jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Luan G. F. dos Santos | Benjamin Nebgen | Alice E. A. Allen | Brenden W. Hamilton | Sakib Matin | Justin S. Smith | Richard A. Messerly
Source: Journal of Chemical Information and Modeling (article, 2025-01-28)
Field: Materials Science — Materials Chemistry

Improving Bond Dissociations of Reactive Machine Learning Potentials through Physics-Constrained Data Augmentation In the field of computational chemistry, predicting bond dissociation energies (BDEs) presents well-known challenges, particularly due to the multireference character of reactive systems. Many chemical reactions involve configurations where single-reference methods fall short, as the electronic structure can significantly change during bond breaking. As generating training data for partially broken bonds is a challenging task, even state-of-the-art reactive machine learning interatomic potentials (MLIPs) often fail to predict reliable BDEs and smooth dissociation curves. By contrast, simple and inexpensive physics-based models, such as the well-established Morse potential, do not suffer from any such limitations. This work leverages the Morse potential to improve reactive MLIPs by augmenting the training data set with inexpensive Morse data along the dissociation pathways. This physics-constrained data augmentation (PCDA) approach results in MLIPs with smooth bond dissociation curves as well as near coupled-cluster level BDEs, all without requiring any expensive multireference quantum mechanical calculations. A case study for methane combustion demonstrates how the PCDA approach can improve an existing reactive MLIP, namely, ANI-1xnr. Not only are the BDEs and bond dissociation curves for all radicals and molecules significantly improved compared to ANI-1xnr but the PCDA-trained MLIP retains the reliability of ANI-1xnr when performing reactive molecular dynamics simulations. Physics ; Computer science ; Machine learning ; Chemistry ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Catalysis and Oxidation Reactions
Authors: Sudharm Rathore | Avanish Kumar | Anurag Kumar | Kushal Mishra | Aparna Singh
Source: International Journal of Fatigue (article, 2025-01-05)
Field: Materials Science — Metals and Alloys

Prediction of sub-critical fatigue crack growth rate in a high-carbon tempered martensitic steel at varying R ratio: Experimental investigation and machine learning based modelling Martensite ; Materials science ; Paris' law ; Carbon fibers ; Metallurgy ; Carbon steel ; Fatigue testing ; Composite material ; Fracture mechanics ; Crack closure ; Microstructure ; Composite number ; Corrosion Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Fatigue and fracture mechanics
Authors: Yutong Han | Ziyang Yin | Haozhe Zhuang | Yu Yao | Yu Ying | Xingyu Huang | Ze Long | Zhigang Zhu
Source: Advanced Functional Materials (article, 2025-06-23)
Field: Materials Science — Materials Chemistry

Synergistic Effects of Photoactivation and Photothermal in MXene Heterostructures for the Enhanced H <sub>2</sub> S Detection Capability Abstract Semiconductor‐based H 2 S gas sensors offer promising applications in human health detection, food monitoring, and industrial safety. Designing novel sensitive materials and coupling regulation mechanisms is crucial to achieving excellent response and response/recovery speed for room‐temperature gas sensors. Herein, MXene/PbS heterostructures dual‐modulated by visible‐light activation and photothermal catalysis are proposed. Under near‐infrared light activation, MXene/PbS sensor achieves a significant response value (90%, threefold higher than in dark conditions) for 10 ppm H 2 S, which also demonstrates rapid response/recovery kinetics (2 s/89 s), broad detection range (10 ppb–50 ppm), long‐term stability (60 days), and excellent selectivity. Experimental and theoretical calculations reveal that the enhanced sensing performance of MXene/PbS arises from improved near‐infrared light absorption and efficient photogenerated carrier separation by the constructed heterostructures, which promotes the dynamic generation of reactive radicals (·O 2 − , ·OH) on the sensitive material, and reduces the activation energy of H 2 S. Further, the self‐generated photothermal effectively promotes the response and recovery speeds of the MXene/PbS sensor. Eventually, an intelligent monitoring system integrating MXene/PbS sensors and deep learning algorithms is successfully applied to human oral health diagnosis and egg spoilage warning, providing novel strategies for gas sensing mechanisms and practical applications of room‐temperature semiconductor gas sensors. Photothermal therapy ; Materials science ; Heterojunction ; Nanotechnology ; Photothermal effect ; Optoelectronics MXene and MAX Phase Materials ; Gas Sensing Nanomaterials and Sensors ; Advanced Photocatalysis Techniques
Authors: Jason Gibson | Ajinkya C. Hire | Philip M. Dee | O.P. Albino Barrera | Benjamin Geisler | P. J. Hirschfeld | Richard G. Hennig
Source: npj Computational Materials (article, 2025-01-09)
Field: Materials Science — Materials Chemistry

Accelerating superconductor discovery through tempered deep learning of the electron-phonon spectral function Integrating deep learning with the search for new electron-phonon superconductors represents a burgeoning field of research, where the primary challenge lies in the computational intensity of calculating the electron-phonon spectral function, α2F(ω), the essential ingredient of Midgal-Eliashberg theory of superconductivity. To overcome this challenge, we adopt a two-step approach. First, we compute α2F(ω) for 818 dynamically stable materials. We then train a deep-learning model to predict α2F(ω), using a training strategy tailored for limited data to temper the model’s overfitting, enhancing predictions. Specifically, we train a Bootstrapped Ensemble of Tempered Equivariant graph neural NETworks (BETE-NET), obtaining an MAE of 0.21, 45 K, and 43 K for the moments derived from α2F(ω): λ, $${\omega }_{\log }$$ , and ω2, respectively, yielding an MAE of 2.5 K for the critical temperature, Tc. Further, we incorporate domain knowledge of the site-projected phonon density of states to impose inductive bias into the model’s node attributes and enhance predictions. This methodological innovation decreases the MAE to 0.18, 29 K, and 28 K, respectively, yielding an MAE of 2.1 K for Tc. We illustrate the practical application of our model in high-throughput screening for high-Tc materials. The model demonstrates an average precision nearly five times higher than random screening, highlighting the potential of ML in accelerating superconductor discovery. BETE-NET accelerates the search for high-Tc superconductors while setting a precedent for applying ML in materials discovery, particularly when data is limited. Superconductivity ; Condensed matter physics ; Phonon ; Electron ; Spectral function ; Physics ; Function (biology) ; Materials science ; Quantum mechanics ; Biology Machine Learning in Materials Science ; High-pressure geophysics and materials ; Nuclear Physics and Applications
Authors: Muhammad Irfan | Amrita Kumar Sana | Alishba Maryam | Naveed Muhammad
Source: BioNanoScience (article, 2025-04-09)
Field: Materials Science — Materials Chemistry

Exploring How Microbial Extracellular Metabolites Drive Nanoparticle Synthesis: A Bioinformatics Approach Extracellular ; Nanoparticle ; Computational biology ; Chemistry ; Computer science ; Bioinformatics ; Biology ; Nanotechnology ; Biochemistry ; Materials science Nanoparticles: synthesis and applications ; Bioeconomy and Sustainability Development
Authors: P. Chandrasekaran | Ting-Yu Lo | Hou-Chien Chang | Hung‐Chung Li | Ming-Der Yang | Chih‐Ming Chen
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-assisted optimization design for enhanced oxygen evolution reaction based on vanadium-doped nickel–cobalt layered double hydroxides The increasing demand for sustainable energy has driven significant research into efficient water splitting, particularly the development of electrocatalysts for the oxygen evolution reaction (OER) which is limited by sluggish kinetics. Cobalt ; Nickel ; Vanadium ; Oxygen evolution ; Layered double hydroxides ; Materials science ; Doping ; Oxygen ; Chemical engineering ; Inorganic chemistry ; Chemistry ; Metallurgy ; Optoelectronics ; Physical chemistry ; Engineering ; Organic chemistry Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Zhi Huang | Yu Jiang | Jiang Yu | Wei He | Jie Yu | Siwei Deng | Yinying Jiang | Chun Yang | Weiwei Zhu | Yi Wu | Xiufeng Liu
Source: Process Safety and Environmental Protection (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Large-scale chemical language models for accurate and interpretable prediction of reactivity and toxicity Chemical space ; Reactivity (psychology) ; Quantum chemical ; Process safety ; Similarity (geometry) ; Molecular descriptor ; Process (computing) ; Chemical process ; Hazard ; Chemical reaction Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Jiahao Jiang | Shuai Zhao | Yanqiu Sun | Xuedong Wang
Source: Angewandte Chemie International Edition (review, 2025-06-05)
Field: Chemistry — Physical and Theoretical Chemistry

