jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Franklin F. da Silva Filho | Luiz Felipe C. Pereira
Source: Nano Letters (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

Accelerated Discovery of Graphene Kirigami with an Enhanced Elastocaloric Effect via Machine Learning Recent studies have examined the elastocaloric response of graphene kirigami (GK) and shown how it may be tailored through geometric design. This tunability makes GK a promising platform for applications in nanoscale solid-state thermal devices. In this work, we combine molecular dynamics (MD) simulations and machine learning (ML) to explore how GK geometries affect the elastocaloric coefficient (ECC), defined as the adiabatic ratio between temperature change and applied tensile stress. A data set of 16,807 GK configurations was generated through systematic cut patterns and evaluated via MD at room temperature. Using this data, both classical and deep-learning models were trained, with a convolutional neural network (CNN) achieving the best performance (RMSE = 0.064 K GPa<sup>-1</sup>; <i>R</i><sup>2</sup> = 0.96). Model-guided optimization identified high-ECC designs 10 times faster than random search, demonstrating the power of ML-assisted strategies for the accelerated discovery of advanced elastocaloric materials. Graphene ; Convolutional neural network ; Nanoscopic scale ; Adiabatic process ; Power (physics) ; Artificial neural network Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Thermal properties of materials
Authors: Abdul Haleem | Muhammad Zaheer Afzal | Tooba Saeed | Mohib Ullah | Laraib Kiran | Hao Li
Source: Next Nanotechnology (article, 2026-03-24)
Field: Chemistry — Organic Chemistry

Synthesis methods, network selection, catalytic mechanism, and stability challenges of silver, gold, palladium, nickel, and cobalt nanoparticles: A review This review provides a comparative assessment of the synthesis, better network selection, catalytic applications, and stabilization of silver, gold, palladium, nickel, and cobalt nanoparticles. It focuses particularly on nickel and cobalt, which exhibit high surface energies and tend to aggregate due to van der Waals forces and magnetic dipole–dipole interactions. This aggregation presents a significant challenge for stabilization. Magnetic nickel nanoparticles are particularly susceptible to surface oxidation; therefore, most synthesis protocols utilize organic media and hydrophobic capping agents to prevent agglomeration and oxidation. While cobalt nanoparticles hold promise for magnetic and catalytic applications, they exhibit poor stability against oxidation and hydrolysis, which limits their use in catalytic contexts. The electronic properties also contribute to the stability challenges faced by these five nanoparticles. We review various synthesis methods, including chemical, biological, and green approaches, as well as stabilization strategies such as surfactant capping, confinement within metal-organic frameworks or covalent frameworks, and polymeric gels. Additionally, we summarize advanced characterization techniques and propose a data-driven framework that combines density functional theory, materials databases, and machine learning to predict synthesis parameters and surface modifications. This review highlights that when properly stabilized, nickel and cobalt nanoparticles can serve as cost-effective alternatives to noble metals, providing high catalytic efficiencies in reactions such as the reduction of nitro compounds and the degradation of dyes. By comparing noble and base metal nanoparticles and highlighting the underexplored systems of nickel and cobalt, we provide mechanistic insights and design principles that will facilitate the rational development of durable catalysts for environmental remediation and energy conversion. Catalysis ; Cobalt ; Nickel ; Nanoparticle ; van der Waals force ; Noble metal ; Rational design ; Characterization (materials science) Nanomaterials for catalytic reactions ; Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications
Authors: Ayhan Aydın | Ümit Kaya Eryılmaz | Onur Bahattin Alkan | Pınar Kocagöz | Fatih Ekinci | Mehmet Serdar Güzel
