jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nicholas Casetti (16986666) | Dylan Anstine (19686916) | Olexandr Isayev (1352004) | Connor W. Coley (1496653)
Source: Figshare (article, 2025-10-14)
Field: Materials Science — Materials Chemistry

Anticipating the Selectivity of Intramolecular CyclizationReaction Pathways with Neural Network Potentials Reaction mechanism search tools have demonstrated the ability to provide insights into likely products and rate-limiting steps of reacting systems. However, reactions involving several concerted bond changesas can be found in many key steps of natural product synthesescan complicate the search process. To mitigate these complications, we present a mechanism search strategy particularly suited to help expedite exploration of an exemplary family of such complex reactions, cyclizations. We provide a cost-effective strategy for identifying relevant elementary reaction steps by combining graph-based enumeration schemes and machine learning techniques for intermediate filtering. Key to this approach is our use of a neural network potential (NNP), AIMNet2-rxn, for computational evaluation of each candidate reaction pathway. In this article, we evaluate the NNP’s ability to estimate activation energies, demonstrate the correct anticipation of stereoselectivity, and recapitulate complex enabling steps in natural product synthesis. Artificial neural network ; Key (lock) ; Mechanism (biology) ; Anticipation (artificial intelligence) ; Product (mathematics) ; Natural product Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Microbial Natural Products and Biosynthesis
Authors: Bence Balázs Mészáros | András Szabó | János Daru
Source: ArXiv.org (preprint, 2025-02-24)
Field: Materials Science — Materials Chemistry

Short-range $Δ$-Machine Learning: A cost-efficient strategy to transfer chemical accuracy to condensed phase systems DFT-based machine-learning potentials (MLPs) are now routinely trained for condensed-phase systems, but surpassing DFT accuracy remains challenging due to the cost or unavailability of periodic reference calculations. Our previous work (PRL 2022, 129, 226001) demonstrated that high-accuracy periodic MLPs can be trained within the CCMD framework using extended yet finite reference calculations. Here, we introduce short-range $Δ$-Machine Learning (sr$Δ$ML), which builds on periodic MLPs while accurately reproducing the observables of the high-level method. Unavailability ; Observable ; Work (physics) ; Phase (matter) ; Transfer (computing) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Li, Zihan | Wan, Mingyang | Gao, Mingyu | Tai, Xishi | Chen, Zhongshan | Wang, Xiangke | Zhang, Feifan
Source: arXiv (Cornell University) (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

COFAP: A Universal Framework for COFs Adsorption Prediction through Designed Multi-Modal Extraction and Cross-Modal Synergy Covalent organic frameworks (COFs) are promising adsorbents for gas adsorption and separation, while identifying the optimal structures among their vast design space requires efficient high-throughput screening. Conventional machine-learning predictors rely heavily on specific gas-related features. However, these features are time-consuming and limit scalability, leading to inefficiency and labor-intensive processes. Herein, a universal COFs adsorption prediction framework (COFAP) is proposed, which can extract multi-modal structural and chemical features through deep learning, and fuse these complementary features via cross-modal attention mechanism. Without relying on explicit gas-specific thermodynamic descriptors, COFAP achieves state-of-the-art prediction performance on the hypoCOFs dataset under the conditions investigated in this study, outperforming existing approaches. Based on COFAP, we also found that high-performing COFs for gas separation concentrate within a narrow range of pore size and surface area. A weight-adjustable prioritization scheme is also developed to enable flexible, application-specific ranking of candidate COFs for researchers. Superior efficiency and accuracy render COFAP directly deployable in crystalline porous materials. Adsorption ; Fuse (electrical) ; Ranking (information retrieval) ; Extraction (chemistry) ; Prioritization ; Limit (mathematics) Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Jiale Zhang (5891141) | Aocheng Fan (22058757) | Ziqi Wang (1525060) | Lei Zheng (106363) | Yu Liu (6938) | Zhijian Wang (310214) | Peng Kang (707736)
Source: Figshare (article, 2025-08-15)
Field: Materials Science — Materials Chemistry

Data-Driven Designof High-Temperature-Resistant PolyimidesUsing Hierarchical Gaussian Process Regression Accurate prediction of Tg for polyimides (PIs) is essential for assessing material performance in high-temperature applications in aerospace, electronics, microelectronics, and flexible display technology. However, experimental measurements remain critically challenging due to the labor-intensive synthesis, conventional instrument limits, and time-consuming characterization processes. Meanwhile, force field limitations, timescale discrepancy, and validation difficulties exist in the prediction of Tg for PIs using molecular dynamic simulation. In this study, we introduce a hierarchical Gaussian process regression machine learning method that integrates prior knowledge to predict Tg for PIs with small-sample data sets. We employ RDKit for molecular descriptor calculation and feature selection. Twenty-one key descriptors are identified, and exceptional model performance with a coefficient of determination <i>R</i><sup>2</sup> of 0.98/0.74 on the training/test set is achieved, surpassing conventional machine learning approaches. We further use Shapley additive explanations analysis to study the actionable insights for designing thermally stable PIs. The number of rotatable bonds and minimum partial charge act as dominant factors influencing Tg. Validations through experimental synthesis and molecular dynamics simulations confirm that the prediction errors are below 15%, while a Bayesian update strategy employing a radial basis function kernel corrected systematic underestimation in the high-Tg regime (>270 °C). This work provides a robust, validated Tg prediction tool, elucidates critical structure–property relationships, and establishes a transferable framework for data-driven materials design, advancing the development of high-performance polymers. Gaussian process ; Kriging ; Kernel (algebra) ; Process (computing) ; Regression ; Gaussian ; Bayesian probability ; Set (abstract data type) ; Field (mathematics) ; Function (biology) Machine Learning in Materials Science ; Synthesis and properties of polymers ; Model Reduction and Neural Networks
Authors: Tiancheng Ma | Zihan Zhang | Shuting Wu | Defang Duan | Tian Cui
Source: ArXiv.org (preprint, 2025-02-07)
Field: Materials Science — Materials Chemistry

FF7: A Code Package for High-throughput Calculations and Constructing Materials Database Decades accumulation of theory simulations lead to boom in material database, which combined with machine learning methods has been a valuable driver for the data-intensive material discovery, i.e., the fourth research paradigm. However, construction of segmented databases and data reuse in generic databases with uniform parameters still lack easy-to-use code tools. We herein develop a code package named FF7 (Fast Funnel with 7 modules) to provide command-line based interactive interface for performing customized high-throughput calculations and building your own handy databases. Data correlation studies and material property prediction can progress by built-in installation-free artificial neural network module and various post processing functions are also supported by auxiliary module. This paper shows the usage of FF7 code package and demonstrates its usefulness by example of database driven thermodynamic stability high-throughput calculation and machine learning model for predicting the superconducting critical temperature of clathrate hydrides. Throughput ; Computer science ; Code (set theory) ; Database ; R package ; Parallel computing ; Programming language ; Operating system Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Lavendra Kumar
Source: International Journal of Advanced Chemistry Research (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Theoretical analysis of electron density distribution in organic compounds This study provides an in-depth theoretical examination of electron density distribution within organic molecules, a fundamental factor influencing molecular structure, reactivity, and intermolecular interactions. Electron density, which dictates the spatial probability of electron presence, is pivotal in determining key molecular properties such as bonding characteristics, dipole moments, and polarizability. Employing quantum mechanical principles, specifically density functional theory (DFT) with the B3LYP functional and 6-31G(d,p) basis set, we conducted precise analyses of electron density across a diverse set of organic compounds, including alkanes, alcohols, aromatic hydrocarbons, carbonyl compounds, and heterocycles. Advanced computational techniques, enriched datasets, and sophisticated visualization tools, such as Laplacian maps and electrostatic potential surfaces, were utilized to elucidate how electron density variations correlate with molecular behavior. The study integrates machine learning models to enhance predictive accuracy of electron density trends, offering novel insights into molecular interactions. The findings reveal distinct electron density patterns, localized in polar molecules like formaldehyde and delocalized in aromatic systems like benzene, directly impacting reactivity and stability. These results advance the field of computational chemistry by providing a robust framework for understanding molecular properties at a quantum level. Furthermore, the insights gained lay a solid foundation for future research in organic synthesis, molecular design, and material science, facilitating the development of innovative compounds and materials with tailored properties. Distribution (mathematics) ; Electron ; Electron density ; Environmental chemistry ; Chemical physics ; Chemistry ; Environmental science ; Materials science ; Statistical physics ; Physics ; Mathematics ; Nuclear physics ; Mathematical analysis Chemical Thermodynamics and Molecular Structure ; Inorganic and Organometallic Chemistry ; Organic Chemistry Cycloaddition Reactions
Authors: Xiaolei Li | Chao Huang | Sen Zhao | Linlin Cui | Shirui Guo | Bo Zheng | Yinghao Cui | Yongqian Chen | Yue Zhao | Lujun Cui | Chunjie Xu
Source: Crystals (article, 2025-06-18)
Field: Materials Science — Materials Chemistry

In Situ Capture of High-Temperature Precipitate Phases in Ti-48Al-2Cr-2Nb Alloy Using Convolutional Neural Networks TiAl intermetallic alloy is a crucial high-performance material, and its microstructure evolution at high temperatures is closely related to the process parameters. Observing the lamellar structure is key to exploring growth kinetics, and the feature extraction of precipitate phases can provide an effective basis for subsequent evolution studies and process parameter settings. Traditional observation methods struggle to promptly grasp the growth state of lamellar structures, and conventional object detection has certain limitations for clustered lamellar structures. This paper introduces a novel method for high-temperature precipitate phase feature extraction based on the YOLOv5-obb rotational object detection network, and a corresponding precipitate phase dataset was created. The improved YOLOv5-obb network was compared with other detection networks. The results show that the proposed YOLOv5-obb network model achieved a precision rate of 93.6% on the validation set for detecting and identifying lamellar structures, with a detection time of 0.02 s per image. It can effectively and accurately identify γ lamellar structures, providing a reference for intelligent morphology detection of alloy precipitate phases under high-temperature conditions. This method achieved good detection performance and high robustness. Additionally, the network can obtain precise positional information for target structures, thus determining the true length of the lamellar structure, which provides strong support for subsequent growth rate calculations. Lamellar structure ; Materials science ; Intermetallic ; Alloy ; Convolutional neural network ; Robustness (evolution) ; Biological system ; Computer science ; GRASP ; Feature (linguistics) ; Artificial intelligence ; Metallurgy ; Chemistry Machine Learning in Materials Science ; Intermetallics and Advanced Alloy Properties ; X-ray Diffraction in Crystallography
Authors: Paulina Baczewska | Damian Nowak | Joanna A. Jaszczewska‐Adamczak | Rafał A. Bachorz | Marcin Hoffmann | Jacek Młynarski
Source: ChemRxiv (article, 2025-10-23)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Enantioselectivity in Asymmetric Catalysis from Small, Curated Datasets: Case Studies in Magnesium-Catalyzed Epoxidation and Thia-Michael Addition Enantioselective synthesis ; Enantiomeric excess ; Catalysis ; Fidelity ; Enantiomer ; Range (aeronautics) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Asymmetric Hydrogenation and Catalysis
Authors: Kiichi Obuchi | Yuta Yahagi | Kiyohiko Toyama | Shukichi Tanaka | Kota Matsui
Source: ArXiv.org (preprint, 2025-05-06)
Field: Materials Science — Materials Chemistry

Multi-modal cascade feature transfer for polymer property prediction In this paper, we propose a novel transfer learning approach called multi-modal cascade model with feature transfer for polymer property prediction.Polymers are characterized by a composite of data in several different formats, including molecular descriptors and additive information as well as chemical structures. However, in conventional approaches, prediction models were often constructed using each type of data separately. Our model enables more accurate prediction of physical properties for polymers by combining features extracted from the chemical structure by graph convolutional neural networks (GCN) with features such as molecular descriptors and additive information. The predictive performance of the proposed method is empirically evaluated using several polymer datasets. We report that the proposed method shows high predictive performance compared to the baseline conventional approach using a single feature. Cascade ; Property (philosophy) ; Feature (linguistics) ; Transfer of learning ; Pattern recognition (psychology) ; Convolutional neural network ; Graph ; Polymer ; Artificial neural network Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Polymer composites and self-healing
Authors: Egor Rumiantsev | Marcel F. Langer | Tulga-Erdene Sodjargal | Michele Ceriotti | Philip Loche
Source: arXiv (Cornell University) (preprint, 2025-07-25)
Field: Materials Science — Materials Chemistry

Learning Long-Range Representations with Equivariant Messages Machine learning interatomic potentials trained on first-principles reference data are becoming valuable tools for computational physics, biology, and chemistry. Equivariant message-passing neural networks, including transformers, achieve state-of-the-art accuracy but rely on cutoff-based graphs, limiting their ability to capture long-range effects such as electrostatics or dispersion, as well as electron delocalization. While long-range correction schemes based on inverse power laws of interatomic distances have been proposed, they are unable to communicate higher-order geometric information and are thus limited in applicability. To address this shortcoming, we propose the use of equivariant, rather than scalar, charges for long-range interactions, and design a graph neural network architecture, LOREM, around this long-range message passing mechanism. We consider several datasets specifically designed to highlight non-local physical effects, and compare short-range message passing with different receptive fields to invariant and equivariant long-range message passing. Even though most approaches work for careful dataset-specific choices of their model hyperparameters, LOREM works consistently without such changes, with excellent benchmark performance. Equivariant map ; Robustness (evolution) ; Artificial neural network ; Graph ; Message passing ; Scaling ; Visualization ; Expressive power Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Quantum many-body systems
Authors: Mohamed Barhoumi
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Two-Dimensional (2D) Materials Variety (cybernetics) ; Graphene ; Quantum ; Hexagonal boron nitride ; Heterojunction ; Boron nitride ; Density functional theory Graphene research and applications ; Aerogels and thermal insulation ; Zeolite Catalysis and Synthesis
Authors: Venkata Shivakumar Remella | N. Haridharan
Source: Research Square (preprint, 2025-05-14)
Field: Chemistry — Organic Chemistry

Integrating Density Functional Theory, Finite Element Analysis &amp;amp; Machine Learning in the study of mechano-thermal properties of a Triclinic Pentafluorophenyl-Urea Derivative Triclinic crystal system ; Derivative (finance) ; Density functional theory ; Finite element method ; Urea ; Chemistry ; Computer science ; Computational chemistry ; Materials science ; Physics ; Crystallography ; Thermodynamics ; Biochemistry ; Crystal structure ; Economics Chemical Thermodynamics and Molecular Structure ; Crystallography and molecular interactions ; Thermal properties of materials
Authors: Thomas
Michael Pruyn (22593433) | Amro Aswad (22593436) | Sartaaj Takrim Khan (22593439) | Ju Huang (806291) | Robert Black (412336) | Seyed Mohamad Moosavi (3610628)
Source: Figshare (article, 2025-11-10)
Field: Chemistry — Inorganic Chemistry

MOF-ChemUnity: Literature-InformedLarge LanguageModels for Metal–Organic Framework Research Artificial intelligence (AI) is transforming research in metal–organic frameworks (MOFs), where models trained on structured computational data routinely predict new materials and optimize their properties. This raises a central question: What if we could leverage the full breadth of MOF knowledge, not just structured data sets, but also the scientific literature? For researchers, the literature remains the primary source of knowledge, yet much of its content, including experimental data and expert insight, remains underutilized by AI systems. We introduce MOF-ChemUnity, a structured, extensible, and scalable knowledge graph that unifies MOF data by linking literature-derived insights to crystal structures and computational data sets. By disambiguating MOF names in the literature and connecting them to crystal structures in the Cambridge Structural Database, MOF-ChemUnity unifies experimental and computational sources and enables cross-document knowledge extraction and linking. We showcase how this enables multiproperty machine learning across simulated and experimental data, compilation of complete synthesis records for individual compounds by aggregating information across multiple publications, and expert-guided materials recommendations via structure-based machine learning descriptors for pore geometry and chemistry. When used as a knowledge source to augment large language models (LLMs), MOF-ChemUnity enables a literature-informed AI assistant that operates over the full scope of MOF knowledge. Expert evaluations show improved accuracy, interpretability, and trustworthiness across tasks such as retrieval, inference of structure–property relationships, and materials recommendation, outperforming standard LLMs. This work lays the foundation for literature-informed materials discovery, enabling both scientists and AI systems to reason over the full existing knowledge in a new way. Leverage (statistics) ; Inference ; Knowledge graph ; Scalability ; Applications of artificial intelligence ; Knowledge base ; Trustworthiness ; Information extraction ; Scope (computer science) ; Graph Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Nanocluster Synthesis and Applications
Authors: Edelen,Jonathan | Krishna,Bhargavi | Hoffmann,Christina | Hall,Christopher | Einstein-Curtis,Joshua | Henderson,Morgan | Calder,Stuart
Source: JACOW (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

Automation of sample alignment for neutron scattering experiments Neutron scattering experiments are a critical tool for the exploration of molecular structure in compounds. The TOPAZ single crystal diffractometer at the Spallation Neutron Source and the Powder Diffractometer at the High Flux Isotope Reactor study these samples by illuminating them with different energy neutron beams and recording the scattered neutrons. Aligning and maintaining the alignment of the sample during an experiment is key to ensuring high quality data are collected. At present this process is performed manually by beamline scientists. RadiaSoft in collaboration with the beamline scientists and engineers at ORNL has developed a machine learning based alignment software automating this process. We utilize a fully-connected convolutional neural network configured in a U-net architecture to identify the sample center of mass. We then move the sample using a custom python-based EPICS IOC interfaced with the motors. In this poster we provide an overview of our machine learning tools and show our results aligning samples at ORNL. Beamline ; Neutron scattering ; Sample (material) ; Diffractometer ; Neutron ; Spallation Neutron Source ; Automation ; Software ; Small-angle neutron scattering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Susanta Kumar Patro | Manoj Kumar Nigam | Amaresh Kumar Patro | Pruthwijeet Patnayak
Source:  (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Analysis of Biomedical Materials for Bone Plate Applications Using Bending Tests and Finite Element Simulation In this paper, we worked on three biological materials that are commonly utilised in orthopaedic implants that are Ti-6Al-4V, AZ31B magnesium alloy, Polylactic Acid (PLA), and Hydroxyapatite (HA). As part of the investigation, CAD models of bone plates were constructed. We also used Finite Element Analysis (FEA) to evaluate and simulate the models in three-point bending. The bending strength, deflection, von Mises stress, and deformation for each material are analysis. Ti-6Al-4V showed the best mechanical performance and the least degree of deformation. Strong magnesium metal AZ31B breaks down readily in nature. PLA was elastic but weak, while HA broke rapidly under tension. The simulation almost matched the real data, proving the FEA model was correct. These results show how important it is to choose the right material for the job. We also suggest that AZ31B and PLA would be good materials to use for implants that can be absorbed. This study makes bone plate materials better for use in programs that help with orthopaedic problems. Finite element method ; Bending ; Bending of plates ; Hot plate Machine Learning in Materials Science ; Bone health and osteoporosis research ; Elasticity and Material Modeling