jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenxuan Jiang | Yawen Wang | Chendong Puyang | Shoufeng Tang | He Guo
Source: Journal of Colloid and Interface Science (article, 2025-01-27)
Field: Materials Science — Materials Chemistry

Periodate activation by plasma coupled with Fe N C for contaminant removal: Machine learning assisted catalyst optimization and electron shuttle mechanism Emerging contaminants (ECs) pose great challenges to water treatment technology due to their complexity and high harm. In this paper, the method of dielectric barrier discharge (DBD) plasma coupled with iron-based catalyst (FeNC) activating periodate (PI) was first designed for ECs removal. The ingenious introduction of FeNC not only promotes the Fenton-like reaction of DBD system but also reduces the PI activation energy barrier and accelerates the electron shuttle between PI and pollutants. Based on the parameters evaluation of machine learning (ML), the calcination temperature of 575 ℃ and 17 % N addition were determined for best catalytic performance. XRD, Raman spectroscopy, XPS and density functional theory (DFT) analysis show that optimized catalyst has better electron shuttle characteristics and PI activation ability. Compared to DBD (78 %) and DBD/PI (94 %), DBD/FeNC/PI could achieve 100 % degradation efficiency of sulfadiazine (SDZ) in 12 min with high reaction rate. In addition to the effects of ROSs (<sup>1</sup>O<sub>2</sub>, OH and O<sub>2</sub><sup>-</sup>), the efficient electron transfer mediated by FeNC and PI is the key to promoting the degradation of pollutants. The progressive dissociation of pyrimidine ring under the action of OH and electron transfer is the main pathway of SDZ degradation. The toxicity of intermediate products produced by the system is generally lower than that of SDZ. The system still has a high SDZ removal efficiency in actual water and has a good removal effect for other types of ECs, which also makes the system have a better practical prospect. Periodate ; Catalysis ; Mechanism (biology) ; Chemistry ; Reaction mechanism ; Plasma ; Chemical engineering ; Combinatorial chemistry ; Engineering ; Organic chemistry ; Physics Catalytic Processes in Materials Science ; Advanced Photocatalysis Techniques ; Environmental remediation with nanomaterials
Authors: Lijia Zhang | Kun Song | Liu G | Xiaobai Li | Yang He | Yunhui Meng | Yuchen Yang | Tao Gong | Hongmei Wang | Ke Sun | Lintao Zeng | Shilong Yang | Hongwei Ma
Source: Coordination Chemistry Reviews (article, 2025-05-09)
Field: Chemistry — Spectroscopy

Solid-state fluorescent films for detection of chemical warfare agents: Mechanisms, film engineering and integration with machine learning Chemistry ; Fluorescence ; Nanotechnology ; Solid-state ; Biochemical engineering ; Combinatorial chemistry ; Artificial intelligence Molecular Sensors and Ion Detection ; Luminescence and Fluorescent Materials ; Advanced Chemical Sensor Technologies
Authors: Ryosuke Jinnouchi | Saori Minami
Source: ACS Nano (review, 2025-06-18)
Field: Materials Science — Materials Chemistry

Machine Learning Force Fields in Electrochemistry: From Fundamentals to Applications This article reviews the foundations and applications of machine learning force fields (MLFFs) in electrochemistry, highlighting their role as a transformative tool in materials science. We first provide an overview of MLFFs, then discuss their applications in ionics and electrochemical reactions, and finally outline future directions. Most MLFF approaches use invariant or equivariant descriptors derived from body-order expansions to represent many-body atomic interactions. These descriptors feed into linear regression models, kernel methods, or neural networks to construct potential energy surfaces for gases, liquids, solids, and interfaces involving inorganic and organic materials. MLFFs have enabled a wide range of advances, including all-atom molecular dynamics (MD), data extraction from MD, and accelerated materials discovery. In MD simulations, MLFFs allow accurate evaluation of ionic conductivity via the fluctuation-dissipation theorem and nonequilibrium MD under electric fields, applied to both solid and polymer electrolytes. For electrochemical reactions, MLFFs and Δ-ML models have been used to predict redox potentials in homogeneous and interfacial systems through thermodynamic integration. MLFFs also enable the extraction of key thermodynamic and kinetic information-such as free energy landscapes and local transport coefficients-from atomic trajectories, facilitating coarse-grained modeling of mass transport and reactions in complex electrolytes. In materials discovery, MLFFs have allowed high-throughput screening of 10<sup>7</sup> to 10<sup>8</sup> crystal structures, leading to the identification of promising Li-ion and Na-ion solid electrolytes. MLFFs are expected to continue evolving as a core technology in computational materials science, spanning a wide range from high-precision calculations to large-scale materials exploration. Nanotechnology ; Materials science ; Chemical physics ; Computer science ; Chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Computational Drug Discovery Methods
Authors: Patrick G. Sahrmann | Gregory A. Voth
Source: Current Opinion in Structural Biology (review, 2025-01-02)
Field: Materials Science — Materials Chemistry

