-validation analysis. The bottlenecks in the automatic processing of experimental data are the lack of chemically diverse XANES reference libraries and the systematic differences between theory and experiment. Therefore, compiling experimental reference libraries across the periodic table and rational application of ML methodology to small (in terms of data science) training data sets becomes increasingly important. This work revises the classical XANES fingerprint analysis by database augmentation, feature extraction, cross-validation, and uncertainty analysis. We apply the developed methodology to decipher the oxidation state and local coordination of supported vanadium-oxo species (VOx), which change their structure by participating in oxidative dehydrogenation catalysis. The developed Cr K-edge and V K-edge experimental library and instruments for analysis may serve as a starting point for a unified platform for fingerprint XANES data analysis. Fingerprint (computing) ; Analytical Chemistry (journal) ; Absorption (acoustics) ; X-ray ; Spectral line ; Absorption spectroscopy ; Materials science ; Chemistry ; Artificial intelligence ; Computer science ; Chromatography ; Optics ; Physics Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Medical Imaging Techniques and Applications
Authors: Wubshet Getachew Mengesha | Kaveer Nagessar
Source: Discover Materials (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

A critical review on electronic materials properties and multifunctional applications The rapid advancement of electronic technologies necessitates the development of materials with tailored properties for multifunctional applications. However, there are significant challenges include a fundamental gap in connecting quantum-level behavior to macroscopic properties, data scarcity, and difficulties in integrating multidisciplinary datasets. This paper aims to analyze recent advancements and propose integrated frameworks to bridge these gaps by leveraging artificial intelligence (AI) and machine learning (ML) with a comprehensive review methodology and critical analysis of properties, types, their diverse applications coupled with AI-driven approaches, including generative models, physics-informed neural networks, and autonomous laboratories, for predicting and optimizing electronic materials. Key findings highlight their diverse applications and discovery such as perovskites, 2D mate- rials, and high-temperature superconductors—and in optimizing electronic, thermal, and magnetic characteristics. Recent studies indicate that AI-driven approaches can improve prediction accuracy and enable inverse design in selected systems. These approaches have the potential for significant impact on materials discovery and integration, potentially leading to a transformation of the electronic materials landscape. This paper underscores the future potential of AI-driven paradigms to revolutionize the electronic materials landscape by integrating computational prediction with experimental validation for multifunctional real-world applications. Multidisciplinary approach ; Bridge (graph theory) ; Key (lock) ; Electronic materials ; Electronics ; Generative grammar Machine Learning in Materials Science ; Quantum many-body systems ; Heusler alloys: electronic and magnetic properties
Authors: Johann Lüder
Source: Physical review. B./Physical review. B (article, 2025-02-04)
Field: Materials Science — Materials Chemistry

Machine learning approach to predict <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>L</mml:mi></mml:mrow></mml:math>-edge x-ray absorption spectra of light transition metal ion compounds Simulation of spectra of x-ray absorption spectroscopy (XAS) at the $L$ edge is a well-established and reliable computational tool that, in combination with experimental measurements, reveals details about the local electronic structure and the chemical environment of transition metal ions (TMIs). For light TMIs, $2p$ XAS model Hamiltonian approaches (MHAs) are fast and accurate in computing spectra. Here, an alternative method using artificial neural networks (ANNs) is employed. This approach predicts x-ray absorption spectra at the $L$ edge of light transition metal ions for elements from Ti to Ni, including different oxidation states, from sets of physical input parameters. Computational performance and accuracy were optimized through a systematic exploration of ANN design and compared to MHA simulations. While the MHA remains more accurate due to the statistical nature of the developed machine learning method, the ANN-based prediction of $2p$ XAS spectra achieved good accuracy of more than 99%, in addition to a noticeable advantage in computational speed. This makes the method intrinsically more suitable for computational high-throughput setups as well as in support of data-intense experimental workflows. However, the extrapolation weakness of artificial neural networks remained for most cases and displayed a strong dependence on the TMI. For one TMI, an extrapolation accuracy of 78% was maintained. Algorithm ; Physics ; Mathematics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods