jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yan-Yu Xie | Xiao-Dong Li | Chengxiang Liu | Jiaxin Li | Xiuying Liu | Jing-Xin Yu
Source: Langmuir (article, 2025-10-15)
Field: Chemistry — Inorganic Chemistry

Multivariate Metal Organic Frameworks for High-Efficiency H <sub>2</sub> S/CO <sub>2</sub> /CH <sub>4</sub> Adsorption and Separation: A Combined Molecular Simulation and Machine Learning Study Natural gas with high sulfur content frequently contains harmful impurities, such as CO<sub>2</sub> and H<sub>2</sub>S, which poses a critical demand for the development of highly efficient and selective adsorbent materials to achieve effective purification. In this study, we have systematically investigated and evaluated the adsorption and separation capabilities of metal-organic frameworks (MTV-MOFs) for H<sub>2</sub>S/CO<sub>2</sub>/CH<sub>4</sub> ternary mixture by combining molecular simulation with machine learning techniques. Grand Canonical Monte Carlo (GCMC) simulations reveals that the framework functionalized with the NH<sub>2</sub>-F-F group not only exhibits excellent H<sub>2</sub>S adsorption capacity, but also has excellent separation ability for ternary mixed gas. The cuf_6586 containing functional group NH<sub>2</sub>-F-F can achieve an adsorption capacity of 18.12 mmol/g under the conditions of 298 K and 1 bar. Based on Henry's constant and adsorption heat, the interaction mechanism between the gas and the framework was deeply analyzed. For separation, cuf_10289, which also contains functional group NH<sub>2</sub>-F-F, is identified as the most effective separation adsorbent. Within the pressure range of 0.1-5 bar, its selectivity for H<sub>2</sub>S and CO<sub>2</sub> is higher than 45. Furthermore, machine learning models were constructed using structural, chemical, and thermodynamic descriptors to predict adsorption capacity and selectivity. Among them, the XGBoost model achieved excellent performance, with an R<sup>2</sup> of 0.93 for regression tasks and a precision of 0.90 for high-performance material classification. SHAP analysis further confirmed the dominant role of thermodynamic descriptors in driving model predictions. This study can provide some theoretical references and an efficient strategy for the high-throughput screening and rational design of advanced adsorbents targeting the purification of high-sulfur natural gas. Ternary operation ; Adsorption ; Monte Carlo method ; Metal-organic framework ; Grand canonical ensemble ; Multivariate statistics ; Separation (statistics) ; Molecular descriptor Metal-Organic Frameworks: Synthesis and Applications ; Industrial Gas Emission Control ; Carbon Dioxide Capture Technologies
Authors: M. Mohan | R. Sapna | Sachin R. Rondiya | K. Hareesh
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A review on copper-based chalcogenide materials for supercapacitor application: exploring through experimental evidence and machine learning Copper chalcogenides (CuS, CuSe, CuTe) are emerging as promising electrode materials for high-performance supercapacitors due to their tuneable structural and electrochemical properties. Supercapacitor ; Chalcogenide ; Electrode ; Electrochemistry Supercapacitor Materials and Fabrication ; Nanoporous metals and alloys
Authors: Sanwal Piracha | Xinrui Gu | Yifei Zhang | Yu‐Xin Miao | Gao Li
Source: Chemical Physics Reviews (article, 2025-04-07)
Field: Materials Science — Materials Chemistry

Unlocking new frontiers in ultrasmall-seized metal nanoclusters for boosting electrochemical energy conversions Ultrasmall metal nanoclusters are revolutionary developments in sustainable chemical synthesis and electrochemical energy conversion. Critical activities including the oxygen evolution reaction, oxygen reduction reaction, and the electrosynthesis of urea via nitrate and carbon dioxide co-reduction are addressed by their distinctive atomic-scale designs, which provide remarkable catalytic activity, selectivity, and stability. Recent advancements in the design and synthesis of these materials are compiled in this study, which highlights how their electrical characteristics and structural tunability enable high-efficiency energy conversions. Important issues are examined critically, along with new approaches to overcome them, such as operational stability, scalable manufacturing, and the financial constraints of noble metals. Innovative strategies including machine learning-guided catalyst tuning, hybrid material systems, and synergistic effects are given special attention. This analysis offers a road map for overcoming these sophisticated catalysts' present drawbacks by combining computational modeling with experimental observations. Their potential to transform sustainable energy technologies and promote the creation of environmentally benign, scalable solutions for chemical and energy applications is highlighted in the debate. The importance of single-atom catalysts and ultrasmall nanoclusters in meeting the urgent worldwide need for sustainable energy solutions is highlighted by this study. Nanoclusters ; Boosting (machine learning) ; Nanotechnology ; Materials science ; Electrochemistry ; Electrochemical energy conversion ; Metal ; Environmental science ; Computer science ; Metallurgy ; Chemistry ; Artificial intelligence Nanocluster Synthesis and Applications ; Electrocatalysts for Energy Conversion ; Advanced Photocatalysis Techniques
Authors: Ademola Soyemi | Khagendra Baral | Tibor Szilvási
Source: The Journal of Physical Chemistry A (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Modeling Equilibrium Solid–Liquid Interfaces under Effective Constant Chemical Potential Using Machine Learning Interatomic Potentials The chemical potential (μ) of species in solution is essential for understanding various chemical processes at interfaces. Molecular dynamics (MD) simulations, constrained by fixed compositions, cannot maintain constant chemical potential with reference to a targeted concentration or chemical potential under nonequilibrium or dynamic conditions, as solute species can migrate to the interface and deplete (or enrich) the bulk due to solute-interface interactions. In this study, we introduce a simple and computationally efficient approach named iterative quasi-constant chemical potential molecular dynamics (iqCμMD) simulation, which helps simulate targeted molar concentrations of species in solution. iqCμMD overcomes the limitations of conventional MD by adjusting the number of species in the solution to reach a target bulk concentration (chemical potential), which allows simulation of the interface under the bulk conditions comparable to experiment. We demonstrate our approach using machine learning interatomic potential (MLIP)-based MD simulations of the Na<sub>2</sub>SO<sub>4,aq</sub>-graphene interface, and to show the transferability of our approach, we also perform classical force field-based MD simulations of NaCl<sub>aq</sub>-air and NaCl<sub>aq</sub>-graphite interfaces, which produce comparable results to previous CμMD simulations. Our results also show that the iqCμMD approach efficiently achieves the desired bulk ion concentration within two iterations, and by utilizing MLIPs, we can achieve converged results using relatively small-scale simulations compared to previous CμMD simulations. By combining iqCμMD with MLIP-driven simulations, solid-liquid interfaces can be modeled under an effective constant chemical potential with DFT-level accuracy. We show that iqCμMD offers a robust and simple computational framework for constant chemical potential simulations, as its only requirement is to be able to converge interfacial simulations with a measurable bulk region. Interatomic potential ; Molecular dynamics ; Transferability ; Constant (computer programming) ; Non-equilibrium thermodynamics ; Chemical species ; Simple (philosophy) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Nanopore and Nanochannel Transport Studies
Authors: Luca Capriotti | Fidelma Giulia Di Lemma | Daniele Salvato | Fei Xu | Yalei Tang | Kyle M. Paaren | Alexander Lee Swearingen | Colby Jensen | Yachun Wang | David Porter
Source: Journal of Nuclear Materials (article, 2025-08-06)
Field: Materials Science — Materials Chemistry

An integrated approach to examine fuel-cladding chemical interaction in HT9/U-10Zr metallic fast reactor fuels: Coupling machine learning with electron microscopy and local mechanical properties analysis Cladding (metalworking) ; Nuclear engineering ; Materials science ; Coupling (piping) ; Composite material ; Engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Fusion materials and technologies
Authors: Johannes Karwounopoulos | Jasper De Landsheere | Leonard Galustian | Tobias Jechtl | Esther Heid
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states The accurate prediction of reaction barrier heights is crucial for understanding chemical reactivity and guiding reaction design. Recent advances in machine learning (ML) models, particularly graph neural networks, have shown great promise in capturing complex chemical interactions. Here, directed message-passing neural networks (D-MPNNs) on graph overlays of the reactant and product structures were shown to provide promising accuracies for reaction property prediction. They rely solely on molecular graph changes as input and thus require no additional information during inference. However, the reaction barrier height intrinsically depends on the conformations of the reactants, transition state, and products, which are not taken into account in standard D-MPNNs. In this work, we present a hybrid approach where we combine the power of D-MPNNs predicting barrier heights with generative models predicting transition state geometries on-the-fly for organic reactions. The resulting model thus only requires two-dimensional graph information as input, while internally leveraging three-dimensional information to increase accuracy. We furthermore evaluate the influence of additional physical features on D-MPNN models of reaction barrier heights, where we find that additional features only marginally enhance predictive accuracy and are especially helpful for small datasets. In contrast, our hybrid graph/coordinate approach reduces the error of barrier height predictions for the two investigated datasets RDB7 and RGD1. Graph ; Artificial neural network ; Graph theory ; Transition state ; Generative model ; Predictive power ; Property (philosophy) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Mass Spectrometry Techniques and Applications
Authors: Elham Soroush | Eiman Alibakhshi | Moein Malekli | Javad Ramezanpour | Bahram Ramezanzadeh | H. Eivaz Mohammadloo
Source: Advanced Composites and Hybrid Materials (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Advances in hybrid organic–inorganic coordination complexes (HOICCs) as a new generation of smart anti-corrosive materials Hybrid organic–inorganic coordination complexes (HOICCs) are an emerging class of materials that combine the structural robustness of inorganic components with the tunable functionality of organic ligands. This unique synergy imparts HOICCs with high coordination bond strength, structural diversity, and adaptability, distinguishing them from conventional corrosion inhibitors. These advantages translate into superior anticorrosive performance, including the ability to form stable protective films, modulate electrochemical environments, and provide dynamic, stimulus-responsive protection in aggressive conditions. This review systematically explores the role of HOICCs in corrosion protection. First, the fundamental structural classifications of HOICCs—framework-based, discrete, and polymeric complexes—and their influence on bond strength, architecture, and functional tunability have been outlined. Next, recent progress in synthetic approaches is summarized, including solution-based, electrolytic processing (EP), layer-by-layer (LbL), and gas-phase methods (Chemical vapor deposition (CVD), atomic layer deposition (ALD), molecular layer deposition (MLD)), which enable precise control over composition and structure. The review then discusses the mechanisms by which HOICCs inhibit corrosion, supported by electrochemical evaluation methods such as electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization (PDP). Finally, key challenges—including scalability, long-term stability, and environmental risks—are analyzed alongside forward-looking opportunities for integrating HOICCs with machine learning, green chemistry, and multifunctional coating design. The purpose of this review is to consolidate current knowledge of HOICCs as corrosion inhibitors, highlight their advantages over conventional strategies, and provide insights into future directions for their development as sustainable, high-performance solutions in industrial corrosion protection. Corrosion ; Coating ; Dielectric spectroscopy ; Electrochemistry ; Atomic layer deposition ; Layer (electronics) ; Polarization (electrochemistry) Corrosion Behavior and Inhibition ; Metal-Organic Frameworks: Synthesis and Applications ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Yuyuan Huang | Haiyan Zhu | Yongle Wang | Geng Yin | Shanlin Chen | Tingting Li | Chou Wu | Shaobo Jia | Jingzhi Shang | Zhifeng Ren | Tianhao Ding | Yawei Li
Source: Catalysts (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Metal-Organic Frameworks and Covalent Organic Frameworks for CO2 Electrocatalytic Reduction: Research Progress and Challenges This paper provides a systematic review of the latest advancements in metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) for electrocatalytic carbon dioxide reduction. Both materials exhibit high specific surface areas, tunable pore structures, and abundant active sites. MOFs enhance CO2 conversion efficiency through improved conductivity, optimized stability, and selective regulation—including bimetallic synergy, pulse potential strategies, and tandem catalysis. COFs achieve efficient catalysis through precise design of single or multi-metal active sites, optimization of framework conjugation, and photo/electro-synergistic systems. Both types of materials demonstrate excellent selectivity toward high-value-added products (CO, formic acid, C2+ hydrocarbons), but they still face challenges such as insufficient stability, short operational lifespan, high scaling-up costs, and poor electrolyte compatibility. Future research should integrate in situ characterization with machine learning to deepen mechanistic understanding and advance practical applications. Bimetallic strip ; Electrocatalyst ; Tandem ; Metal-organic framework ; Catalysis ; Covalent bond Covalent Organic Framework Applications ; CO2 Reduction Techniques and Catalysts ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Tianze Zheng | Xingyuan Xu | Zhi Wang | Zhenze Yang | Yuanheng Wang | Xu Han | Chen, Lei | Zhenliang Mu | Ziqing Zhang | Siyuan Liu | Sheng Gong | Yu Kuang | Wen Yan
Source: ArXiv.org (preprint, 2025-08-12)
Field: Materials Science — Materials Chemistry

Bridging Quantum Mechanics to Organic Liquid Properties via a Universal Force Field Molecular dynamics (MD) simulations are essential tools for unraveling atomistic insights into the structure and dynamics of condensed-phase systems. However, the universal and accurate prediction of macroscopic properties from ab initio calculations remains a significant challenge, often hindered by the trade-off between computational cost and simulation accuracy. Here, we present ByteFF-Pol, a graph neural network (GNN)-parameterized polarizable force field, trained exclusively on high-level quantum mechanics (QM) data. Leveraging physically-motivated force field forms and training strategies, ByteFF-Pol exhibits exceptional performance in predicting thermodynamic and transport properties for a wide range of small-molecule liquids and electrolytes, outperforming state-of-the-art (SOTA) classical and machine learning force fields. The zero-shot prediction capability of ByteFF-Pol bridges the gap between microscopic QM calculations and macroscopic liquid properties, enabling the exploration of previously intractable chemical spaces. This advancement holds transformative potential for applications such as electrolyte design and custom-tailored solvent, representing a pivotal step toward data-driven materials discovery. Bridging (networking) ; Force field (fiction) ; Molecular dynamics ; Polarizability ; Quantum ; Ab initio ; Field (mathematics) ; Quantum chemical Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly
Authors: Ji Hyun Nam | Seul Lee | Seongil Jo | Jaeoh Kim | Jooyeon Lee | Jahyun Koo | Byounghwak Lee | Keunhong Jeong | Donghyeon Yu
Source: Journal of Chemometrics (article, 2025-02-01)
Field: Chemistry — Spectroscopy

Improving Vapor Pressure Prediction Through Integration of Multiple Molecular Representations: A Super Learner Approach ABSTRACT Accurate prediction of vapor pressure is essential in chemical engineering, environmental science, and pharmaceutical development, impacting the volatility and stability of compounds. Traditional methods often fall short for complex and new molecular structures. This study introduces an advanced machine learning approach, integrating graph neural networks (GNNs), and CHEM‐BERT models to improve prediction accuracy. Utilizing the largest dataset to date, we derived comprehensive chemical descriptors and fingerprints. We evaluated 19 predictive models, including ridge regression, random forest, support vector regression, and feed‐forward neural networks, trained on diverse features like PaDEL and Morgan fingerprints, chemical descriptors, and Chem‐BERT embeddings. Central to our methodology is the super learner architecture, which combines 19 multiple models to enhance accuracy. The super learner achieved a root mean squared error (RMSE) of 0.8200, outperforming individual models and previous reports. These successful results highlight the effectiveness of integrating GNNs and Chem‐BERT for capturing detailed molecular information, setting a new benchmark for vapor pressure prediction. This study underscores the value of advanced machine learning techniques and comprehensive datasets, offering a robust tool for researchers and paving the way for future advancements in chemical property prediction. Vapor pressure ; Computer science ; Chemistry ; Organic chemistry Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Chemical Thermodynamics and Molecular Structure
