jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Can Polat | Erchin Serpedin | Mustafa Kurban | Hasan Kurban
Source: arXiv (Cornell University) (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

How Far Can You Grow? Characterizing the Extrapolation Frontier of Graph Generative Models for Materials Science Every generative model for crystalline materials harbors a critical structure size beyond which its outputs quietly become unreliable -- we call this the extrapolation frontier. Despite its direct consequences for nanomaterial design, this frontier has never been systematically measured. We introduce RADII, a radius-resolved benchmark of ${\sim}$75,000 nanoparticle structures (55-11,298 atoms) that treats radius as a continuous scaling knob to trace generation quality from in-distribution to out-of-distribution regimes under leakage-free splits. RADII provides frontier-specific diagnostics: per-radius error profiles pinpoint each architecture's scaling ceiling, surface-interior decomposition tests whether failures originate at boundaries or in bulk, and cross-metric failure sequencing reveals which aspect of structural fidelity breaks first. Benchmarking five state-of-the-art architectures, we find that: (i) all models degrade by ${\sim}13\%$ in global positional error beyond training radii, yet local bond fidelity diverges wildly across architectures -- from near-zero to over $2\times$ collapse; (ii) no two architectures share the same failure sequence, revealing the frontier as a multi-dimensional surface shaped by model family; and (iii) well-behaved models obey a power-law scaling exponent $α\approx 1/3$ whose in-distribution fit accurately predicts out-of-distribution error, making their frontiers quantitatively forecastable. These findings establish output scale as a first-class evaluation axis for geometric generative models. The dataset and code are available at https://github.com/KurbanIntelligenceLab/RADII. Extrapolation ; Scaling ; Benchmark (surveying) ; Benchmarking ; Generative grammar ; Fidelity ; Generative model ; Frontier Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Block Copolymer Self-Assembly
Authors: Can Polat | Erchin Serpedin | Mustafa Kurban | Hasan Kurban
Source: Open MIND (preprint, 2026-02-10)
Field: Materials Science — Materials Chemistry

How Far Can You Grow? Characterizing the Extrapolation Frontier of Graph Generative Models for Materials Science Every generative model for crystalline materials harbors a critical structure size beyond which its outputs quietly become unreliable -- we call this the extrapolation frontier. Despite its direct consequences for nanomaterial design, this frontier has never been systematically measured. We introduce RADII, a radius-resolved benchmark of ${\sim}$75,000 nanoparticle structures (55-11,298 atoms) that treats radius as a continuous scaling knob to trace generation quality from in-distribution to out-of-distribution regimes under leakage-free splits. RADII provides frontier-specific diagnostics: per-radius error profiles pinpoint each architecture's scaling ceiling, surface-interior decomposition tests whether failures originate at boundaries or in bulk, and cross-metric failure sequencing reveals which aspect of structural fidelity breaks first. Benchmarking five state-of-the-art architectures, we find that: (i) all models degrade by ${\sim}13\%$ in global positional error beyond training radii, yet local bond fidelity diverges wildly across architectures -- from near-zero to over $2\times$ collapse; (ii) no two architectures share the same failure sequence, revealing the frontier as a multi-dimensional surface shaped by model family; and (iii) well-behaved models obey a power-law scaling exponent $α\approx 1/3$ whose in-distribution fit accurately predicts out-of-distribution error, making their frontiers quantitatively forecastable. These findings establish output scale as a first-class evaluation axis for geometric generative models. The dataset and code are available at https://github.com/KurbanIntelligenceLab/RADII. Extrapolation ; Scaling ; Benchmark (surveying) ; Benchmarking ; Generative grammar ; Fidelity ; Generative model ; Frontier Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Block Copolymer Self-Assembly
Authors: Guang Yang | Jianing Li | Qiong Wang | Lianlian Wu | Zhengshan Chen | Peng-Cheng Zhao | Haoyang Wang | Qinglong Wang | Bing Wen | Chao Song | Wenxin Xu | Xiongfei He | Xiao Zhang | Sophia Tsoka | Siu-Ming Yiu | Fang‐Xiang Wu | Meng Xiao | Shirui Pan | Hui Yu | Song He | Xiaoli Li | Xiaochen Bo | Min Wu | Jian‐Yu Shi
Source: The Innovation Life (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A comprehensive survey on artificial intelligence for biomolecule design &lt;p&gt;Artificial Intelligence (AI), particularly Generative AI (GenAI), has revolutionized molecule design by enabling the creation of novel and complex molecular structures, advancing drug discovery, materials science, and synthetic biology. This survey provides a comprehensive exploration of AI-driven molecule design, categorized within and beyond the central dogma of molecular biology. We first introduce the foundational concepts of GenAI, focusing on architectures such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), and Diffusion Models (DMs), which underpin molecular design applications. Then, we examine how GenAI facilitates molecule design within the central dogma, addressing DNA, RNA, and protein design. Beyond the central dogma, we explore applications in designing small molecules, lipids, glycans, and other biomaterials. Finally, we conclude by discussing current challenges, emerging trends, and potential future directions for GenAI in molecule design. Overall, this survey aims to equip researchers with a thorough understanding of this rapidly-evolving field and inspire novel developments and breakthroughs in GenAI for molecule design.&lt;/p&gt; Generative grammar ; Field (mathematics) ; Synthetic biology ; Design elements and principles Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Yuchen Hua | Xingang Peng | Jianzhu Ma | Muhan Zhang
Source: arXiv (Cornell University) (preprint, 2026-03-13)
Field: Materials Science — Materials Chemistry

VecMol: Vector-Field Representations for 3D Molecule Generation Generative modeling of three-dimensional (3D) molecules is a fundamental yet challenging problem in drug discovery and materials science. Existing approaches typically represent molecules as 3D graphs and co-generate discrete atom types with continuous atomic coordinates, leading to intrinsic learning difficulties such as heterogeneous modality entanglement and geometry-chemistry coherence constraints. We propose VecMol, a paradigm-shifting framework that reimagines molecular representation by modeling 3D molecules as continuous vector fields over Euclidean space, where vectors point toward nearby atoms and implicitly encode molecular structure. The vector field is parameterized by a neural field and generated using a latent diffusion model, avoiding explicit graph generation and decoupling structure learning from discrete atom instantiation. Experiments on the QM9 and GEOM-Drugs benchmarks validate the feasibility of this novel approach, suggesting vector-field-based representations as a promising new direction for 3D molecular generation. Coherence (philosophical gambling strategy) ; Parameterized complexity ; Representation (politics) ; Quantum entanglement ; Dot product ; Field (mathematics) ; Graph ; Molecular graph ; Euclidean geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Quantum many-body systems
Authors: Hokyun Kim | Jonghun Kim | Mansu Kim | Hyunjin Park | Bo-yong Park
Source: NeuroImage (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

LS-MAT: Lifespan structural magnetic resonance imaging synthesis for microstructural covariance profile analysis toolbox Human brain structure changes dynamically across the lifespan, closely linked to cognitive development and decline. T1-weighted and T2-weighted magnetic resonance imaging (MRI) are widely used to assess anatomical and microstructural properties. However, acquiring longitudinal multimodal MRI is resource-intensive, limiting characterization of age-related trajectories. Here, we propose LS-MAT, a generative framework for synthesizing personalized, multimodal, and age-conditioned structural MRIs to support lifespan neuroimaging research. The framework integrates: (i) a variational autoencoder with a generative adversarial network for efficient latent encoding, (ii) a latent diffusion model for high-resolution conditional synthesis, and (iii) a ControlNet for modality-guided structural consistency. We trained and evaluated the model on large-scale, publicly available datasets spanning ages 5-100 years. LS-MAT achieved strong performance in modality conversion, measured by peak signal-to-noise ratio, structural similarity index measure, and mean squared error. The generated images captured established developmental and aging trends, including ventricular enlargement, cortical thinning, and age-related trajectories of T1/T2-weighted ratio-based microstructural profiles. Compared with existing methods, our model outperformed previous approaches in both modality conversion and age-conditioned synthesis tasks. These findings highlight the potential of generative modeling to overcome data scarcity in lifespan neuroimaging and provide a powerful tool for studying structural brain changes. The pipeline not only supports longitudinal analyses but also enables the derivation of microstructural profile features and is openly available at https://github.com/hobacteria/LS-MAT. Toolbox ; Covariance ; Magnetic resonance imaging ; Analysis of covariance ; Pattern recognition (psychology) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Jed A. Duersch | Elohan Veillon | Astrid Klipfel | Adlane Sayede | Zied Bouraoui
Source: Open MIND (preprint, 2026-02-12)
Field: Materials Science — Materials Chemistry

Fourier Transformers for Latent Crystallographic Diffusion and Generative Modeling The discovery of new crystalline materials calls for generative models that handle periodic boundary conditions, crystallographic symmetries, and physical constraints, while scaling to large and structurally diverse unit cells. We propose a reciprocal-space generative pipeline that represents crystals through a truncated Fourier transform of the species-resolved unit-cell density, rather than modeling atomic coordinates directly. This representation is periodicity-native, admits simple algebraic actions of space-group symmetries, and naturally supports variable atomic multiplicities during generation, addressing a common limitation of particle-based approaches. Using only nine Fourier basis functions per spatial dimension, our approach reconstructs unit cells containing up to 108 atoms per chemical species. We instantiate this pipeline with a transformer variational autoencoder over complex-valued Fourier coefficients, and a latent diffusion model that generates in the compressed latent space. We evaluate reconstruction and latent diffusion on the LeMaterial benchmark and compare unconditional generation against coordinate-based baselines in the small-cell regime ($\leq 16$ atoms per unit cell). Fourier transform ; Scaling ; Fourier analysis ; Generative model ; Algebraic number ; Fourier series ; Transformer ; Periodic boundary conditions ; Representation (politics) ; Anisotropy Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Electron Microscopy Techniques and Applications
Authors: Chonnipa Chuaypat | Santi Bardeeniz | Mohamed Azlan Hussain | Chanin Panjapornpon
Source: Digital Chemical Engineering (article, 2026-02-21)
Field: Materials Science — Materials Chemistry

Energy efficiency modeling considered chemical process anomalies using contrastive learning-guided generative adversarial imputation network for operation-aligned data reconstruction • A generative adversarial imputation network with contrastive learning is proposed • Contrastive learning drives imputations toward positives and away from negatives • GAIN uses identified pairs to reconstruct data for improving energy prediction • The proposed model can precisely predict energy efficiency with a test R2 of 0.9585 • Positive/negative pairs substantially improve generalization and imputation error. Effective industrial energy management in petrochemical operations relies on dependable measurement sensors; however, process data frequently exhibits systematic missingness due to sensor malfunctions and multirate sampling, which adversely affects the estimation of specific energy consumption. To address this problem, this study proposes a self-supervised contrastive learning that integrates with a generative adversarial imputation network for an LSTM energy efficiency predictor (LSTM-SSCL-GAIN). Positive/negative pairs pull imputations toward physically similar operating conditions and push them away from dissimilar regimes, guiding the generative adversarial imputation network to produce operating-consistent data based on the modified adversarial, reconstruction, and contrastive losses. The self-supervised contrastive learning integrates with a generative adversarial imputation network reconstructed data is then passed through the long short-term memory energy efficiency model to perform the prediction. The proposed model is verified by a large crude-glycerin refinery. According to the results, LSTM-SSCL-GAIN outperforms machine learning and deep learning models by achieving a testing r-squared of 0.9585, a root mean square error value of 216.36 kJ/kg, and a percentage error value of 1.04% in energy efficiency prediction. Robustness analysis indicates that the model with full contrastive loss maintains high accuracy in both reconstruction and prediction, even under missing rates of up to 60%. Moreover, the ablation study further shows that combining both positive and negative pairs delivers the highest performance and stronger generalization. Through operational adjustments, the proposed model indicates a 10.43% reduction in energy efficiency, equivalent to an annual emissions reduction of approximately 1,550 tons of carbon dioxide. Missing data ; Imputation (statistics) ; Outlier ; Robustness (evolution) ; Efficient energy use ; Generative grammar ; Adversarial system ; Generative adversarial network Machine Learning in Materials Science ; Fault Detection and Control Systems ; Energy Efficiency and Management
Authors: Daiki Koge | Naoaki Ono | Takashi Abe | Shigehiko Kanaya
Source: Journal of Cheminformatics (article, 2026-03-16)
Field: Materials Science — Materials Chemistry

Graph latent diffusion-based molecular representation learning for enhanced generalization in molecular property prediction This study aims to evaluate the effect of latent diffusion models on molecular representation learning from the perspective of generalization performance in molecular property prediction. To this end, we formulate a deep generative model for molecular representation learning based on a latent diffusion-based prior distribution, and introduce an evaluation methodology of generalization for learned molecular representations using the widely applicable information criterion (WAIC) and the widely applicable Bayesian information criterion (WBIC). Furthermore, we propose an analysis framework based on smoothness and multi-modality to analyze the factor of generalization in molecular representations. We constructed the graph latent diffusion autoencoder (Graph LDA), a deep molecular generative model that combines a transformer-based graph variational autoencoder and latent-diffusion-based latent prior distribution, designed to construct graph-level molecular representations through unsupervised learning. We compared the generalization performance of Graph LDA with other molecular representation learning models using WBIC and WAIC across multiple molecular properties, including HOMO energy, solubility, and biological activities. The results demonstrate that molecular representations learned by different models exhibit distinct generalization behaviors, and that representations learned by Graph LDA-using a latent diffusion-based prior-consistently show improved generalization in molecular property prediction. Using our proposed framework, we empirically demonstrate that the superior generalization performance of Graph LDA is attributable to the smoothness and multimodality of its learned molecular latent representation. These findings provide a principled understanding of the role of latent diffusion-based molecular representation learning in improving generalization performance. Scientific contribution: This work systematically analyzed the effect of latent diffusion-based priors in molecular representation learning from the perspective of generalization performance in molecular property prediction. Through generalization evaluation using WBIC and WAIC, together with an analysis framework for molecular representations, it is empirically demonstrated that latent diffusion-based priors contribute to deep generative models extracting smooth and multimodal latent representations, which in turn lead to enhanced generalization performance of molecular representations. These findings offer a principled guideline for developing molecular representation learning models with high generalization. Generalization ; Molecular graph ; Autoencoder ; Graph ; Representation (politics) ; Feature learning ; Generative model ; Latent variable Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Minkyu Kim | Nayoung Kim | Honghui Kim | Sungsoo Ahn
Source: Open MIND (preprint, 2026-02-05)
Field: Materials Science — Materials Chemistry

CatFlow: Co-generation of Slab-Adsorbate Systems via Flow Matching Discovering heterogeneous catalysts tailored for specific reaction intermediates remains a fundamental bottleneck in materials science. While traditional trial-and-error methods and recent generative models have shown promise, they struggle to capture the intrinsic coupling between surface geometry and adsorbate interactions. To address this limitation, we propose CatFlow, a flow matching-based framework for de novo design and structure prediction of heterogeneous catalysts. Our model operates on a primitive cell-based factorized representation of the slab-adsorbate complex, reducing the number of learnable variables by an average of 9.2x while explicitly encoding the surface orientation of the slab-adsorbate interface. Experiments on the Open Catalyst 2020 dataset demonstrate that CatFlow significantly improves the structural fidelity of generated catalysts compared to autoregressive and sequential baselines. Further experiments show that the generated structures accurately capture the adsorption energy distributions of physically plausible interfaces and lie closer to thermodynamic local minima. Representation (politics) ; Bottleneck ; Coupling (piping) ; Orientation (vector space) ; Surface (topology) ; Flow (mathematics) ; Encoding (memory) ; Autoregressive model ; Matching (statistics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Catalysis and Oxidation Reactions
Authors: Akta Singh | Nayana Mukherjee | Jagannath Mondal | Pushpita Ghosh
Source: ArXiv.org (article, 2026-01-22)
Field: Materials Science — Biomaterials

Emergence of spatiotemporal patterns in a fuel-driven coupled cooperative supramolecular system Chemically fueled supramolecular systems can exhibit complex, time-dependent behaviors reminiscent of living matter when maintained far from equilibrium by continuous energy or fuel consumption. Here, we introduce a minimal reaction-diffusion model that captures the essential dynamics of a cooperative supramolecular polymerization network driven by monomer activation and deactivation. We show that a balance between autocatalytic growth and inhibitory decay sustains a nonequilibrium steady state in the model that undergoes a Hopf bifurcation, giving rise to autonomous oscillations. When spatial transport is introduced through diffusion, the system displays rich spatiotemporal phenomena, such as traveling wavefronts and transient polygonal patterns. Our results demonstrate that the interplay between reaction kinetics and diffusion can spontaneously generate self-organized, life-like dynamics in synthetic supramolecular polymer systems. This theoretical framework not only bridges molecular self-assembly and active matter dynamics but also provides design principles for creating adaptive, oscillatory, and self-patterning materials powered by chemical fuels. Supramolecular chemistry ; Non-equilibrium thermodynamics ; Autocatalysis ; Supramolecular polymers ; Autocatalytic reaction ; Monomer ; Diffusion ; Bistability ; Energy landscape ; Transient (computer programming) Supramolecular Self-Assembly in Materials ; Micro and Nano Robotics ; Supramolecular Chemistry and Complexes
Authors: Satish Prajapati | Saibal Ray | Prasanta Kumar
Source: ChemRxiv (article, 2026-03-24)
Field: Materials Science — Polymers and Plastics

A Thermodynamically Consistent Multiscale Framework for Additive Transport in Pharmaceutical Polymers: The Pharm-PATIK Theory The incorporation of functional additives into pharmaceutical polymers underpins modern drug delivery systems, yet the predictive modeling of additive mass transfer during processing remains a fundamental challenge. Existing frameworks rely on decoupled Fickian diffusion models that fail to capture the complex interplay between polymer rheology, crystallization dynamics, and mechano-chemical degradation-phenomena critically important for pharmaceutical applications where additive uniformity directly impacts drug product safety and efficacy. Here, we present the Pharm-PATIK (Pharmaceutical Polymer Additive Transport and Interaction Kinetics) framework: a thermodynamically consistent, multiscale computational theory for additive mass transfer in polymer matrices. The framework is built upon the GENERIC (General Equation for Non-Equilibrium Reversible-Irreversible Coupling) formalism and integrates: (i) a multicomponent Maxwell-Stefan diffusion model with shear-dependent Flory-Huggins interaction parameters; (ii) a phase-field description of polymer crystallization with additive exclusion dynamics; (iii) a finite-strain viscoelastic model capturing stress-induced additive migration via configurational temperature theory; and (iv) mechano-chemical degradation kinetics for predictive assessment of leachables formation. The governing equations are formulated as a system of coupled nonlinear partial differential equations with degenerate mobility, for which we establish mathematical well-posedness under physically relevant conditions. Homogenization theory bridges molecular-scale free-volume effects to equipmentscale transport phenomena. Validation against experimental benchmarks including forced Rayleigh scattering diffusivity measurements, X-ray fluorescence mapping, and twin-screw extrusion trials demonstrates predictive accuracy with mean absolute percentage error below 7%. The framework provides a rigorous foundation for digital design of pharmaceutical polymer processing operations, enabling predictive control of additive fate from extrusion through shelflife storage under quality-by-design (QbD) paradigms. Thermal diffusivity ; Homogenization (climate) ; Polymer ; Nonlinear system ; Viscoelasticity ; Extrusion ; Mass transfer ; Additive model Polymer crystallization and properties ; Membrane Separation and Gas Transport ; Rheology and Fluid Dynamics Studies