jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rita C. O. Sebastião | Natália R. S. Araújo | Felipe Silva Carvalho | Bárbara D. L. Ferreira | J. P. Braga
Source: Journal of the Brazilian Chemical Society (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Determination of Thermal Process Kinetics by Artificial Neural Networks: A Review Considering Isothermal and Non-Isothermal TG and DSC Data The integration of artificial neural networks (ANNs) with thermal analysis techniques, such as thermogravimetry (TG) and differential scanning calorimetry (DSC), presents great potential to accurately determine kinetics of thermal processes and thermal behavior. This review examines the application of ANNs, including Multilayer Perceptron (MLP) and Hopfield Neural networks (HNN), to analyze both isothermal and non-isothermal TG and DSC data. These networks provide robust tools for predicting thermal behavior, determining kinetic parameters, and modeling complex reaction mechanisms, significantly improving accuracy when compared to traditional methods. The review highlights advances in ANN-based methodologies, including the ability to integrate multiple kinetic models and manage noisy experimental data, thereby offering new insights into thermal decomposition and reaction kinetics. Moreover, the use of HNN in distributed activation energy models (DAEM) is explored, indicating their potential in understanding heterogeneous systems. This study emphasizes the transformative potential of ANNs for improving kinetic studies, suggesting future directions to explore machine learning in material characterization and thermal process optimization. Isothermal process ; Kinetics ; Artificial neural network ; Materials science ; Process (computing) ; Thermodynamics ; Thermal ; Computer science ; Artificial intelligence ; Physics Thermal and Kinetic Analysis
Authors: Eleftheria Roumeli | Sourena Azidhak | Ana F. Costa | Anlan Chen | Io Saito | Yiyang Sun | L. Cate Brinson | Cynthia Rudin | Linda S. Schadler | Kayla Sprenger
Source: MRS Bulletin (article, 2025-11-01)
Field: Materials Science — Biomaterials

From biomatter to bioplastics: A perspective on modeling, structure, and data-driven design Perspective (graphical) ; Function (biology) ; Path (computing) ; Inverse ; Materials processing ; Finite element method ; Biological organism Calcium Carbonate Crystallization and Inhibition ; Machine Learning in Materials Science ; Nanocomposite Films for Food Packaging
Authors: Zhenyu Zhou | Salman Bin Kashif | Jin‐Hu Dou | Chris Wolverton | Kaihang Shi | Tao Deng | Zhenpeng Yao
Source: ArXiv.org (preprint, 2025-09-19)
Field: Materials Science — Materials Chemistry

Interpretable Nanoporous Materials Design with Symmetry-Aware Networks Nanoporous materials hold promise for diverse sustainable applications, yet their vast chemical space poses challenges for efficient design. Machine learning offers a compelling pathway to accelerate the exploration, but existing models lack either interpretability or fidelity for elucidating the correlation between crystal geometry and property. Here, we report a three-dimensional periodic space sampling method that decomposes large nanoporous structures into local geometrical sites for combined property prediction and site-wise contribution quantification. Trained with a constructed database and retrieved datasets, our model achieves state-of-the-art accuracy and data efficiency for property prediction on gas storage, separation, and electrical conduction. Meanwhile, this approach enables the interpretation of the prediction and allows for accurate identification of significant local sites for targeted properties. Through identifying transferable high-performance sites across diverse nanoporous frameworks, our model paves the way for interpretable, symmetry-aware nanoporous materials design, which is extensible to other materials, like molecular crystals and beyond. Nanoporous ; Interpretability ; Property (philosophy) ; Identification (biology) ; Fidelity ; Computation ; Key (lock) Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Electrocatalysts for Energy Conversion
Authors: Javad Tamnanloo (1438264) | Abdol Hadi Mokarizadeh (10917568) | Farzad Toiserkani (20665223) | Hansini Abeysinghe (21082729) | Abraham Joy (1579327) | Mesfin Tsige (1483108)
Source: Figshare (article, 2025-04-13)
Field: Materials Science — Materials Chemistry