From Binary to Higher‐Order Organic Cocrystals: Design Principles and Performance Optimization Organic cocrystals, particularly the evolution from binary to higher-order structures, have garnered considerable attention due to their tunable intermolecular interactions and unique material properties. Binary cocrystals, formed through π-π stacking, charge transfer, and hydrogen/halogen bonding, allow for precise control over molecular packing and enhanced optoelectronic properties. In contrast, higher-order cocrystals, incorporating three or more components, enable greater complexity and functional diversity. Strategies such as homologation via isostructural substitution, hierarchical intermolecular interactions, and long-range Synthon Aufbau Modules facilitate the synthesis of these advanced materials. The shift toward higher-order cocrystals paves the way for novel applications in fields such as deep learning for cocrystal prediction, drug design, organic solar cells, and NIR-II photothermal conversion. However, challenges related to molecular screening, ratio optimization, scalable synthesis, and long-term stability remain critical hurdles for the broader implementation of these materials in practical applications. Cocrystal ; Synthon ; Stacking ; Isostructural ; Materials science ; Crystal engineering ; Intermolecular force ; Organic solar cell ; Nanotechnology ; Hydrogen bond ; Molecule ; Chemistry ; Organic chemistry ; Polymer ; Crystal structure Crystallography and molecular interactions ; Energetic Materials and Combustion ; Luminescence and Fluorescent Materials
Authors: Pietro Novelli | Giacomo Meanti | Pedro J. Buigues | Lorenzo Rosasco | Michele Parrinello | Massimiliano Pontil | Leo H. Bonati
Source: npj Computational Materials (article, 2025-09-25)
Field: Materials Science — Materials Chemistry

Fast and Fourier features for transfer learning of interatomic potentials Training machine learning interatomic potentials that are both computationally and data-efficient is a key challenge for enabling their routine use in atomistic simulations. To this effect, we introduce franken, a scalable and lightweight transfer learning framework that extracts atomic descriptors from pre-trained graph neural networks and transfers them to new systems using random Fourier features - an efficient and scalable approximation of kernel methods. It also provides a closed-form fine-tuning strategy for general-purpose potentials such as MACE-MP0, enabling fast and accurate adaptation to new systems or levels of quantum mechanical theory with minimal hyperparameter tuning. On a benchmark dataset of 27 transition metals, franken outperforms optimized kernel-based methods in both training time and accuracy, reducing model training from tens of hours to minutes on a single GPU. We further demonstrate the framework's strong data-efficiency by training stable and accurate potentials for bulk water and the Pt(111)/water interface using just tens of training structures. Our open-source implementation (https://franken.readthedocs.io) offers a fast and practical solution for training potentials and deploying them for molecular dynamics simulations across diverse systems. Scalability ; Fourier transform ; Hyperparameter ; Molecular dynamics ; Artificial neural network ; Benchmark (surveying) ; Kernel (algebra) ; Generalization ; Fast Fourier transform Machine Learning in Materials Science ; Nuclear Physics and Applications ; Force Microscopy Techniques and Applications
Authors: Chunyu Yang | Bing Yan
Source: ACS Applied Materials & Interfaces (article, 2025-05-28)
Field: Chemistry — Spectroscopy

