jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Luneng Zhao | Hongsheng Liu | Yuan Chang | Xiaoran Shi | Jijun Zhao | Feng Ding | Junfeng Gao
Source: Nature Communications (preprint, 2026-02-23)
Field: Materials Science — Materials Chemistry

Intermediates of forming transition metal dichalcogenide heterostructures revealed by machine learning simulations Two-dimensional (2D) transition metal dichalcogenide (TMD) van der Waals heterostructures (vdWHs) hold promise for high-performance electronics, but their large-scale synthesis remains limited by size constraints and alloying contaminations. Recently, a two-step vapor deposition method was reported for growing wafer-size TMD vdWHs with minimal impurities. In this study, we develop a machine learning potential (MLP) that captures the atomic-scale dynamic growth process of bilayer MoS<sub>2</sub>/WS<sub>2</sub> vdWHs under feasible growth conditions. Our simulations uncover a crucial metastable SMMS (M = Mo or W) intermediate structure that facilitates metal atom swap and alloying. Eliminating the alloying contamination requires preventing the embedding of bare metal atoms. The results also show that the SMMS structure exhibits favorable electronic properties and emerges as a low Schottky barrier contact electrode for MoS<sub>2</sub> field-effect transistors (FETs). Mechanism (biology) ; Ab initio ; Dynamics (music) ; Chemical physics ; Molecular dynamics ; Transition (genetics) ; Materials science ; Computational chemistry ; Physics ; Chemistry ; Quantum mechanics Machine Learning in Materials Science ; 2D Materials and Applications
Authors: Xitai Cai | Yuxun Wu | Libo Li | Lijun Liao | Penghua Ying | Yanying Wei
Source: Nano Research (article, 2026-02-14)
Field: Materials Science — Materials Chemistry

HULU: A unified Monte Carlo framework for adsorption simulations with machine learning potentials Adsorption in nanoporous materials is pivotal for addressing global challenges in gas storage, separation, sensing, catalysis, and atmospheric water harvesting. Consequently, molecular simulations are essential for understanding adsorption mechanisms and accelerating material discovery. Key thermodynamic descriptors, such as adsorption isotherms, density distributions, and Henry constants, are particularly valuable for high-throughput screening and predicting separation performance. Recently, machine learning potentials (MLPs) have emerged as a powerful tool, offering near-ab-initio accuracy with high computational efficiency. While MLPs have been extensively applied in molecular dynamics simulations, their integration into Monte Carlo (MC) simulations for adsorption remains largely untapped. This limitation arises primarily because mainstream MC simulation codes are designed for empirical force fields and lacks native support for MLPs. In this work, we developed a flexible Python package, HULU (High-throughput Universal Learning-enabled Utility for Adsorption), to bridge this gap. We present the first demonstration of calculating full adsorption isotherms using state-of-the-art foundation MLPs (MACE-MATPES-PBE-0, NEP89, and ORB v3). Furthermore, we systematically benchmark these models against standard baselines, such as available experimental or density-functional-theory calculation data, and elucidate the microscopic origins of deviations in the simulation results. Ultimately, HULU paves the way for incorporating high-fidelity MLPs into high-throughput screening workflows, significantly enhancing the predictive design of nanoporous materials for energy and environmental applications. Nanoporous ; Monte Carlo method ; Python (programming language) ; Adsorption ; Benchmark (surveying) ; Molecular dynamics ; Thermodynamic integration Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Phase Equilibria and Thermodynamics
Authors: Jelena Pavlović | Nevenka Rajić
Source: Minerals (article, 2026-02-06)
Field: Chemistry — Inorganic Chemistry

