jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Salma Zahran | Zhou Ao | Zhengyang Zhang | Chen Chi | Chenchen Yuan | Yanming Wang
Source: Open MIND (preprint, 2026-02-02)
Field: Materials Science — Materials Chemistry

Physics Informed Generative AI Enabling Labour Free Segmentation For Microscopy Analysis Semantic segmentation of microscopy images is a critical task for high-throughput materials characterisation, yet its automation is severely constrained by the prohibitive cost, subjectivity, and scarcity of expert-annotated data. While physics-based simulations offer a scalable alternative to manual labelling, models trained on such data historically fail to generalise due to a significant domain gap, lacking the complex textures, noise patterns, and imaging artefacts inherent to experimental data. This paper introduces a novel framework for labour-free segmentation that successfully bridges this simulation-to-reality gap. Our pipeline leverages phase-field simulations to generate an abundant source of microstructural morphologies with perfect, intrinsically-derived ground-truth masks. We then employ a Cycle-Consistent Generative Adversarial Network (CycleGAN) for unpaired image-to-image translation, transforming the clean simulations into a large-scale dataset of high-fidelity, realistic SEM images. A U-Net model, trained exclusively on this synthetic data, demonstrated remarkable generalisation when deployed on unseen experimental images, achieving a mean Boundary F1-Score of 0.90 and an Intersection over Union (IOU) of 0.88. Comprehensive validation using t-SNE feature-space projection and Shannon entropy analysis confirms that our synthetic images are statistically and featurally indistinguishable from the real data manifold. By completely decoupling model training from manual annotation, our generative framework transforms a data-scarce problem into one of data abundance, providing a robust and fully automated solution to accelerate materials discovery and analysis. Segmentation ; Generative grammar ; Automation ; Pipeline (software) ; Synthetic data ; Intersection (aeronautics) ; Noise (video) ; Generative model ; Image segmentation Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Generative Adversarial Networks and Image Synthesis
Authors: Adrián Fuente-Ballesteros | V. Zuin Zeidler
Source: Analytical Chemistry (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Artificial Intelligence for Academic Text Generation in Analytical Chemistry: Current Risks, Indicators, and Perspectives toward Greener and More Sustainable Approaches The fast adoption of large language models has introduced new possibilities and challenges in scientific writing. While artificial intelligence (AI) tools have long supported researchers through grammar checking, reference management, and data processing, recent generative models such as ChatGPT are pretending now to be capable of producing complete sections of academic text that are comparable to conventional manuscripts. This development raises important questions regarding authorship, responsibility, and the integrity of the scientific record. In this work, we propose to examine how AI-generated and AI-assisted text is currently used in analytical chemistry writing, with particular emphasis on recurring linguistic, structural, and bibliographic patterns that may indicate automated drafting in a number of journals' submissions. We propose a reflection on the risks, including superficial, fast, unsustainable, or even misconceptions on science, biased reasoning driven by prompt formulation, and integrity failures linked to AI misuse. Finally, when is the case, we suggest a set of practical indicators and good practices aiming at supporting responsible, transparent, and critically supervised use of AI in analytical chemistry publications. Generative grammar ; Set (abstract data type) ; Grammar ; Reflection (computer programming) ; Generative model ; Parsing Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education ; Academic Writing and Publishing
Authors: Mianzhi Pan | J. Li | Peishuo Liu | Botian Wang | Yawen Ouyang | Yiming Rong | Hao Zhou | Jianbing Zhang
Source: arXiv (Cornell University) (preprint, 2026-01-14)
Field: Chemistry — Inorganic Chemistry

Enhancing Spatial Reasoning in Large Language Models for Metal-Organic Frameworks Structure Prediction Metal-organic frameworks (MOFs) are porous crystalline materials with broad applications such as carbon capture and drug delivery, yet accurately predicting their 3D structures remains a significant challenge. While Large Language Models (LLMs) have shown promise in generating crystals, their application to MOFs is hindered by MOFs' high atomic complexity. Inspired by the success of block-wise paradigms in deep generative models, we pioneer the use of LLMs in this domain by introducing MOF-LLM, the first LLM framework specifically adapted for block-level MOF structure prediction. To effectively harness LLMs for this modular assembly task, our training paradigm integrates spatial-aware continual pre-training (CPT), structural supervised fine-tuning (SFT), and matching-driven reinforcement learning (RL). By incorporating explicit spatial priors and optimizing structural stability via Soft Adaptive Policy Optimization (SAPO), our approach substantially enhances the spatial reasoning capability of a Qwen-3 8B model for accurate MOF structure prediction. Comprehensive experiments demonstrate that MOF-LLM outperforms state-of-the-art denoising-based and LLM-based methods while exhibiting superior sampling efficiency. Modular design ; Stability (learning theory) ; Generative grammar ; Domain (mathematical analysis) ; Reinforcement learning ; Prior probability Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Block Copolymer Self-Assembly
Authors: Liang Shi | Jiarui Lu | Junqi Liu | Chence Shi | Zhi Qi Yang | Tang, Jian, 1968-
Source: arXiv (Cornell University) (preprint, 2026-03-18)
Field: Materials Science — Materials Chemistry

Atomic Trajectory Modeling with State Space Models for Biomolecular Dynamics Understanding the dynamic behavior of biomolecules is fundamental to elucidating biological function and facilitating drug discovery. While Molecular Dynamics (MD) simulations provide a rigorous physical basis for studying these dynamics, they remain computationally expensive for long timescales. Conversely, recent deep generative models accelerate conformation generation but are typically either failing to model temporal relationship or built only for monomeric proteins. To bridge this gap, we introduce ATMOS, a novel generative framework based on State Space Models (SSM) designed to generate atom-level MD trajectories for biomolecular systems. ATMOS integrates a Pairformer-based state transition mechanism to capture long-range temporal dependencies, with a diffusion-based module to decode trajectory frames in an autoregressive manner. ATMOS is trained across crystal structures from PDB and conformation trajectory from large-scale MD simulation datasets including mdCATH and MISATO. We demonstrate that ATMOS achieves state-of-the-art performance in generating conformation trajectories for both protein monomers and complex protein-ligand systems. By enabling efficient inference of atomic trajectory of motions, this work establishes a promising foundation for modeling biomolecular dynamics. Trajectory ; Molecular dynamics ; State space ; Function (biology) ; Basis (linear algebra) ; Inference ; Biomolecular structure ; Autoregressive model ; Generative model Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Dong Xu | Qihua Pan | Sisi Yuan | Jianqiang Li | Zexuan Zhu | Junkai Ji
Source: ArXiv.org (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Unveiling Scaling Behaviors in Molecular Language Models: Effects of Model Size, Data, and Representation Molecular generative models, often employing GPT-style language modeling on molecular string representations, have shown promising capabilities when scaled to large datasets and model sizes. However, it remains unclear and subject to debate whether these models adhere to predictable scaling laws under fixed computational budgets, which is a crucial understanding for optimally allocating resources between model size, data volume, and molecular representation. In this study, we systematically investigate the scaling behavior of molecular language models across both pretraining and downstream tasks. We train 300 models and conduct over 10,000 experiments, rigorously controlling compute budgets while independently varying model size, number of training tokens, and molecular representation. Our results demonstrate clear scaling laws in molecular models for both pretraining and downstream transfer, reveal the substantial impact of molecular representation on performance, and explain previously observed inconsistencies in scaling behavior for molecular generation. Additionally, we publicly release the largest library of molecular language models to date to facilitate future research and development. Code and models are available at https://github.com/SZU-ADDG/MLM-Scaling. Scaling ; Representation (politics) ; String (physics) ; Language model ; Generative grammar ; Molecular model ; Scaling law Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Lei Peng | Zhaohuan Zhang | Mingjun Zhang | Huang Zhang | Bin Du | Penghui Xiao | Huaqiang Yin | Xuedong He | Tao Ma | Xiao Ma
Source: Journal of Nuclear Materials (article, 2026-02-06)
Field: Materials Science — Materials Chemistry

New insights into binary gas diffusion in nuclear-grade graphite IG-110 by an improved Wicke–Kallenbach diffusion experiments and Lattice Boltzmann method Diffusion ; Graphite ; Gaseous diffusion ; Lattice Boltzmann methods ; Diffusion process ; Work (physics) ; Boltzmann constant ; Molecular diffusion ; Adsorption Graphite, nuclear technology, radiation studies ; Nuclear reactor physics and engineering ; Heat transfer and supercritical fluids
Authors: Dong Xu | Qihua Pan | Sisi Yuan | Jianqiang Li | Zexuan Zhu | Junkai Ji
Source: Open MIND (preprint, 2026-01-30)
Field: Materials Science — Materials Chemistry

Unveiling Scaling Behaviors in Molecular Language Models: Effects of Model Size, Data, and Representation Molecular generative models, often employing GPT-style language modeling on molecular string representations, have shown promising capabilities when scaled to large datasets and model sizes. However, it remains unclear and subject to debate whether these models adhere to predictable scaling laws under fixed computational budgets, which is a crucial understanding for optimally allocating resources between model size, data volume, and molecular representation. In this study, we systematically investigate the scaling behavior of molecular language models across both pretraining and downstream tasks. We train 300 models and conduct over 10,000 experiments, rigorously controlling compute budgets while independently varying model size, number of training tokens, and molecular representation. Our results demonstrate clear scaling laws in molecular models for both pretraining and downstream transfer, reveal the substantial impact of molecular representation on performance, and explain previously observed inconsistencies in scaling behavior for molecular generation. Additionally, we publicly release the largest library of molecular language models to date to facilitate future research and development. Code and models are available at https://github.com/SZU-ADDG/MLM-Scaling. Scaling ; Representation (politics) ; String (physics) ; Language model ; Generative grammar ; Molecular model ; Scaling law Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Dilipkumar N. Asthagiri | Dmitry V. Matyushov
Source: arXiv (Cornell University) (preprint, 2026-01-15)
Field: Materials Science — Materials Chemistry

Rotational Memory Function of SPC/E water Memory effects are essential for dynamics of condensed materials and are responsible for non-exponential relaxation of correlation functions of dynamic variables through the memory function. Memory functions of dipole rotations for polar liquids have never been calculated. We present here calculations of memory functions for single-dipole rotations and for the overall dipole moment of the sample for SPC/E water. The memory functions for single-particle and collective dipole dynamics turn out to be nearly identical. This result validates theories of dielectric spectroscopy in terms of single-particle time correlation functions and the connection between the collective and single-particle relaxation times through the Kirkwood factor. The dielectric function in this formalism contains no new dynamic information that does not exist in the single-dipole correlation function. A short memory time, $\lesssim 1$ fs, justifies the use of rotational diffusion model to describe dynamics of a single molecular dipole moment in bulk water. Dipole ; Dielectric ; Formalism (music) ; Moment (physics) ; Relaxation (psychology) ; Dynamics (music) ; Polar ; Electric dipole moment ; Molecular dynamics Material Dynamics and Properties ; Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies
Authors: Danial Ebrahimzadeh | Safura Sharifi | Yaser 'Mike' M. Banad
Source:  (article, 2026-01-08)
Field: Materials Science — Polymers and Plastics

Machine Learning-Driven Discovery of Stable Vanadium Oxide Compositions for Aerospace Applications The demanding environments of aerospace systems require materials with exceptional properties, yet discovering thermodynamically stable, synthesizable candidates remains a critical challenge for next-generation electronics and protective coatings. We introduce an inverse-design pipeline that couples a voxel-aware variational autoencoder with aWasserstein GAN whose loss incorporates formation-energy constraints, enabling direct generation of viable vanadium-oxide (V–O) candidates from a continuous latent space. The framework encodes atomic positions and lattice parameters on 3D grids, then decodes GAN samples back to real-space structures for ab-initio vetting. Across 1,622 generated compositions, we achieve a remarkable 20% stability rate under strict thermodynamic criteria (negative formation energy and ≤ 300 meV/atom distance to the convex hull), with an additional 15% meeting a metastability threshold (≤ 500 meV/atom). Phonon dispersion calculations on selected stable compositions confirm dynamic stability, with minor imaginary modes attributable to finitesize effects rather than fundamental structural instabilities. The approach yields physically consistent structures with superior generative stability versus conventional GANs, revealing V–O compositions with potential for radiation-tolerant, low-power aerospace systems, thereby demonstrating a transformative computational pathway for developing advanced materials for space-grade electronics, thermal management systems, and radiation-shielding applications. Aerospace ; Metastability ; Thermal stability ; Stability (learning theory) ; Oxide ; Electronics ; Welding ; Structural stability Transition Metal Oxide Nanomaterials ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Maciej J. Karcz | Luca Messina | Eiji Kawasaki | Emeric Bourasseau
Source: Scientific Reports (article, 2026-03-23)
Field: Materials Science — Materials Chemistry

AI-driven data-efficient estimation of partition functions in disordered materials This work presents PULSE (Partition function Unsupervised Learning Sampling and Evaluation), a novel generative approach to efficiently estimate the partition function of disordered compounds, without requiring pre-existing training datasets and at a fraction of the computational cost compared to traditional methods. By enabling targeted calculations of atomic-scale properties, PULSE overcomes key limitations of existing sampling techniques, which either require prohibitive computational effort (Monte Carlo) or result in a partial exploration of the configuration space (Special Quasirandom Structures). The approach, based on an inverse variational autoencoder architecture, can also generate representative configuration sets tailored to specific properties, offering a powerful tool for constructing optimized datasets for training interatomic potentials. We demonstrate the capabilities of our method by computing point-defect formation energies and concentrations in uranium-plutonium mixed oxides. Furthermore, we show that the generated configurations provide physical insight into the role of local environments on defect behavior. Beyond this specific application, PULSE is broadly applicable to studying disorder-driven properties in complex materials, including mixed oxides and high-entropy alloys. Autoencoder ; Partition function (quantum field theory) ; Partition (number theory) ; Sampling (signal processing) ; Key (lock) ; Function (biology) ; Inverse ; Space (punctuation) ; Generative grammar Machine Learning in Materials Science ; Nuclear Materials and Properties ; High Entropy Alloys Studies
Authors: Utiwe Ezekiel | Enjamamul Hoq | Osama Aljarrah | Jun Li | Alfa Heryudono | Wenzhen Huang
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Physics-Informed Generative Surrogate Modeling for Full-Field Plasticity Prediction in Al/SiC Nanocomposites Nonlinear system ; Uncertainty quantification ; Scalability ; Inference ; Surrogate model ; Generative grammar ; Finite element method ; Plasticity Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Composite Material Mechanics
Authors: Mostafa M. Hamed | Mahmoud M. S. Ali | Refaat F. Aglan
Source: Water Air & Soil Pollution (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Thermal Decomposition Route for Synthesis of Pure-Phase Zirconium Dioxide Nanocrystals and their Application in the Sorption of Molybdenum Ions in Nitric Acid Solution Abstract For the first time, pure monoclinic ZrO₂ nanocrystals were successfully synthesized via a simple, one-step, and surfactant-free thermal decomposition method using a single precursor compound. In this synthesis, no chemical additives were used; only the metal–organic precursor was employed. This one-step synthesis is notable for its environmental friendliness, low cost, and scalability. Pure-monoclinic structured ZrO 2 has been synthesized in a one step and for the first time applied as an adsorbent to remove molybdenum ions from 3.0 M HNO 3 solution. The nano-oxide was characterized by XRD, SEM, TEM, FTIR, and TGA/DTA to evaluate its structure, morphology, functional groups, and thermal stability. Synthesized ZrO 2 nanocrystals have a pure-monoclinic phase with an average particle size ranging from 14 to 28 nm. To evaluate its performance in radioactive waste treatment, the sorption capacity of pure monoclinic nano-zirconium oxide (Pm-ZrO₂) was investigated for the removal of Mo(VI) from simulated high-level liquid radioactive waste (3.0 M HNO 3 ). The equilibrium sorption behavior of Mo(VI) on Pm-ZrO₂ was evaluated using Langmuir, Freundlich, and Dubinin–Radushkevich isotherm models, whereas the kinetic analysis was conducted based on pseudo-first-order, pseudo-second-order, and intraparticle diffusion models. Kinetic analysis indicated that the sorption process is best described by the pseudo-second-order model, implying that chemisorption is likely the rate-controlling mechanism. The equilibrium sorption isotherm of Mo(VI) onto Pm-ZrO₂ was best represented by the Langmuir and Dubinin–Radushkevich models, suggesting monolayer adsorption occurring on a uniform surface. The maximum equilibrium sorption capacity was found to be 66.70 mg/g, confirming the promising potential of Pm-ZrO₂ as an efficient adsorbent for Mo(VI) removal from acidic radioactive media. Graphical Abstract Sorption ; Chemisorption ; Thermal decomposition ; Adsorption ; Monoclinic crystal system ; Langmuir adsorption model ; Particle size ; Langmuir ; Nitric acid Magnesium Oxide Properties and Applications ; Environmental remediation with nanomaterials ; Extraction and Separation Processes
Authors: Fayçal Adrar | Nicolas Folastre | Chloé Pablos | Stefan Stanescu | Sufal Swaraj | Raghvender Raghvender | François Cadiou | Laurence Croguennec | Matthieu Bugnet | Arnaud Demortière
Source: Open MIND (preprint, 2026-03-08)
Field: Materials Science — Materials Chemistry