Multifunctional Hydrogen-Bonded Organic Frameworks for Intelligent Anti-Counterfeiting and Food Safety Monitoring In an era of increasing digital threats and product counterfeiting, this study introduces MA-IPA@NPA, a groundbreaking hydrogen-bonded organic framework (HOF) material designed for advanced anticounterfeiting applications. This innovative material showcases distinctive optical properties, including dual fluorescence-phosphorescence emission and an ultralong phosphorescence lifetime, making it exceptionally difficult to replicate. Additionally, MA-IPA@NPA demonstrates efficacy as a fluorescence sensor for detecting tryptamine and gallic acid in food products, addressing both security and food safety concerns. Through electrospinning technology, we successfully developed a multifunctional composite nanofiber membrane by integrating MA-IPA@NPA with poly(vinyl alcohol) nanofibers, thereby combining the superior optical properties of the HOF with the flexibility and durability of polymer nanofibers. This advanced composite forms the foundation of our Multiple-Responsive Intelligent Anticounterfeiting label, which exhibits dynamic responses to diverse stimuli and incorporates multilevel verification through sophisticated machine learning methods, including binary image recognition and a Back-Propagation Neural Network. Our comprehensive research not only presents a novel strategy for developing advanced, multifunctional anticounterfeiting systems but also offers a timely solution to the escalating demand for enhanced security and authenticity verification in our increasingly digital world, paving the way for more robust and reliable anticounterfeiting measures across various industries. Materials science ; Nanotechnology ; Metal-organic framework ; Systems engineering ; Process engineering ; Organic chemistry ; Engineering Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Advanced Chemical Sensor Technologies
Authors: Sungin Kim | Valentín Briega‐Martos | Shikai Liu | Kwanghwi Je | Chuqiao Shi | K. Stephens | Steven E. Zeltmann | Zhijing Zhang | Rafael Guzman-Soriano | Wenqi Li | Jiahong Jiang | Juhyung Choi | Yafet J. Negash | Franklin S. Walden | Nelson Luis Marthe | Patrick S. Wellborn | Yaofeng Guo | John Damiano | Yimo Han | Erik H. Thiede | Yao Yang
Source: Journal of the American Chemical Society (article, 2025-05-23)
Field: Chemistry — Electrochemistry

<i>Operando</i> Heating and Cooling Electrochemical 4D-STEM Probing Nanoscale Dynamics at Solid–Liquid Interfaces <i>Operando</i>/<i>in situ</i> methods have revolutionized our fundamental understanding of molecular and structural changes at solid-liquid interfaces and enabled the vision of "watching chemistry in action". <i>Operando</i> transmission electron microscopy (TEM) emerges as a powerful tool to interrogate time-resolved nanoscale dynamics, which involve local electrical fields and charge transfer kinetics distinctly different from those of their bulk counterparts. Despite early reports on electrochemical or heating liquid-cell TEM, developing <i>operando</i> TEM with simultaneous electrochemical and thermal control remains a formidable challenge. Here, we developed <i>operando</i> heating and cooling electrochemical liquid-cell scanning TEM (EC-STEM). By integrating a three-electrode electrochemical circuit and an additional two-electrode thermal circuit, we can investigate heterogeneous electrochemical kinetics across a wide temperature range of -50 to 300 °C. We used Cu electrodeposition/stripping processes as a model system to demonstrate quantitative electrochemistry from -40 to 95 °C in both transient and steady states in aqueous and organic solutions, which paves the way for investigating energy materials operating in extreme climates. Machine learning-assisted quantitative 4D-STEM structural analysis in cold liquids (-40 °C) reveals a distinct two-stage growth of nanometer-scale mossy Cu nanoislands with random orientations followed by μm-scale Cu dendrites with preferential orientations. This work benchmarked electrochemistry in the three-electrode EC-STEM and systematically investigated the temperature and pH dependence of the Pt pseudoreference electrode (RE). At room temperature, the Pt pseudo-RE shows a reliable potential of 0.8 ± 0.1 V vs the standard hydrogen electrode and remains pH-independent on the reversible hydrogen electrode scale. We anticipate that <i>operando</i> heating/cooling EC-STEM will become invaluable for understanding fundamental temperature-controlled nanoscale electrochemistry and advancing renewable energy technologies (e.g., catalysts and batteries) in realistic climates. Chemistry ; Nanoscopic scale ; Electrochemistry ; Dynamics (music) ; Nanotechnology ; Chemical physics ; Chemical engineering ; Electrode ; Physical chemistry Electrochemical Analysis and Applications ; Force Microscopy Techniques and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Neeraj Dhariwal | Preety Yadav | Akanksha | Savita Bisht | Ramesh Chandra | O. P. Thakur | Paul V. Braun | Sung Bum Kang | Amit Sanger | Vinod Kumar
Source: Advanced Energy Materials (article, 2025-07-26)
Field: Materials Science — Materials Chemistry

