jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shanshan Wang | Dian Xu | Jianmin Shen | Feng Gao | Wei Li | Weibing Deng
Source: ArXiv.org (article, 2025-07-05)
Field: Materials Science — Materials Chemistry

Siamese Neural Network for Label-Efficient Critical Phenomena Prediction in 3D Percolation Models Percolation theory serves as a cornerstone for studying phase transitions and critical phenomena, with broad implications in statistical physics, materials science, and complex networks. However, most machine learning frameworks for percolation analysis have focused on two-dimensional systems, oversimplifying the spatial correlations and morphological complexity of real-world three-dimensional materials. To bridge this gap and improve label efficiency and scalability in 3D systems, we propose a Siamese Neural Network (SNN) that leverages features of the largest cluster as discriminative input. Our method achieves high predictive accuracy for both site and bond percolation thresholds and critical exponents in three dimensions, with sub-1% error margins using significantly fewer labeled samples than traditional approaches. This work establishes a robust and data-efficient framework for modeling high-dimensional critical phenomena, with potential applications in materials discovery and complex network analysis. Percolation (cognitive psychology) ; Percolation theory ; Artificial neural network ; Directed percolation ; Discriminative model ; Scalability ; Critical exponent ; Complex system ; Complex network Machine Learning in Materials Science ; Theoretical and Computational Physics ; Quantum many-body systems
Authors: Yuncheng Du | Cristina Sanchez Cereceda | Dongping Du
Source: Computational Materials Science (article, 2025-10-16)
Field: Chemistry — Inorganic Chemistry

Incremental learning with heterogeneous data for morphology prediction in metal-organic frameworks Domain adaptation ; Incremental learning ; Adaptation (eye) ; Domain (mathematical analysis) ; Predictive modelling ; Unsupervised learning Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Machine Learning and ELM
Authors: Alexander Quinn | Rebecca Walker | Naila Tufaha | John M. D. Storey | Corrie T. Imrie | Ingo Dierking
Source: Soft Matter (article, 2025-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Distinguishing liquid crystalline nematic variants by machine learning Two different machine learning architectures - sequential convolutional neural networks (CNN) and parallel inception models were evaluated with respect to their ability to identify nematic liquid crystal variants, including the ferroelectric and the twist-bend nematic phases. Varying levels of model complexity were employed from 1- to 5-layer CNNs, to 1- to 3-block inception models. Various types of augmentations like flip, contrast and brightness were used, together with dropout-layer regularisation. Flip was the only augmentation trialled to yield positive results with an acceptable level of accuracy and error, while the inclusion of dropout regularisation almost exclusively led to lower accuracies. From the systematic investigation it is advised that different variants of the nematic phase can be distinguished to an accuracy better than 0.96-0.98 ± 0.01 by the use of 3-layer CNNs or a model with a single inception block, if flip augmentation is applied. Computational restraints therefore suggest that a sequential CNN is sufficient to characterise phase sequences with four or fewer different phases. Higher accuracies, closer to 100%, can be achieved for extended and class-balanced datasets. In the latter case an inception approach would possibly be beneficial, depending on the size of the dataset, but overfitting needs to be avoided. Overfitting ; Liquid crystal ; Dropout (neural networks) ; Convolutional neural network ; Phase (matter) ; Brightness ; Artificial neural network ; Contrast (vision) Liquid Crystal Research Advancements ; Advanced Optical Imaging Technologies ; Electrowetting and Microfluidic Technologies
Authors: Xiaoping Wang
Source: Structural Dynamics (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Single-Crystal Diffraction at ORNL: Historical Development, Current Advances, and Future Perspectives Since the pioneering neutron diffraction experiments by Ernest O. Wollan and Clifford G. Shull in the 1940s, Oak Ridge National Laboratory (ORNL) has played a central role in the advancement of single-crystal diffraction techniques. Over the past 75 years, ORNL's contributions have significantly shaped structural science across a range of disciplines, especially in chemistry, condense matter physics and materials science. Among the suite of single-crystal diffractometers at the High Flux Isotope Reactor (HFIR) and the Spallation Neutron Source (SNS), the TOPAZ instrument plays a central role in extending neutron crystallography beyond traditional three-dimensional frameworks. By combining neutron wavelength-resolved Laue diffraction with event-based neutron detection, TOPAZ enables simultaneous measurement of three-dimensional diffraction space and real-time tracking of structural responses to external stimuli in parameter space—including temperature, pressure, and applied fields. These capabilities are particularly valuable for investigating hydrogen bonding in functional energy materials, local and short-range correlations in quantum materials, and structural and magnetic phase transitions under operando conditions. Looking ahead, the integration of artificial intelligence (AI) and machine learning (ML) into neutron diffraction workflows is expected to enhance data interpretation, support real-time experimental optimization, and enable adaptive exploration of complex structural systems. These developments will broaden the scope of crystallographic research in structural science and create new opportunities for discovery in the study of advanced energy and quantum materials. Neutron diffraction ; Neutron scattering ; Neutron source ; Neutron ; Diffraction ; Spallation X-ray Diffraction in Crystallography ; Nuclear Physics and Applications
Authors: Connor Edwards | Jack D. Evans
Source: ChemRxiv (preprint, 2025-03-18)
Field: Materials Science — Materials Chemistry

Exploring foundational machine learned potentials for treating the high temperature dynamics of metal-organic frameworks Metal-organic framework (MOF) derived materials, formed through high temperature processes, show great potential as catalysts. However, knowledge of the structure-property relationships between the initial MOF and final MOF-derived catalyst is limited, as their amorphous nature challenges standard structural characterization methods. Additionally, current molecular simulation methods, such as ab initio molecular dynamics, are computationally demanding and unable to follow pyrolysis over large temporal and spatial scales. One solution is the use of neural network approaches for learning interatomic potentials from density functional theory (DFT). Here we explored the pyrolysis of CALF-20 and ZIF-8 using machine learned potentials and established potentials that can simulate high temperature decomposition at near DFT accuracy. Standard random sampling and two biased sampling techniques were tested in an effort to sample the phase space of average zinc coordination number and bond length. These biased sampling methods showed significant improvement over random sampling; the resulting models were able to successfully recreate the environments seen in a DFT simulation. Using this model, we then simulated a one nanosecond quench of CALF-20 and ZIF-8 at 1500 K and 1750 K, respectively. This gave atomistic details of how the MOF behaved at high temperatures including gas formation, changes in zinc coordination environment and decomposition of linkers. This demonstrates the potential of using MLPs to simulate complex, high temperature processes in MOFs to gain a better understanding of reactivity and predict the features needed for new catalytic materials. Metal-organic framework ; Dynamics (music) ; Computer science ; Nanotechnology ; Biochemical engineering ; Artificial intelligence ; Materials science ; Chemistry ; Engineering ; Psychology ; Physical chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Catalysis and Oxidation Reactions
Authors: Tian, Zhihui | Suwandi, Ethan | Oppelstrup, Tomas | Bulatov, Vasily V. | Harley, Joel B. | Zhou, Fei
Source: arXiv (Cornell University) (article, 2025-11-22)
Field: Materials Science — Materials Chemistry

Scaling Kinetic Monte-Carlo Simulations of Grain Growth with Combined Convolutional and Graph Neural Networks Graph neural networks (GNN) have emerged as a promising machine learning method for microstructure simulations such as grain growth. However, accurate modeling of realistic grain boundary networks requires large simulation cells, which GNN has difficulty scaling up to. To alleviate the computational costs and memory footprint of GNN, we propose a hybrid architecture combining a convolutional neural network (CNN) based bijective autoencoder to compress the spatial dimensions, and a GNN that evolves the microstructure in the latent space of reduced spatial sizes. Our results demonstrate that the new design significantly reduces computational costs with using fewer message passing layer (from 12 down to 3) compared with GNN alone. The reduction in computational cost becomes more pronounced as the spatial size increases, indicating strong computational scalability. For the largest mesh evaluated (160^3), our method reduces memory usage and runtime in inference by 117x and 115x, respectively, compared with GNN-only baseline. More importantly, it shows higher accuracy and stronger spatiotemporal capability than the GNN-only baseline, especially in long-term testing. Such combination of scalability and accuracy is essential for simulating realistic material microstructures over extended time scales. The improvements can be attributed to the bijective autoencoder's ability to compress information losslessly from spatial domain into a high dimensional feature space, thereby producing more expressive latent features for the GNN to learn from, while also contributing its own spatiotemporal modeling capability. The training was optimized to learn from the stochastic Potts Monte Carlo method. Our findings provide a highly scalable approach for simulating grain growth. Autoencoder ; Scalability ; Convolutional neural network ; Scaling ; Memory footprint ; Inference ; Reduction (mathematics) ; Graph ; Bijection Machine Learning in Materials Science ; Microstructure and mechanical properties ; Quantum many-body systems
Authors: Li, Zihan | Wan, Mingyang | Gao, Mingyu | Tai, Xishi | Chen, Zhongshan | Wang, Xiangke | Zhang, Feifan
Source: arXiv (Cornell University) (preprint, 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: Sithira Liyanage | Anuradha Irugalbandara | Dilantha Thushara | Hiran Chathuranga | Indika Perera | Sandrine Costa
Source:  (article, 2025-08-14)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Discovery of Photocatalysts for Solar Hydrogen Production: Overcoming Bandgap Prediction Challenges with Transfer Learning Establishing hydrogen as a clean and sustainable energy vector relies on advancements in solar-driven green hydrogen production technologies, such as photocatalytic water splitting (PWS). To make PWS commercially viable, it is essential to achieve a solar-to-hydrogen conversion efficiency of at least 10 %. Recent progress in computational materials science and machine learning has led to the development of sophisticated models such as graph neural networks and transformer-based architectures for identifying promising photocatalytic materials. However, their ability to accurately predict critical properties such as bandgap remains limited, primarily due to the lack of large, high-quality datasets computed using advanced density functional theory (DFT) functionals. While functionals like the modified Becke Johnson (mBJ) potential offer significantly improved accuracy, large scale datasets based on mBJ calculations are not yet widely available for training complex machine learning models. This study addresses these limitations by employing a novel transfer learning strategy. Models are first pretrained on large scale but less accurate Perdew-Burke-Ernzerhof (PBE) datasets and then fine-tuned using smaller, high fidelity mBJ datasets. This approach enhanced bandgap prediction accuracy by reducing the MAE by 10.22 % compared to a model trained conventionally on the mBJ dataset, thereby accelerates the discovery of efficient photocatalysts. Solar energy conversion ; Scale (ratio) ; Deep learning ; Band gap ; Transfer of learning ; Photovoltaic system ; Artificial neural network ; Fidelity Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Electrocatalysts for Energy Conversion
Authors: Eris Elianddy Supeni | Azizan As’arry | Haider Jaafar Chilabi
Source: Physical Sciences Reviews (article, 2025-07-23)
Field: Materials Science — Materials Chemistry

Advancement of energy harvesting using smart materials and composites Abstract This study explores advancements in energy harvesting using smart materials and composite structures, focusing on integrating shape memory alloys (SMAs) into wind turbine blades. The research investigates the structural and thermo-mechanical behavior of glass fiber-reinforced polymer (GFRP) composites and SMA wires, emphasizing their potential in improving blade adaptability and stress recovery. Finite Element Analysis (FEA) and experimental methods are utilized to evaluate deflection, load correlation, and actuation efficiency. Results demonstrate that suspended SMA wires enhance deflection recovery with lower energy loss compared to embedded configurations. A neural network model predicts deformation behavior based on load, current, and wire count. Additionally, SMA integration improves the power coefficient (Cp), bringing turbine efficiency closer to the Betz limit. Despite challenges such as nonuniform prestrain and material degradation at attachment points, this study confirms the feasibility of SMA-driven adaptive blades for wind energy applications. Future work includes optimizing SMA deployment in real-scale wind turbines through dynamic analysis and aerodynamic performance testing. Composite material ; Materials science ; Smart material Shape Memory Alloy Transformations ; Transition Metal Oxide Nanomaterials ; Advanced Thermoelectric Materials and Devices
Authors: Blake
E. Ocampo (21808220) | Bilal Altundas (8912018) | Matthew J. Bock (21808223) | Sara Feiz (21808226) | Scott E. Denmark (1310370)
Source: Figshare (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Data-Driven Predictionof Enantioselectivity for theSharpless Asymmetric Dihydroxylation: Model Development and ExperimentalValidation The Sharpless asymmetric dihydroxylation remains a key transformation in chemical synthesis, yet its success hides unexpected cases of lower selectivity. A chemoinformatic workflow was developed to allow data-driven analysis of the reaction. A database of 1007 reactions employing AD-mix α and β was curated from the literature, and an alignment-dependent, fragment-based featurization of alkenes was implemented for modeling. This platform converged on machine learning models capable of predicting the magnitude of enantioselectivity for multiple alkene classes, achieving <i>Q</i><sup>2</sup><sub>F3</sub> values ≥ 0.8, test <i>r</i><sup>2</sup> values ≥ 0.7 and mean absolute errors (MAE) ≤ 0.3 kcal/mol. The features of alkenes contributing to model performance were assessed with SHapley Additive exPlanations (SHAP) analysis to gather insight into factors underlying predictions. Experimental validation demonstrated that the models could achieve meaningful predictions on out-of-sample alkenes. Sharpless asymmetric dihydroxylation ; Workflow ; Alkene ; Representation (politics) ; Development (topology) ; Key (lock) ; Transformation (genetics) Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Microbial Natural Products and Biosynthesis
Authors: Zhao, Siwei | Ying, Penghua | Zhang, Guoqiang | Zhou, Ke | Yue, Shengying | Chen, Yan | Liu, Yilun
Source: arXiv (Cornell University) (preprint, 2025-11-07)
Field: Materials Science — Materials Chemistry

Intrinsic Fracture Nonreciprocity at the Nanoscale We reveal intrinsic fracture nonreciprocity, manifesting as directional asymmetry in crack resistance, in two-dimensional heterostructures engineered through lattice-mismatched interfaces. Density-functional theory combined with machine-learning molecular dynamics show that intrinsic lattice mismatch between bonded component crystals imprints asymmetric prestrain states at crack tips, governing bond-breaking thresholds through charge redistribution. The failure criterion obeys a universal exponential scaling law between normalized charge density and bond strain, insensitive to bonding chemistry and local atomic environment. The magnitude of nonreciprocity scales systematically with lattice mismatch, reaching 49% at 10% mismatch. Validation across hexagonal, square, rectangular, and oblique two-dimensional lattices confirms universality, establishing interface strain engineering as a general design principle that bridges electronic structure to nanoscale failure, enabling rational design of damage-tolerant nanostructures. Asymmetry ; Nanoscopic scale ; Scaling ; Heterojunction ; Lattice (music) ; Oblique case ; Density functional theory ; Molecular dynamics Machine Learning in Materials Science ; Graphene research and applications ; 2D Materials and Applications
Authors: Zhao, Siwei | Ying, Penghua | Zhang, Guoqiang | Zhou, Ke | Yue, Shengying | Chen, Yan | Liu, Yilun
Source: ArXiv.org (article, 2025-11-07)
Field: Materials Science — Materials Chemistry

Intrinsic Fracture Nonreciprocity at the Nanoscale We reveal intrinsic fracture nonreciprocity, manifesting as directional asymmetry in crack resistance, in two-dimensional heterostructures engineered through lattice-mismatched interfaces. Density-functional theory combined with machine-learning molecular dynamics show that intrinsic lattice mismatch between bonded component crystals imprints asymmetric prestrain states at crack tips, governing bond-breaking thresholds through charge redistribution. The failure criterion obeys a universal exponential scaling law between normalized charge density and bond strain, insensitive to bonding chemistry and local atomic environment. The magnitude of nonreciprocity scales systematically with lattice mismatch, reaching 49% at 10% mismatch. Validation across hexagonal, square, rectangular, and oblique two-dimensional lattices confirms universality, establishing interface strain engineering as a general design principle that bridges electronic structure to nanoscale failure, enabling rational design of damage-tolerant nanostructures. Asymmetry ; Nanoscopic scale ; Scaling ; Heterojunction ; Lattice (music) ; Oblique case ; Density functional theory ; Molecular dynamics Machine Learning in Materials Science ; Graphene research and applications ; 2D Materials and Applications
Authors: Pau Rocabert Oriols | Noelia Lopez | Javier Heras-Domingo | Camilla Lo Conte
Source: Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Multi-Modal Constrastive Learning for Chemical Structure Elucidation with VibraCLIP Identifying molecular structures from vibrational spectra is central to chemical analysis but remains challenging due to spectral ambiguity and the limitations of single-modality methods. While deep learning has advanced various spectroscopic characterization techniques, leveraging the complementary nature of infrared (IR) and Raman spectroscopies remains largely underexplored. We introduce VibraCLIP, a contrastive learning framework that embeds molecular graphs, IR and Raman spectra into a shared latent space. A lightweight fine-tuning protocol ensures generalization from theoretical to experimental datasets. VibraCLIP enables accurate, scalable, and data-efficient molecular identification, linking vibrational spectroscopy with structural interpretation. This tri-modal design captures rich structure–spectra relationships, achieving Top-1 retrieval accuracy of 81.7% and reaching 98.9% Top-25 accuracy with molecular mass integration. By integrating complementary vibrational spectroscopic signals with molecular representations, VibraCLIP provides a practical framework for automated spectral analysis, with potential applications in fields such as synthesis monitoring, drug development, and astrochemical detection. Raman spectroscopy ; Characterization (materials science) ; Generalization ; Infrared spectroscopy ; Molecule ; Infrared ; Spectroscopy ; Molecular model Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Xiaoxiang Han | Jun Li | Lin Yuan | Xin Zhang | Weijie Zhu | Yang Liu | Haiyang Zhang | Baoyi Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Pinn and Kan Temperature Prediction of Carbon Fiber/Epoxy Composite Materials Irradiated by Nuclear Environment Simulated Intense Heat Fluxes Epoxy ; Composite number ; Carbon fiber composite ; Composite material ; Materials science ; Irradiation ; Carbon fibers ; Fiber ; Nuclear physics ; Physics Graphite, nuclear technology, radiation studies ; Fiber-reinforced polymer composites