jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Qiuyu Shi | KANGMING LI | Yao Fehlis | RUNZE ZHANG | Daniel Persaud | Robert W. Black | Jason Hattrick‐Simpers
Source: Machine Learning Science and Technology (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Exploring the limitations of kNN noisy feature detection and recovery for self-driving labs Abstract Self-driving laboratories (SDLs) have shown promise to accelerate materials discovery by integrating machine learning with automated experimental platforms. However, errors in the capture of input parameters may corrupt the features used to model system performance, compromising current and future campaigns. This study develops an automated workflow to systematically detect noisy features, determine sample-feature pairings that can be corrected, and finally recover the correct feature values. A systematic study is then performed to examine how dataset size, noise intensity, noise type, and feature value distribution affect both the detectability and recoverability of noisy features on both density functional theory and SDL datasets. In general, high-intensity noise and large training datasets are conducive to the detection and correction of noisy features. Low-intensity noise reduces detection and recovery accuracy but can be compensated for by larger clean training data sets. Detection and correction results vary between features, with continuous and dispersed feature distributions showing greater recoverability compared to features with discrete or narrow distributions. This systematic study not only demonstrates a model-agnostic framework for rational data recovery in the presence of noise, limited data, and differing feature distributions but also provides a tangible benchmark of k-nearest neighbor imputation method in materials datasets. Ultimately, it aims to enhance data quality and experimental precision in automated materials discovery. Noise (video) ; Feature (linguistics) ; Pattern recognition (psychology) ; Robustness (evolution) ; Noisy data ; Workflow ; Benchmark (surveying) ; Training set Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Mineral Processing and Grinding
Authors: Rigoberto Advincula | Jihua Chen
Source: Open MIND (preprint, 2026-01-15)
Field: Materials Science — Materials Chemistry

Advanced Manufacturing with Renewable and Bio-based Materials: AI/ML workflows and Process Optimization Advanced manufacturing with new bio-derived materials can be achieved faster and more economically with first-principle-based artificial intelligence and machine learning (AI/ML)-derived models and process optimization. Not only is this motivated by increased industry profitability, but it can also be optimized to reduce waste generation, energy consumption, and gas emissions through additive manufacturing (AM) and AI/ML-directed self-driving laboratory (SDL) process optimization. From this perspective, the benefits of using 3D printing technology to manufacture durable, sustainable materials will enable high-value reuse and promote a better circular economy. Using AI/ML workflows at different levels, it is possible to optimize the synthesis and adaptation of new bio-derived materials with self-correcting 3D printing methods, and in-situ characterization. Working with training data and hypotheses derived from Large Language Models (LLMs) and algorithms, including ML-optimized simulation, it is possible to demonstrate more field convergence. The combination of SDL and AI/ML Workflows can be the norm for improved use of biobased and renewable materials towards advanced manufacturing. This should result in faster and better structure, composition, processing, and properties (SCPP) correlation. More agentic AI tasks, as well as supervised or unsupervised learning, can be incorporated to improve optimization protocols continuously. Deep Learning (DL), Reinforcement Learning (RL), and Deep Reinforcement Learning (DRL) with Deep Neural Networks (DNNs) can be applied to more generative AI directions in both AM and SDL, with bio-based materials. Workflow ; Reinforcement learning ; Reuse ; Process (computing) ; Artificial neural network ; Renewable energy ; Deep learning Machine Learning in Materials Science ; Additive Manufacturing and 3D Printing Technologies ; 3D Printing in Biomedical Research
Authors: Shreshth A. Malik | Tiarnan A. S. Doherty | Panagiotis Tigas | Muhammed Razzak | Pu Pu | A. T. Walsh | Yarin Gal
Source: Open MIND (preprint, 2026-01-28)
Field: Materials Science — Materials Chemistry

MADE: Benchmark Environments for Closed-Loop Materials Discovery Existing benchmarks for computational materials discovery primarily evaluate static predictive tasks or isolated computational sub-tasks. While valuable, these evaluations neglect the inherently iterative and adaptive nature of scientific discovery. We introduce MAterials Discovery Environments (MADE), a novel framework for benchmarking end-to-end autonomous materials discovery pipelines. MADE simulates closed-loop discovery campaigns in which an agent or algorithm proposes, evaluates, and refines candidate materials under a constrained oracle budget, capturing the sequential and resource-limited nature of real discovery workflows. We formalize discovery as a search for thermodynamically stable compounds relative to a given convex hull, and evaluate efficacy and efficiency via comparison to baseline algorithms. The framework is flexible; users can compose discovery agents from interchangeable components such as generative models, filters, and planners, enabling the study of arbitrary workflows ranging from fixed pipelines to fully agentic systems with tool use and adaptive decision making. We demonstrate this by conducting systematic experiments across a family of systems, enabling ablation of components in discovery pipelines, and comparison of how methods scale with system complexity. Benchmark (surveying) ; Workflow ; Oracle ; Benchmarking ; Knowledge extraction ; Drug discovery ; Service discovery ; Scale (ratio) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Shreshth A. Malik | Tiarnan A. S. Doherty | Panagiotis Tigas | Muhammed Razzak | Pu Pu | A. T. Walsh | Yarin Gal
Source: ArXiv.org (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

MADE: Benchmark Environments for Closed-Loop Materials Discovery Existing benchmarks for computational materials discovery primarily evaluate static predictive tasks or isolated computational sub-tasks. While valuable, these evaluations neglect the inherently iterative and adaptive nature of scientific discovery. We introduce MAterials Discovery Environments (MADE), a novel framework for benchmarking end-to-end autonomous materials discovery pipelines. MADE simulates closed-loop discovery campaigns in which an agent or algorithm proposes, evaluates, and refines candidate materials under a constrained oracle budget, capturing the sequential and resource-limited nature of real discovery workflows. We formalize discovery as a search for thermodynamically stable compounds relative to a given convex hull, and evaluate efficacy and efficiency via comparison to baseline algorithms. The framework is flexible; users can compose discovery agents from interchangeable components such as generative models, filters, and planners, enabling the study of arbitrary workflows ranging from fixed pipelines to fully agentic systems with tool use and adaptive decision making. We demonstrate this by conducting systematic experiments across a family of systems, enabling ablation of components in discovery pipelines, and comparison of how methods scale with system complexity. Benchmark (surveying) ; Workflow ; Oracle ; Benchmarking ; Knowledge extraction ; Drug discovery ; Service discovery ; Scale (ratio) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Turab Lookman | YuJie Liu | Zhibin Gao
Source: arXiv (Cornell University) (article, 2026-01-02)
Field: Materials Science — Materials Chemistry

Materials Informatics: Emergence To Autonomous Discovery In The Age Of AI This perspective explores the evolution of materials informatics, from its foundational roots in physics and information theory to its maturation through artificial intelligence (AI). We trace the field's trajectory from early milestones to the transformative impact of the Materials Genome Initiative and the recent advent of large language models (LLMs). Rather than a mere toolkit, we present materials informatics as an evolving ecosystem, reviewing key methodologies such as Bayesian Optimization, Reinforcement Learning, and Transformers that drive inverse design and autonomous self-driving laboratories. We specifically address the practical challenges of LLM integration, comparing specialist versus generalist models and discussing solutions for uncertainty quantification. Looking forward, we assess the transition of AI from a predictive tool to a collaborative research partner. By leveraging active learning and retrieval-augmented generation (RAG), the field is moving toward a new era of autonomous materials science, increasingly characterized by "human-out-of-the-loop" discovery processes. Transformative learning ; Field (mathematics) ; Informatics ; Perspective (graphical) ; Generative grammar ; Scientific discovery ; Reinforcement learning Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Block Copolymer Self-Assembly
Authors: Turab Lookman | YuJie Liu | Zhibin Gao
Source: arXiv (Cornell University) (preprint, 2026-01-02)
Field: Materials Science — Materials Chemistry

Materials Informatics: Emergence To Autonomous Discovery In The Age Of AI This perspective explores the evolution of materials informatics, from its foundational roots in physics and information theory to its maturation through artificial intelligence (AI). We trace the field's trajectory from early milestones to the transformative impact of the Materials Genome Initiative and the recent advent of large language models (LLMs). Rather than a mere toolkit, we present materials informatics as an evolving ecosystem, reviewing key methodologies such as Bayesian Optimization, Reinforcement Learning, and Transformers that drive inverse design and autonomous self-driving laboratories. We specifically address the practical challenges of LLM integration, comparing specialist versus generalist models and discussing solutions for uncertainty quantification. Looking forward, we assess the transition of AI from a predictive tool to a collaborative research partner. By leveraging active learning and retrieval-augmented generation (RAG), the field is moving toward a new era of autonomous materials science, increasingly characterized by "human-out-of-the-loop" discovery processes. Transformative learning ; Field (mathematics) ; Informatics ; Perspective (graphical) ; Generative grammar ; Scientific discovery ; Reinforcement learning Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Block Copolymer Self-Assembly
Authors: Lena Angelika Mittmann | Hampus Näsström | Eugene P. Bertin | Sarthak Kapoor | Inês Diogo | Anat Itzhak | Giulia Dalmonte | Rasmus Thorup Danielsen | Malthe Emil Skytte | Foster | Andrea Crovetto | José A Márquez
Source: ChemRxiv (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Digital infrastructure to support daily high-throughput experimental materials research High-throughput and autonomous experimentation generate complex, heterogeneous datasets whose reuse, large-scale analysis, and linking to data from first-principles calculations remain challenging. Here, we report on an operational deployment of the open-source NOMAD Oasis data infrastructure as a digital backbone for daily high-throughput experimental work in a materials discovery group at DTU Nanolab. The customized platform supports the complete experimental workflow in a thin-film laboratory, linking physical inventory items, automated synthesis logs, high-throughput characterization data, and cloud-based analysis within a unified data model. The platform preserves full sample provenance across multi-step workflows and uses unified sample coordinates to enable direct cross-technique correlation. We demonstrate the capabilities of the platform on a combined experimental-computational project centered on phosphosulfide thin-film libraries. The data consists of 12,000 experimental samples acquired during routine operation over 18 months, complemented by more than 800 first-principles calculations - all managed in a unified data platform. We show how an open, extensible data infrastructure system supports reproducible workflows, scalable analysis, and long-term data reuse. Workflow ; Software deployment ; Scalability ; Sample (material) ; Experimental data ; Extensibility ; Work (physics) ; Data modeling Machine Learning in Materials Science ; Scientific Computing and Data Management ; Catalysis and Oxidation Reactions
Authors: Chuntian Cao | Hyeong Jin Kim | Matthew R. Carbone | Kristofer Reyes | Shinjae Yoo | Fang Lu | Yugang Zhang
Source: Photon Science (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

Uncertainty-Aware Machine Learning for Small-Angle X-ray Scattering Analysis in Autonomous Experimentation Small-angle X-ray scattering (SAXS) is a powerful high-throughput characterization tool for probing nanoscale structure in native sample environments, providing real-time morphological information such as nanoparticle size and shape during synthesis. However, automated SAXS data analysis for extracting meaningful structural parameters is non-trivial and remains a bottleneck in closed-loop experimentation towards autonomous materials discovery, which demands fast, reliable, and uncertainty-aware data analysis. Here, we develop a machine-learning approach for automated SAXS analysis tailored to closed-loop nanoparticle synthesis. A Random Forest (RF) regression model is trained on 100,000 synthetic SAXS curves generated from polydisperse spherical nanoparticles with realistic background contributions. Using normalized one-dimensional SAXS intensity profiles as input, the RF model directly predicts nanoparticle radius, size polydispersity, and background parameters, while the ensemble standard deviation across trees provides built-in uncertainty quantification (UQ). On synthetic data, we show that combining fit-quality metrics (R2, MAE) with thresholds on prediction uncertainty reliably identifies accurate parameter estimates without access to ground truth. We then apply the trained model to 365 experimental SAXS profiles of citrate-reduced gold nanoparticles synthesized using an automated droplet-flow microreactor with in situ SAXS at a synchrotron beamline, classifying the results into high- and low-confidence subsets based on UQ metrics. Finally, we integrate RF-based SAXS analysis into a simulated closed-loop optimization campaign using Gaussian process Bayesian optimization to minimize nanoparticle polydispersity, benchmarking against conventional automated Levenberg–Marquardt fitting. The RF-guided campaign exhibits substantially faster convergence and lower relative opportunity cost (∼0.07 vs ∼0.3), demonstrating that uncertainty-aware machine-learning SAXS analysis significantly enhances the efficiency and robustness of autonomous nanomaterials synthesis workflows. Small-angle X-ray scattering ; Kriging ; Robustness (evolution) ; Gaussian process ; Scattering ; Random forest ; Characterization (materials science) ; Ensemble learning Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Computational Drug Discovery Methods
Authors: Min Peng | Yuanjun Tang | De Dong | Yang Zhang | Cheng Wang | Shulin Jiao | Xiaotong Ma | Shichao Zhang | Jingchen Wang | Huiying Wang | Yongxin Zhang | Huiping Zhu | Yue-Wen Fang | Fan Zhang | Zhenping Wu
Source: arXiv (Cornell University) (article, 2026-03-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Small-Data Machine Learning Uncovers Decoupled Control Mechanisms of Crystallinity and Surface Morphology in $β$-Ga2O3 Epitaxy The ultrawide-bandgap semiconductor $β$-Ga2O3 holds exceptional promise for next-generation power electronics and deep-ultraviolet optoelectronics, yet its widespread application is hindered by the lack of cost-effective, high-quality heteroepitaxial thin films. Here, we demonstrate an interpretable machine learning framework that efficiently navigates the complex, multiparameter process space of pulsed laser deposition (PLD) to achieve high-crystallinity $β$-Ga2O3 epitaxy on c-plane sapphire. By systematically benchmarking nine regression algorithms under limited experimental data conditions, we identify quadratic polynomial ridge regression as the optimal surrogate model, which combines predictive accuracy (R$^2$ $\approx$ 0.86) with full physical transparency through explicit analytical coefficients. Coupling this model with SHAP (SHapley Additive exPlanations) analysis and iterative experimental design, we construct a closed-loop optimization workflow that progressively refines the process-performance landscape over only three experimental rounds. This data-efficient strategy reduces the X-ray rocking curve (RC) full-width at half-maximum (FWHM) by 70$\%$ from > 3$^{\circ}$ to 0.92$^{\circ}$, which is the best reported value for PLD-grown $β$-Ga2O3 on sapphire. Intriguingly, concurrent modeling of surface roughness reveals that crystalline quality and surface morphology are governed by distinct dominant factors: temperature primarily controls bulk crystallinity, whereas oxygen pressure dictates surface kinetics. This decoupled mechanism, quantitatively captured for the first time via feature importance analysis, provides actionable physical insight for independent optimization of structural and morphological properties. Our work establishes a generalizable, resource-efficient paradigm for intelligent process development in oxide epitaxy and beyond. Interpretability ; Epitaxy ; Surface roughness ; Surface (topology) ; Dimensionality reduction ; Polynomial regression ; Cadmium telluride photovoltaics ; Workflow ; Thin film ; Kriging Ga2O3 and related materials ; Machine Learning in Materials Science ; Artificial Intelligence in Healthcare and Education
Authors: Jiaxin Chen | Tianjiao Wan | Hui Geng | Liang Xiong | Guohong Wang | Yihan Zhao | Linfeng Deng | Gao, Zijian | Fang, Susu | Luo, Zheng | Shanshan Wang | Wang, Shanshan | Kele Xu
Source: ArXiv.org (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Data-driven active learning approaches for accelerating materials discovery Materials discovery is a cornerstone of modern technological advancement, yet it remains constrained by traditional trial-and-error paradigms and the inherent bias of human intuition. Artificial intelligence (AI) has emerged as a transformative tool in materials science by effectively modeling structure-property relationships. Despite substantial efforts to enhance model expressiveness, data efficiency remains an equally critical challenge, given the limited availability of experimental and computational resources. Active learning (AL), as a data-driven machine learning paradigm, has shown great promise for discovering novel materials and enabling the efficient navigation of vast materials spaces. In this review, we follow the evolution of sampling strategy design techniques in AL, from Bayesian optimization to advanced deep learning-based strategies. We then highlight how AL enhances data efficiency across various data regimes, ranging from task-specific settings with limited data to the development of general-purpose datasets and large-scale models. We further provide a systematic overview of AL applications throughout the materials research pipeline, including computational simulation, composition and structural design, process optimization, and self-driving laboratory systems. Finally, we pinpoint key challenges and future perspectives of AL in materials discovery. Bayesian optimization ; Process (computing) ; Transformative learning ; Key (lock) ; Active learning (machine learning) ; Cornerstone ; Deep learning Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks
Authors: Abdulhalim Musa Abubakar | Mahlon Kida Marvin
Source: The Canadian Journal of Chemical Engineering (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

Computational intelligence approaches for hydrogen storage material design Abstract Hydrogen is increasingly recognized as a clean energy carrier in the transition toward low‐carbon and sustainable energy systems. However, the development of safe, efficient, and cost‐effective hydrogen storage technologies remains a major barrier to its large‐scale deployment. This paper presents a review of how advanced computational intelligence encompassing machine learning (ML), deep learning (DL), high‐throughput screening, and multi‐scale modelling is transforming the discovery, design, and optimization of hydrogen storage materials. The integration of graph neural networks (GNNs), inverse material design, and Bayesian optimization is enabling accelerated screening and performance prediction across diverse material classes such as metal hydrides, complex hydrides, porous frameworks, and carbon nanostructures. Materials informatics platforms and autonomous discovery pipelines, including self‐driving laboratories and active learning workflows, are further streamlining the experimental validation of computationally designed materials. In addition, the application of quantum ML and multi‐fidelity modelling is improving accuracy while reducing simulation costs. Through a series of case study reviews, this paper demonstrates the critical role of these tools in enhancing thermodynamic tuning, adsorption capacity, and kinetic response in next‐generation hydrogen storage systems. This review study highlight that computational intelligence is central to the hydrogen materials innovation cycle. Bayesian optimization ; Materials informatics ; Hydrogen storage ; Computational model ; Artificial neural network ; Computational intelligence ; Bayesian network ; Energy carrier Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion