jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sergei V. Kalinin | Boris N. Slautin | Yu Liu | Mahshid Ahmadi | Kamyar Barakati | N. M. Anoop Krishnan | Heng Ji | Charles Qing Cao | Raymundo Arroyave
Source: APL Machine Learning (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Bringing ML to the real world: Rewards are all we need Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision-making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that the performance measure can be exactly and correctly specified in the form of a utility or reward function” [S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach (Pearson, 2020)]. Once the reward function is known, any problem can be formulated as an optimization or search problem—the areas well explored within the AI community. However, while the long-term objectives of a specific activity are often well defined, constructing short-term rewards that remain aligned with those goals and consistent with real-world constraints remains a major unresolved challenge. Such alignment has been achieved in domains such as chess, Go, and supervised machine learning problems, where objectives are well defined and easily simulated. However, no universal solution exists for defining intermediate rewards for complex, evolving scientific goals, which remains an open challenge. Correspondingly, the key to operationalizing automated instruments, integrating multi-instrument self-driving laboratories, and building geographically distributed research facilities is to generate experiment-aligned, probabilistic, domain-specific reward functions. These rewards must be consistent with long-term experimental objectives while remaining actionable on the timescales of decision-making on laboratory tools in microscopy and materials synthesis labs—proper reward definition operationalizes scientific intent. Here, we review the extant reward structures in the physical sciences and summarize opportunities for reward design informed by physical principles, human heuristics, and LLM-based reasoning. Operationalization ; Extant taxon ; Function (biology) ; Key (lock) ; Measure (data warehouse) ; Human intelligence ; Crowdsourcing Machine Learning in Materials Science ; Scientific Computing and Data Management ; Cell Image Analysis Techniques
Authors: Yihang Chen | Zhongwei Yu | Yang Li | Xingyu Lu | Qiang Li | Hongxia Yu | Jun Wang
Source:  (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

Position: Autonomous Scientific Discovery Needs Embodied Experimentation with Learnability Autonomous scientific discovery, particularly through self-driving laboratories (SDLs) integrated with large language models, has achieved breakthroughs in materials science, drug design, and beyond. However, current implementations exhibit a critical limitation: cognitive parameters remain immutable post-training, motor behaviours adhere to pre-programmed sequences, and experimental optimisation proceeds without leveraging institutional knowledge. Thus, we propose a formalised trinity of learnability comprising cognitive design, experimentation optimisation, and embodied execution, where an evolving cognitive core serves as the foundation for discovery. This architecture synergises the cognitive core with adaptive embodied skills to translate lab-specific variances into robustness while refining active experimental designs to prune explosive search spaces. This synergy enables SDLs to foster the emergence of high-efficiency discovery. Taken together, this position paper argues that autonomous discovery requires learnability through embodied experimentation, not merely automation sophistication. As a proof of concept, we developed a bespoke embodied laboratory prototype for the Electro-Fenton task. By leveraging historical memory and adaptive skills, our agent navigated a 200-million search space to achieve 99.7% efficiency in just 15 rounds. Embodied cognition ; Learnability ; Cognitive robotics ; Cognitive architecture ; Robustness (evolution) ; Cognition ; Bespoke ; Architecture Machine Learning in Materials Science ; Modular Robots and Swarm Intelligence ; Micro and Nano Robotics
Authors: Jawad Chowdhury | Ganesh Narasimha | Jan-Chi Yang | Yongtao Liu | Rama K. Vasudevan
Source: arXiv (Cornell University) (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Quality-Controlled Active Learning via Gaussian Processes for Robust Structure-Property Learning in Autonomous Microscopy Autonomous experimental systems are increasingly used in materials research to accelerate scientific discovery, but their performance is often limited by low-quality, noisy data. This issue is especially problematic in data-intensive structure-property learning tasks such as Image-to-Spectrum (Im2Spec) and Spectrum-to-Image (Spec2Im) translations, where standard active learning strategies can mistakenly prioritize poor-quality measurements. We introduce a gated active learning framework that combines curiosity-driven sampling with a physics-informed quality control filter based on the Simple Harmonic Oscillator model fits, allowing the system to automatically exclude low-fidelity data during acquisition. Evaluations on a pre-acquired dataset of band-excitation piezoresponse spectroscopy (BEPS) data from PbTiO3 thin films with spatially localized noise show that the proposed method outperforms random sampling, standard active learning, and multitask learning strategies. The gated approach enhances both Im2Spec and Spec2Im by handling noise during training and acquisition, leading to more reliable forward and inverse predictions. In contrast, standard active learners often misinterpret noise as uncertainty and end up acquiring bad samples that hurt performance. Given its promising applicability, we further deployed the framework in real-time experiments on BiFeO3 thin films, demonstrating its effectiveness in real autonomous microscopy experiments. Overall, this work supports a shift toward hybrid autonomy in self-driving labs, where physics-informed quality assessment and active decision-making work hand-in-hand for more reliable discovery. Active learning (machine learning) ; Noise (video) ; Quality (philosophy) ; Active vision ; Gaussian ; Sampling (signal processing) ; Filter (signal processing) ; Active noise control Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Model Reduction and Neural Networks
Authors: Jawad Chowdhury | Ganesh Narasimha | Jan-Chi Yang | Yongtao Liu | Rama K. Vasudevan
Source: arXiv (Cornell University) (preprint, 2026-03-31)
Field: Materials Science — Materials Chemistry

Quality-Controlled Active Learning via Gaussian Processes for Robust Structure-Property Learning in Autonomous Microscopy Autonomous experimental systems are increasingly used in materials research to accelerate scientific discovery, but their performance is often limited by low-quality, noisy data. This issue is especially problematic in data-intensive structure-property learning tasks such as Image-to-Spectrum (Im2Spec) and Spectrum-to-Image (Spec2Im) translations, where standard active learning strategies can mistakenly prioritize poor-quality measurements. We introduce a gated active learning framework that combines curiosity-driven sampling with a physics-informed quality control filter based on the Simple Harmonic Oscillator model fits, allowing the system to automatically exclude low-fidelity data during acquisition. Evaluations on a pre-acquired dataset of band-excitation piezoresponse spectroscopy (BEPS) data from PbTiO3 thin films with spatially localized noise show that the proposed method outperforms random sampling, standard active learning, and multitask learning strategies. The gated approach enhances both Im2Spec and Spec2Im by handling noise during training and acquisition, leading to more reliable forward and inverse predictions. In contrast, standard active learners often misinterpret noise as uncertainty and end up acquiring bad samples that hurt performance. Given its promising applicability, we further deployed the framework in real-time experiments on BiFeO3 thin films, demonstrating its effectiveness in real autonomous microscopy experiments. Overall, this work supports a shift toward hybrid autonomy in self-driving labs, where physics-informed quality assessment and active decision-making work hand-in-hand for more reliable discovery. Active learning (machine learning) ; Noise (video) ; Quality (philosophy) ; Active vision ; Gaussian ; Sampling (signal processing) ; Filter (signal processing) ; Active noise control Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Model Reduction and Neural Networks
Authors: Jinyuan Guo | Honglei Yu | Dexi Wang | Lin Fan
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Cavitation-enhanced carbonation for nano-ZnO synthesis via an ultrasonic–jet coupled reactor: machine learning prediction and multi-objective GA optimization Cavitation ; Nucleation ; Ultrasonic sensor ; Carbonation ; Mass transfer ; Surface roughness ; Crystallite ; Jet (fluid) ; Carbonization ; Crystal (programming language) Ultrasound and Cavitation Phenomena ; Calcium Carbonate Crystallization and Inhibition ; Subcritical and Supercritical Water Processes
Authors: Min Peng | Yuanjun Tang | De Dong | Yang Zhang | Cheng 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) (preprint, 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 &gt; 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: Boris N. Slautin | Kamyar Barakati | Utkarsh Pratiush | Christopher D. Lowe | Catherine Bodinger | Brandi M. Cossairt | Mahshid Ahmadi | Austin Houston | Timur Bazhirov | Jaehyung Lee | Kamal Choudhary | Gerd Duscher | Sergei Kalinin
Source: arXiv (Cornell University) (article, 2026-03-21)
Field: Materials Science — Materials Chemistry

From Photons to Electrons: Accelerated Materials Discovery via Random Libraries and Automated Scanning Transmission Electron Microscopy The real-world implementation of materials prediction algorithms remains limited by persistent characterization bottlenecks in materials discovery, where photon-based probe techniques (e.g., XRD or Raman) impose long acquisition times and access latencies, restricting exploration to quasi-ternary composition spaces typically realized as compositional libraries. Here, we argue that a paradigm shift from photon- to electron-based characterization can realign materials characterization with modern high-throughput synthesis. We formulate cost functions and exploration strategies for STEM-based chemical and structural characterization and use Monte Carlo simulations to show that random chemical libraries, where compositionally distinct regions are co-located within a single specimen and interrogated in situ by electron spectroscopies, can sample high-dimensional composition and phase spaces with orders-of-magnitude greater effective coverage than conventional spread-library/X-ray approaches. We further demonstrate autonomous discovery on a laboratory STEM platform, where ML-based autotuning and scripted control enable iterative region selection and characterization without human intervention. Finally, we outline extensions to labeled or position-encoded libraries that preserve compositional and processing metadata, enabling joint exploration of composition and process spaces. Together, these results establish electron-based, ML-enabled STEM as a scalable pathway toward combinatorially rich materials discovery. Characterization (materials science) ; Scalability ; Monte Carlo method ; Process (computing) ; Random access ; In situ ; Selection (genetic algorithm) ; Scanning transmission electron microscopy Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electronic and Structural Properties of Oxides
Authors: Zhan‐Long Wang | Zhifen Ma | Wenxing Song | Guolai Jiang | Bo Jiang | Mingyang Jiang | Weiliang Shu | Bing Wang | Zhiyuan Wan | Shengyong Geng | Zhen Zhao | Wenhua Zhou | Xue‐Feng Yu
Source: Advanced Science (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

AP‐Lab: An AI‐Driven Autonomous Pilot‐Scale Platform Bridging Materials Discovery and Industrial Manufacturing Artificial intelligence (AI) has accelerated materials discovery, yet its translation to industrial manufacturing remains limited due to two critical gaps: the scarcity of proprietary industrial datasets and the absence of application-oriented benchmarks. To address these challenges, we develop the AP-Lab, an AI-Driven Autonomous Pilot-Scale Laboratory workstation designed to bridge research and manufacturing. Using magnetic nanoparticles (MNPs) for viral nucleic acids (NAs) extraction as a case study, the AP-Lab integrates four agent-controlled systems for user interaction, optimization scheme generation, autonomous synthesis and testing, and data management. By leveraging localized industrial datasets and adopting Polymerase Chain Reaction (PCR) cycle threshold (Ct) values as an application-specific benchmark, the AP-Lab achieves rapid optimization of MNPs-based NAs extraction products at pilot-scale corresponding to 50,000 tests per batch within three weeks, and enables scale-up manufacturing of 1 million tests per batch in two months. Compared to conventional manual workflows, the AP-Lab reduces development timelines from four to six months to three weeks while delivering performance superior to leading commercial products. This work demonstrates a scalable strategy for AI-driven pilot-scale production and offers a blueprint for accelerating industrial adoption of advanced materials. Scalability ; Bridging (networking) ; Industrial production ; Blueprint ; Timeline ; Workstation Machine Learning in Materials Science ; Advanced Nanomaterials in Catalysis ; Advanced biosensing and bioanalysis techniques
Authors: Jeffrey Hu | Rongzhi Dong | Ying Feng | Ming Hu | Jianjun Hu
Source: ArXiv.org (article, 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: Yuki K. Wakabayashi | Yui Ogawa | Franz Benedict Romero | Takuma Otsuka | Yoshitaka Taniyasu
Source: arXiv (Cornell University) (article, 2026-02-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Interpretable self-driving sputter epitaxy: from black-box optimization to human-usable growth rules Self-driving laboratories have emerged as powerful tools for navigating high-dimensional process spaces, yet systems remain black-box optimizers that yield limited transferable process understanding. Here, we demonstrate an interpretable self-driving laboratory framework that transforms autonomous optimization into human-usable growth rules. As a stringent benchmark, we apply this framework to RF magnetron sputtering, addressing a long-standing challenge of achieving high-quality beta-Ga2O3 heteroepitaxy and single-crystalline beta-Ga2O3 homoepitaxy via sputtering. By combining Bayesian optimization with automated optical evaluation of the Urbach energy as a metric of sub-bandgap disorder, the self-driving system efficiently identifies heteroepitaxial growth conditions yielding a minimum Urbach energy of 182 meV, the lowest value for sputtered beta-Ga2O3 films. Importantly, the optimized growth window is transferable, realizing single-crystalline beta-Ga2O3 homoepitaxy without further optimization, corroborated by scanning transmission electron microscopy. To convert the closed-loop dataset into interpretable growth rules, we train a random forest surrogate and distill it into response curves and quantified pairwise interactions across the four-dimensional growth-parameter space. This analysis identifies substrate temperature as the primary control knob, with RF power and gas flows acting largely additively and only a modest temperature-oxygen coupling delineating the narrow window for high-quality growth, establishing a general route from autonomous experimentation to transferable growth rules. Bayesian optimization ; Metric (unit) ; Coupling (piping) ; Process (computing) ; Pairwise comparison ; Substrate (aquarium) ; Process window ; Window (computing) Ga2O3 and related materials ; Electronic and Structural Properties of Oxides ; GaN-based semiconductor devices and materials
Authors: Anupama S. | Senthilkumar D
Source: NanoNEXT (article, 2026-03-07)
Field: Materials Science — Materials Chemistry

2D Materials Beyond Graphene for Energy Storage and Conversion: Recent Advances and Future Perspectives Two-dimensional (2D) materials have emerged as a major class of advanced materials for energy storage and conversion technologies. Although graphene initiated this research field, its zero bandgap, limited chemical tunability, and restacking tendency restrict its performance in many practical energy systems. These limitations have driven the development of a broad range of 2D materials beyond graphene, including MXenes, transition metal dichalcogenides, black phosphorus, metal oxides and hydroxides, layered double hydroxides, and other emerging atomically thin systems. These materials offer diverse crystal structures, tunable electronic properties, rich surface chemistry, and high surface-to-volume ratios, making them attractive for batteries, supercapacitors, electrocatalysis, and photoelectrochemical applications. This review provides a comprehensive overview of recent progress in 2D materials beyond graphene for energy storage and conversion. Key aspects discussed include synthesis strategies, defect and interface engineering, heterostructure design, and structure–property relationships. The performance of these materials in lithium-ion and beyond-lithium batteries, supercapacitors, electrocatalytic reactions, and solar-driven energy systems is critically examined. Recent advances in characterization techniques, computational modelling, and machine learning-assisted discovery are also highlighted for their role in accelerating material development. Despite significant laboratory-level success, several challenges remain, particularly in scalable synthesis, long-term stability, standardised benchmarking, and system-level integration. Addressing these issues is essential for translating promising material properties into reliable and commercially viable energy technologies. Overall, this review outlines current achievements, identifies critical knowledge gaps, and presents future perspectives for advancing 2D materials beyond graphene toward sustainable and practical energy solution. Graphene ; Characterization (materials science) ; Energy storage ; Energy transformation ; Scalability ; Heterojunction ; Interface (matter) MXene and MAX Phase Materials ; Supercapacitor Materials and Fabrication ; 2D Materials and Applications