jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mario Wolter | Christoph R. Jacob
Source: LeoPARD - TU Braunschweig Publications And Research Data (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

An Ontology for Theoretical Chemistry Ontologies, as formal and structured representations of knowledge within a domain, have been instrumental in enabling data integration, knowledge sharing, and interoperability. For instance in chemistry experimental techniques, instrumentation, and chemical entities are codified in ontologies such as the Chemical Methods Ontology[1] and the Chemical Information Ontology[2]. Surprisingly, no comparably comprehensive ontology exists for theoretical chemistry. Anontology is the foundation of the necessary standardization for building reusable software libraries, workflow engines, and data repositories. Classes in software code could be directly derived from the ontology and therefore facilitate the modular construction of software components. Thereby greatly enhancing the efficiency and reproducibility of computational workflows. Creating this ontology, however, is intrinsically a community endeavour. Several partly overlapping projects already address fragments of theoretical chemistry or focus on specific software packages (e.g. OntoCompChem[3] or MolSSI QCSchema[4]). In the process of building the “comprehensive ontology” we should align with these existing vocabularies to maximise interoperability. For community inclusion an iterative, open development on collaborative platforms (e.g., GitHub) employing continuous integration should be enfostered. Early adoption can be catalysed by embedding identifiers into widely used computational packages (e.g., ORCA) and data repositories (e.g., Chemotion, NOMAD, QCArchive), thus ensuring immediate practical value. By uniting theoretical chemists around a shared semantic framework, the proposed ontology will accelerate method innovation, improve software sustainability, and unlock new possibilities for machine learning. [1] https://github.com/rsc-ontologies/rsc-cmo [2] Hastings J., Chepelev L., Willighagen E., Adams N., Steinbeck C., Dumontier M., PLoS One, 6(10):e25513 (2011) [3] Krdzavac N., Mosbach S., Nurkowski D., Buerger P., Akroyd J., Martin J., Menon A., Kraft M., J Chem Inf Model., 22;59(7):3154-3165 (2019) [4] https://molssi.org/software/qcschema-2/ &amp; https://github.com/MolSSI/QCSchema Ontology ; Workflow ; Identifier ; Software ; Process ontology ; Process (computing) ; Ontology-based data integration ; Semantic Web Machine Learning in Materials Science ; Scientific Computing and Data Management ; Semantic Web and Ontologies
Authors: Tianze Gai | Ruohan Luan
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Understanding of Dynamic Structural Restructuring in Perovskite Fluorides for Efficient Oxygen Evolution Catalysis Interpretability ; Oxygen evolution ; Perovskite (structure) ; Restructuring ; Electrochemistry ; Benchmarking ; Workflow ; Catalysis Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Wu, Min
Source: Lirias (KU Leuven) (article, 2025-06-23)
Field: Materials Science — Materials Chemistry

Chemische processen versterken met Artificiële Intelligentie: Auto-encoders, zwermintelligentie en Reinforcement Learning voor modellering en optimalisatie van complexe reacties Artificial Intelligence (AI) is now applied successfully to a number of disciplines including chemical engineering. With the help of AI, it is possible to predict an optimal chemical process unit as well as to obtain the optimal factors of the chemical process unit based on the existing data sources. This thesis uses AI methods, such as artificial neural networks to predict the optimal conditions of chemical processes and to give accurate parametric suggestions. For this purpose, the economic and operational optimization of the chemical processes can be achieved. Artificial neural network ; Process (computing) ; Chemical process ; Reinforcement learning ; Unit (ring theory) Machine Learning in Materials Science ; Advanced Control Systems Optimization ; Process Optimization and Integration
Authors: Junmeng Li | Dezhi Chen | Zhou Fang | Xing Hu | Jun Xu | Yi Wang | Sheng Su | Song Hu | Jun Xiang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Fourier Transform Infrared Spectroscopy Combined with Machine Learning Models for Rapid Prediction of Cellulose, Hemicellulose, and Lignin Content in Biomass Hemicellulose ; Lignin ; Cellulose ; Biomass (ecology) ; Fourier transform infrared spectroscopy ; Pulp and paper industry ; Infrared spectroscopy ; Fourier transform ; Materials science ; Chemistry ; Chemical engineering ; Organic chemistry ; Mathematics ; Agronomy ; Engineering ; Biology Spectroscopy and Chemometric Analyses
Authors: Mack Cleveland | Andrew Nelson | Ericmoore Jossou
Source: ArXiv.org (preprint, 2025-10-09)
Field: Materials Science — Materials Chemistry

Chromium-doped uranium dioxide fuels: A review UO2 doped with parts per million Cr2O3 powder is considered a potential near term accident tolerant fuel candidate. Here, the results of decades of industry and academic research into Cr-doped UO2 are analyzed and their shortcomings are critiqued. Focusing on the incorporation mechanisms of Cr into the fuel matrix, we explore a mechanistic understanding of the characteristic properties of Cr-doped UO2, notably, enhanced fission gas retention attributed to enlarged grain sizes following sintering, along with marginal improvements in the thermophysical properties. The findings of recent X-ray Adsorption Near Edge Spectroscopy studies were compared and put into conversation with historic data regarding the incorporation of Cr in UO2. On the basis of defect mechanisms, the case is made for the substitutional incorporation of Cr governing the lattice solubility but not the enhanced U diffusivity. Instead, Cr/Cr2O3 redox chemistry in a well-defined oxygen potential explains the differences in the U diffusivity and O/M ratio. The primary mechanism of doping-enhanced grain growth is found to be liquid assisted sintering due to a CrO(l) eutectic phase at the grain boundaries. The role of inhomogeneities in Cr concentration in UO2 at various length scales across the materials microstructure is highlighted and connected to promising experimental and modeling work to fill in the gaps in the current understanding of Cr-doped UO2. The review ends with an outline of future works that combine meticulous irradiation studies and high resolution experiments with next generation modeling and simulations techniques empowered by machine learning advances to accelerate the fabrication and adoption of Cr-doped UO2 light water reactors. Uranium dioxide ; Eutectic system ; Sintering ; Microstructure ; Fabrication ; Uranium ; Oxygen ; Adsorption Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Radioactive element chemistry and processing
Authors: Xuan Zhang (56049) | Wenjia Shang (22788075)
Source: Figshare (article, 2025-12-05)
Field: Chemistry — Inorganic Chemistry

Prediction of Rn,N<sub>2</sub>, and O<sub>2</sub> Adsorption and Separation in MOFsand MOF@AC Composites via MachineLearning and Graph Neural Networks This study establishes a high-throughput computational framework that integrates Grand Canonical Monte Carlo (GCMC) simulations, machine learning (ML), and crystal graph convolutional neural networks (CGCNN) to evaluate the adsorption and separation performance of metal–organic frameworks (MOFs) and their activated carbon composites (MOF@AC) for radon (Rn), nitrogen (N<sub>2</sub>), and oxygen (O<sub>2</sub>). Structural-adsorption analysis revealed that pressure, accessible surface area, and pore volume fraction are the dominant factors governing Rn uptake. ML models achieved high predictive accuracy (<i>R</i><sup>2</sup> > 0.9), while CGCNN models enabled rapid though slightly less precise predictions, underscoring their complementary roles in large-scale screening. MOF@AC composites exhibited markedly enhanced adsorption, particularly for Rn, with ZEDVUB showing outstanding selectivity. Ideal adsorbed solution theory (IAST) confirmed that all MOF@AC systems outperformed pristine MOFs, and breakthrough curve simulations further validated the preferential retention of Rn over N<sub>2</sub> and O<sub>2</sub>. These results identify MOF@AC composites as promising candidates for efficient radon capture and separation, and more broadly highlight the power of coupling physics-based simulations with data-driven models to accelerate the discovery and optimization of high-performance adsorbents. Adsorption ; Artificial neural network ; Convolutional neural network ; Monte Carlo method ; Graph ; Coupling (piping) ; Ideal (ethics) Metal-Organic Frameworks: Synthesis and Applications ; Adsorption and biosorption for pollutant removal ; Covalent Organic Framework Applications
Authors: Dian Jin
Source: ArXiv.org (preprint, 2025-07-01)
Field: Materials Science — Materials Chemistry

Rotational Sampling: A Plug-and-Play Encoder for Rotation-Invariant 3D Molecular GNNs Graph neural networks (GNNs) have achieved remarkable success in molecular property prediction. However, traditional graph representations struggle to effectively encode the inherent 3D spatial structures of molecules, as molecular orientations in 3D space introduce significant variability, severely limiting model generalization and robustness. Existing approaches primarily focus on rotation-invariant and rotation-equivariant methods. Invariant methods often rely heavily on prior knowledge and lack sufficient generalizability, while equivariant methods suffer from high computational costs. To address these limitations, this paper proposes a novel plug-and-play 3D encoding module leveraging rotational sampling. By computing the expectation over the SO(3) rotational group, the method naturally achieves approximate rotational invariance. Furthermore, by introducing a carefully designed post-alignment strategy, strict invariance can be achieved without compromising performance. Experimental evaluations on the QM9 and C10 Datasets demonstrate superior predictive accuracy, robustness, and generalization performance compared to existing methods. Moreover, the proposed approach maintains low computational complexity and enhanced interpretability, providing a promising direction for efficient and effective handling of 3D molecular information in drug discovery and material design. Rotational invariance ; ENCODE ; Generalization ; Property (philosophy) ; Encoder ; Computational complexity theory ; Encoding (memory) ; Invariant (physics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Raj Kumar Gupta | Pappu Kumar | Sudhir Kumar | V. Padmavathi | Dinesh Kumar | Khushboo Singh
Source:  (article, 2025-09-10)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hybrid CNN–GRU System for Real-Time Environmental Pollutant Monitoring using BiFeO3-based Photocatalytic Reactors A lot of people are interested in photocatalysis because it can use sun energy to quickly remove organic contaminants without making any bad byproducts. Due to its low cost, significant absorption in the visible spectrum, and relatively narrow band gap, multiferroic bismuth ferrite has become an attractive choice among materials that respond to visible light. The photocatalytic effectiveness of bulk BFO is reduced because to its flaws, which include structural instability, unattractive band topologies, fast recombination of photogenerated electron-hole pairs, and impurity phases. A thorough data preprocessing procedure was carried out to remove outliers and correct missing or incorrect results in order to improve predictive analysis of the performance of BFO-based photocatalytic reactors. To further improve the input dataset, feature extraction approaches were then employed. To acquire knowledge of patterns in space and time, an interconnected network of convolutional neural networks and recurrent units called CNN-GRU was suggested. they compared the model’s performance to three other methods and used real data from photocatalytic reactors based on BiFeO to evaluate it. With a simulated accuracy of $97.60 \%$, the CNN-GRU model was the most effective. These findings demonstrate the promise of deep learning for improving photocatalytic devices and expanding the environmental applications of $\mathbf{B F O}$. Photocatalysis ; Bismuth ferrite ; Outlier ; Convolutional neural network ; Preprocessor ; Absorption (acoustics) ; Visible spectrum ; Deep learning Multiferroics and related materials ; Advanced Photocatalysis Techniques ; Machine Learning in Materials Science
Authors: Pelin Sezgin (21662562) | Seda Keskin (536519)
Source: Figshare (article, 2025-07-04)
Field: Chemistry — Inorganic Chemistry

Integrating MolecularSimulations with Machine Learningto Discover Selective MOFs for CH<sub>4</sub>/H<sub>2</sub> Separation As the number of synthesized and hypothetical metal–organic frameworks (MOFs) continues to grow, identifying the most selective adsorbents for CH<sub>4</sub>/H<sub>2</sub> separation through experimental or computational methods has become increasingly complex. This study integrates molecular simulations with machine learning (ML) to evaluate the CH<sub>4</sub>/H<sub>2</sub> separation performance of 126605 distinct types of MOFs. Grand canonical Monte Carlo (GCMC) simulations were performed to produce CH<sub>4</sub> and H<sub>2</sub> adsorption data for synthesized MOFs at various pressures, which were then used to train ML models incorporating structural, chemical, and energetic features of the MOFs. These ML models were subsequently transferred to hypothetical MOFs, enabling the rapid and accurate screening of promising adsorbents for CH<sub>4</sub>/H<sub>2</sub> separation. The top-performing MOFs were identified based on their CH<sub>4</sub>/H<sub>2</sub> selectivities, and their key structural and chemical characteristics were analyzed. Synthesized (hypothetical) MOFs having narrow pores and pyridine-, histidine-, and imidazole-based (carboxylate-, benzoate-, and cubane-based) linkers demonstrated high selectivities up to 85 (115) at 1 bar and 298 K. Our findings highlight the potential of MOFs as superior alternatives to traditional adsorbent materials for CH<sub>4</sub>/H<sub>2</sub> separation. Adsorption ; Separation (statistics) ; Key (lock) ; Monte Carlo method ; Separation method ; Training set Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies
Authors: Can Polat | Hasan Kurban | Erchin Serpedin | Mustafa Kurban
Source: ArXiv.org (preprint, 2025-05-30)
Field: Materials Science — Materials Chemistry

Beyond Atomic Geometry Representations in Materials Science: A Human-in-the-Loop Multimodal Framework Most materials science datasets are limited to atomic geometries (e.g., XYZ files), restricting their utility for multimodal learning and comprehensive data-centric analysis. These constraints have historically impeded the adoption of advanced machine learning techniques in the field. This work introduces MultiCrystalSpectrumSet (MCS-Set), a curated framework that expands materials datasets by integrating atomic structures with 2D projections and structured textual annotations, including lattice parameters and coordination metrics. MCS-Set enables two key tasks: (1) multimodal property and summary prediction, and (2) constrained crystal generation with partial cluster supervision. Leveraging a human-in-the-loop pipeline, MCS-Set combines domain expertise with standardized descriptors for high-quality annotation. Evaluations using state-of-the-art language and vision-language models reveal substantial modality-specific performance gaps and highlight the importance of annotation quality for generalization. MCS-Set offers a foundation for benchmarking multimodal models, advancing annotation practices, and promoting accessible, versatile materials science datasets. The dataset and implementations are available at https://github.com/KurbanIntelligenceLab/MultiCrystalSpectrumSet. Benchmarking ; Annotation ; Bridging (networking) ; Key (lock) ; Domain (mathematical analysis) ; Implementation Machine Learning in Materials Science ; History and advancements in chemistry
Authors: P. V. Shravan | Latha G | Santosh S | Sujith R N | Mohamed Javeed A | Sudha Priyanga G
Source: The Journal of Physical Chemistry C (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Machine Learning Analysis of Dimensionality Effects on Bandgap Prediction Bandgap engineering is central to tailoring the electronic and optical properties of semiconductors and low-dimensional materials. In this work, we present a machine learning (ML) framework capable of accurately predicting key material properties such as formation energy, energy above the hull, and bandgap across various dimensionalities. Unlike conventional approaches, our model explicitly incorporates quantum confinement effects by integrating structural descriptors and dimensionality-based features, enabling systematic insights into confinement-induced bandgap variations. Our data set, curated from a diverse set of materials, includes atomic composition, crystallographic symmetry, and structural features but excludes DFT or experimentally derived property data such as the information on stability, bandgap, and their nature. To compensate, we developed predictive models trained on external property references, achieving strong performance with an XGBRegressor yielding an R2 score of 0.875. A k-fold cross-validation strategy ensures robustness and generalizability, while Optuna-based hyperparameter tuning minimizes overfitting and optimizes predictive accuracy. Analysis reveals that bandgap trends are strongly influenced by average ionic character and dimensionality, with 0D materials exhibiting the most pronounced quantum confinement effects. To demonstrate practical utility, we applied our model to the crystallography materials repository (CMR), identifying promising candidates for light-harvesting applications. Selected materials were further validated using density functional theory (DFT) calculations to confirm structural stability and electronic properties. This work provides a scalable pathway for high-throughput discovery and bandgap prediction in low-dimensional materials. Band gap ; Overfitting ; Robustness (evolution) ; Curse of dimensionality ; Stability (learning theory) ; Crystal structure prediction ; Quantum dot ; Hyperparameter ; Scalability ; Electronic structure Machine Learning in Materials Science ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides
Authors: Nathan J. Szymanski | Christopher J. Bartel
Source: arXiv (Cornell University) (preprint, 2025-01-04)
Field: Materials Science — Materials Chemistry

Establishing baselines for generative discovery of inorganic crystals Generative artificial intelligence offers a promising avenue for materials discovery, yet its advantages over traditional methods remain unclear. In this work, we introduce and benchmark two baseline approaches - random enumeration of charge-balanced prototypes and data-driven ion exchange of known compounds - against four generative techniques based on diffusion models, variational autoencoders, and large language models. Our results show that established methods such as ion exchange are better at generating novel materials that are stable, although many of these closely resemble known compounds. In contrast, generative models excel at proposing novel structural frameworks and, when sufficient training data is available, can more effectively target properties such as electronic band gap and bulk modulus. To enhance the performance of both the baseline and generative approaches, we implement a post-generation screening step in which all proposed structures are passed through stability and property filters from pre-trained machine learning models including universal interatomic potentials. This low-cost filtering step leads to substantial improvement in the success rates of all methods, remains computationally efficient, and ultimately provides a practical pathway toward more effective generative strategies for materials discovery. By establishing baselines for comparison, this work highlights opportunities for continued advancement of generative models, especially for the targeted generation of novel materials that are thermodynamically stable. Generative grammar ; Computer science ; Materials science ; Artificial intelligence Crystallization and Solubility Studies ; Diatoms and Algae Research ; Optics and Image Analysis
Authors: Fionna Samuels
Source: C&EN Global Enterprise (article, 2025-09-12)
Field: Materials Science — Materials Chemistry

Karl S. Booksh If you come to Karl S. Booksh at the University of Delaware with an interesting chemistry problem, he might strike up a partnership to help you solve it. “I’m a Southerner at heart,” he says, born and raised in Louisiana. And in the Deep South, “you get to know somebody, and if you like them, you can find a way to collaborate,” he says. The strategy has proven fruitful for the chemometrician. Using chemometric models—machine learning algorithms built and interpreted using chemical knowledge—Booksh has probed questions in a variety of disciplines. He hops from field to field “just based upon what sort of fun collaborator I have.” These days, one of Booksh’s projects is a collaboration with the US Forest Service to develop a model to trace the origins of timber with handheld spectroscopic sensors. And, with another collaborator, Booksh is developing a way to analyze scanning electron microscope images “to divine chemical composition based upon shapes of inclusions in nanoparticles.” “One of the things I find interesting about Karl is that over the course of his career, he's been able to reinvent himself a few times,” says Barry Lavine, a computational chemist at Oklahoma State University and one of Booksh’s past and current collaborators. This ability to change gears is something Lavine sees as key to success. “Karl's been very good at that,” he adds.Booksh began his path into chemometrics at the University of Alaska Fairbanks as a National Merit Scholar working in an analytical chemistry lab. “Then, during Variety (cybernetics) ; TRACE (psycholinguistics) ; Field (mathematics) ; sort ; State (computer science) ; Chemometrics ; Product (mathematics) ; General partnership Machine Learning in Materials Science ; History and advancements in chemistry ; Various Chemistry Research Topics