On the emergence of machine-learning methods in bottom-up coarse-graining Machine-learning methods have gained significant attention in the computational chemistry community as a viable approach to molecular modeling and analysis. Recent successes in utilizing neural networks to learn atomistic force-fields which 'coarse-grain' electronic structure have inspired similar applications to the thermodynamic coarse-graining of chemical and biological systems. In this review, we discuss the current viability and challenges in using machine-learning methods to represent coarse-grained force-fields, as well as the utility of machine-learning in various aspects of coarse-grained modeling. Granularity ; Top-down and bottom-up design ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Enhanced Oil Recovery Techniques
Authors: Han-Chung Chang | Ming‐Hsuan Tsai | Yi‐Pei Li
Source: Journal of Chemical Information and Modeling (article, 2025-01-25)
Field: Materials Science — Materials Chemistry

Enhancing Activation Energy Predictions under Data Constraints Using Graph Neural Networks Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this work, we explore and compare three innovative approaches (transfer learning, delta learning, and feature engineering) to enhance the accuracy of activation energy predictions using graph neural networks, specifically focusing on methods that incorporate low-cost, low-level computational data. Using the Chemprop model, we systematically evaluated how these methods leverage data from semiempirical quantum mechanics (SQM) calculations to improve predictions. Delta learning, which adjusts low-level SQM activation energies to align with high-level CCSD(T)-F12a targets, emerged as the most effective method, achieving high accuracy with substantially reduced data requirements. Notably, delta learning trained with just 20-30% of high-level data matched or exceeded the performance of other methods trained with full data sets, making it advantageous in data-scarce scenarios. However, its reliance on transition state searches imposes significant computational demands during model application. Transfer learning, which pretrains models on large data sets of low-level data, provided mixed results, particularly when there was a mismatch in the reaction distributions between the training and target data sets. Feature engineering, which involves adding computed molecular properties as input features, showed modest gains, particularly in thermodynamic properties. Our study highlights the trade-offs between accuracy and computational demand in selecting the best approach for enhancing activation energy predictions. These insights provide valuable guidelines for researchers aiming to apply machine learning in chemical reaction engineering, helping to balance accuracy with resource constraints. Artificial neural network ; Computer science ; Graph ; Energy (signal processing) ; Biological system ; Artificial intelligence ; Data mining ; Mathematics ; Theoretical computer science ; Statistics ; Biology Machine Learning in Materials Science ; Advanced Memory and Neural Computing ; Fuel Cells and Related Materials
Authors: Hasan Kurban | Parichit Sharma | Mehmet Dalkılıç | Mustafa Kurban
Source: Scientific Reports (article, 2025-08-20)
Field: Materials Science — Materials Chemistry

Accelerating density of states prediction in Zn-doped MgO nanoparticles via kernel-optimized weighted k-NN This study presents an integrated approach combining Density Functional based Tight Binding (DFTB) calculations with machine learning (ML) techniques to predict the density of states (DOS) in pristine and Zn-doped MgO nanoparticles (NPs). A range of over 60 ML models, including linear models, tree-based ensembles, and neural networks, were evaluated for predictive performance. Among these, the weighted k-nearest neighbor (wkNN) algorithm, particularly when using triweight and biweight kernels, consistently outperformed others, achieving a median RMSE of 0.241 for pristine MgO and 0.386 for Zn-doped samples. The models demonstrated robust performance across various doping concentrations (5–25%) and NP sizes (0.8 nm and 0.9 nm), with minimal impact of doping levels on prediction accuracy. This integration of DFTB with ML offers a powerful and efficient framework for accelerating electronic property predictions in materials science, supporting the rapid design of advanced materials for applications in electronics, catalysis, and energy storage. The code and data are publicly available at: https://github.com/KurbanIntelligenceLab/DOS-Nanoparticles-Weighted-kNN . Nanoparticle ; Doping ; Kernel (algebra) ; Materials science ; Kernel density estimation ; Computer science ; Biological system ; Mathematics ; Statistics ; Nanotechnology ; Optoelectronics ; Discrete mathematics ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Zhiming Zhang | Athulya S. Palakkal | Xiaoyu Wu | Jianwen Jiang | Zhongyi Jiang
Source: Environmental Science & Technology (article, 2025-05-02)
Field: Chemistry — Inorganic Chemistry

Discovering Ultra-Stable Metal–Organic Frameworks for CO<sub>2</sub> Capture from A Wet Flue Gas: Integrating Machine Learning and Molecular Simulation The rapid increase in atmospheric CO<sub>2</sub>, arising from anthropogenic sources, has posed a severe threat to global climate and raised widespread environmental concern. Metal-organic frameworks (MOFs) are promising adsorbents to potentially reduce CO<sub>2</sub> emissions from flue gases. However, many MOFs suffer from structural degradation and performance deterioration upon exposure to water in flue gases. Aiming to discover stable and efficient MOFs for CO<sub>2</sub> capture from a wet flue gas, we propose a hierarchical high-throughput computational screening (HTCS) strategy. Machine learning (ML)-assisted stability analysis is incorporated within the HTCS, leveraging prior experimental experience to predict ultrastable (including water-, thermal-, and activation-stable) MOFs from ∼280,000 candidates in the ab initio REPEAT charge MOF (ARC-MOF) database. Among 9755 shortlisted MOFs, molecular simulations identify 1000 top-performing MOFs. Remarkably, several vanadium-based MOFs are revealed to be ultrastable, exhibiting high CO<sub>2</sub> capture capability of 3-7 mmol/g and CO<sub>2</sub>/N<sub>2</sub> selectivity of 95-401. Subsequently, ML regressors are developed to derive design principles for MOFs capable of overcoming the trade-off effect. Furthermore, an ML classifier is developed to analyze the impact of water on CO<sub>2</sub> capture by comparing dry and wet conditions. The proposed hierarchical HTCS and developed ML models lay a solid foundation for the potential transition of MOFs into practical applications. Flue gas ; Metal-organic framework ; Environmental science ; Environmental chemistry ; Computer science ; Chemistry ; Waste management ; Engineering ; Adsorption ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Naoki Noto | Ryuga Kunisada | Tabea Rohlfs | Manami Hayashi | Ryosuke Kojima | Olga Garcı́a Mancheño | Takeshi Yanai | Susumu Saito
Source: Nature Communications (article, 2025-04-10)
Field: Chemistry — Organic Chemistry

Transfer learning across different photocatalytic organic reactions While seasoned organic chemists can often predict suitable catalysts for new reactions based on their past experiences in other catalytic reactions, developing this ability is costly, laborious and time-consuming. Therefore, replicating this remarkable expertize of human researchers through machine learning (ML) is compelling, albeit that it remains highly challenging. Herein, we apply a domain-adaptation-based transfer-learning (TL) approach to photocatalysis. Despite being different reaction types, the knowledge of the catalytic behavior of organic photosensitizers (OPSs) from photocatalytic cross-coupling reactions is successfully transferred to ML for a [2+2] cycloaddition reaction, improving the prediction of the photocatalytic activity compared with conventional ML approaches. Furthermore, a satisfactory predictive performance is achieved by using only ten training data points. This experimentally readily accessible small dataset can also be used to identify effective OPSs for alkene photoisomerization, thereby showcasing the potential benefits of TL in catalyst exploration. Photocatalysis ; Catalysis ; Cycloaddition ; Transfer of learning ; Alkene ; Computer science ; Adaptation (eye) ; Photochemistry ; Electron transfer ; Biochemical engineering ; Nanotechnology ; Chemistry ; Materials science ; Artificial intelligence ; Organic chemistry ; Biology ; Neuroscience Radical Photochemical Reactions ; Advanced Photocatalysis Techniques ; Advanced Chemical Sensor Technologies
Authors: Jaeyun Moon | Zhiting Tian
Source: npj Computational Materials (article, 2025-05-17)
Field: Materials Science — Materials Chemistry

Crystal-like thermal transport in amorphous carbon Thermal transport in amorphous carbon has attracted immense attention due to its extreme thermal properties: It has been reported to have among the highest thermal conductivity for bulk amorphous solids up to ~37 W m−1 K−1, comparable to crystalline sapphire (α-Al2O3). However, mechanism behind the high thermal conductivity remains elusive due to many variables at play. In this work, we perform large-scale (~105 atoms) molecular dynamics simulations utilizing a machine learning potential based on neural networks with first-principles accuracy. Through spectral decomposition of thermal conductivity which enables a quantum correction to classical heat capacity, we find that propagating vibrational excitations govern thermal transport in amorphous carbon (~100 % of thermal conductivity) in sharp contrast to the convention that diffusive vibrational excitations dominate thermal transport in amorphous solids. This remarkable behavior resembles thermal transport in simple crystals. Our work, therefore, provides a perspective that deepens our understanding of intermediate thermal transport mechanisms between the two ends of spectrum of solids: crystalline and amorphous solids. Materials science ; Amorphous solid ; Crystal (programming language) ; Thermal ; Carbon fibers ; Amorphous carbon ; Crystallography ; Chemistry ; Composite material ; Physics ; Computer science ; Thermodynamics ; Composite number Thermal properties of materials ; Diamond and Carbon-based Materials Research ; Graphene research and applications
Authors: Wang Tang | Tianxiong Liu | Hongxia Gao | Shaofei Wang | Hui Zhou | Ningbo Yu | Zhiwu Liang
Source: Separation and Purification Technology (article, 2025-02-02)
Field: Chemistry — Spectroscopy

Study on artificial neural networks and structure–activity relationship for constructing viscosity correlations of amine aqueous solutions based on chemical structure information Aqueous solution ; Amine gas treating ; Viscosity ; Artificial neural network ; Chemistry ; Network structure ; Biological system ; Thermodynamics ; Materials science ; Organic chemistry ; Computer science ; Artificial intelligence ; Physics ; Machine learning ; Biology Analytical Chemistry and Chromatography ; Thermodynamic properties of mixtures ; Advanced Chemical Sensor Technologies
Authors: Priyanka Panchal | Protima Rauwel | S.P. Nehra | Priyanka Singh | Mamta Karla | Glemarie C. Hermosa | Erwan Rauwel
Source: Pharmaceuticals (review, 2025-05-29)
Field: Materials Science — Materials Chemistry

A Review on Biomedical Applications of Plant Extract-Mediated Metallic Ag, Au, and ZnO Nanoparticles and Future Prospects for Their Combination with Graphitic Carbon Nitride Since the publication of the 12 principles of green chemistry in 1998 by Paul Anastas and John Warner, the green synthesis of metal and metal oxide nanoparticles has emerged as an eco-friendly and sustainable alternative to conventional chemical methods. Plant-based synthesis utilizes natural extracts as reducing and stabilizing agents, minimizing harmful chemicals and toxic by-products. Ag nanoparticles (Ag-NPs) exhibit strong antibacterial activity; Au nanoparticles (Au-NPs) are seen as a promising carrier for drug delivery and diagnostics because of their easy functionalization and biocompatibility; and ZnO nanoparticles (ZnO-NPs), on the other hand, produce reactive oxygen species (ROS) that kill microorganisms effectively. These nanoparticles also demonstrate antioxidant properties by scavenging free radicals, reducing oxidative stress, and preventing degenerative diseases. Green syntheses based on plant extracts enhance biocompatibility and therapeutic efficacy, making them suitable for antimicrobial, anticancer, and antioxidant applications. Applying a similar "green synthesis" for advanced nanostructures like graphitic carbon nitride (GCN) is an environmentally friendly alternative to the traditional ways of doing things. GCN exhibits exceptional photocatalytic activity, pollutant degradation efficiency, and electronic properties, with applications in environmental remediation, energy storage, and biomedicine. This review highlights the potential of green-synthesized hybrid nanocomposites combining nanoparticles and GCN as sustainable solutions for biomedical and environmental challenges. The review also highlights the need for the creation of a database using a machine learning process that will enable providing a clear vision of all the progress accomplished till now and identify the most promising plant extracts that should be used for targeted applications. Biocompatibility ; Nanotechnology ; Graphitic carbon nitride ; Nanoparticle ; Environmentally friendly ; Materials science ; Nanocomposite ; Drug delivery ; Chemistry ; Photocatalysis ; Organic chemistry ; Catalysis ; Biology ; Metallurgy Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis
Authors: Junwei Sun | Rui Yu | Dominik Legut | Joseph S. Francisco | Ruifeng Zhang
Source: Advanced Materials (article, 2025-05-06)
Field: Materials Science — Materials Chemistry

Rational Design of Highly Stable and Active Single‐Atom Modified S‐MXene as Cathode Catalysts for Li‐S Batteries The practical application of Li-S batteries is hindered by the shuttle effect and sluggish sulfur conversion kinetics. To address these challenges, this work proposes an efficient strategy by introducing single atoms (SAs) into sulfur-functionalized MXenes (S-MXenes) catalysts and evaluate their potential in Li-S batteries through first-principles calculations. Using high-throughput screening of various SA-modified S-MXenes, this work identifies 73 promising candidates that exhibit exceptional thermodynamic and kinetic stability, along with the effective immobilization of polysulfides. Notably, the incorporation of Ni, Cu, or Zn as SAs into S-MXenes results in a significant Gibbs free energy barrier reduction by 51%-75%, outperforming graphene-based catalysts. This reduction arises from SA-induced surface electron density that influences the adsorption energies of intermediates and thereby disrupts the scaling relations between Li₂S₂ and other key intermediates. Further enhancement in catalytic performance is achieved through strain engineering by shifting the d-band center of metal atoms to higher energy levels, increasing the chemical affinity for intermediates. To elucidate the intrinsic adsorption properties of intermediates, this work develops a machine learning model with high accuracy (R<sup>2</sup> = 0.88), which underscores the pivotal roles of SA electronegativity and local coordination environment in determining adsorption strength, offering valuable insights for the rational design of catalysts. MXenes ; Electronegativity ; Catalysis ; Rational design ; Materials science ; Adsorption ; Gibbs free energy ; Chemical physics ; Graphene ; Chemical engineering ; Nanotechnology ; Physical chemistry ; Chemistry ; Thermodynamics ; Organic chemistry MXene and MAX Phase Materials ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Qin Zhu | Yuming Gu | Jing Ma
Source: The Journal of Physical Chemistry Letters (review, 2025-02-26)
Field: Materials Science — Materials Chemistry

Digital Descriptors in Predicting Catalysis Reaction Efficiency and Selectivity Accurately controlling the interactions and dynamic changes between multiple active sites (e.g., metals, vacancies, and lone pairs of heteroatoms) to achieve efficient catalytic performance is a key issue and challenge in the design of complex catalytic reactions involving 2D metal-supported catalysts, metal-zeolites, metal-organic catalysts, and metalloenzymes. With the aid of machine learning (ML), descriptors play a central role in optimizing the electrochemical performance of catalysts, elucidating the essence of catalytic activity, and predicting more efficient catalysts, thereby avoiding time-consuming trial-and-error processes. Three kinds of descriptors─active center descriptors, interfacial descriptors, and reaction pathway descriptors─are crucial for understanding and designing metal-supported catalysts. Specifically, vacancies, as active sites, synergize with metals to significantly promote the reduction reactions of energy-relevant small molecules. By combining some physical descriptors, interpretable descriptors can be constructed to evaluate catalytic performance. Future development of descriptors and ML models faces the challenge of constructing descriptors for vacancies in multicatalysis systems to rationally design the activity, selectivity, and stability of catalysts. Utilization of generative artificial intelligence and multimodal ML to automatically extract descriptors would accelerate the exploration of dynamic reaction mechanisms. The transferable descriptors from metal-supported catalysts to artificial metalloenzymes provide innovative solutions for energy conversion and environmental protection. Selectivity ; Catalysis ; Chemistry ; Combinatorial chemistry ; Computer science ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions