jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: B. Zheng | Peixuan Zhu
Source: Materials (article, 2025-12-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advances in Metamaterials: Structure, Properties and Applications Metamaterials, leveraging the unique characteristics of subwavelength structures, have led to significant research progress in fields such as acoustics, optics, and electromagnetics [...]. Electromagnetics ; Field (mathematics) ; Electromagnetic field ; Computational electromagnetics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Advanced Antenna and Metasurface Technologies
Authors: Rongge Xu | Hui Dai | Yiming Fu | Jiedong Jiang | Tianjiao Nie | Hongwei Wang | Junkai Wang | Zhang, Zhi-Hao
Source: arXiv (Cornell University) (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

LeanCat: A Benchmark Suite for Formal Category Theory in Lean (Part I: 1-Categories) While large language models (LLMs) have demonstrated impressive capabilities in formal theorem proving, current benchmarks fail to adequately measure library-grounded abstraction -- the ability to reason with high-level interfaces and reusable structures central to modern mathematics and software engineering. We introduce LeanCat, a challenging benchmark comprising 100 fully formalized category-theory tasks in Lean. Unlike algebra or arithmetic, category theory serves as a rigorous stress test for structural, interface-level reasoning. Our evaluation reveals a severe abstraction gap: the best state-of-the-art model solves only 12.0% of tasks at pass@4, with performance collapsing from 55.0% on Easy tasks to 0.0% on High-difficulty tasks, highlighting a failure in compositional generalization. To overcome this, we evaluate LeanBridge, a retrieval-augmented agent that employs a retrieve-generate-verify loop. LeanBridge achieves a peak success rate of 24.0% -- doubling the performance of the best static baseline. These results empirically demonstrate that iterative refinement and dynamic library retrieval are not merely optimizations but strict necessities for neuro-symbolic reasoning in abstract domains. LeanCat offers a compact, reusable testbed for tracking progress toward reliable, research-level formalization. Benchmark (surveying) ; Suite ; Abstraction ; Testbed ; Test suite ; Measure (data warehouse) ; Software ; Iterative refinement Machine Learning in Materials Science ; Mathematics, Computing, and Information Processing ; Model-Driven Software Engineering Techniques
Authors: Dilshod Nematov | Iskandar Raufov
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

The Future of Materials Science with AI: Self-Driving Laboratories and Closed-Loop Discovery Transformative learning ; Characterization (materials science) ; Workflow ; Quantum dot ; Biomedicine ; Energy (signal processing) Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Electron and X-Ray Spectroscopy Techniques
Authors: Guoyue Xu | Renzheng Zhang | Tengfei Luo
Source: ArXiv.org (preprint, 2025-09-02)
Field: Materials Science — Polymers and Plastics

Self-Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers To overcome the inherent inefficiencies of traditional trial-and-error materials discovery, the scientific community is increasingly developing autonomous laboratories that integrate data-driven decision-making into closed-loop experimental workflows. In this work, we realize this concept for thermoresponsive polymers by developing a low-cost, "frugal twin" platform for the optimization of the lower critical solution temperature (LCST) of poly(N-isopropylacrylamide) (PNIPAM). Our system integrates robotic fluid-handling, on-line sensors, and Bayesian optimization (BO) that navigates the multi-component salt solution spaces to achieve user-specified LCST targets. The platform demonstrates convergence to target properties within a minimal number of experiments. It strategically explores the parameter space, learns from informative "off-target" results, and self-corrects to achieve the final targets. By providing an accessible and adaptable blueprint, this work lowers the barrier to entry for autonomous experimentation and accelerates the design and discovery of functional polymers. Lower critical solution temperature ; Convergence (economics) ; Work (physics) ; Bayesian optimization ; Polymer ; Thermoresponsive polymers in chromatography ; Upper critical solution temperature Synthesis and properties of polymers
Authors: Yuncheng Du | Cristina Sanchez Cereceda | Dongping Du
Source: MRS Communications (article, 2025-06-16)
Field: Materials Science — Materials Chemistry

Data-driven approaches for autonomous experimentation: Insights into modeling and prediction dynamics of metal–organic frameworks synthesis Materials science ; Metal-organic framework ; Systems engineering ; Dynamics (music) ; Nanotechnology ; Biochemical engineering ; Engineering ; Physical chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Computational Drug Discovery Methods
Authors: Suponya, Alexander
Source: Rutgers University Community Repository (Rutgers University) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Self-driving lab for the data-driven discovery of single chain polymer nanoparticles Careful mapping of protein structure-activity relationships connecting amino acid sequence to its tertiary structure has spurred the rise of rational protein design using computational and experimental methods. Drawing inspiration from proteins for synthetic materials, careful choice of monomers enables the creation of novel single-chain polymer nanoparticles (SCNPs) with self-assembling characteristics. Rationally designed SCNPs permit the necessary structural complexity for use as protein mimics and polymer-protein hybrids, while granting access to a broad chemical design space, straightforward preparation, and tunable properties. Advances in oxygen-tolerant photoinduced polymerization chemistries have greatly facilitated efficient preparation of SCNPs. Here, we describe a high-throughput, autonomous workflow for active learning to discover structure-property relationships and use them to iteratively predict and synthesize novel SCNPs. We developed a control system consisting of a liquid handling robot to mix polymerization reagents, a custom-built lightbox to catalyze PET-RAFT polymerization, a dynamic light scattering (DLS) plate reader to characterize polymer hydrodynamic radius (Rh), and a robotic arm to transport polymer-containing well plates from one instrument to another. By first testing low feature, rationally designed polymer libraries, we gained experimental context for the development of higher-feature, randomly sampled seed libraries as training sets for Gaussian process regressor (GPR) models. Over multiple generations of Bayesian optimization (BO), additional generations of polymer synthesis were found not only to successfully improve model performance but also to represent the impact of specific monomer content on Rh. This automated polymer discovery platform serves as a useful prototype for SCNP design using more complex design features as well as more advanced optimization targets, including polymers whose structure can be tailored for enzyme stabilization and other biomedical applications. Rational design ; Workflow ; Context (archaeology) ; Polymer ; Monomer ; Polymerization ; Nanoparticle ; Process (computing) ; Dynamic light scattering Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Abhishek Soni | Shipeng Jia | C. Y. Chao | Iwnetim Abate
Source: ChemRxiv (preprint, 2025-05-06)
Field: Materials Science — Materials Chemistry

Accelerated Development of Energy Materials through Automation and Artificial Intelligence Batteries, fuel cells, and electrolyzers mostly rely on advances in solid-state inorganic materials, yet discovering and optimizing these materials remains a complex and time-intensive challenge. Self-driving laboratories (SDLs), through advances in computation, automation and artificial intelligence (AI), are revolutionizing the pace of solid-state material discovery. SDLs can rapidly navigate vast chemical spaces, optimizing multi-dimensional synthesis and processing conditions and accelerating the identification of high-performance materials relevant for commercialization. The approach not only enhances efficiency and experimental precision but also mitigates the trial-and-error limitations of traditional methods. In this perspective, we examine how computation, high throughput automation, and advanced AI techniques can accelerate the discovery of energy materials, highlighting key capabilities, current applications, and providing a perspective on future directions in the field. We will also discuss how despite challenges such as high implementation costs and the complexity of automating solid-state synthesis, the continued success of SDLs relies on more democratized, accessible, and collaborative frameworks. Automation ; Development (topology) ; Energy (signal processing) ; Computer science ; Artificial intelligence ; Manufacturing engineering ; Engineering ; Systems engineering ; Mechanical engineering ; Physics ; Mathematics Machine Learning in Materials Science
Authors: Torresi, Luca | Friederich, Pascal
Source: ArXiv.org (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Multi-stage Bayesian optimisation for dynamic decision-making in self-driving labs Self-driving laboratories (SDLs) are combining recent technological advances in robotics, automation, and machine learning based data analysis and decision-making to perform autonomous experimentation toward human-directed goals without requiring any direct human intervention. SDLs are successfully used in materials science, chemistry, and beyond, to optimise processes, materials, and devices in a systematic and data-efficient way. At present, the most widely used algorithm to identify the most informative next experiment is Bayesian optimisation. While relatively simple to apply to a wide range of optimisation problems, standard Bayesian optimisation relies on a fixed experimental workflow with a clear set of optimisation parameters and one or more measurable objective functions. This excludes the possibility of making on-the-fly decisions about changes in the planned sequence of operations and including intermediate measurements in the decision-making process. Therefore, many real-world experiments need to be adapted and simplified to be converted to the common setting in self-driving labs. In this paper, we introduce an extension to Bayesian optimisation that allows flexible sampling of multi-stage workflows and makes optimal decisions based on intermediate observables, which we call proxy measurements. We systematically compare the advantage of taking into account proxy measurements over conventional Bayesian optimisation, in which only the final measurement is observed. We find that over a wide range of scenarios, proxy measurements yield a substantial improvement, both in the time to find good solutions and in the overall optimality of found solutions. This not only paves the way to use more complex and thus more realistic experimental workflows in autonomous labs but also to smoothly combine simulations and experiments in the next generation of SDLs. Workflow ; Bayesian probability ; Bayesian optimization ; Set (abstract data type) ; Proxy (statistics) ; Range (aeronautics) ; Bayesian inference Machine Learning in Materials Science ; Scientific Computing and Data Management ; Gaussian Processes and Bayesian Inference
Authors: Torresi, Luca | Friederich, Pascal
Source: arXiv (Cornell University) (preprint, 2025-12-17)
Field: Materials Science — Materials Chemistry

Multi-stage Bayesian optimisation for dynamic decision-making in self-driving labs Self-driving laboratories (SDLs) are combining recent technological advances in robotics, automation, and machine learning based data analysis and decision-making to perform autonomous experimentation toward human-directed goals without requiring any direct human intervention. SDLs are successfully used in materials science, chemistry, and beyond, to optimise processes, materials, and devices in a systematic and data-efficient way. At present, the most widely used algorithm to identify the most informative next experiment is Bayesian optimisation. While relatively simple to apply to a wide range of optimisation problems, standard Bayesian optimisation relies on a fixed experimental workflow with a clear set of optimisation parameters and one or more measurable objective functions. This excludes the possibility of making on-the-fly decisions about changes in the planned sequence of operations and including intermediate measurements in the decision-making process. Therefore, many real-world experiments need to be adapted and simplified to be converted to the common setting in self-driving labs. In this paper, we introduce an extension to Bayesian optimisation that allows flexible sampling of multi-stage workflows and makes optimal decisions based on intermediate observables, which we call proxy measurements. We systematically compare the advantage of taking into account proxy measurements over conventional Bayesian optimisation, in which only the final measurement is observed. We find that over a wide range of scenarios, proxy measurements yield a substantial improvement, both in the time to find good solutions and in the overall optimality of found solutions. This not only paves the way to use more complex and thus more realistic experimental workflows in autonomous labs but also to smoothly combine simulations and experiments in the next generation of SDLs. Workflow ; Bayesian probability ; Bayesian optimization ; Set (abstract data type) ; Proxy (statistics) ; Range (aeronautics) ; Bayesian inference Machine Learning in Materials Science ; Scientific Computing and Data Management ; Gaussian Processes and Bayesian Inference
Authors: Chaturvedi
Source: International Journal of Pharmacy and Life Sciences (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

AI-Driven Discovery of Novel Polymers: A Comprehensive Review The discovery and optimization of polymeric materials have traditionally relied on experiment-driven, trial-and-error workflows that are time-consuming and resource intensive. Artificial intelligence (AI) — particularly machine learning (ML), graph neural networks (GNNs), generative models, and active learning — is transforming polymer science by enabling rapid property prediction, inverse design, and closed-loop experimental optimization. This review surveys recent advances (2018–2025) in data resources, polymer representations, predictive and generative AI models, optimization strategies (including Bayesian optimization and active learning), and automated/self-driving laboratories. We highlight landmark platforms (Polymer Genome, Open Macromolecular Genome), methodological progress (multitask GNNs, chemical language models such as polyBERT, and benchmarks for deep generative models), and successful demonstrations of AI-assisted polymer discovery in energy, electronics, healthcare, and sustainable plastics. Key challenges remain: scarcity and heterogeneity of polymer data, difficulty representing polymer ensembles and architectures, synthetic feasibility of generated candidates, model interpretability, and integration with experimental workflows. We discuss strategies to address these issues — standardized databases and representations, self-supervised learning, synthesis-aware generative models, and tighter AI–robotics integration — and outline opportunities where AI can accelerate green polymer chemistry, circular-economy polymers, and application-directed multi-property optimization. The review concludes by advocating for community efforts in data curation, open benchmarks, and interdisciplinary training to realize AI’s promise for fast, cost-effective polymer innovation. Generative grammar ; Workflow ; Scarcity ; Deep learning ; Generative model ; Resource (disambiguation) Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Saaketh Desai | Sadhvikas Addamane | J. Y. Tsao | Igal Brener | Rémi Dingreville | Prasad P. Iyer
Source: Nature Communications (article, 2025-12-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Self-driving lab discovers principles for steering spontaneous emission beyond conventional Fourier optics We develop an autonomous experimentation platform to accelerate interpretable scientific discovery in ultrafast nanophotonics, targeting a novel method to steer spontaneous emission from reconfigurable semiconductor metasurfaces. Despite the potential of reconfigurable semiconductor metasurfaces with embedded sources for spatiotemporal control, achieving arbitrary far-field control remains challenging. Here, we present a self-driving lab (SDL) platform that addresses this challenge by discovering the governing equations for predicting the far-field emission profile from light-emitting metasurfaces. We discover that both the spatial gradient (grating-like) and the curvature (lens-like) of the local refractive index are key factors in steering spontaneous emission. The SDL employs a machine-learning framework comprising: (1) a variational autoencoder for generating complex spatial refractive index profiles, (2) an active learning agent for guiding experiments with real-time closed-loop feedback, and (3) a neural network-based equation learner to uncover structure-property relationships. The SDL demonstrates up to a four-fold enhancement in peak emission directivity (up to 77%) over a 74° field of view within ~300 experiments. Our findings reveal that combinations of positive gratings and lenses are as effective as negative lenses and gratings for all emission angles, offering a novel strategy for controlling spontaneous emission beyond conventional Fourier optics. Spontaneous emission ; Fourier transform ; Autoencoder ; Curvature ; Refractive index ; Key (lock) ; Amplified spontaneous emission ; Directivity Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Thermal Radiation and Cooling Technologies
Authors: Dahms, Selma | Torresi, Luca | Bandesha, Shahbaz Tareq | Hansmann, Jan | Röhm, Holger | Colsmann, Alexander | Schott, Marco | Friederich, Pascal
Source: ArXiv.org (article, 2025-11-28)
Field: Materials Science — Polymers and Plastics

