jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Peter S. Rice | Jan Groh | Nicolas S Dwarica | Noah J. Gibson | James M. Mayer | Simone Raugei
Source: ACS Catalysis (article, 2025-08-20)
Field: Materials Science — Materials Chemistry

Unraveling Adsorbate-Induced Structural Evolution of Iron Carbide Nanoparticles Iron carbide (FexCy) nanoparticles (NPs) are promising candidates for replacing platinum group metals in industrial applications, such as high-temperature Fischer–Tropsch synthesis. However, due to their amorphous nature, characterization of the active sites has been challenging experimentally and computationally. Here, using a combined density functional theory (DFT), neural network interatomic potential-assisted global optimization, and ensemble learning study, we evaluate dynamic surface changes associated with syngas (H and CO) interactions. For this purpose, we have developed a general procedure that we use to model an experimentally relevant 270-atom Fe182C88 NP using the neural network-assisted stochastic surface walk global optimization algorithm (SSW-NN). Once generated, the Fe182C88 NP active sites and particle morphology are thoroughly characterized before the effects of syngas adsorbate interactions are explored by using DFT and molecular dynamics simulations. Lastly, we explore correlations between geometric and electronic features of the active sites and the adsorption of H (Hads), using a regularized random forest machine learning algorithm. In doing so, we identified the Fe–C coordination number and p orbital occupancy as the most important descriptors affecting Hads. Using a combined ML and quantum chemistry approach, our work demonstrates a general and efficient procedure for generating and probing complex surface phenomena on binary nanoparticles. Nanoparticle ; Carbide ; Catalysis ; Materials science ; Nanotechnology ; Chemical engineering ; Chemistry ; Metallurgy ; Organic chemistry Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Manickam Minakshi | Ashish Sharma | Ferdous Sohel | Almantas Pivrikas | Pragati A. Shinde | Katsuhiko Ariga | Lok Kumar Shrestha
Source: ChemPlusChem (article, 2025-08-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning—Guided Design of Biomass‐Based Porous Carbon for Aqueous Symmetric Supercapacitors Biomass-derived porous carbon electrodes have attracted significant attention for high-performance supercapacitor applications due to their sustainability, cost-effectiveness, and tunable porosity. To accelerate the design and evaluation of these materials, it is essential to develop accurate and efficient strategies for optimizing their physicochemical and electrochemical properties. Herein, a machine learning (ML) approach is employed to analyze experimental data from previously reported sources, enabling the prediction of specific capacitance (F g<sup>-1</sup>) based on various material characteristics and processing conditions. The trained ML model evaluates the influence of factors such as biomass type, electrolyte, activating agent, and key synthesis parameters, including activation and carbonization temperatures and durations, on supercapacitor performance. Despite growing interest, comprehensive studies that correlate these variables with performance metrics remain limited. This work addresses this gap by using ML algorithms to uncover the interrelationships between biomass-derived carbon properties, synthesis conditions, and specific capacitance. Herein, it is demonstrated that an optimal combination of a carbonized honeydew peel to H<sub>3</sub>PO<sub>4</sub> ratio of 1:4 and an activation temperature of 500 °C yields a highly porous carbon material. When used in a symmetric device with 1 M H<sub>2</sub>SO<sub>4</sub> electrolyte, this material, rich in oxygen and phosphorus species, achieves a high specific capacitance of 611 F g<sup>-1</sup> at a current density of 1.3 A g<sup>-1</sup>. Correlation analysis reveals a strong synergy between surface area and pore volume (correlation coefficient = 0.8473), and the ML-predicted capacitance closely aligns with experimental results. This ML-assisted framework offers valuable insights into the critical physicochemical and electrochemical parameters that govern supercapacitor performance, providing a powerful tool for the rational design of next-generation energy storage materials. Supercapacitor ; Aqueous solution ; Porosity ; Biomass (ecology) ; Carbon fibers ; Materials science ; Chemical engineering ; Nanotechnology ; Activated carbon ; Process engineering ; Electrochemistry ; Chemistry ; Engineering ; Organic chemistry ; Composite material ; Composite number ; Electrode ; Geology Supercapacitor Materials and Fabrication ; Nanomaterials for catalytic reactions ; Electrospun Nanofibers in Biomedical Applications
Authors: Mohammad Rahimi | Shakirudeen A. Salaudeen
Source: Materials Chemistry and Physics (article, 2025-09-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synthesis-feature-coupled machine learning approaches to predict the capacitance of biomass-derived carbon electrodes in supercapacitors Supercapacitor ; Capacitance ; Electrode ; Carbon fibers Supercapacitor Materials and Fabrication ; Conducting polymers and applications ; Electrochemical sensors and biosensors
Authors: Yue Fang | Hanyu Gao
Source: Current Opinion in Chemical Engineering (article, 2025-06-06)
Field: Chemistry — Organic Chemistry

Kinetic modeling for radical polymerization and depolymerization Depolymerization ; Polymerization ; Polymer science ; Polymer chemistry ; Materials science ; Chemistry ; Polymer ; Organic chemistry Advanced Polymer Synthesis and Characterization ; Photopolymerization techniques and applications ; Polymer crystallization and properties
Authors: Guobin Zhao | Pengyu Zhao | Yongchul G. Chung
Source: Journal of the American Chemical Society (article, 2025-08-11)
Field: Materials Science — Materials Chemistry

MOFClassifier: A Machine Learning Approach for Validating Computation-Ready Metal–Organic Frameworks The computational discovery and design of new crystalline materials, particularly metal-organic frameworks (MOFs), heavily rely on high-quality, "computation-ready" structural data. However, recent studies have revealed significant error rates within existing MOF databases, posing a critical data problem that hinders efficient high-throughput computational screening. While rule-based algorithms like MOSAEC, MOFChecker, and the Chen and Manz method (Chen-Manz) have been developed to address this, they often suffer from inherent limitations and misclassification of structures. To overcome this challenge, we developed MOFClassifier, a novel machine learning approach built upon a positive-unlabeled crystal graph convolutional neural network (PU-CGCNN) model. MOFClassifier learns intricate patterns from perfect crystal structures to predict a "crystal-likeness score" (CLscore), effectively classifying MOFs as computation-ready. Our model achieves an ROC value of 0.979 (previous best value 0.912) and, importantly, can identify subtle structural and chemical errors that are undetectable by current rule-based methods. By accurately recovering previously misclassified false-negative structures, the MOFClassifier reduces the risk of overlooking promising material candidates in large-scale computational screening campaigns. This user-friendly tool is freely available and has been integrated into the preparation workflow for the updated CoRE MOF DB 2025 v1.0, contributing to the accelerated computational discovery of MOF materials. Workflow ; Computation ; Computer science ; Convolutional neural network ; Artificial intelligence ; Machine learning ; Chemical space ; Computational model ; Chemistry ; Algorithm ; Drug discovery ; Database Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Qinrui Liu | Michael Forrester | Dhananjay Dileep | Aadhi Subbiah | Vivek Garg | Demetrius Finley | Eric W. Cochran | George A. Kraus | Scott Broderick
Source: International Journal of Molecular Sciences (article, 2025-03-18)
Field: Materials Science — Materials Chemistry

Data-Driven Modeling and Design of Sustainable High Tg Polymers This paper develops a machine learning methodology for the rapid and robust prediction of the glass transition temperature (Tg) for polymers for the targeted application of sustainable high-temperature polymers. The machine learning framework combines multiple techniques to develop a feature set encompassing all relative aspects of polymer chemistry, to extract and explain correlations between features and Tg, and to develop and apply a high-throughput predictive model. In this work, we identify aspects of the chemistry that most impact Tg, including a parameter related to rotational degrees of freedom and a backbone index based on a steric hindrance parameter. Building on this scientific understanding, models are developed on different types of data to ensure robustness, and experimental validation is obtained through the testing of new polymer chemistry with remarkable Tg. The ability of our model to predict Tg shows that the relevant information is contained within the topological descriptors, while the requirement of non-linear manifold transformation of the data also shows that the relationships are complex and cannot be captured through traditional regression approaches. Building on the scientific understanding obtained from the correlation analyses, coupled with the model performance, it is shown that the rigidity and interaction dynamics of the polymer structure are key to tuning for achieving targeted performance. This work has implications for future rapid optimization of chemistries. Polymer ; Robustness (evolution) ; Steric effects ; Degrees of freedom (physics and chemistry) ; Quantitative structure–activity relationship ; Computer science ; Biological system ; Rigidity (electromagnetism) ; Glass transition ; Machine learning ; Artificial intelligence ; Nanotechnology ; Materials science ; Chemistry ; Thermodynamics ; Organic chemistry ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Carbon dioxide utilization in catalysis
Authors: Simon Bbumba | John Ssekatawa | Ibrahim Karume | Emmanuel Tebandeke | Moses Kigozi | Solomon Yiga | Robert Setekera | Joseph Ssebuliba | Steven Sekitto | Ruth Mbabazi | Ivan Kiganda | Maximillian Kato | Patrick Taremwa | Moses Murungi | Chinaecherem Tochukwu Arum | Collins Yiiki Letibo | Geofrey Kaddu | Margret Namugwanya | John Kusasira | Peace Mwesigwa | Muhammad Ntale
Source: BMC Chemistry (article, 2025-07-22)
Field: Chemistry — Inorganic Chemistry

Prediction and optimization of Rhodamine B removal from water using metal-organic frameworks: RSM-CCD, ANN, non-linear kinetics, and isotherm studies This study involved the chemical synthesis of Metal-organic Frameworks (MOFs). The synthesized MOFs were characterized using Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR), and Powder X-ray diffraction (PXRD). Artificial intelligence models such as Response Surface Methodology (RSM) and Artificial Neural Networks (ANN) were used to predict and optimize the adsorptive removal of Rhodamine B (RhB) from water. The adsorption process was optimized using RSM with a Central Composite Design (CCD), which predicted a maximum removal efficiency of 95.91% under the following conditions: initial dye concentration (10 mg/L), adsorbent dosage (15 mg), pH (6), and temperature (25 °C). ANN was also optimized using similar conditions and the resulting predictive removal efficiency of 97.18% was obtained. Non-linear isotherm studies strongly correlated with the Freundlich (R² = 0.9987) and Sips (R² = 0.9928) models, indicating multilayer and monolayer adsorption. Non-linear Pseudo-first-order, Pseudo-second-order, and Elovich model correlation coefficients of 0.9644, 0.9998, and 0.952 suggested that the mechanisms were by chemisorption and physisorption on energetically stable heterogeneous surfaces. The findings of this study show a dual approach based on metal-organic framework and machine learning models as efficient alternatives to understanding the removal of RhB from water. Kinetics ; Rhodamine B ; Response surface methodology ; Rhodamine ; Metal-organic framework ; Materials science ; Chemistry ; Chemical engineering ; Biological system ; Physical chemistry ; Chromatography ; Fluorescence ; Adsorption ; Physics ; Engineering ; Organic chemistry ; Optics Metal-Organic Frameworks: Synthesis and Applications ; Molecular Sensors and Ion Detection ; Sulfur Compounds in Biology
Authors: Erhan Onat | Selma Ekıncı | Mehmet Sait İzgi̇ | Emre Erkan | Serdal Atiç | Behçet Kocaman | Vedat Tümen
Source: Fuel (article, 2025-10-06)
Field: Materials Science — Materials Chemistry

Sustainable hydrogen production from NaBH4 using Co@CHE Catalyst: Experimental and MLP-Based modeling for autonomous fuel systems Sodium borohydride ; Catalysis ; Hydrogen production ; Hydrogen ; Hydrolysis ; Hydrogen fuel ; Multilayer perceptron ; Artificial neural network Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Ammonia Synthesis and Nitrogen Reduction
Authors: Hesam Makki | Colm Burke | Christian B. Nielsen | Alessandro Troisi
Source: Materials Horizons (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Mapping the structure-function landscape of semiconducting polymers The molecular design of semiconducting polymers (SCPs) has been largely guided by varying monomer combinations and sequences by leveraging a robust understanding of charge transport mechanisms. However, the connection between controllable structural features and resulting electronic disorder remains elusive, leaving design rules for next-generation SCPs undefined. Using high-throughput computational methods, we analyse 100+ state-of-the-art p- and n-type polymer models. This exhaustive dataset allows for deriving statistically significant design rules. Our analysis disentangles the impact of key structural features, examining existing hypotheses, and identifying new structure-property relationships. For instance, we show that polymer rigidity has minimal impact on charge transport, while the planarity persistence length, introduced here, is a superior structural characteristic. Additionally, the predictive power of machine learning models trained on our dataset highlights the potential of data-driven approaches to SCP design, laying the groundwork for accelerated discovery of materials with tailored electronic properties. Structure function ; Materials science ; Polymer ; Function (biology) ; Nanotechnology ; Business ; Composite material ; Physics ; Evolutionary biology ; Biology Conducting polymers and applications
Authors: Saifullahi Kabiru Sa’adu | Cheng Seong Khe | M.F. Shukur | Kwok Feng Chong | Chin Wei Lai | Kok Yeow You | Nik Roselina Nik Roseley | Eslam Aboelazm
Source: Royal Society Open Science (article, 2025-08-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Transition metal coordination polymer-derived materials for supercapacitor applications: recent advances and future perspectives With the rising demand for efficient, sustainable and scalable energy storage, researchers are continuously exploring innovative materials for next-generation supercapacitors. Among these, transition metal coordination polymer (TMCP)-derived materials have emerged as promising candidates due to their high porosity, redox activity and structural adaptability. These materials offer significant potential for energy storage, but challenges like low electrical conductivity, structural instability and limited charge retention have restricted their widespread application. To overcome these hurdles, researchers have developed transformation strategies such as carbonization, phosphorization, sulfidation and oxide formation, enhancing the conductivity, stability and overall electrochemical performance of TMCPs. This review investigates the latest breakthroughs in TMCP-derived electrode materials, highlighting key advancements in synthesis techniques, structural engineering and hybrid material integration to improve charge transport and long-term durability. The incorporation of green chemistry principles, such as low-temperature synthesis, the use of non-toxic precursors, and strategies for recycling or reducing harmful byproducts, is highlighted, consequently promoting the fabrication of environmentally friendly supercapacitors. Furthermore, it explores how nanostructured designs and composite materials are unlocking new possibilities for high-performance supercapacitors. It also provides a perspective on the future of TMCPs in energy storage by fusing theoretical insights with experimental evidence. Theoretical frameworks like density functional theory and emerging machine learning models are other methods employed to better understand redox behaviour and drive material design. It addresses present issues and suggests viable future directions for their useful application. Supercapacitor ; Nanotechnology ; Electrochemical energy storage ; Materials science ; Energy storage ; Environmentally friendly ; Capacitance ; Electrode ; Chemistry Supercapacitor Materials and Fabrication ; Advanced battery technologies research ; Conducting polymers and applications
Authors: Darya R. Ahmed | Fahmi F. Muhammadsharif
Source: Processes (review, 2025-02-01)
Field: Materials Science — Materials Chemistry

A Review of Machine Learning in Organic Solar Cells Organic solar cells (OSCs) are a promising renewable energy technology due to their flexibility, lightweight nature, and cost-effectiveness. However, challenges such as inconsistent efficiency and low stability limit their widespread application. Addressing these issues requires extensive experimentation to optimize device performance, a process hindered by the complexity of OSC molecular structures and device architectures. Machine learning (ML) offers a solution by accelerating material discovery and optimizing performance through the analysis of large datasets and prediction of outcomes. This review explores the application of ML in advancing OSC technologies, focusing on predicting critical parameters such as power conversion efficiency (PCE), energy levels, and absorption spectra. It emphasizes the importance of supervised, unsupervised, and reinforcement learning techniques in analyzing molecular descriptors, processing data, and streamlining experimental workflows. Concludingly, integrating ML with quantum chemical simulations, alongside high-quality datasets and effective feature engineering, enables accurate predictions that expedite the discovery of efficient and stable OSC materials. By synthesizing advancements in ML-driven OSC research, the gap between theoretical potential and practical implementation can be bridged. ML can viably accelerate the transition of OSCs from laboratory research to commercial adoption, contributing to the global shift toward sustainable energy solutions. Organic solar cell ; Environmental science ; Computer science ; Engineering physics ; Engineering ; Photovoltaic system ; Electrical engineering Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Silicon and Solar Cell Technologies
Authors: Xijun Wu | Xuan Zhao | Jiangtao Wang | Shixin Wang | Yungang Zhang
Source: Analytica Chimica Acta (article, 2025-08-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Chemically and Electromagnetically dual-enhanced COFs-Au@AgNPs SERS sensor integrated with deep learning for ultrasensitive detection of neonicotinoid pesticides Chemistry ; Neonicotinoid ; Dual (grammatical number) ; Pesticide ; Nanotechnology ; Imidacloprid Gold and Silver Nanoparticles Synthesis and Applications ; Insect and Pesticide Research ; Pesticide Residue Analysis and Safety
Authors: Jiaojiao Deng | Xiaozhen Chen | Qiang Cai | Jinhan Mo | Xiaoliang Yu
Source: Chinese Journal of Chemistry (article, 2025-11-27)
Field: Materials Science — Materials Chemistry

Data‐Driven Electrolyte Design for Advanced Rechargeable Lithium Batteries <sup>†</sup> Comprehensive Summary The development of high‐performance liquid electrolytes is pivotal for advancing rechargeable lithium batteries, which are central to global electrification and renewable energy integration. Conventional electrolyte design, heavily reliant on empirical trial‐and‐error approaches, faces significant challenges in simultaneously optimizing a complex set of properties, including ionic conductivity, electrochemical stability window, thermal resilience, and most critically, compatibility with electrode interfaces. The efficiency of charge transfer processes and the stability of interphases formed on electrode surfaces, such as the solid electrolyte interphase (SEI) and cathode electrolyte interphase (CEI), are fundamentally governed by electrolyte composition. The nonlinear dependencies among these properties and the vast, unexplored chemical space render traditional methods inefficient. Emerging data‐driven strategies represent a paradigm shift, leveraging artificial intelligence (AI) and machine learning (ML) to accelerate the discovery and rational design of next‐generation electrolytes. This review comprehensively surveys recent progress in this rapidly evolving field. We begin by systematically outlining the fundamental properties of liquid electrolytes and establishing advanced descriptors for quantifying ion‐solvent and ion‐anion interactions. The core AI workflow encompassing data acquisition from diverse sources, feature engineering, and the application of various models from supervised learning to generative AI is critically examined. We then showcase the transformative applications of data‐driven methodologies, including performance‐targeted electrolyte formulation for extreme conditions, prediction of interfacial reaction pathways and SEI/CEI evolution mechanisms, and the development of novel AI algorithms and integrated computational platforms for end‐to‐end discovery. Despite promising advances, challenges remain, such as data scarcity and standardization, limited model generalizability, and the difficulty of multi‐objective optimization balancing performance, safety, and sustainability. By synthesizing these developments and outlining a clear research trajectory, this review aims to provide novel perspectives and inspire continued innovation in the design of high‐performance, safe, and sustainable electrolytes, ultimately enabling more reliable and powerful rechargeable lithium batteries for a clean energy future. Key Scientists Electrolyte ; Workflow ; Electrochemistry ; Cathode ; Chemical space ; Electrode ; Lithium (medication) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Huapeng Wu | Xiumin Chen | Lianfeng Yang | Dexi Zheng | Jie Zhou | Enhao Zhang | Ru Jia | Pengju Liu | Yunmin Chen
Source: Materials Today Communications (article, 2025-02-10)
Field: Materials Science — Materials Chemistry

Mechanism study of rare earth Y enhancing A356 tensile performance: Deep learning potential and Ab initio molecular dynamics Materials science ; Molecular dynamics ; Mechanism (biology) ; Ab initio ; Chemical physics ; Rare earth ; Ultimate tensile strength ; Dynamics (music) ; Earth (classical element) ; Nanotechnology ; Computational chemistry ; Composite material ; Metallurgy ; Organic chemistry Machine Learning in Materials Science ; Microstructure and mechanical properties ; Electron and X-Ray Spectroscopy Techniques