Source: Journal of Chemical Information and Modeling (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Hybrid Graph–Machine Learning Framework for Accurate and Interpretable Band Gap Prediction Accurate prediction of the electronic band gap is essential for accelerating the discovery and design of semiconducting and energy materials. Conventional density functional theory (DFT) methods, while physically rigorous, remain computationally expensive and limited in scalability. In this study, we propose a hybrid artificial intelligence framework that combines graph-based deep learning embeddings with classical machine learning algorithms to achieve high-accuracy, interpretable, and computationally efficient band gap prediction. The model integrates embeddings obtained from CGCNN, MEGNet, and SchNet architectures with physically meaningful crystal descriptors─including maximum electronegativity, crystal system, space group, and spin-orbit coupling─and trains them using optimized gradient-boosting and neural architectures. Trained on 136,000 crystal structures from the Materials Project database, the hybrid model achieves <i>R</i><sup>2</sup> = 0.921, MAE = 0.191, and MSE = 0.155, outperforming both classical models (Ward et al., 2016) and standalone graph neural networks such as CGCNN (Xie and Grossman, 2018). The achieved accuracy is statistically comparable to the state-of-the-art ALIGNN model (Choudhary et al., 2021), while requiring lower computational resources and offering enhanced generalization due to the integration of multisource structural information. SHAP-based interpretability analysis highlights that the model captures physically consistent relationships, with metallicity and magnetic site features emerging as dominant factors in band gap prediction. These findings demonstrate that the synergy between deep structural embeddings and classical algorithms provides a powerful, scalable approach for materials informatics. The proposed framework establishes a foundation for multiproperty prediction, transfer learning across databases, and inverse materials design driven by interpretable artificial intelligence. Interpretability ; Scalability ; Artificial neural network ; Generalization ; Deep learning ; Inverse ; Band gap Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Inorganic Chemistry and Materials
Authors: Farag M. A. Altalbawy | Raed H. C. Alfilh | Tarak Vora | Anurag Yadav | R. Manjunatha | Rishabh Thakur | B. Angel | Priyadarshi Das | Anup Singh Negi | Muyassar Norberdiyeva | Ahmad Abumalek
Source: Polymers for Advanced Technologies (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Predicting the Storage Modulus of Chromium‐Crosslinked Polymer Gels Through Advanced Machine Learning Techniques ABSTRACT The accurate prediction of the storage modulus of chromium‐crosslinked polymer gels presents a complex but critical challenge due to the interplay of structural, chemical, and mechanical factors. This study implements a robust ensemble learning framework, combining Decision Trees (DT), Random Forests (RF), AdaBoost (AB), and Ensemble Learning (EL) to model and predict the storage modulus with high accuracy. A dataset incorporating key features, such as polymer and chromium concentrations, strain, temperature, and pH, was used to train these models. Among them, RF presented superior accuracy with R 2 of 0.7995 and mean squared error (MSE) of approximately 170.7 million in the test phase, outperforming its counterparts in generalizability. However, Decision Trees and AdaBoost exhibited severe overfitting, with AdaBoost reaching a training R 2 of 0.9989 but failing in testing ( R 2 = 0.8989, MSE = ~86 million). To address overfitting, hyperparameter optimization, and regularization strategies were implemented, including limiting tree depth and controlling estimator numbers. Feature importance analysis using SHAP (SHapley Additive exPlanations) identified strain (%) as the most significant negative factor, while chromium concentration positively influenced modulus. These findings align with physical and chemical principles, as strain disrupts gel networks while increased crosslink density enhances storage modulus. The novelty of this methodology lies in its departure from conventional reinforcement learning‐based optimization approaches. Unlike reward‐driven route optimization frameworks, this ensemble learning model integrates experimental physicochemical parameters with explainable inference. This enables direct mechanistic insight into the structural behaviors governing gel elasticity, establishing a distinctive and interpretable computational pathway for polymer‐gel modulus prediction. Random forest ; AdaBoost ; Ensemble learning ; Decision tree ; Hyperparameter ; Dynamic mechanical analysis ; Rheometry ; Polymer ; Modulus Machine Learning in Materials Science ; Hydrogels: synthesis, properties, applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Liangliang Xu | Jian Zhou | Aliaksandr S. Bandarenka | Zhongfang Chen
Source: Accounts of Materials Research (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Data-Driven Electrocatalyst Discovery: Recent Trends in Machine Learning Approaches and Descriptor-Based Design Principles ConspectusThe urgent transition to sustainable energy systems has intensified the search for advanced electrocatalysts that efficiently promote key reactions, including hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), carbon dioxide reduction reaction (CO2RR), and nitrogen reduction reaction (NRR). However, the enormous chemical and structural diversity among candidate materials makes traditional trial-and-error screening highly inefficient. Recent advances in data-driven discovery, especially descriptor-based strategies and machine learning (ML), are transforming this landscape. By combining high-throughput first-principles calculations, curated materials databases, and interpretable ML algorithms, researchers can systematically reveal quantitative relationships between structure and activity, identify promising catalyst candidates, and accelerate the design and screening of efficient catalytic systems. This Account highlights how computational modeling, ML algorithms, data mining techniques, and descriptor engineering (e.g., d-band center, eg orbital filling, and ionization energies), along with experimental verification, together provide a robust framework for rational catalyst development.In this Account, we survey recent progress in data-driven electrocatalyst discovery across the major energy conversion reactions. The integration of interpretable ML models, such as least absolute shrinkage and selection operator (LASSO), sure independence screening and sparsifying operator (SISSO), and subgroup discovery (SGD), with high-quality data sets enables the discovery of both global and local relationships between structure and activity, overcoming limitations in conventional models like volcano plots and linear scaling relations.Through a series of case studies, we demonstrate that the end-to-end, data-driven workflows enable rapid, reliable screening of large catalyst libraries, including single-atom and dual-atom motifs on two-dimensional (2D) materials, basal planes of 2D substrates, and metal–organic frameworks (MOFs). Together, descriptor-based design and interpretable ML not only yield mechanistic insights that guide experimental synthesis and optimization, but also establishes a new paradigm for catalyst discovery, paving the way for breakthroughs in sustainable energy technologies. Electrocatalyst ; Workflow ; Dimensionality reduction ; Operator (biology) ; Oxygen reduction reaction ; Reduction (mathematics) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Kai Li | Lei Xu | Huiwen Guan | Junyi Li | Linlin Wu | You‐Nian Liu
Source: Applied Surface Science (article, 2026-03-24)
Field: Chemistry — Organic Chemistry

Unveiling the adsorption of N-PHA on graphene-supported single-atom catalysts: a DFT and machine learning study Adsorption ; Density functional theory ; Feature (linguistics) ; Computational learning theory Nanomaterials for catalytic reactions ; Catalysis and Hydrodesulfurization Studies ; Environmental remediation with nanomaterials
Authors: Jiyoon Kim | Chuhong Wang | Aayush R. Singh | Tyler Sours | Shivang Agarwal | AJ Nish | Paul Abruzzo | Ang Xiao | Omar Allam
Source: arXiv (Cornell University) (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

AQVolt26: High-Temperature r$^2$SCAN Halide Dataset for Universal ML Potentials and Solid-State Batteries The demand for safe, high-energy-density batteries has spotlighted halide solid-state electrolytes, which offer the potential for enhanced ionic mobility, electrochemical stability, and interfacial deformability. Accelerating their discovery requires extensive molecular dynamics, which has been increasingly enabled by universal machine learning interatomic potentials trained on foundational datasets. However, the dynamic softness of halides poses a stringent test of whether general-purpose models can reliably replace first-principles calculations under the highly distorted, elevated-temperature regimes necessary to probe ion transport. Here, we present AQVolt26, a dataset of 322,656 r$^2$SCAN single-point calculations for lithium halides, generated via high-temperature configurational sampling across $\sim$5K structures. We demonstrate that foundational datasets provide a strong baseline for stable halide chemistries and transfer local forces well, however absolute energy predictions degrade in distorted higher-temperature regimes. Co-training with AQVolt26 resolves this blind spot. Furthermore, incorporating Materials Project relaxation data improves near-equilibrium performance but degrades extreme-strain robustness without enhancing high-temperature force accuracy. These results demonstrate that domain-specific configurational sampling is essential for the reliable dynamic screening of halide electrolytes. Furthermore, our findings suggest that while foundational models provide a robust base, they are most effective for dynamically soft solid-state chemistries when augmented with targeted, high-temperature data. Finally, we show that near-equilibrium relaxation data serves as a task-specific complement rather than a universally beneficial addition. Halide ; Robustness (evolution) ; Ionic bonding ; Electrochemistry ; Molecular dynamics ; Sampling (signal processing) ; Lithium (medication) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Jiyoon Kim | Chuhong Wang | Aayush R. Singh | Tyler Sours | Shivang Agarwal | AJ Nish | Paul Abruzzo | Ang Xiao | Omar Allam
Source: arXiv (Cornell University) (preprint, 2026-04-02)
Field: Materials Science — Materials Chemistry

AQVolt26: High-Temperature r$^2$SCAN Halide Dataset for Universal ML Potentials and Solid-State Batteries The demand for safe, high-energy-density batteries has spotlighted halide solid-state electrolytes, which offer the potential for enhanced ionic mobility, electrochemical stability, and interfacial deformability. Accelerating their discovery requires extensive molecular dynamics, which has been increasingly enabled by universal machine learning interatomic potentials trained on foundational datasets. However, the dynamic softness of halides poses a stringent test of whether general-purpose models can reliably replace first-principles calculations under the highly distorted, elevated-temperature regimes necessary to probe ion transport. Here, we present AQVolt26, a dataset of 322,656 r$^2$SCAN single-point calculations for lithium halides, generated via high-temperature configurational sampling across $\sim$5K structures. We demonstrate that foundational datasets provide a strong baseline for stable halide chemistries and transfer local forces well, however absolute energy predictions degrade in distorted higher-temperature regimes. Co-training with AQVolt26 resolves this blind spot. Furthermore, incorporating Materials Project relaxation data improves near-equilibrium performance but degrades extreme-strain robustness without enhancing high-temperature force accuracy. These results demonstrate that domain-specific configurational sampling is essential for the reliable dynamic screening of halide electrolytes. Furthermore, our findings suggest that while foundational models provide a robust base, they are most effective for dynamically soft solid-state chemistries when augmented with targeted, high-temperature data. Finally, we show that near-equilibrium relaxation data serves as a task-specific complement rather than a universally beneficial addition. Halide ; Robustness (evolution) ; Ionic bonding ; Electrochemistry ; Molecular dynamics ; Sampling (signal processing) ; Lithium (medication) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Yingdi Jin | Xinming Qin | Ruichen Liu | Jie Liu | Zhenyu Li | Jinlong Yang
Source: ArXiv.org (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

V2Rho-FNO: Fourier Neural Operator for Electronic Density Prediction Density functional theory (DFT) is a cornerstone of computational chemistry and materials science, but its computational cost limits its use in large-scale and high-throughput applications. While machine learning has accelerated energy prediction for specific molecular classes, transferable prediction of electron density across diverse chemical spaces remains challenging. Here, we present a universal framework based on Fourier Neural Operators (FNOs) that directly learns the mapping from external potentials to electron density distributions. Unlike conventional approaches that rely on explicit atomic orbitals, basis sets, or handcrafted descriptors, the proposed method captures global electronic interactions and long-range correlations through operator learning in the spatial-frequency domain. Trained on datasets spanning multiple elements and molecular geometries, the model achieves zero-shot generalization to entirely unseen molecular systems and accurately predicts their electron densities without retraining. This transferability arises from the intrinsic ability of FNOs to represent global structure in continuous fields. Our work establishes neural operator learning as a promising route for fast, accurate, and transferable electronic structure prediction, with potential applications in high-throughput screening and chemical space exploration. Generalization ; Operator (biology) ; Artificial neural network ; Fourier transform ; Basis (linear algebra) ; Density functional theory ; Electronic structure ; Space (punctuation) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Alcides Gustavo López Paredes | Patricia Esther Ochoa Pesantes | Karla Alexandra Ávila Hidalgo
Source: Dialnet (Universidad de la Rioja) (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Reconocimiento asistido por redes neuronales de grupos funcionales en espectros FTIR de compuestos obtenidos por pirólisis Objective: The description of pyrolysis products using FTIR spectroscopy has problems due to the complexity and overlap of spectral bands. In this context, the study aimed to evaluate how deep learning models can automatically recognize functional groups in FTIR spectra of pyrolytic compounds. Methodology: Three neural network architectures were tested: 1D CNN, Inception V3, and Transformer for automated multi-label recognition of 18 functional groups. A set of 12,500 spectra was created, sourced from reference spectral databases (NIST and SDBS) and experimental samples of bio-oils produced by the pyrolysis of biomass and plastics. Results: The Transformer model achieved the best performance, with a macro F1 score of 0.896, an AUC-ROC of 0.967, and a molecular perfection rate of 72.4%, significantly outperforming conventional methods (SVM+PCA: F1 = 0.783). Additionally, the interpretability analysis with SHAP confirmed that the models identified spectral areas that chemically matched the spectroscopic assignments. Conclusions: The results demonstrated the viability of deep neural networks as an assisted tool for the prompt and reliable identification of functional groups in pyrolytic products, highlighting their potential to support chemical characterization processes and spectral analysis in research contexts and analytical applications. Interpretability ; Fourier transform infrared spectroscopy ; Pattern recognition (psychology) ; Pyrolytic carbon ; Artificial neural network ; Spectral analysis ; Pyrolysis ; Partial least squares regression Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Bee Products Chemical Analysis
Authors: Guoao Li | Haobo Ling | Chaoxu Su | Zhengxuan Liu | G. H. Wang | Manyi Yang | Shuhua Li
Source: CCS Chemistry (article, 2026-03-22)
Field: Materials Science — Materials Chemistry

A data-efficient reactive machine learning potential to accelerate automated exploration of complex reaction networks Artificial neural network ; Automation ; Key (lock) ; Production (economics) ; Feature (linguistics) Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Advanced Graph Neural Networks
Authors: Di Zhang | Yuanzheng Chen | Chuanyu Liu | Yan Liu | Hongliang Xin | Jiayu Peng | Pengfei Ou | H. C. Li
Source: Angewandte Chemie (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Accelerating Catalyst Materials Discovery With Large Artificial Intelligence Models ABSTRACT The integration of artificial intelligence (AI) into catalysis is fundamentally reshaping the research paradigm of catalyst discovery. Unlike traditional trial‐and‐error approaches, AI‐empowered data‐driven technologies, particularly large AI models such as universal machine learning interatomic potentials (MLIPs) and large language models (LLMs), offer unprecedented capabilities in exploring complex spaces, predicting catalytic performance, and accelerating rational design. Standing at the forefront of data‐driven science, we underscore how databases, universal MLIPs, and LLMs are revolutionizing the traditional catalysis paradigm and bridging the ontology‐concept‐computation‐experiment continuum. We then demonstrate significant recent progress, and discuss their potential and challenges in the catalytic field. By leveraging cutting‐edge universal MLIPs and LLMs, researchers can conduct large‐scale simulations, highly efficient data acquisition, training, and prediction, and even self‐directed research in the field of catalysis. Looking ahead, these advantages enable the rapid development of target catalysts, which will be propelled by integrated universal MLIPs, multimodal LLMs, and automation systems. Developments in these domains will pave the way toward AI‐empowered closed‐loop platforms and cross‐disciplinary Digital Materials Ecosystems that broaden the discovery landscape and foster cross‐materials innovation, marking the dawn of a new era in which catalyst materials discovery is perpetually accelerating. Field (mathematics) ; Bridging (networking) ; Automation ; Paradigm shift Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Artificial Intelligence in Healthcare and Education