A self-driving lab for solution-processed electrochromic thin films Solution-processed electrochromic materials offer high potential for energy-efficient smart windows and displays. Their performance varies with material choice and processing conditions. Electrochromic thin film electrodes require a smooth, defect-free coating for optimal contrast between bleached and colored states. The complexity of optimizing the spin-coated electrochromic thin layer poses challenges for rapid development. This study demonstrates the use of self-driving laboratories to accelerate the development of electrochromic coatings by coupling automation with machine learning. Our system combines automated data acquisition, image processing, spectral analysis, and Bayesian optimization to explore processing parameters efficiently. This approach not only increases throughput but also enables a pointed search for optimal processing parameters. The approach can be applied to various solution-processed materials, highlighting the potential of self-driving labs in enhancing materials discovery and process optimization. Electrochromism ; Thin film ; Throughput ; Electrochromic devices ; Electrode ; Process (computing) ; Coating ; Automation Transition Metal Oxide Nanomaterials ; Conducting polymers and applications ; Nanomaterials and Printing Technologies
Authors: Dahms, Selma | Torresi, Luca | Bandesha, Shahbaz Tareq | Hansmann, Jan | Röhm, Holger | Colsmann, Alexander | Schott, Marco | Friederich, Pascal
Source: Fraunhofer-Publica (Fraunhofer-Gesellschaft) (preprint, 2025-11-28)
Field: Materials Science — Polymers and Plastics

A self-driving lab for solution-processed electrochromic thin films Solution-processed electrochromic materials offer high potential for energy-efficient smart windows and displays. Their performance varies with material choice and processing conditions. Electrochromic thin film electrodes require a smooth, defect-free coating for optimal contrast between bleached and colored states. The complexity of optimizing the spin-coated electrochromic thin layer poses challenges for rapid development. This study demonstrates the use of self-driving laboratories to accelerate the development of electrochromic coatings by coupling automation with machine learning. Our system combines automated data acquisition, image processing, spectral analysis, and Bayesian optimization to explore processing parameters efficiently. This approach not only increases throughput but also enables a pointed search for optimal processing parameters. The approach can be applied to various solution-processed materials, highlighting the potential of self-driving labs in enhancing materials discovery and process optimization. Electrochromism ; Thin film ; Throughput ; Electrochromic devices ; Electrode ; Process (computing) ; Coating ; Automation Transition Metal Oxide Nanomaterials ; Conducting polymers and applications ; Nanomaterials and Printing Technologies
Authors: Ritesh Kumar
Source: Journal of Emerging Technologies and Innovative Research (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data Driven Approaches in Polymer Research Data-driven polymer research is reshaping how macromolecules are conceived, synthesized, and deployed by linking curated, FAIR data to physics-informed machine learning and autonomous experimentation. Computational approaches are now an important part of polymer chemistry. They let scientists guess a material's bulk properties based on its molecular structure. The goal is to figure out which structural elements control certain behaviours so that new materials can be designed in a logical way. But this procedure is made more difficult by a number of real-world problems, such as short datasets, changing molecular weights, and complicated polymer topologies. As a result, generic computational models are typically not very useful unless they are heavily customised. To get over these problems, chemists and data scientists need to work together in a way that helps both groups. The chemist's job is to help define the problem by using their knowledge of the field, and the data scientist's job is to make models work in the unique chemical context. This research seeks to bridge the multidisciplinary divide by tackling issues related to data quality and emphasising how recent scientific progress has enhanced data accessibility. We look at how materials are used in the field, from anticipating how well they will work to designing them for drug delivery. Finally, we talk about how important it is to share data (FAIR Data Principles) and how powerful it is to combine conventional polymer theory with new, data-driven insights. Data-driven ; Multidisciplinary approach ; Bridge (graph theory) ; Work (physics) ; Quality (philosophy) ; Data quality ; Open research Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Qiang Sun | Zhiwen Zhu | Qi Huang | Teng Yang | Hao Jiang | Shaoxuan Yuan | Juan Xiang | Liangliang Cai
Source: Research Square (preprint, 2025-09-23)
Field: Materials Science — Materials Chemistry

Deep learning driving autonomous molecular reactions with single-bond selectivity Deep learning ; Scalability ; Deep neural networks ; Reinforcement learning ; Artificial neural network ; Dissociation (chemistry) Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation