jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiean Wang | Pin Chen | Liqin Tan | Yutong Lu | Qingsong Zou
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

FUSION: Dataset Pruning via Fusing Uncertainty with Structural Information for Optimal Neural Training in Crystal Property Prediction The rapid expansion of materials databases offers unprecedented opportunities for accelerating materials discovery via machine learning. However, the widespread assumption that larger datasets inherently produce better models does not hold in practice. We propose FUSION (Fusing Uncertainty with Structural Information for Optimal Neural training), an offline dataset pruning strategy that synergistically combines uncertainty quantification with crystallographic structure analysis via geometric fingerprinting, framing dataset pruning as a discrete optimization problem. Through evaluation across 3 benchmark datasets, FUSION consistently outperforms baselines, including random pruning, uncertainty sampling, weighting factor pruning, diversity sampling, and active learning. It demonstrates robust transferability across 11 diverse architectures, outperforming random pruning by 1.91–13.65% across different datasets, with an average improvement of 6.36%. Moreover, our analysis suggests that different models exhibit varying robustness characteristics when faced with pruned training data, highlighting the importance of model selection tailored to dataset composition. We identify optimal pruning points where removing just 0–8% of training data improves model performance, yielding gains up to 12.67% in specific model–dataset combinations. These results establish a new paradigm for materials informatics that prioritizes data quality over quantity, offering a pathway toward more efficient and sustainable machine learning workflows in computational materials science. Pruning ; Robustness (evolution) ; Weighting ; Random forest ; Leverage (statistics) ; Benchmark (surveying) ; Initialization ; Workflow Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Qibo Xu | Xiaoyu Xuan | C. X. Yu | Feng Xu | Wanlin Guo | Zhuhua Zhang
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Data-Driven Description and Prediction of Two-Dimensional Materials with Negative Poisson's Ratios Nonlinear system ; Artificial neural network ; Moduli ; Sensitivity (control systems) ; Deformation (meteorology) ; Material properties ; Identification (biology) ; Smart material ; Elastic modulus 2D Materials and Applications ; MXene and MAX Phase Materials ; Boron and Carbon Nanomaterials Research
Authors: YaoKun Lei | Yi Isaac Yang
Source: arXiv (Cornell University) (preprint, 2026-03-31)
Field: Materials Science — Materials Chemistry

Electronic Collective Variables for Chemical Reactions Chemical reaction sampling critically depends on collective variables (CVs) that capture the slow degrees of freedom governing reactive transformations. However, existing reaction CVs are often defined in geometric space or learned in a system-specific manner, which limits their transferability and leaves open the more fundamental question of how reaction progress should be represented. From a physical perspective, chemical reactions are defined by electron redistribution. Here, we introduce a charge-space electronic collective variable that describes the electronic component of reaction progress in a common linear form based on atomic charges. To enable its use in enhanced sampling, atomic charges and the corresponding CV gradients are provided by a neural-network model trained on QM/MM data within an iterative sampling-training workflow. Across multiple reactions in aqueous and enzymatic environments, we show that this electronic CV can be constructed in a common charge-space form, with the corresponding coefficients assigned in a simple manner from charge differences between relevant states. Our simulations further show that reaction progress generally involves coupled electronic and conformational components, and that the same framework can also be extended to restrain side reactions. These findings support charge-based electronic CVs as a physically motivated framework for describing the electronic component of chemical reaction progress with reduced reliance on handcrafted geometric descriptors. Electronic structure ; Chemical reaction ; Degrees of freedom (physics and chemistry) ; Simple (philosophy) ; Component (thermodynamics) ; Transferability ; Chemical space ; Space (punctuation) ; Variable (mathematics) Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Ryan Jacobs | Lane E. Schultz | Dane Morgan
Source: Open MIND (preprint, 2026-01-27)
Field: Materials Science — Materials Chemistry

Kolmogorov-Arnold Networks Applied to Materials Property Prediction Kolmogorov-Arnold Networks (KANs) were proposed as an alternative to traditional neural network architectures based on multilayer perceptrons (MLP-NNs). The potential advantages of KANs over MLP-NNs, including significantly enhanced parameter efficiency and increased interpretability, make them a promising new regression model in supervised machine learning problems. We apply KANs to prediction of materials properties, focusing on a diverse set of 33 properties consisting of both experimental and calculated data. We compare the KAN results to random forest, a method that generally gives excellent performance on a wide range of properties predictions with very little optimization. The KANs were worse, on par, or better than random forest about 35%, 60%, and 5% of the time, respectively, and KANs are in practice more difficult to fit than random forest. By tuning the network architecture, we found property fits often resulted in 10-20% lower errors compared to the standard KAN, and typically gave results comparable to random forest. In the specific context of predicting reactor pressure vessel transition temperature shifts, we explored the parameter efficiency and the interpretable power of KANs by comparing predictions of simple KAN models (e.g., &lt; 50 parameters) and closed-form expressions suggested by the KAN fits to previously published deep MLP-NNs and hand-tuned models created using domain expertise of embrittlement physics. We found that simple KAN models and the resulting closed-form expressions produce prediction errors on par with established hand-tuned models with a comparable number of parameters, and required essentially no domain expertise to produce. These findings reinforce the potential applicability of KANs for machine learning in materials science and suggest that KANs should be explored as a regression model for prediction of materials properties. Random forest ; Artificial neural network ; Context (archaeology) ; Property (philosophy) ; Range (aeronautics) ; Perceptron ; Set (abstract data type) ; Regression Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Computational Drug Discovery Methods
Authors: Masanori Kodera | Kazuhiro Sayama
Source: Journal of Solid State Chemistry (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Experimental and machine learning-assisted prediction of nitridation of Ta2O5 to TaON Nitriding ; Degree (music) ; Gaussian process ; Process (computing) ; Ammonia ; Nonlinear system ; Characterization (materials science) ; Nitrogen Machine Learning in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Electrocatalysts for Energy Conversion
Authors: Ayoub Elajjani | Chaoyang Sun | Rami Farhat | Amer Baras | Yuyan Zhang | Xianyou Mo | Sun Zhihui
Source: JOM (article, 2026-02-02)
Field: Materials Science — Biomaterials

AI-Optimized Prediction of Hot-Compression Behavior of 2D-Reinforced Magnesium Alloy for Cleaner Production Scrap ; Magnesium alloy ; Cleaner production ; Energy consumption ; Metaheuristic ; Work (physics) ; Magnesium ; Predictive modelling ; Production (economics) Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Cellular and Composite Structures
Authors: Dilpreet Singh | Akshay Thakur | Kuldeep Singh
Source: Drug Delivery Letters (article, 2026-01-22)
Field: Materials Science — Biomaterials

Recent Advancements in Hierarchically Porous Polymeric Scaffolds forStem Cell Engineering and Regenerative Medicine Hierarchically porous polymer scaffolds have gained significant attention in the field of stem cell-based tissue engineering due to their ability to replicate the complex microstructure of natural extracellular matrices. These scaffolds, with pores ranging from microscale to macroscale, facilitate cellular infiltration, migration, and differentiation, making them ideal for supporting stem cell proliferation and regenerative applications. The design and fabrication of these scaffolds involve a variety of polymeric materials, such as synthetic, natural, and hybrid polymers, which are chosen based on their biodegradability, mechanical properties, and ability to support cellular activities. Additionally, the integration of biophysical cues, such as mechanical stiffness, pore size, and alignment, plays a critical role in directing stem cell behavior, including adhesion, proliferation, and differentiation. Recent advances in stimuli-responsive materials have further enhanced the functionality of these scaffolds, allowing for dynamic control of stem cell differentiation through external stimuli, like temperature, pH, and electrical or magnetic fields. Moreover, the application of computational modeling and machine learning techniques has revolutionized scaffold design by optimizing material properties and predicting cellular interactions. This review provides an in-depth analysis of the fabrication techniques, functionalization strategies, and regenerative applications of hierarchically porous polymer scaffolds, highlighting their potential in the treatment of various degenerative diseases and tissue repair. The ongoing developments in this area offer promising pathways for advancing personalized regenerative therapies. Regenerative medicine ; Scaffold ; Tissue engineering ; Stem cell ; Microscale chemistry ; Stem cell biology ; Biofabrication Electrospun Nanofibers in Biomedical Applications ; Tissue Engineering and Regenerative Medicine ; Hydrogels: synthesis, properties, applications
Authors: N Renugadevi | Raahath Shaik | Kartheek V L N Cherukuri | Ravi Kumar Kennedy
Source: Critical Reviews in Food Science and Nutrition (article, 2026-02-23)
Field: Chemistry — Analytical Chemistry

A comprehensive review of non-destructive technologies for banana ripeness assessment: implications for food quality, safety, and supply chain management Banana (<i>Musa</i> spp.) is a primary climacteric fruit characterized by a rapid surge in ethylene production and respiration post-harvest. Accurate ripeness detection is critical for supply chain management, export logistics, and reducing global food waste, which accounts for nearly 30% of harvested bananas. Traditional methods-visual inspection and destructive chemical testing [e.g., Total Soluble Solids (TSS) and Titratable Acidity (TA)]-are subjective and labor-intensive. Recent years have seen a paradigm shift toward non-destructive testing (NDT) powered by Deep Learning (DL), Computer Vision (CV), and multi-modal sensor fusion. This comprehensive review critically examines diverse methodologies employed for identifying banana ripening stages, including dielectric properties, deep learning, artificial intelligence and neural networks, image processing, laser-induced backscattering imaging, and spectroscopy. We evaluate the underlying principles, effectiveness, current limitations, and practical applicability of each approach. The review highlights the challenges associated with standardizing ripeness identification across the popular banana cultivars and environmental conditions, as well as the computational and practical hurdles of advanced technologies. Finally, we discuss emerging trends and propose future research directions, emphasizing the integration of multi-sensory data and advanced computational models for developing robust, cost-effective, and scalable solutions that enhance sustainable post-harvest management and reduce food waste. Ripeness ; Supply chain ; Identification (biology) ; Food supply ; Sustainability ; Staple food ; Postharvest Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Microwave and Dielectric Measurement Techniques
Authors: R. Sudo | Tomoya Ueno | Shogo Yamashita | Mikihiko Oogane
Source: AIP Advances (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Prediction of magnetocrystalline anisotropy constants in FeCoNi thin films using density of states features We developed an integrated dataset by combining experimentally measured magnetocrystalline anisotropy constants (K1) of FeCoNi alloy thin films with density of states (DOS) calculated from first-principles calculations without spin–orbit coupling (SOC). The DOS was transformed into statistical descriptors and directly used as features for machine learning (ML) models to predict K1. The highest predictive performance was achieved when using DOS features within approximately ± 0.1 eV of the Fermi level, yielding a sign prediction accuracy of 83%. This result demonstrates that low K1 materials can be efficiently identified from the DOS even without the SOC. Furthermore, feature importance analysis revealed that the minority-spin DOS near the Fermi level plays the dominant role in the model predictions, reflecting the fact that most alloys in the dataset are Ni–rich with fcc structures, where contributions from majority-spin DOS are negligible. Magnetocrystalline anisotropy ; Thin film ; Anisotropy ; Feature (linguistics) ; Fermi level ; Density of states ; Coupling (piping) ; Alloy Machine Learning in Materials Science ; Magnetic properties of thin films ; Heusler alloys: electronic and magnetic properties
Authors: Jinzhou Zheng | Jinrong Huo | Kai Zhang | Chaozheng He
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Predicting Crystal Structures and Calculating Properties of Copper Oxide at Different Scales Using Innovative Search Software and the First Principle Methods Stability (learning theory) ; Oxide ; Copper ; Crystal structure ; Material properties ; Crystal (programming language) ; Chemical stability ; Crystal structure prediction ; Kinetic energy ; Software Copper-based nanomaterials and applications ; Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties
Authors: Masoud Aman Mohammadi | Behrouz Tajdar-oranj | Ebrahim Esmaeili
Source: Food Control (article, 2026-03-16)
Field: Chemistry — Analytical Chemistry

Spectral-AI technologies for food safety: Advances, challenges, and future directions Emerging technologies ; Food systems ; Food supply ; Food security ; Food processing Spectroscopy and Chemometric Analyses ; Melamine detection and toxicity ; Smart Agriculture and AI
Authors: Mohammad Mostafanejad | Paul Saxe | T. Daniel Crawford
Source: ArXiv.org (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

BERTology of Molecular Property Prediction Chemical language models (CLMs) have emerged as promising competitors to popular classical machine learning models for molecular property prediction (MPP) tasks. However, an increasing number of studies have reported inconsistent and contradictory results for the performance of CLMs across various MPP benchmark tasks. In this study, we conduct and analyze hundreds of meticulously controlled experiments to systematically investigate the effects of various factors, such as dataset size, model size, and standardization, on the pre-training and fine-tuning performance of CLMs for MPP. In the absence of well-established scaling laws for encoder-only masked language models, our aim is to provide comprehensive numerical evidence and a deeper understanding of the underlying mechanisms affecting the performance of CLMs for MPP tasks, some of which appear to be entirely overlooked in the literature. Property (philosophy) ; Benchmark (surveying) ; Competitor analysis ; Scaling law ; Scaling ; Series (stratigraphy) ; Experimental data Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Joakim Brorsson | Henrik Klein Moberg | Joel Hildingsson | J. Gastaldi | Tobias Mattisson | Anders Hellman
Source: arXiv (Cornell University) (preprint, 2026-01-07)
Field: Materials Science — Materials Chemistry

Material exploration through active learning -- METAL The discovery and design of new materials are paramount in the development of green technologies. High entropy oxides represent one such group that has only been tentatively explored, mainly due to the inherent problem of navigating vast compositional spaces. Thanks to the emergence of machine learning, however, suitable tools are now readily available. Here, the task of finding oxygen carriers for chemical looping processes has been tackled by leveraging active learning-based strategies combined with first-principles calculations. High efficiency and efficacy have, moreover, been achieved by exploiting the power of recently developed machine learning interatomic potentials. Firstly, the proposed approaches were validated based on an established computational framework for identifying high entropy perovskites that can be used in chemical looping air separation and dry reforming. Chief among the insights thus gained was the identification of the best performing strategies, in the form of greedy or Thompson-based sampling based on uncertainty estimates obtained from Gaussian processes. Building on this newfound knowledge, the concept was applied to a more complex problem, namely the discovery of high entropy oxygen carriers for chemical looping oxygen uncoupling. This resulted in both qualitative as well as quantitative outcomes, including lists of specific materials with high oxygen transfer capacities and configurational entropies. Specifically, the best candidates were based on the known oxygen carrier CaMnO3 but also contained a variety of additional species, of which some, e.g., Ti; Co; Cu; and Ti, were expected while others were not, e.g., Y and Sm. The results suggest that adopting active learning approaches is critical in materials discovery, given that these methods are already shifting research practice and soon will be the norm. Entropy (arrow of time) ; Oxygen ; Turbocharger ; Task (project management) ; Variety (cybernetics) ; Gaussian ; Cross-entropy method Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; High Entropy Alloys Studies
Authors: Chikashi Shinagawa | So Takamoto | Daiki Shintani | Yong-Bin Zhuang | Yuta Tsuboi | Katsuhiko Nishimra | Kohei Shinohara | Shigeru Iwase | Yuta Tanaka | Ju Li
Source: arXiv (Cornell University) (preprint, 2026-03-09)
Field: Materials Science — Materials Chemistry

Matlantis-PFP v8: Universal Machine Learning Interatomic Potential with Better Experimental Agreements via r2SCAN Functional Universal Machine Learning Interatomic Potentials (uMLIPs) enable atomistic simulations and high-throughput screening at scales far beyond those accessible with density functional theory (DFT). However, most existing uMLIPs are trained on Perdew--Burke--Ernzerhof (PBE) generalized gradient approximation (GGA) data and are therefore fundamentally limited by PBE-level accuracy. In this paper, we argue that better zero-shot predictions versus experiments must be an explicit design target for uMLIPs and present PFP v8, a uMLIP available on the Matlantis service that overcomes the inherent limitations of the PBE functional by being trained to reproduce the regularized-restored strongly constrained and appropriately normed (r2SCAN) meta-GGA potential-energy surface across a wide range of chemical domains. Without requiring domain-specific fine-tuning, PFP v8 delivers systematically improved agreement with experimental data or high-accuracy references for crystals, molecules, and surfaces, outperforming PBE-based DFT calculations. Crucially, in long-time molecular dynamics simulations that are computationally impractical with DFT, PFP v8 predicts melting points with an average error of approximately 130 K, halving the error relative to PBE-trained models. These results establish that uMLIPs can move beyond the limitations of their training approximations and achieve substantially improved agreement with experiment across diverse chemical domains, further narrowing the gap between simulation and reality. Range (aeronautics) ; Experimental data ; Density functional theory ; Surface (topology) ; Approximation error ; Training set Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Chemical Physics Studies
Authors: Henry  Reynolds Nana Benyin Enninful | Muhammad Abdullah Khan
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Feasibility Mapping in Latent Space Reveals Transferability Limits Across Electrochemical Energy Storage Materials Software deployment ; Transfer of learning ; Dimensionality reduction ; Curse of dimensionality ; Scaling ; Artificial neural network ; Representation (politics) ; Embedding Supercapacitor Materials and Fabrication ; Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts
Authors: Jonathan Broadbent | Jiří Vymětal | Saeed Moayedpour | Michael Bailey | Saleh Riahi | Akshay Balsubramani | Peter Mikochik | Luc Even | Naresh Gunaganti | Ramesh Dasari | Saswata Karmakar | Hongfeng Deng | Vikram Agarwal | Ziv Bar-Joseph | Sven Jäger
Source: ACS Omega (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

MolQuery: Prediction of Lipid Synthesizability Using Active Learning In the field of molecular design, Generative Artificial Intelligence (GenAI) has the potential to create extensive synthetic data sets encompassing a wide range of chemical properties. However, the practical application of these data sets is often constrained by the synthesizability of the molecules within. To address this, it is essential to develop a robust platform for assessing synthesizability, which is crucial for constructing effective GenAI-based models for molecular systems. Here, we introduces MolQuery, a comprehensive pipeline that integrates active learning (AL) to improve the accuracy of chemical synthesizability predictions for lipid molecules designed for mRNA delivery via lipid nanoparticles (LNPs). By leveraging AL, MolQuery efficiently trains machine learning models using small data sets which greatly improves upon current solutions for this tasks. Our results demonstrate that MolQuery produces highly accurate predictions of lipid synthesizability, making it a valuable tool for filtering synthetic LNP data sets. Pipeline (software) ; Field (mathematics) ; Active learning (machine learning) ; Generative grammar ; Synthetic data ; Range (aeronautics) ; Generative model Machine Learning in Materials Science ; RNA Interference and Gene Delivery ; Computational Drug Discovery Methods