jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: S.K. Jalali | M.J. Beigrezaee | Nicola M. Pugno
Source: Journal of Cleaner Production (article, 2025-11-17)
Field: Materials Science — Polymers and Plastics

Physically based machine learning for the multi-objective optimal design of partially recycled materials: The case study of rubber resources in the automotive industry Flexibility (engineering) ; Automotive industry ; Sustainability ; Natural rubber ; Scalability Polymer composites and self-healing ; Polymer crystallization and properties ; Machine Learning in Materials Science
Authors: Tsitsvero, Mikhail | Nakao, Atsuyuki | Ikebata, Hisaki
Source: arXiv (Cornell University) (preprint, 2025-11-26)
Field: Materials Science — Materials Chemistry

Accelerating Materials Discovery: Learning a Universal Representation of Chemical Processes for Cross-Domain Property Prediction Experimental validation of chemical processes is slow and costly, limiting exploration in materials discovery. Machine learning can prioritize promising candidates, but existing data in patents and literature is heterogeneous and difficult to use. We introduce a universal directed-tree process-graph representation that unifies unstructured text, molecular structures, and numeric measurements into a single machine-readable format. To learn from this structured data, we developed a multi-modal graph neural network with a property-conditioned attention mechanism. Trained on approximately 700,000 process graphs from nearly 9,000 diverse documents, our model learns semantically rich embeddings that generalize across domains. When fine-tuned on compact, domain-specific datasets, the pretrained model achieves strong performance, demonstrating that universal process representations learned at scale transfer effectively to specialized prediction tasks with minimal additional data. Property (philosophy) ; Representation (politics) ; Process (computing) ; Limiting ; Graph ; Artificial neural network ; Feature learning ; Scale (ratio) ; Transfer of learning Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Di Hu | Zhiming Li | Jiarui Shen | J. Tu | Zi-Hao Ye | J. L. Zhang
Source: ArXiv.org (preprint, 2025-12-16)
Field: Materials Science — Materials Chemistry

Dual-Axis RCCL: Representation-Complete Convergent Learning for Organic Chemical Space Machine learning is profoundly reshaping molecular and materials modeling; however, given the vast scale of chemical space (10^30-10^60), it remains an open scientific question whether models can achieve convergent learning across this space. We introduce a Dual-Axis Representation-Complete Convergent Learning (RCCL) strategy, enabled by a molecular representation that integrates graph convolutional network (GCN) encoding of local valence environments, grounded in modern valence bond theory, together with no-bridge graph (NBG) encoding of ring/cage topologies, providing a quantitative measure of chemical-space coverage. This framework formalizes representation completeness, establishing a principled basis for constructing datasets that support convergent learning for large models. Guided by this RCCL framework, we develop the FD25 dataset, systematically covering 13,302 local valence units and 165,726 ring/cage topologies, achieving near-complete combinatorial coverage of organic molecules with H/C/N/O/F elements. Graph neural networks trained on FD25 exhibit representation-complete convergent learning and strong out-of-distribution generalization, with an overall prediction error of approximately 1.0 kcal/mol MAE across external benchmarks. Our results establish a quantitative link between molecular representation, structural completeness, and model generalization, providing a foundation for interpretable, transferable, and data-efficient molecular intelligence. Valence (chemistry) ; Chemical space ; Graph ; Representation (politics) ; Deep learning ; Convolutional neural network ; Feature learning ; Encoding (memory) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Yiyuan Zhang | Shih-Kuang Lee | Sharon C. Glotzer | Rebecca Lindsey
Source: ChemRxiv (preprint, 2025-06-02)
Field: Materials Science — Materials Chemistry

A Generalized Machine-Learning Framework for Developing Alchemical Many-Body Interaction Models for Polymer Grafted Nanoparticles Alchemy ; Nanoparticle ; Polymer ; Computer science ; Artificial intelligence ; Nanotechnology ; Materials science ; Art ; Composite material ; Art history Machine Learning in Materials Science
Authors: Reshma Devi | Keith T. Butler | Gopalakrishnan Sai Gautam
Source: ArXiv.org (preprint, 2025-08-08)
Field: Chemistry — Electrochemistry

Leveraging transfer learning for accurate estimation of ionic migration barriers in solids Ionic mobility determines the rate performance of several applications, such as batteries, fuel cells, and electrochemical sensors and is exponentially dependent on the migration barrier ($E_m$), a difficult to measure/calculate quantity. Previous approaches to identify materials with high ionic mobility have relied on imprecise descriptors given the lack of generalizable models to predict $E_m$. Here, we present a graph neural network based architecture that leverages principles of transfer learning to efficiently and accurately predict $E_m$ across a diverse set of materials. We use a model pre-trained simultaneously on seven distinct bulk properties (labeled MPT), modify the MPT model to classify different migration pathways in a structure, and fine-tune (FT) on a manually-curated literature-derived dataset of 619 $E_m$ data points calculated with density functional theory. Importantly, our best-performing FT model (labeled MODEL-3) demonstrates substantial improvements in prediction accuracy compared to classical machine learning methods, graph models trained from scratch, and a universal machine learned interatomic potential, with a R$^2$ score of 0.703 and a mean absolute error of 0.261 eV on the test set. Notably, MODEL-3 is able to distinguish different migration pathways within a structure and also demonstrates excellent ability to generalize across intercalant compositions and chemistries. As a classifier, MODEL-3 exhibits 80\% accuracy and 82.8\% precision in identifying materials that are `good' ionic conductors (i.e., structures with $E_m &lt;$0.65~eV). Thus, our work demonstrates the effective use of FT strategies and architectural modifications necessary for making swift and accurate $E_m$ predictions, which will be useful for materials discovery in batteries and for predicting other data-scarce material properties. Graph ; Artificial neural network ; Data point ; Transfer of learning ; Work (physics) ; Applicability domain ; Test set ; Set (abstract data type) ; Density estimation Electrochemical Analysis and Applications
Authors: A.G. Leyva
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-07-14)
Field: Materials Science — Materials Chemistry

WITHDRAWN: Bridging Molecular Simulations and Experimental Observables: Predicting FRET Signals and Conformational Transitions Using MD and Deep Learning in Cas9 Withdrawal Statement The author has withdrawn this manuscript because it was posted without institutional approval, proper authorship attribution, or PI oversight. Therefore, the author does not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author. Bridging (networking) ; Observable ; Förster resonance energy transfer ; Molecular dynamics ; Computer science ; Statistical physics ; Chemistry ; Nanotechnology ; Physics ; Materials science ; Computational chemistry ; Quantum mechanics ; Fluorescence Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Yixin Liu | Grace Dykstra | Rourke Sylvain | Isabel Chapa | Vivian Torres Vera
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Chemistry — Analytical Chemistry

<i>(Invited)</i> Engineering Electropolymerized Molecularly Imprinted Polymers for Synthetic Electrochemical Biosensors Molecularly imprinted polymers (MIPs) have emerged as robust and cost-effective synthetic recognition elements for selective molecular sensing; however, challenges remain in translating them into scalable, high-performance electrochemical sensors. Electropolymerized MIPs (eMIPs) offer a direct and versatile route to creating functional thin films on electrode surfaces with tunable morphology and controllable thickness. This work presents recent advancements in the design and fabrication of eMIP-based electrochemical sensors targeting analytes relevant to both health monitoring and food safety, including cortisol, glucose, and antibiotic residues. Machine learning techniques have been employed to correlate fabrication parameters with sensing performance, enabling data-driven optimization of material synthesis. Real-time, in-situ monitoring of the electropolymerization process was used to investigate how fabrication conditions affect polymer film properties and to guide process refinement for improved sensitivity. Multiple transduction mechanisms have been explored, including: redox-integrated MIPs for reagentless detection of electro-inactive analytes; incorporation of nanostructured catalysts into the MIP matrix for non-enzymatic glucose sensing; and nanomaterial-enhanced direct detection of electroactive targets. Flexible, high-performance transducers have also been developed to improve sensor stability, sensitivity, and expand application versatility. Through case studies in health monitoring and food safety, this work demonstrates how the integration of rational design, advanced materials, and real-time process control contributes to the development of low-cost, scalable, and high-performance electrochemical sensing platforms. Molecularly imprinted polymer ; Fabrication ; Biosensor ; Polymer ; Electrode ; Rational design ; Conductive polymer ; Electrochemical gas sensor Analytical chemistry methods development ; Electrochemical sensors and biosensors ; Advanced Chemical Sensor Technologies
Authors: Xuanyu Liu | G.L. Chen | Yue Zhang | Jingang Qi | Pingli Mao
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Investigation of Strengthening Mechanisms in Gradient Nanostructured Polycrystalline Magnesium Using Machine Learning and Molecular Dynamics Magnesium ; Crystallite ; Molecular dynamics ; Dynamics (music) ; Materials science ; Nanotechnology ; Metallurgy ; Chemistry ; Computational chemistry ; Physics Microstructure and mechanical properties ; Metal and Thin Film Mechanics ; Boron and Carbon Nanomaterials Research
Authors: Emily
Xi Tan (21986631) | Lam Bang Thanh Nguyen (11959395) | Yubin Jin (21986634) | Yan Lv (563216) | In Yee Phang (1501696) | Xing Yi Ling (1476664)
Source: Figshare (article, 2025-08-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

SERS Cheminformatics:Opportunities for Data-DrivenDiscovery and Applications Surface-enhanced Raman scattering (SERS) is a powerful analytical technique offering ultrasensitive, nondestructive molecular fingerprinting. However, challenges such as spectral overlap, noise, and signal variability, especially in complex mixtures, limit its reliability and reproducibility. With increasing volumes of complex SERS data, there is a pressing need for advanced tools to manage and interpret this information. Cheminformatics amalgamates chemical knowledge with computational methods to deliver solutions for spectral preprocessing, database management, molecular modeling, pattern recognition, and multimodal data integration. This Outlook presents a vision for uniting SERS and cheminformatics to enhance the reliability of (bio)­chemical analysis and discovery. We propose a conceptual framework built upon four interconnected pillars: (1) centralized SERS databases, (2) molecular modeling for mechanistic insights, (3) machine learning (ML) for spectral analysis, and (4) automation and artificial intelligence for expanding the SERS chemical space. Together, these four pillars form a dynamic, feedback-driven system that enhances interpretability, accelerates data-driven discovery, and facilitates real-time SERS analysis. The symbiotic relationship between SERS and cheminformatics positions this integration at the forefront of data-driven chemical research with transformative applications in materials science, catalysis, biomedical diagnostics, and environmental monitoring. Cheminformatics ; Reliability (semiconductor) ; Automation ; Raman scattering ; Transformative learning Gold and Silver Nanoparticles Synthesis and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation ; Machine Learning in Materials Science
Source: Institutional Repositories DataBase (IRDB) (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Distribution ratio prediction of group 4 elements using machine learning, toward chemical study of element 104, rutherfordium Group (periodic table) ; Distribution (mathematics) ; Element (criminal law) ; Finite element method Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Kai Gu (181400) | Yingping Liang (12631796) | Jiaming Su (484794) | Peihan Sun (18194432) | Jia Peng (508247) | Naihua Miao (2627770) | Zhimei Sun (1665508) | Ying Fu (173963) | Haizheng Zhong (1395337) | Jun Zhang (48506)
Source: Figshare (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

DeepLearning Models for Colloidal Nanocrystal Synthesis Colloidal synthesis of nanocrystals usually includes complex chemical reactions and multistep synthesis processes. Despite the great success in the past 30 years, it remains challenging to clarify the correlations between the synthetic parameters of the chemical reaction and the physical properties of nanocrystals. Here, we developed a deep learning-based nanocrystal synthesis model that correlates synthetic parameters with the final size and shape of target nanocrystals, using a data set of 3508 recipes covering 348 distinct nanocrystal compositions. The size and shape labels were obtained from transmission electron microscope images using a segmentation model trained with a semi-supervised algorithm on a data set comprising around 1.2 million nanocrystals. By applying the reaction intermediate-based data augmentation method and elaborated descriptors, the synthesis model was able to predict the nanocrystal’s size with a mean absolute error of 1.39 nm, while reaching an 89% average accuracy for shape classification. The synthesis model shows knowledge transfer capabilities across different nanocrystals with the input of new recipes. With that, the influence of chemicals on the final size of nanocrystals was further evaluated, revealing the descending order of importance of the nanocrystal composition, precursor or ligand, and solvent. Overall, the deep learning-based nanocrystal synthesis model offers a powerful tool to expedite the development of high-quality nanocrystals. Nanocrystal ; Deep learning ; Colloid ; Set (abstract data type) ; Transmission electron microscopy Machine Learning in Materials Science ; Quantum Dots Synthesis And Properties ; Nanocluster Synthesis and Applications
Authors: Guobin Zhao (16641096) | Pengyu Zhao (5848832) | Yongchul G. Chung (3369290)
Source: Figshare (article, 2025-08-11)
Field: Chemistry — Inorganic Chemistry

MOFClassifier: A MachineLearning Approach for ValidatingComputation-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 ; Convolutional neural network ; Artificial neural network ; Computational model ; Graph ; Core (optical fiber) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Xiaoyan Sun | Yining Hu | Bifen Shu | Yintong Zhou | X. G. Liu
Source: ChemRxiv (preprint, 2025-10-08)
Field: Chemistry — Organic Chemistry

Machine Learning-Optimized Methyl Oleate Production Using Synthesized [Lys][CF₃SO₃]₂ Ionic Liquid as catalyst Ionic liquid ; Catalysis ; Response surface methodology ; Methanol ; Oleic acid ; Process (computing) ; Biodiesel Chemical Synthesis and Reactions
Authors: Jiaxu Duan | Ganxin Jie | Jun Wang | Yong Nie | Tianyi Lv | Zhiping Ma
Source: Journal of Physics Conference Series (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Predicting polyester Performance of powder coating material using 3D graph network Polyester resin, as a key substrate for powder coatings, has a direct impact on the product’s applicability and performance because of its glass transition temperature ( T g ). However, the current industry relies mainly on experience for formula development, with high trial and error costs and long research and development cycles. There is an urgent need for a scalable method of predicting structural performance to guide material design. This article relies on material gene technology and constructs molecular fingerprint and graph neural network (GNN) models based on more than 500 industrial grade formulas. We propose a GNN-based method for predicting the properties of polyester resin formulations. Each monomer is represented as a 3D molecular graph of its Simplified molecular input line entry system (SMILES) and a molar percentage to obtain a formulation-level embedding, followed by a multilayer perceptron for regression. Compared with traditional molecular fingerprints, this model improved structural representation and performance under small sample conditions. A large number of experimental results have shown that the method proposed in this paper has significant advantages over existing mainstream methods in predicting the key performance accuracy and formula generalization ability of polyester powder coatings. The research results of this article provide a theoretical basis for the intelligent design of polyester formulations for the development of new powder coatings, and also demonstrate the broad prospects of graph neural networks in polymer material design. Polyester ; Graph ; Artificial neural network ; Scalability ; Coating ; Generalization ; Epoxy ; Polymer Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Epoxy Resin Curing Processes
Authors: Anshuman Kumar
Source:  (preprint, 2025-04-04)
Field: Materials Science — Materials Chemistry

Harnessing active learning with machine learning potentials to investigate deactivation mechanisms in transition metal-doped alumina catalysts Doping ; Catalysis ; Transition metal ; Active learning (machine learning) ; Materials science ; Nanotechnology ; Chemistry ; Computer science ; Artificial intelligence ; Optoelectronics ; Organic chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Materials Characterization Techniques
Authors: Tsitsvero, Mikhail | Nakao, Atsuyuki | Ikebata, Hisaki
Source: ArXiv.org (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Accelerating Materials Discovery: Learning a Universal Representation of Chemical Processes for Cross-Domain Property Prediction Experimental validation of chemical processes is slow and costly, limiting exploration in materials discovery. Machine learning can prioritize promising candidates, but existing data in patents and literature is heterogeneous and difficult to use. We introduce a universal directed-tree process-graph representation that unifies unstructured text, molecular structures, and numeric measurements into a single machine-readable format. To learn from this structured data, we developed a multi-modal graph neural network with a property-conditioned attention mechanism. Trained on approximately 700,000 process graphs from nearly 9,000 diverse documents, our model learns semantically rich embeddings that generalize across domains. When fine-tuned on compact, domain-specific datasets, the pretrained model achieves strong performance, demonstrating that universal process representations learned at scale transfer effectively to specialized prediction tasks with minimal additional data. Property (philosophy) ; Representation (politics) ; Process (computing) ; Limiting ; Graph ; Artificial neural network ; Feature learning ; Scale (ratio) ; Transfer of learning Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Daehee Yang | Young‐Hoon Kim | Hyo June Lee | Sang‐Hyeok Yang | Minji Jung | Eun‐Byeol Park | Hyonchol Kim | Yerin Jeon | Young-Moo Heo | K.-H. Kim | Sungyong Cho | Yun Sik Kang | Ki Kang Kim | Hangil Lee | Sung‐Dae Yim | Jae Hyuck Jang | Sung-Chul Lee | Young‐Min Kim
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Integrated Probing of Cycling-Induced Degradation of Multi-Component Electrode in Hydrogen Fuel Cells Via Machine Learning-Empowered Spectroscopic Imaging Degradation (telecommunications) ; Electrode ; Cycling ; Fuel cells ; Component (thermodynamics) ; Hydrogen ; Materials science ; Chemical engineering ; Chemistry ; Computer science ; Engineering ; Physics ; Telecommunications ; Organic chemistry Machine Learning in Materials Science ; Fuel Cells and Related Materials