Authors: Guosen Wang | Xia Huang | Changmin Guo | Hong Zhang | Chuanyu Zhang | Xinlu Cheng
Source: The Journal of Physical Chemistry A (article, 2025-02-14)
Field: Materials Science — Materials Chemistry

Dynamics Study of the CaC (<i>X</i><sup>3</sup>∑<sup>–</sup>) + C(<sup>3</sup>P<sub>g</sub>) → Ca+C<sub>2</sub> (∑<i>v</i>) Reaction: Based on a Full-Dimensional Neural Network Potential Energy Surface of CaC<sub>2</sub> The CaC<sub>2</sub> molecule, as an interstellar species that has already been detected, has attracted significant attention. To date, studies on the potential energy surface (PES) and the reaction dynamics of CaC<sub>2</sub> are largely lacking. In this work, <i>ab initio</i> energy values were obtained for 3877 configurations using the icMRCI+Q method, and these energies were subsequently fitted using a neural network approach. During parameter optimization, the trust region framework (TRF) method, which has superior performance compared to the previously used Levenberg-Marquardt (LM) method, was used. The root-mean-squared error (RMSE) for both the training and testing sets meets the requirement for chemical accuracy (error less than 1 kcal/mol). Using the neural network PES, we identified one stable structure and two metastable structures for the ground state (<i>X̃</i><sup>1</sup>A') of CaC<sub>2</sub>. The stable structure is T-shaped, while the two metastable structures are linear. The potential well depths of the stable structure and the two metastable structures are -10.98, -9.75, and -4.70 eV, respectively. Based on the obtained full-dimensional neural network PES, we investigated the CaC(<i>X</i><sup>3</sup>Σ<sup>-</sup>) + C(<sup>3</sup>P<sub>g</sub>) → Ca + C<sub>2</sub> (Σ<i>v</i>) reaction dynamics under different initial conditions. Under the condition that all other parameters remain unchanged, the reaction cross section and rate constant were found to be largest when the initial condition was <i>v</i> = 0 and <i>j</i> = 0. These findings indicate that the reaction rate is fastest when the CaC molecule is in its ground state. Nuclear reaction ; Physics ; Chemistry ; Atomic physics ; Radiochemistry ; Analytical Chemistry (journal) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Prithwish Biswas | Lingcheng Kong | Zhiting Tian
Source: Nanoscale (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Donor–acceptor conjugated polymers as high-mobility semiconductors: prospects for organic thermoelectrics Donor-acceptor conjugated polymers are emerging as a new class of organic semiconductors, where the donor and acceptor moieties function as hole and electron transporters, respectively. The potential of being doped as both p-type and n-type makes them attractive for scalable manufacturing, and they have been widely explored for organic photovoltaics. They can be particularly appealing for organic thermoelectrics, primarily due to their high interchain mobility alongside intrachain mobility. The high intrinsic mobility, resulting from the push-pull effect of the donor-acceptor moieties, ensures high electrical conductivity with minimal doping, which is crucial for maintaining a high Seebeck coefficient in thermoelectric materials. In this review, we explain the molecular structure and energetics, as well as their relationship to the electronic structure of donor-acceptor polymers. We also review the existing literature on how structural and energetic modifications can be implemented to modulate interchain transport, intrachain transport, and doping efficiencies. Based on these, we propose that improvements in molecular design, characterization methods, and the integration of data science and machine learning can accelerate research on donor-acceptor polymers for thermoelectrics and beyond. Conjugated system ; Thermoelectric materials ; Polymer ; Seebeck coefficient ; Thermoelectric effect ; Doping ; Characterization (materials science) ; Acceptor Advanced Thermoelectric Materials and Devices ; Perovskite Materials and Applications ; Conducting polymers and applications