Engineering Zeolite Acidity and Porosity for Improved Esterification: A Review of Mechanisms, Kinetics, and Sustainability Processes Esterification, the reaction between carboxylic acids and alcohols that produces esters and water, plays a vital role in many industries, especially in biodiesel and pharmaceutical manufacturing. Traditional methods using homogeneous mineral acids pose environmental issues, prompting the search for sustainable alternatives—solid acid catalysts. Zeolites offer unique structural advantages, including shape selectivity and adjustable acidity, which improve reaction efficiency and reduce waste. This review provides a detailed examination of how zeolite topology—particularly pore structure and connectivity—influences the kinetics of long-chain fatty acid (LCFA) esterification. It investigates the optimization of acid sites via modifications to the silicon-to-aluminum ratio (Si/Al), ion exchange, and pore engineering to improve mass transfer. The study investigates key reaction mechanisms, specifically the Langmuir–Hinshelwood (LH) and Eley–Rideal (ER) models, to address issues such as mass-transfer limitations and water inhibition. The paper highlights recent advances in sustainable catalyst design, such as hierarchical zeolites and membrane-integrated reactors, for converting biomass-derived feedstocks into valuable esters. It also discusses current research challenges and suggests future directions, including the use of 3D-printed monoliths and machine learning integration, to develop next-generation, eco-friendly zeolite catalysts. Zeolite ; Porosity ; Homogeneous ; Sustainability ; Catalysis ; Chemical reaction engineering Zeolite Catalysis and Synthesis ; Catalysis for Biomass Conversion ; Mesoporous Materials and Catalysis
Authors: Syed Quadir Moinuddin | P. Hariharasakthisudhan | Jitendra Kumar Katiyar | K Sathickbasha | Mohammad Faseeulla Khan | Akbar Niaz
Source: Journal of Materials Research and Technology (article, 2026-02-19)
Field: Materials Science — Biomaterials

Tribological behavior of Sr and Yb modified AZ91 magnesium alloy using machine learning prediction and multi-objective optimization for biomedical applications Tribology ; Magnesium alloy ; Magnesium ; Alloy Magnesium Alloys: Properties and Applications ; Titanium Alloys Microstructure and Properties ; Metallurgy and Material Forming
Authors: Yuxin Qiu | G. L. Chen | Qian Liu | Zhiwen Qi | Kake Zhu | Zhen Song
Source: ENGINEERING Chemical Engineering (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Bridging machine learning and COSMO-SAC for accurate prediction of infinite dilute activity coefficients of binary mixtures Interpretability ; Binary number ; Residual ; COSMO-RS ; Boosting (machine learning) ; Bridging (networking) ; Activity coefficient ; Dilution ; Data point ; Scalability Machine Learning in Materials Science ; Process Optimization and Integration ; Computational Drug Discovery Methods
Authors: Konrad Friedrichs | Franz Dammaß | Karl A. Kalina | Markus Kästner
Source: Computer Methods in Applied Mechanics and Engineering (article, 2026-03-11)
Field: Materials Science — Materials Chemistry

Precise, efficient and flexible modeling of crystallizing elastomers based on physics-augmented neural networks We propose a precise and efficient physics-augmented neural network (PANN) to model strain-induced crystallization in rubbery polymers. We demonstrate that the model can be flexibly employed for both unfilled and filled natural rubber (NR). The approach is based on a two potential framework, similar to the concept of generalized standard materials (GSMs). To describe the material behavior, neural network-based free energy and dissipation potentials are employed. The evolution of crystallinity is derived from the two potentials. To ensure boundedness of the crystallinity, a novel constrained GSM-type evolution problem is proposed. To this end, two additional Lagrange multipliers together with the corresponding Karush-Kuhn-Tucker conditions are introduced. As a result, it is guaranteed that crystallinity can be interpreted as a variable of concentration type. The neural network-based potentials ensure all physically desirable properties by construction. Most importantly, objectivity, material symmetry and thermodynamic consistency are automatically fulfilled. In addition, an alternative derivation of the governing model equations in time-discrete form is presented based on an incremental variational framework, which also serves as the basis for a finite element implementation. We demonstrate the predictive capability of the PANN using three different experimental data sets from literature, considering both stress and crystallinity evolution at material point level as well as the corresponding field distributions in a notched specimen. Moreover, we show that model parameterization is also possible when experimental crystallinity data is not available, still enabling suitable stress predictions. Artificial neural network ; Elastomer ; Finite element method ; Control theory (sociology) Machine Learning in Materials Science ; Advanced Materials and Mechanics ; Advanced Sensor and Energy Harvesting Materials
Authors: Yunji Choi | Seokhyun Choung | Jaebeom Han | Jae-eon Hwang | Hyeon Jin | Yunkyung Kim | Jeongjin Kim | Jeong Young Park | Jeong Woo Han | Hyunjoo Lee
Source: Nature Communications (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Understanding oxygen transfer on ceria with Pt single atoms for surface reaction Reducible metal oxides are widely used in surface reactions, primarily due to their ability to activate and transfer oxygen. Ceria, known for its rapid Ce<sup>3+</sup>/Ce<sup>4+</sup> redox property, is well-known to follow Mars van Krevelen mechanism. In this study, we prepare Pt/CeO<sub>2</sub>-Al<sub>2</sub>O<sub>3</sub> catalysts with different ceria domain sizes of 3.7, 5.6, and 7.3 nm to understand oxygen transfer, mainly O<sub>2</sub> activation and lattice oxygen transfer. The ceria domains are isolated on the alumina, preventing oxygen transfer between the ceria. Pt single atomic structures are meticulously prepared to exclude O<sub>2</sub> activation on Pt nanoparticles and provide uniform active sites. Interestingly, the activity trend for CO oxidation is reversed in O<sub>2</sub>-rich and O<sub>2</sub>-deficient conditions. O<sub>2</sub> activation occurs efficiently in small ceria domains of 3.7 nm. In contrast, larger ceria domains exhibit less O<sub>2</sub> activation but significant lattice oxygen transfer. This behavior is also modeled using large-scale molecular dynamics simulations with a neural network potential trained on first-principles data. Based on these understanding, the catalyst for methane oxidation is proposed by accelerating O<sub>2</sub> activation in O<sub>2</sub>-rich condition. This work provides an impactful platform for understanding metal oxide catalysts. Catalysis ; Redox ; Oxygen ; Oxide ; Metal ; Nanoparticle ; Methane ; Activation energy Catalytic Processes in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Elena-Theodora Moldoveanu | Adelina-Gabriela Niculescu | Denisa Alexandra Florea | Tony Hadibarata | Alexandru Mihai Grumezescu | Dan-Eduard Mihaiescu
Source: Toxics (article, 2026-01-26)
Field: Chemistry — Spectroscopy

Bridging Material Innovation and Environmental Safety: Aerogel-Based Magnetic Nanocomposites as Emerging Platforms for Water Decontamination Currently, water pollution is one of the major global environmental sustainability and public health issues that requires efficient and viable remediation technologies, as existing decontamination methods face limitations. In this sense, this review aims to highlight the potential of multifunctional aerogel-based magnetic nanocomposites as a novel strategy for water decontamination by integrating magnetic nanostructures into aerogel matrices that promote high adsorption capacity, selective catalysis, and facile magnetic recovery. In this regard, providing a comprehensive analysis of their functional design, contaminant-removal mechanisms, and multifunctional performance is crucial for developing and optimizing a system capable of addressing complex pollutants through multiple mechanisms (e.g., adsorption, photocatalysis, and reductive pathways). However, ecotoxicological evaluations focus on the potential for nanoparticles to leach, induce oxidative stress, and cause aquatic toxicity, supporting the development of strategies that comply with safety principles. Additionally, this review examines the aerogels' capabilities for regeneration, operational stability, and scalability across repeated-use cycles, as well as their potential for real-world wastewater applications. Moreover, future directions for these aerogels include the development of smart, stimuli-responsive aerogels, machine-learning-based modeling, and the use of green synthesis approaches to enable sustainable water remediation strategies. Human decontamination ; Environmental remediation ; Bridging (networking) ; Sustainability ; Nanocomposite ; Groundwater remediation ; Sustainable development ; Environmental pollution ; Pollutant Aerogels and thermal insulation ; Advanced Photocatalysis Techniques ; Fluoride Effects and Removal
Authors: Zhengqian Jin | Zhenjiang Cao | Li Jin | S. Y. Ding | Kai Xi
Source: Nano-Micro Letters (article, 2026-01-07)
Field: Materials Science — Materials Chemistry

High-Entropy Layered Hydroxides: Pioneering Synthesis, Mechanistic Insights, and Multifunctional Applications in Sustainable Energy and Biomedicine High-entropy layered hydroxides (HELHs), an emerging frontier in entropy-stabilized materials derived from layered double hydroxides (LDHs), have captivated attention with their unparalleled tunability, thermodynamic stability, and electrochemical performance. The integration of the high-entropy concept into LDHs empowers HELHs to surmount the constraints of conventional materials through compositional diversity, structurally disordered configurations, and synergistic multi-element interactions. This review systematically embarks on their synthesis methodologies, functional mechanisms, and applications in energy conversion/storage and biomedicine. Advanced synthesis strategies, such as plasma-assisted hydrothermal methods, facilitate precise control over HELH architectures while supporting scalable production. HELHs demonstrate superior electrochemical performance in critical reactions, including oxygen evolution reaction, water oxidation, hydrogen evolution, and glucose electrooxidation. Future directions encompass integrating in situ characterization with simulations, leveraging machine learning for composition screening, and expanding HELHs application through interdisciplinary collaborations. This work establishes a comprehensive roadmap for advancing HELHs as next-generation multifunctional platforms for sustainable energy and biomedical technologies. Sustainable energy ; Oxygen evolution ; Characterization (materials science) ; Electrochemical energy storage ; Biomedicine ; Work (physics) ; Scalability ; Scope (computer science) ; Thermal management of electronic devices and systems Layered Double Hydroxides Synthesis and Applications ; Electrocatalysts for Energy Conversion ; Subcritical and Supercritical Water Processes
Authors: Yong Liu | Chenqing Hua | Menglong Xu | Tao Zeng | Jiahua Rao | Zhongyue Zhang | Rongjie Wu | Jing-Ke Weng Jing-Ke Weng | Connor W. Coley | Shuangjia Zheng
Source: Nature Catalysis (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

A geometric foundation model for enzyme retrieval with evolutionary insights Function (biology) ; Identification (biology) ; Foundation (evidence) ; Enzyme ; Protein engineering ; Range (aeronautics) ; Evolutionary algorithm Machine Learning in Materials Science ; Enzyme Structure and Function ; Protein Structure and Dynamics
Authors: G Vargas G | Anuja Sudarsanan | Sreelatha K S | Beena Saraswathyamma
Source: Progress in Solid State Chemistry (article, 2026-01-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A comprehensive review on advancements, challenges, and future possibilities of biomass-derived porous carbon for supercapacitors Supercapacitor ; Carbon fibers ; Capacitance ; Raw material ; Energy storage ; Electrochemical energy storage ; Porosity Supercapacitor Materials and Fabrication ; Catalysis for Biomass Conversion ; Advancements in Battery Materials
Authors: xiuMin Chen | Yunmin Chen | Jie Zhou
Source: npj Computational Materials (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Short bond evaluation method for rapidly assessing the generalization ability of deep neural network potential function models and its effectiveness verification When training deep neural networks using first-principles calculation data to obtain potential functions for molecular dynamics simulations, extensive model capability evaluation work is required. However, the commonly used validation sets for model evaluation are limited by the high cost of obtaining first-principles data, making it difficult to comprehensively assess the strong generalization ability of deep neural network trained models, which requires coverage of a much larger space than the training set samples. This manuscript proposes a short bond evaluation method and conducts evaluation experiments using this method and the self-consistent field labeling evaluation method on multiple tasks under different structures generalization in two complex reaction systems. It also performs correlation analysis between the results of the two methods to validate and explain the applicability and effectiveness of the proposed method. Although this method has the necessary and insufficient characteristics, the results show that this method can accelerate the assessment of model generalization capabilities while maintaining the reliability of the evaluation results. Moreover, this method can particularly accelerate the high-accuracy filter of poor-performing models, thereby helping to improve the convergence speed during the model training iteration process. At the same time, it achieves a significant reduction in evaluation costs. Generalization ; Artificial neural network ; Evaluation methods ; Reliability (semiconductor) ; Set (abstract data type) ; Convergence (economics) ; Field (mathematics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Protein Structure and Dynamics
Authors: Apri Wahyudi | Natthapong Sueviriyapan | Thirasak Rirksomboon | Uthaiporn Suriyapraphadilok
Source: ChemRxiv (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

DeepThermoMix: a local composition graph neural networks model for multicomponent activity coefficients We propose DeepThermoMix, a local-composition-based graph neural network model that predicts multicomponent activity coefficients directly from molecular graphs. Unlike classical local composition models, which rely on fixed algebraic expressions, DeepThermoMix generalizes the local composition assumption by learning interaction patterns without assuming predefined functional forms, such as the Boltzmann distribution. Unlike existing constrained machine learning models that remain limited by the pairwise additivity assumption, DeepThermoMix can capture emergent N-body interactions while preserving linear scaling with the number of components, thereby avoiding the combinatorial explosion inherent to pairwise approaches. Furthermore, thermodynamic consistency is directly embedded into the architecture by predicting the molar excess Gibbs free energy as a scalar potential from which activity coefficients are derived via automatic differentiation. Despite being trained on quantum-based COSMO-RS data, DeepThermoMix naturally converges toward surfaces resembling classical formalisms, a phenomenon indicative of "structural regularization." This study positions DeepThermoMix as a thermodynamically consistent and scalable model for multicomponent activity coefficient prediction. Pairwise comparison ; Artificial neural network ; Scaling ; Graph ; Additive function ; Boltzmann machine ; Gibbs free energy ; Scalar (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Bastien Casier | M. Hamdi | B. Herzog
Source: Journal of Chemical Theory and Computation (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Selective Configuration Interaction for Accurate Ground and Excited State Calculations In this work, we introduce a perturbative Selective Configuration Interaction (SCI) approach guided by a binary machine-learning classifier. The method leverages a lightweight feedforward neural network (FNN), purposefully designed for fast and efficient training. Within this framework, we show that our model attains the accuracy of the state-of-the-art SCI method CIPSI (Configuration Interaction using a Perturbative Selection done Iteratively). Across all tested configuration-space sizes, our approach reliably identifies the most important Slater determinants using a binary cross-entropy metric, achieving FCI/CASCI-level accuracy within 10<sup>-4</sup> Hartree for both ground and excited states. Finally, the method remains robust for strained geometries and conformational changes, successfully classifying Slater determinants across multiple points of potential energy curves and surfaces. This capability opens the door to new regression-based strategies for molecular electronic structure and the construction of machine-learning-driven potential energy surfaces. Excited state ; Binary number ; Artificial neural network ; Ground state ; Energy (signal processing) ; Configuration interaction ; Potential energy ; Energy minimization Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum many-body systems
Authors: Dengshan Li | Zhenhao Xu | Rongxi Lin | Yixing Huang | Qin Ouyang | Zhonghua Liu
Source: Computers and Electronics in Agriculture (article, 2026-01-14)
Field: Chemistry — Analytical Chemistry

SIFNet: A spectrum-image fusion neural network in hyperspectral imaging for intelligent quality assessment of crude tea to ensure tea product availability Hyperspectral imaging ; Pattern recognition (psychology) ; Feature extraction ; Artificial neural network ; Convolutional neural network ; Feature (linguistics) ; Fuzzy logic ; Standardization ; Image quality Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Smart Agriculture and AI
Authors: Vladislav A. Voloshkin | Cecile Valsecchi | Florian Medina | Laurent Lefort | Mikko Muuronen | Matthieu Jouffroy | Steven P. Nolan
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Challenges in data-driven catalysis modelling: case study on palladium-NHC catalyzed Suzuki–Miyaura reactions In this study, we synthesized a set of 21 N-heterocyclic carbene (NHC)Pd complexes and evaluated them in a benchmark reaction for Suzuki-Miyaura coupling under 12 different conditions, resulting in a high-quality dataset tailored for machine learning applications. We present a detailed analysis of the data, enabling a thorough assessement of the various parameters (ligand structure and reaction parameters) influencing the reaction yield. We used a new workflow to select descriptors for building linear regression models. The models achieved satisfactory performance in interpolation across all reaction conditions. To ensure these results were not artifacts, we critically examined our models, assessing features explainability, featurization strategies, the impact of train-test splits, and the influence of conformer sets. This work highlights key practical considerations for modeling catalytic activity using machine learning. Workflow ; Benchmark (surveying) ; Catalysis ; Carbene ; Set (abstract data type) ; Work (physics) ; Interpolation (computer graphics) ; Key (lock) Machine Learning in Materials Science ; N-Heterocyclic Carbenes in Organic and Inorganic Chemistry ; Catalytic Cross-Coupling Reactions