Beyond Heat Harvesting: Thermoelectric Materials and Hybrid Devices for Smart Sensing and Sustainable Technologies Abstract Heat is a plentiful energy source available within us and in our surroundings, yet it is still underutilized and wasted. Capturing even a small fraction of this energy provides an opportunity to contribute to an eco‐friendly and sustainable society. Although traditional solid‐state inorganic semiconductor materials have led the research stage of thermoelectric (TE) energy conversion, carbon‐based hybrid materials and hydrogels provide an emerging TE platform for wearable, flexible, and low‐temperature energy harvesting devices for powering sensors. These materials have expanded the application of these thermoelectric materials beyond heat‐to‐electrical energy conversion to power various smart devices for use in healthcare monitoring and environmental remediation. This review is divided into three main categories. First, this work focuses on the fundamentals of TE generators and their classifications. In the second section, a thorough discussion of integrated sensor fabrication, including single and hybrid systems and their performance matrices, is given. Special attention is given to self‐healing materials that improve device longevity and achieve environmental sustainability. Section three discusses the incorporation of machine learning (ML) techniques into TE‐powered sensors. By emphasizing the latest discoveries to tabulate the entire range of thermoelectric‐powered sensors, this review bridges materials science, wearable technologies, and advanced engineering. Materials science ; Thermoelectric effect ; Thermoelectric materials ; Energy harvesting ; Nanotechnology ; Smart material ; Engineering physics ; Optoelectronics ; Composite material ; Thermal conductivity ; Engineering Advanced Thermoelectric Materials and Devices ; Advanced Sensor and Energy Harvesting Materials ; Gas Sensing Nanomaterials and Sensors
Authors: Seema Agarwal | Akhlak Mahmood | Rampi Ramprasad
Source: ACS Materials Letters (article, 2025-04-25)
Field: Materials Science — Materials Chemistry

Polymer Solubility Prediction Using Large Language Models Traditional approaches in polymer informatics often require labor-intensive data curation, time-consuming preprocessing such as fingerprinting, and choosing suitable learning algorithms. Large language models (LLMs) represent a compelling alternative by addressing these limitations with their inherent flexibility, ease of use, and scalability. In this study, we propose a novel approach utilizing fine-tuned LLMs to classify solvents and nonsolvents for polymers, a property critical to polymer synthesis, purification, and diverse applications. Our results show that fine-tuned GPT-3.5 achieves predictive performance comparable to or exceeding traditional machine learning methods, even with limited data sets. The model achieved predictive accuracies of 0.90 and 0.83 for identifying soluble and insoluble solvent–polymer pairs, respectively. Remarkably, these models accurately classify solvents and nonsolvents in entirely unseen scenarios, indicating that they are able to effectively leverage the components embedded in their base models. The operational simplicity and accuracy of LLMs highlight their potential for advancing polymer research. Solubility ; Polymer ; Polymer science ; Materials science ; Computer science ; Chemistry ; Composite material ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Li Xiao | Reza Amirmoshiri | Colton R. Davis | Indu Muthancheri | Antoine de Gombert | Saeed Moayedpour | Sven Jäger | Andreas R. Rötheli | Yasser Jangjou
Source: Industrial & Engineering Chemistry Research (article, 2025-03-07)
Field: Materials Science — Materials Chemistry

Mechanistic Exploration and Kinetic Modeling Through In Silico Data Generation and Probabilistic Machine Learning Analysis First-principles-based kinetic models are powerful tools for developing and optimizing chemical reactions. Capable of describing the transient behavior of reactions, these models are particularly enabling for designing, optimizing, and controlling processes in a fully digital fashion. Despite advancements in kinetic modeling methods, challenges persist due to resource-intensive experimentation, the need for chemistry and engineering expertise, and difficulties in quantifying uncertainties. This paper introduces a workflow and open-source Python package, the Sanofi Kinetic AI (SKAI) tool, that simplifies kinetic modeling. The proposed method democratizes kinetic hypothesis testing by leveraging Bayesian inference, allowing scientists to evaluate reaction pathways without repeated trial-and-error experimentation. To further enhance accessibility, we incorporate a prompt-engineered large language model (LLM) that converts reaction descriptions into system equations. Additionally, pretrained machine learning models, trained on in silico time-course data, support hypothesis generation by providing data-driven assumptions about reaction pathways in low-data regimes. We validate this framework with two industrially relevant case studies involving series and parallel reactions, demonstrating its efficacy in pathway elucidation, kinetic modeling, and uncertainty quantification. This approach offers a robust and accessible toolset for advancing kinetic modeling practices. Computer science ; Probabilistic logic ; In silico ; Machine learning ; Artificial intelligence ; Statistical model ; Data mining ; Chemistry Machine Learning in Materials Science ; Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics