jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jeffrey Hu | Rongzhi Dong | Ying Feng | Ming Hu | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2026-03-13)
Field: Materials Science — Materials Chemistry

Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discovery Active learning (AL) has emerged as a powerful paradigm for accelerating materials discovery by iteratively steering experiments toward promising candidates, reducing the number of costly synthesis-and-characterization cycles needed to identify optimal materials. However, current AL relies predominantly on Gaussian Process (GP) and Random Forest (RF) surrogates, which suffer from complementary limitations: GP underfits complex composition-property landscapes due to rigid kernel assumptions, while RF produces unreliable heuristic uncertainty estimates in small-data regimes. This small-data challenge is pervasive in materials science, making reliable surrogate modeling extremely difficult with models trained from scratch on each new dataset. Here we propose In-Context Active Learning (ICAL), which addresses this bottleneck by replacing conventional surrogates with TabPFN, a transformer-based foundation model (FM) pre-trained on millions of synthetic regression tasks to meta-learn a universal prior over tabular data, upon which TabPFN performs principled Bayesian inference in a single forward pass without dataset-specific retraining, delivering strong small-data regression performance and well-calibrated predictive uncertainty (required for effective AL). We benchmark ICAL against GP and RF across 10 materials datasets and TabPFN wins on 8 out of 10 datasets, achieving a mean saving of 52% in extra evaluations relative to GP and 29.77% relative to RF. Cross-validation analysis confirms that TabPFN's advantage stems from superior uncertainty calibration, achieving the lowest Negative Log-Likelihood and Area Under the Sparsification Error curve among all surrogates. These results demonstrate that pre-trained FMs can serve as effective surrogates for active learning, enabling data-efficient discovery across diverse materials systems and small-data experimental sciences. Gaussian process ; Bayesian inference ; Active learning (machine learning) ; Random forest ; Inference ; Bayesian probability ; Foundation (evidence) ; Bayesian optimization Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Domain Adaptation and Few-Shot Learning
Authors: Joakim Brorsson | Henrik Klein Moberg | J. Gastaldi | Joel Hildingsson | Tobias Mattisson | Anders Hellman
Source: Materials Today Energy (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Discovery of high-entropy perovskite oxygen carriers for chemical looping applications via an autonomous active learning protocol The discovery and design of new materials are paramount for advancing green technologies. High-entropy oxides represent one such group that has been only tentatively explored, mainly due to the inherent problem of navigating vast compositional spaces. Here, oxygen carriers for chemical looping processes have been identified using active learning-based strategies and first-principles-informed calculations. The proposed approaches were validated using an established computational framework for identifying high-entropy perovskites suitable for chemical looping air separation and dry reforming. The central insight gained was the identification of effective strategies, including greedy and Thompson-based sampling, informed by uncertainty estimates from Gaussian processes. Building on this knowledge, the concept was applied to the challenge of discovering high-entropy oxygen carriers for chemical-looping oxygen uncoupling. This resulted in both qualitative and quantitative outcomes, including lists of materials with high oxygen transfer capacities and configurational entropies. The top candidates were based on the known oxygen carrier CaMnO 3 and included expected elements such as titanium, cobalt, and copper, as well as unexpected ones such as yttrium and samarium. The results suggest that adopting active learning approaches is critical for materials discovery, as these methods are already reshaping research practice and will soon become the norm. • Demonstration of an active-learning-based strategy for material discovery based on first-principles-informed calculations. • The approach is proven to be efficient for finding oxygen carriers for chemical looping applications. • The process is validated and thoroughly tested for dry reforming and chemical looping air separation, while also yielding a wide range of potential candidates. • More detailed calculations lead to the identification of a multitude of high-entropy oxygen carriers for chemical oxygen uncoupling. Chemical looping combustion ; Oxygen ; Perovskite (structure) ; Solid oxygen ; Process (computing) ; Protocol (science) Advancements in Solid Oxide Fuel Cells ; Chemical Looping and Thermochemical Processes ; High-Temperature Coating Behaviors
Authors: Guihua Luo | Jianxiang Zhu | Runqiu Shen | Yan Rockee Zhang | Weike Su | An Su
Source: Journal of Industrial and Engineering Chemistry (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Meta-learning accelerates multi-objective Bayesian optimization of chemical reaction: a monoacylation case study Bayesian optimization ; Bayesian probability ; Optimization problem ; Optimization algorithm ; Bayesian inference Machine Learning in Materials Science ; Machine Learning and Data Classification ; Advanced Multi-Objective Optimization Algorithms
Authors: Joakim Brorsson | Henrik Klein Moberg | Joel Hildingsson | J. Gastaldi | Tobias Mattisson | Anders Hellman
Source: ACS Materials Au (article, 2026-01-22)
Field: Materials Science — Materials Chemistry

Data-Efficient Design of High-Entropy Oxygen Carriers for Chemical Looping Using Active Learning High-entropy materials, first demonstrated in metallic alloys and later extended to oxides and other systems, unlock a vast compositional space with properties suited for catalysis, energy, and structural materials. However, the high compositional complexity makes systematic exploration challenging, and only a small portion of the design space has been studied. To address this, we introduce an active learning strategy that integrates predictive modeling, uncertainty estimation, and iterative sampling to efficiently navigate embedded compositional material spaces. This approach continuously learns from previous evaluations, focusing subsequent searches on the most promising regions while reducing both time and data requirements. We demonstrate this methodology in the search for high-entropy oxygen carriers for chemical looping, where it rapidly accelerates discovery and identifies promising candidates more effectively than conventional trial-and-error or grid-search approaches. Importantly, this strategy is general and well-suited to exploring the vast space of multicomponent materials. Active learning (machine learning) ; Chemical space ; Space (punctuation) ; Key (lock) ; Chemical looping combustion Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: C. J. A. P. Martins | Daniela Andrade Damasceno | Keat Yung Hue | C. Miranda | Erich A. Müller | Rodrigo A. Vargas–Hernández
Source: Spiral (Imperial College London) (article, 2026-01-02)
Field: Materials Science — Materials Chemistry

Bayesian optimization for high-dimensional coarse-grained model parameterization: a case study on Pebax polymer Coarse-grained (CG) force field models are extensively utilized in material simulations because of their scalability. Ordinarily, these models are parameterized using hybrid strategies that sequentially integrate top-down and bottom-up approaches. However, this combination restricts the capacity to jointly optimize all parameters. Although Bayesian optimization (BO) has been explored as an alternative search strategy to identify well-optimized CG parameters, its application has conventionally been limited to low-dimensional scenarios. This has contributed to the assumption that BO is unsuitable for more complex CG models, which often involve a large number of parameters. In this study, we challenge this assumption by successfully extending BO, using the tree-structured Parzen estimator (TPE) model, to optimize a high-dimensional CG model. Specifically, we show that a 41-parameter CG model of Pebax-1657, a copolymer composed of alternating polyamide and polyether segments, can be effectively parameterized using BO, resulting in a model that accurately reproduces the key physical properties of its parent atomistic representation. Our optimization framework simultaneously targets structural and thermodynamic properties, namely density, radius of gyration, and glass transition temperature. Compared to traditional search algorithms, BO-TPE not only converges faster but also delivers consistent improvements over more standard parametrization approaches. Bayesian optimization ; Parameterized complexity ; Parametrization (atmospheric modeling) ; Bayesian probability ; Estimator ; Field (mathematics) ; Key (lock) ; Force field (fiction) Block Copolymer Self-Assembly ; Composite Material Mechanics ; Rheology and Fluid Dynamics Studies
Authors: Alexander Pletl | Roman-David Kulko | Andreas Hanus | Benedikt Elser
Source:  (article, 2026-01-01)
Field: Chemistry — Analytical Chemistry

Data-Efficient Polymer Classification Using Spectra Simulation and Bayesian Optimization Plastic recycling represents an essential element of strategies aimed at lowering global carbon emissions while supporting a circular plastics economy. However, the effectiveness of current plastic sorting systems remains limited by data scarcity, spectral variability, and the complexity of real world waste streams. This study introduces a CNN-based polymer classification framework that integrates physics-informed spectral simulation, adaptive data augmentation, and Bayesian hyperparameter optimization to enable robust classification under data limited conditions. Our framework combines near-infrared (NIR) spectral data from technical scale measurements with synthetically generated spectra. With only 100 measured spectra per polymer, the proposed framework achieves average balanced accuracies of 0.9739 in multi-target polymer classification tasks. By using technical scale spectral data, this study bridges the gap between laboratory model development and real sorting conditions. Hyperparameter ; Sorting ; Bayesian probability ; Bayesian optimization ; Scale (ratio) ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Microplastics and Plastic Pollution ; Gaussian Processes and Bayesian Inference
Authors: Alexander Pletl | Roman-David Kulko | Andreas Hanus | Benedikt Elser
Source: Recycling (article, 2026-02-03)
Field: Chemistry — Analytical Chemistry

Data-Efficient Polymer Classification Using Spectra Simulation and Bayesian Optimization Plastic recycling represents an essential element of strategies aimed at lowering global carbon emissions while supporting a circular plastics economy. However, the effectiveness of current plastic sorting systems remains limited by data scarcity, spectral variability, and the complexity of real world waste streams. This study introduces a CNN-based polymer classification framework that integrates physics-informed spectral simulation, adaptive data augmentation, and Bayesian hyperparameter optimization to enable robust classification under data limited conditions. Our framework combines near-infrared (NIR) spectral data from technical scale measurements with synthetically generated spectra. With only 100 measured spectra per polymer, the proposed framework achieves average balanced accuracies of 0.9739 in multi-target polymer classification tasks. By using technical scale spectral data, this study bridges the gap between laboratory model development and real sorting conditions. Hyperparameter ; Sorting ; Bayesian probability ; Bayesian optimization ; Scale (ratio) ; Pattern recognition (psychology) ; Statistical classification Spectroscopy and Chemometric Analyses ; Microplastics and Plastic Pollution ; Machine Learning in Materials Science
Authors: Zhongjie Xie | Jiaze Wang | Feijian Chen | Yi Li
Source: Chemical Research in Chinese Universities (article, 2026-03-24)
Field: Chemistry — Inorganic Chemistry

Enhancing Reproducibility in Zeolite Synthesis via Active Learning Reproducibility ; Zeolite ; Active learning (machine learning) ; Active carbon Zeolite Catalysis and Synthesis ; Nanopore and Nanochannel Transport Studies ; Machine Learning in Materials Science
Authors: Kelun Liu | Yingchao Hai | Ying Ruan | Bingbo Wei
Source: Computational Materials Science (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Surface-informed active learning prediction of thermophysical properties for liquid refractory multicomponent alloy Alloy ; Refractory (planetary science) ; Active learning (machine learning) ; Specific heat ; Work (physics) Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Model Reduction and Neural Networks
Authors: Jeongon Eom | Sewon Kim | Seokjun Hong | Chang Je Park
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Uncertainty-Aware Superpixel Segmentation and Bayesian Optimization for Sampling Strategy Development Using Activation Analysis of OPR-1000 Sampling (signal processing) ; Bayesian probability ; Uncertainty quantification ; Bayesian optimization ; Curse of dimensionality ; Gaussian process ; Pooling ; Uncertainty analysis ; Probabilistic logic ; Adaptive sampling Graphite, nuclear technology, radiation studies ; Nuclear reactor physics and engineering ; Radiation Shielding Materials Analysis
Authors: Tianhong Tan | Ting-Yeh Chen | Jacob R. Breese | Lisa M. Hall | Joel A. Paulson
Source: The Journal of Chemical Physics (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

MD-BAX: A general-purpose Bayesian design framework for molecular dynamics simulations with input-dependent noise Molecular dynamics (MD) simulations are a powerful tool for understanding complex molecular behavior, but exhaustively exploring the large space of input parameters can be computationally prohibitive, especially when the outcomes are noisy and/or expensive to evaluate. In this work, we introduce MD-Bayesian algorithm execution (BAX), a general-purpose, automated design framework that builds on BAX acquisition strategy to efficiently guide simulation campaigns toward learning meaningful features of the system. Unlike optimization-centric Bayesian optimization approaches, MD-BAX seeks to identify broader system properties (e.g., phase transition boundaries, level sets, and threshold crossings) by strategically selecting input/parameter settings based on uncertainty. To accurately represent the variability in simulation outcomes, MD-BAX incorporates a Gaussian process surrogate model with input-dependent noise, estimated directly from MD trajectory statistics at each simulation setting. This enables construction of reliable uncertainty estimates for guiding the next simulation. We demonstrate the approach on a case study involving coil-to-globule transitions in amphiphilic block copolymers, highlighting that explicitly including trajectory-derived noise improves uncertainty calibration and enables our framework to more efficiently map the relationship between polymer structure, solvent quality, and conformational behavior. MD-BAX represents a domain-informed specialization of the BAX framework for MD and is broadly applicable to molecular modeling problems where the goal is to infer key system behaviors from stochastic, trajectory-based simulation outputs rather than to locate a single optimal condition. Noise (video) ; Bayesian optimization ; Bayesian probability ; Process (computing) ; Trajectory ; Molecular dynamics ; Gaussian process ; Key (lock) ; Calibration Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Rheology and Fluid Dynamics Studies
Authors: Alex Berlaga | Renyu Zheng | Zeqian Zhang | J Lee | Diya Gandhi | Chun‐Long Chen | N. Fazzini
Source: Macromolecular Rapid Communications (article, 2026-02-24)
Field: Materials Science — Biomaterials

Data‐Driven Engineering of Thermostable Collagen‐Mimetic Peptoid Triple Helices Collagen-mimetic peptides (CMPs) are engineered molecules designed to replicate the triple-helical structure of natural collagen. A repeating x-y-Gly sequence is the defining motif of CMPs and is critical to their triple-helical structure and stability. Substitutions to the residues occupying the x and y positions present a means to modulate the CMP structure and properties. Peptoid residues-N-substituted glycine derivatives-present an attractive potential substitution due to their thermal stability, proteolytic resistance, biocompatibility, and diverse palette of non-natural side chains, but also tend to introduce a high degree of backbone flexibility that can diminish the stability of the triple helix. In this work, we report a computational active learning cycle comprising molecular dynamics simulation, Gaussian process regression, and Bayesian optimization to computationally identify a number of promising peptoid substitutions predicted to stabilize the desired quaternary structure through side chain interactions and produce stable peptoid-based collagen-like triple helices. To experimentally test the computational predictions, a top candidate identified by the screen was synthesized and imaged using scanning electron microscopy to resolve fibril-like bundles consistent with collagen-like triple helices. This work predicts a number of CMP peptoid substitutions capable of forming stable triple-helical structures, presents a generalizable design strategy for engineering desired peptoid structures, and opens new avenues for the design of peptoid-based biomimetic materials. Peptoid ; Side chain ; Cheminformatics ; Protein design ; Protein engineering ; Molecular dynamics ; Thermal stability Collagen: Extraction and Characterization ; Chemical Synthesis and Analysis ; Protein Hydrolysis and Bioactive Peptides
Authors: Kang Liang | Xinke Qi | Xu Xiao | Li Wang | Jiujun Zhang
Source: Chemical Science (article, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Correction: Chemically-informed active learning enables data-efficient multi-objective optimization of self-healing polyurethanes [This corrects the article DOI: 10.1039/D5SC07752D.]. Active learning (machine learning) Polymer composites and self-healing ; Machine Learning in Materials Science ; Hydrogels: synthesis, properties, applications
Authors: Francesco Aiello | Jian Zhang | J.C. Brouwer | Michele Cassetta | Mauro Salazar | Diletta Giuntini
Source: Acta Materialia (article, 2026-01-13)
Field: Materials Science — Biomaterials

Double-tough and ultra-strong ceramics: Leveraging multiscale toughening mechanisms through Bayesian optimization An optimization-driven approach is presented to create a “double-tough” ceramic. The material features two main toughening mechanisms - crack deflection in a brick-and-mortar microstructure, and transformation toughening in the mortar - and it is engineered to achieve high strength and fracture toughness levels simultaneously. The material design involves high-strength alumina bricks interconnected via a ceria-stabilized zirconia mortar. Given that the design of the optimal material, featuring multiscale toughening mechanisms, typically requires a laborious trial-and-error approach, a Bayesian optimization framework is proposed to streamline and accelerate the experimental campaign. A Gaussian process is used to emulate the material’s mechanical response, and a cost-aware batch Bayesian optimization is implemented to efficiently identify optimal design process parameters, accounting for the cost of experimentally varying them. This approach expedites the optimization of the material’s mechanical properties. As a result, a bio-inspired all-ceramic composite is developed, exhibiting an exceptional balance between bending strength ( 704 MPa ) and fracture toughness ( 13 . 6 M P a m 0 . 5 ), along with a stress intensity factor at crack initiation of 6 . 7 M P a m 0 . 5 . The material exhibits significantly higher strength than both nacre-like ceramic composites and transformation-toughened zirconia at comparable toughness levels. • Created an all-ceramic material achieving high strength and toughness. • Integrated nacre-like and phase transformation toughening mechanisms. • Applied Bayesian optimization to guide experimental design. • Provided a general framework for materials optimization and design. Fracture toughness ; Toughness ; Deflection (physics) ; Bayesian optimization ; Ceramic ; Toughening ; Cubic zirconia ; Design of experiments ; Material Design Calcium Carbonate Crystallization and Inhibition ; Advanced ceramic materials synthesis ; Machine Learning in Materials Science
Authors: Francesco Aiello | Zhang Jian | J.C. Brouwer | Michele Cassetta | Mauro Salazar | Diletta Giuntini
Source: TUHH Open Research (article, 2026-01-13)
Field: Materials Science — Ceramics and Composites

Double-tough and ultra-strong ceramics: leveraging multiscale toughening mechanisms through Bayesian optimization Fracture toughness ; Deflection (physics) ; Toughness ; Toughening ; Bayesian optimization ; Ceramic ; Cubic zirconia ; Design of experiments ; Stress intensity factor Advanced ceramic materials synthesis ; Composite Material Mechanics ; Calcium Carbonate Crystallization and Inhibition
Authors: Feiyue Lu | Kang An | Qin Wang | Zhipeng Yin | Xinhui Liu | Jialin Wu | Xiaolan Kang | Hongyu Zhang | Xin Zhang | Wei Meng | Lei Ying | Liang Gao | Jiaping Lin | Na Li
Source: Research Square (preprint, 2026-03-27)
Field: Materials Science — Materials Chemistry

Multi-objective AI-driven optimization guides the discovery of high-performance organic photovoltaics Workflow ; Organic solar cell ; Scalability ; Bayesian optimization ; Process (computing) ; Photovoltaics ; Identification (biology) ; Fabrication Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Nanowire Synthesis and Applications