Graph NeuralNetworks for Surface Tension Predictionof Polymers: A Comparative Analysis with Descriptor-Based Models Surface tension is a critical property that influences polymer behavior at interfaces and affects applications ranging from coatings to biomedical devices. Traditional experimental methods for measuring polymer surface tension are time-consuming, costly, and sensitive to environmental conditions. Computational approaches such as molecular dynamics (MD) simulations are valuable but computationally intensive, especially for polymers with long chains. This study investigates the use of machine learning (ML) techniques to predict polymer surface tension using different levels of molecular representation, focusing on multilinear regression (MLR), random forest (RF), and graph neural networks (GNNs). A data set of 317 homopolymers collected from the PolyInfo database is used to train and evaluate these models. Descriptors are derived at various levels of complexity, ranging from manually calculated features to graph-based representations. The GNN approach captures the intrinsic connectivity of polymer structures, while the MLR and RF models rely on manually crafted descriptors. The performance of these models is compared with experimental data, with the GNN model demonstrating superior accuracy due to its ability to directly learn from molecular graphs. Our results show that GNNs can better capture complex nonlinear relationships in polymer structures than traditional descriptor-based methods, suggesting their significant potential for accelerating polymer design and development. The study also includes validation of model predictions against molecular dynamics simulations, highlighting the potential of GNNs to accurately model polymer interfacial properties. Multilinear map ; Surface tension ; Ranging ; Polymer ; Artificial neural network ; Set (abstract data type) ; Nonlinear system ; Experimental data ; Graph Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Polymer Surface Interaction Studies
Authors: Sejun Kim
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Property-Networking Deep Learning Model for Experimental Datasets The demand for artificial intelligence (AI) technologies that can accurately predict material properties, particularly in electrochemistry, is growing rapidly. To meet this need, it is crucial to expand material databases beyond the constraints of density functional theory (DFT) and develop deep learning (DL) models that can be effectively trained with limited high-quality data. In this presentation, I introduce a deep learning model that uses a crystal structure representation based on the orbital field matrix (OFM), with added information about elemental properties and valence electron configurations. This model, called PROFiT-Net (PRoperty-networking Orbital Field maTrix-convolutional neural Network), captures the relationships between elemental properties in the crystal structure. PROFiT-Net demonstrates exceptional accuracy in predicting experimental data relevant to electrochemistry, such as band gaps, formation enthalpies, and other electrochemical properties. It outperforms or matches other leading DL models, providing results comparable to DFT or hybrid functional calculations. Additionally, the model identifies physical trends similar to those observed in the Penn model while maintaining scalability. With its strong predictive capabilities, PROFiT-Net has the potential to significantly advance electrochemical research by bridging the gap between theoretical predictions and experimental outcomes. Deep learning ; Bridging (networking) ; Field (mathematics) ; Valence (chemistry) ; Experimental data ; Representation (politics) ; Artificial neural network ; Data modeling Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Zeng, Yinqi | Li, Renjie
Source: ArXiv.org (article, 2025-11-23)
Field: Chemistry — Organic Chemistry

QuantumChem-200K: A Large-Scale Open Organic Molecular Dataset for Quantum-Chemistry Property Screening and Language Model Benchmarking The discovery of next-generation photoinitiators for two-photon polymerization (TPP) is hindered by the absence of large, open datasets containing the quantum-chemical and photophysical properties required to model photodissociation and excited-state behavior. Existing molecular datasets typically provide only basic physicochemical descriptors and therefore cannot support data-driven screening or AI-assisted design of photoinitiators. To address this gap, we introduce QuantumChem-200K, a large-scale dataset of over 200,000 organic molecules annotated with eleven quantum-chemical properties, including two-photon absorption (TPA) cross sections, TPA spectral ranges, singlet-triplet intersystem crossing (ISC) energies, toxicity and synthetic accessibility scores, hydrophilicity, solubility, boiling point, molecular weight, and aromaticity. These values are computed using a hybrid workflow that integrates density function theory (DFT), semi-empirical excited-state methods, atomistic quantum solvers, and neural-network predictors. Using QuantumChem-200K, we fine tune the open-source Qwen2.5-32B large language model to create a chemistry AI assistant capable of forward property prediction from SMILES. Benchmarking on 3000 unseen molecules from VQM24 and ZINC20 demonstrates that domain-specific fine-tuning significantly improves accuracy over GPT-4o, Llama-3.1-70B, and the base Qwen2.5-32B model, particularly for TPA and ISC predictions central to photoinitiator design. QuantumChem-200K and the corresponding AI assistant together provide the first scalable platform for high-throughput, LLM-driven photoinitiator screening and accelerated discovery of photosensitive materials. Photoinitiator ; Workflow ; Property (philosophy) ; Scalability ; Benchmarking ; Intersystem crossing Photopolymerization techniques and applications ; Nonlinear Optical Materials Studies ; Machine Learning in Materials Science
Authors: Zeng, Yinqi | Li, Renjie
Source: arXiv (Cornell University) (preprint, 2025-11-23)
Field: Chemistry — Organic Chemistry

QuantumChem-200K: A Large-Scale Open Organic Molecular Dataset for Quantum-Chemistry Property Screening and Language Model Benchmarking The discovery of next-generation photoinitiators for two-photon polymerization (TPP) is hindered by the absence of large, open datasets containing the quantum-chemical and photophysical properties required to model photodissociation and excited-state behavior. Existing molecular datasets typically provide only basic physicochemical descriptors and therefore cannot support data-driven screening or AI-assisted design of photoinitiators. To address this gap, we introduce QuantumChem-200K, a large-scale dataset of over 200,000 organic molecules annotated with eleven quantum-chemical properties, including two-photon absorption (TPA) cross sections, TPA spectral ranges, singlet-triplet intersystem crossing (ISC) energies, toxicity and synthetic accessibility scores, hydrophilicity, solubility, boiling point, molecular weight, and aromaticity. These values are computed using a hybrid workflow that integrates density function theory (DFT), semi-empirical excited-state methods, atomistic quantum solvers, and neural-network predictors. Using QuantumChem-200K, we fine tune the open-source Qwen2.5-32B large language model to create a chemistry AI assistant capable of forward property prediction from SMILES. Benchmarking on 3000 unseen molecules from VQM24 and ZINC20 demonstrates that domain-specific fine-tuning significantly improves accuracy over GPT-4o, Llama-3.1-70B, and the base Qwen2.5-32B model, particularly for TPA and ISC predictions central to photoinitiator design. QuantumChem-200K and the corresponding AI assistant together provide the first scalable platform for high-throughput, LLM-driven photoinitiator screening and accelerated discovery of photosensitive materials. Photoinitiator ; Workflow ; Property (philosophy) ; Scalability ; Benchmarking ; Intersystem crossing Photopolymerization techniques and applications ; Nonlinear Optical Materials Studies ; Machine Learning in Materials Science
Authors: M. Chertok | Howard A. Stone | Michael A. Webb
Source: Soft Matter (article, 2025-01-01)
Field: Materials Science — Biomaterials

Effect of shear flow and precursor polymer design on single-chain nanoparticle formation Single-chain nanoparticles (SCNPs) are a class of materials formed by the intramolecular cross-linking and collapse of single polymer chains. Because their morphology dictates suitability for specific applications, such as nanoscale reactors and drug delivery vehicles, understanding how to control or tailor morphologies is of interest. Here, we investigate how the morphology of SCNPs depends on both precursor chain attributes, such as linker fraction and backbone stiffness, and an imposed shear flow. Using coarse-grained molecular dynamics simulations, we generate an ensemble of structures from 10 800 unique SCNPs, some formed under quiescent conditions and some in shear flow-the latter of which has not been studied previously. We then characterize morphologies by analysis of a three-dimensional embedding space obtained through unsupervised learning of the simulated structures. This reveals how SCNP morphology depends on dimensionless parameters, related to precursor-chain attributes and shear rate, and offers insight into their relative influence. We find that shear rate has comparable influence to the degree of polymerization and the blockiness of reactive sites. Furthermore, shear, which can be externally controlled independent of precursor chain synthesis, can have persistent effects on morphology, such as enhancing compaction of SCNPs based on chain stiffness. This work provides guidelines for designing SCNPs with targeted characteristics based on five dimensionless variables and illustrates the utility of machine learning in analyzing SCNPs formed across a range of conditions. Nanoparticle ; Polymer ; Shear (geology) ; Dimensionless quantity ; Nanoscopic scale ; Intramolecular force ; Shear rate ; Polymerization ; Single chain Nanoparticle-Based Drug Delivery ; Advanced Polymer Synthesis and Characterization ; Dendrimers and Hyperbranched Polymers
Authors: Jinjin Dong (21685010) | Wenjun Yang (346091) | Haolin Liu (789542) | Jingwen Wu (80821) | Zongguo Wang (7450790)
Source: Figshare (article, 2025-07-09)
Field: Materials Science — Materials Chemistry

A Generalizable MachineLearning Framework for IdentifyingSustainable Multi-Ion Garnet Electrolytes Lithium-ion (Li-ion) solid-state batteries (SSBs) are highly regarded for their exceptional energy density and prolonged operational lifespan. However, concerns regarding their sustainability have arisen due to the uneven global distribution of Li resources and Li’s relatively low abundance in the Earth’s crust. Consequently, significant interest has shifted toward developing alternative SSBs, such as sodium (Na), magnesium (Mg) and Aluminum (Al)-ion batteries. A key challenge in this pursuit is efficiently identifying viable solid-state electrolytes (SEs) from the vast chemical space, particularly for Na and Mg ions. This study introduces a generalized framework based on machine learning for effectively screening high-performance garnet-type SEs. Utilizing specifically designed chemical descriptors, ML models predict the thermal stability and electrical conductivity of garnet-type SEs, achieving predictive accuracies of 94% and 89%, respectively. The chemical factors influencing stability and conductivity are identified and validated through interpretability analysis. Leveraging these models, 1764 garnet-type SEs exhibiting high thermal stability and wide band gaps were screened from a database of 43,732 compounds. Furthermore, 44 garnet-type SEs with favorable environmental and economic advantages were selected, and verified through first-principles calculations using density functional theory. Given their cost-effectiveness and high performance, these SEs hold great potential for application in Na, Mg, and Al ion SSBs. This study provides crucial insights into developing SSB materials, advances sustainable energy storage, and offers key perspectives for exploring material systems within specific space groups. Interpretability ; Stability (learning theory) ; Sustainability ; Key (lock) ; Energy (signal processing) ; Ensemble learning ; Training (meteorology) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Eunchi Kim | Paula Hartnagel | Barbara Urbano | Leonard Christen | Thomas Kirchartz
Source: JuSER Publikationsportal (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Inferring Material Parameters from Current–Voltage Curves in Organic Solar Cells via Neural Network‐Based Surrogate Models Artificial neural network ; Organic solar cell ; Mathematical model ; Calibration ; Solar energy Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Advanced Memory and Neural Computing
Authors: Yurii Bilak
Source: Bulletin of Taras Shevchenko National University of Kyiv Series Physics and Mathematics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Information system based on a complex model using machine learning for spectral analysis The research is devoted to the design and development of an information system based on a complex model using machine learning methods to automate spectral analysis to increase the accuracy and speed of data processing. The history of the research is connected with the development of analytical methods in physics, chemistry and biology, where spectral analysis has traditionally played a key role. However, modern challenges, in particular the growth of data volumes and the need for automation, have stimulated the introduction of innovative methods based on artificial intelligence. The relevance is due to the need to process large volumes of complex spectral data in real time, which is important for medicine, ecology, chemistry and other industries. Traditional analysis methods have limitations, so the use of machine learning is appropriate to increase the efficiency of the process. The research focuses on the following issues: how to automate spectral data processing, how to ensure the integration of classical methods with machine learning, and how to increase the accuracy and scalability of the analysis. For this purpose, signal processing methods were applied, including noise filtering, smoothing, baseline correction, and peak analysis using derivatives and numerical integration. Machine learning was implemented through Random Forest models and neural networks adapted for predicting spectrum parameters. The results showed that the developed system provides high accuracy and speed of spectral data analysis, interactive visualization of spectrum parameters, as well as the ability to integrate with other information platforms. This significantly simplifies analysis processes, reduces dependence on expert intervention, and increases productivity. Research prospects include optimizing mathematical models for even greater accuracy, integration with IoT systems, and expanding the functionality for analyzing complex multidimensional spectra. This opens up opportunities for application in interdisciplinary projects, such as monitoring environmental changes or diagnosing medical conditions. Computer science ; Spectral analysis ; Artificial intelligence ; Machine learning ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Neural Networks and Applications
Authors: Swarup Maity | Bhanendra Sahu | Nishikanta Singh | Sudev Kumar Padhi | Sk Subidh Ali | Sanjib Banerjee
Source: ACS ES&T Engineering (article, 2025-12-05)
Field: Materials Science — Polymers and Plastics

Fluorescent-Tagged Amphiphilic Polymers for Dual-Functional <i>para</i> -Nitrophenol Sensing and Remediation The development of multifunctional polymeric platforms for toxin detection and environmental remediation is crucial for advancing sustainable pollutant monitoring and removal technologies. Para-nitrophenol (PNP) is a highly toxic environmental contaminant, posing severe health risks, including headaches, cyanosis, and, with prolonged exposure, fatal consequences. Despite extensive research on PNP detection and removal, no prior studies have explored amphiphilic polymers for both solution- and solid-state sensing, adsorption, and deep learning-assisted detection. Here, for the first time, we report the synthesis of poly(methacrylic acid-random-7-methacryloyloxycoumarin) (PCM10S), a fluorescent-active amphiphilic polymer designed for selective and highly sensitive PNP detection. The incorporation of a coumarin-based fluorophore within the polymer backbone ensures exceptional selectivity among aromatic compounds, achieving an ultralow limit of detection (LOD = 20 nM). To enhance pollutant removal, a modified variant of PCM10S, with increased hydrophobic content, was synthesized, promoting PNP encapsulation within polymeric aggregates. This structural modification facilitates efficient sedimentation, achieving ∼96% PNP removal from aqueous solutions with a removal capacity of 0.144 mg PNP per mg of the adsorbent. Furthermore, a paper-based sensor was developed for rapid, user-friendly PNP detection. To enhance detection accuracy, deep learning integration was explored, evaluating seven convolutional neural networks (CNNs) for fluorescence-based colorimetric analysis. Among them, InceptionNet achieved the highest classification accuracy, demonstrating the potential of AI-assisted pollutant monitoring. This study introduces a synergistic approach combining fluorescence sensing and deep learning, paving the way for next-generation smart environmental monitoring. The methodology holds promise for detecting and removing a broader range of organic contaminants, offering a scalable and sustainable solution for intelligent wastewater treatment. Amphiphile ; Environmental remediation ; Polymer ; Fluorophore ; Pollutant ; Aqueous solution ; Nanoparticle Conducting polymers and applications ; Covalent Organic Framework Applications ; Electrochemical sensors and biosensors
Authors: Junqi He | Siyang Li | Hao Sheng | Rui Gao | Ying Luo | Yiming Gai | Xudong Li | Yuyang Miao | Yaoming Zhou | Yang Xu | Tianjun He | Jian Huang
Source: Research Square (preprint, 2025-10-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Efficient Data-Driven Modeling of Core Loss in Magnetic Materials for Power Electronics Systems Waveform ; Artificial neural network ; Process (computing) ; Electronics ; Power electronics ; Core (optical fiber) ; Power (physics) ; Feature (linguistics) ; Empirical modelling Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Non-Destructive Testing Techniques