AI-Driven Phase Identification from X-ray Hyperspectral Imaging of cycled Na-ion Cathode Materials Na-ion batteries have emerged as viable candidates for large-scale energy storage applica- tions due to resource abundance and cost advantages. The constraints imposed on their performance and durability, for instance, by complex phase transformations in positive electrode materials during electrochemical cycling, can be addressed and are thus not detrimental to their development. However, diffusion-limited Na-ion transport can drive spatially heterogeneous phase nucleation and propagation, leading to multiphase coexis- tence and locally non-uniform electrochemical activity, generating complex reaction path- ways that challenge both mechanistic understanding and predictive material optimization. These challenges can be addressed by investigating single-crystalline regions of materials, i.e. down to the scale of individual particles, although such analyses are often constrained by energetically and/or spatially sparse hyperspectral datasets. Here, we developed an AI-driven method to process hyperspectral data under sparse sampling conditions and generate multiphase maps with nanometer-scale resolution over a micrometer-scale field of view. We applied this processing on scanning transmission X-ray microscopy (STXM) data to determine the distribution and coexistence of phases in individual particles of NaxV2(PO4)2F3 cathode materials, at different states of charge. The methodology relies on a workflow which combines a Gaussian mixture variational autoencoder (GMVAE) algorithm with the Pearson corre- lation coefficient to identify the sodium content and map their spatial distribution. Our approach reveals nanoscale phase heterogeneity and evolution within individual particles, and improves the reliability of phase detection by identifying ambiguity zones, false assign- ments, and transition phases localized at grain boundaries. Hyperspectral imaging ; Phase (matter) ; Sampling (signal processing) ; Pattern recognition (psychology) ; Mixture model ; Scale (ratio) ; Reliability (semiconductor) ; Gaussian process ; Gaussian ; Workflow Machine Learning in Materials Science ; Advancements in Battery Materials ; X-ray Diffraction in Crystallography