jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tao Song | Weixing Chen | Xiaobo Li | Hengtao Lei | Liang Zhang | Hongli Zhang | Guifu Zou | Linjiang Chen | Weiwei Shang | Jun Jiang
Source: ChemRxiv (article, 2026-04-08)
Field: Materials Science — Materials Chemistry

ChemVLMate: a multimodal, dual-arm, seeing robotic chemist for adaptive synthesis Chemical synthesis is a dynamic process. Chemists rely on a perception-reasoning-manipulation loop to interpret evolving experimental environments and states and adapt operations real-time. Yet most robotic chemistry platforms remain perception-blind, executing static scripts that assume nominal behaviours and fixed environments. Here we introduce ChemVLMate, a multimodal dualarm robotic chemist that closes the perception–reasoning–manipulation loop by grounding decision making in real-time vision. An open-vocabulary vision–language model converts an overhead image into a structured semantic observation of tools, reagents and instrument readouts; a language-model policy selects the next parameterized laboratory action from a constrained API; and coordinated bimanual manipulation executes it with force-aware control. Across diverse bench chemistry tasks, ChemVLMate autonomously performs solution preparation, precision weighing, hydrogel synthesis and colouring, and room-temperature crystallizations, adapting online under partial observability. Integrated within a self-driving laboratory, it completes an end-to-end synthesis-to-X-ray diffraction characterization workflow. ChemVLMate provides a general framework for reliable, visually grounded laboratory autonomy and adaptive synthesis. Scripting language ; Parameterized complexity ; Chemist ; Action (physics) ; Robotics ; Robot Machine Learning in Materials Science ; Modular Robots and Swarm Intelligence ; Polyoxometalates: Synthesis and Applications
Authors: Mohamed Barhoumi
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

AI-Guided Two-Dimensional Materials: From Discovery and Property Prediction to Device Engineering Deep learning ; Scalability ; Property (philosophy) ; Generative grammar ; Bayesian optimization ; Transformative learning ; Probabilistic logic ; Scale (ratio) Machine Learning in Materials Science ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: AGBOGO Victor Ugbetan | TEFFO Moipone Linda | MAVHUNGU Lucey Mapula | Sadiku Emmanuel Rotimi | INJOR Oryina M.
Source: SHILAP Revista de lepidopterología (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

AUTONOMOUS POLYMER MATERIALS DISCOVERY: ARTIFICIAL INTELLIGENCE ARCHITECTURES FOR GENERATIVE DESIGN AND CLOSED-LOOP ENGINEERING Artificial intelligence (AI) is revolutionizing polymer engineering by shifting materials development from traditional trial-and-error to integrated, data-driven, and autonomous workflows. This review summarizes recent progress in AI-driven polymer science, including structural representation, generative molecular design, property prediction, retrosynthetic planning, and closed-loop experimental processes. We discuss how polymer-specific encodings like BigSMILES and graph-based models enable machine-readable macromolecular structures, supporting transformer models, graph neural networks, and uncertainty-aware predictors that accurately estimate thermal, mechanical, dielectric, and functional properties. Beyond prediction, we examine generative architectures such as variational autoencoders, adversarial models, diffusion models, and reinforcement learning that enable inverse polymer design by exploring vast chemical spaces under performance constraints. Combining these computational approaches with techniques like Bayesian optimization, active learning, and autonomous labs signifies a shift toward closed-loop polymer engineering, where AI continuously suggests, synthesizes, analyzes, and refines materials with minimal human intervention. Key challenges, including data scarcity, polymer-specific representation learning, model interpretability, infrastructure integration, and sustainability goals, are thoroughly considered. Emerging solutions such as FAIR data ecosystems, physics-informed neural networks, explainable AI, and modular self-driving laboratories are evaluated for scalable implementation. Overall, these advancements point toward autonomous polymer discovery, with AI serving not only as a predictive tool but as the foundation for intelligent, adaptive, and sustainable polymer engineering. Generative grammar ; Scalability ; Modular design ; Robotics ; Artificial life ; Reinforcement learning ; Artificial neural network ; Applications of artificial intelligence Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Block Copolymer Self-Assembly
Authors: Sania Ismaeel | Sidra Manzoor | Muhammad Sabir
Source: Organic Electronics (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Toward autonomous discovery in small-molecule organic photovoltaics: A review integrating artificial intelligence and machine learning within the molecular design landscape Deep learning ; Applications of artificial intelligence ; Artificial neural network ; Artificial life Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Photochromic and Fluorescence Chemistry
Authors: Xiao Chen | Kai Shi | C. Q. Jiang | Yunhai Zhu | Xizhong Yu
Source: Future Batteries (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Data-driven inorganic material design for future all-solid-state batteries: Innovations and perspectives Data-driven approaches have emerged as a transformative force in accelerating the discovery and optimization of inorganic materials for all-solid-state batteries (ASSBs), leading to substantial progress in recent years. Earlier reviews often focus on isolated methodologies or specific material classes. Here, this study systematically highlights the pivotal role of machine learning in advancing solid electrolytes and cathode materials in ASSBs, targeting critical properties including high ionic conductivity, wide electrochemical stability windows, and interfacial compatibility. We chart the methodological evolution from conventional descriptor-based predictive models to advanced deep learning architectures that autonomously extract features from composition and crystal structure, and further to integrated active-learning platforms that close the loop between prediction and experimental validation. Beyond summarizing these technological strides, we critically assess current limitations, particularly the gap between idealized models and the complexity of real devices. Finally, a forward-looking research agenda is outlined, advancing physics-guided and multi-fidelity learning frameworks, developing generative models for inverse design under realistic constraints, extending modeling to interface and cell-level complexity, and establishing reliable closed-loop discovery systems. This work will consolidate current knowledge and chart a path toward more predictive, actionable, and integrative artificial intelligence tools that can accelerate the realization of high-performance, commercially viable solid-state batteries. Generative grammar ; Transformative learning ; Interface (matter) ; Chart ; Focus (optics) ; Work (physics) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Yuki K. Wakabayashi | Takuma Otsuka | Yoshiharu Krockenberger | Yoshitaka Taniyasu
Source: Open MIND (preprint, 2026-02-05)
Field: Materials Science — Materials Chemistry

Physics-informed acquisition weighting for stoichiometry-constrained Bayesian optimization of oxide thin-film growth We present a physics-informed Bayesian optimization (PIBO) with a concise modification to its acquisition function to incorporate the physical prior knowledge. Specifically, this method multiplies the expected improvement (EI) by a weight encoding prior crystal growth physics. When applied to LaAlO3 molecular-beam epitaxy, the weighting function defines a flat stoichiometric window and penalizes off-window proposals, thereby steering the optimization toward physically plausible regions while maintaining controlled exploration. In a closed-loop optimization, relative to the bare EI, which often proposes off-stoichiometric conditions, the weighted EI constrains the search toward stoichiometric regions while retaining sufficient flexibility to explore neighboring conditions, eventually identifying an optimum slightly beyond the stoichiometric window. Within only 15 growth runs, the lattice constant of the grown LaAlO3 film converged to the bulk value, evidencing efficient and rapid optimization for the ideal stoichiometric growth. Because physics knowledge is incorporated solely through the weighting function, the approach requires only minimal modification to standard BO workflows and is readily applicable to other material systems, offering a general and practical route to AI-driven materials synthesis. Weighting ; Bayesian optimization ; Bayesian probability ; Flexibility (engineering) ; Stoichiometry ; Function (biology) ; Optimization problem ; Workflow Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Yuki K. Wakabayashi | Takuma Otsuka | Yoshiharu Krockenberger | Yoshitaka Taniyasu
Source: ArXiv.org (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Physics-informed acquisition weighting for stoichiometry-constrained Bayesian optimization of oxide thin-film growth We present a physics-informed Bayesian optimization (PIBO) with a concise modification to its acquisition function to incorporate the physical prior knowledge. Specifically, this method multiplies the expected improvement (EI) by a weight encoding prior crystal growth physics. When applied to LaAlO3 molecular-beam epitaxy, the weighting function defines a flat stoichiometric window and penalizes off-window proposals, thereby steering the optimization toward physically plausible regions while maintaining controlled exploration. In a closed-loop optimization, relative to the bare EI, which often proposes off-stoichiometric conditions, the weighted EI constrains the search toward stoichiometric regions while retaining sufficient flexibility to explore neighboring conditions, eventually identifying an optimum slightly beyond the stoichiometric window. Within only 15 growth runs, the lattice constant of the grown LaAlO3 film converged to the bulk value, evidencing efficient and rapid optimization for the ideal stoichiometric growth. Because physics knowledge is incorporated solely through the weighting function, the approach requires only minimal modification to standard BO workflows and is readily applicable to other material systems, offering a general and practical route to AI-driven materials synthesis. Weighting ; Bayesian optimization ; Bayesian probability ; Flexibility (engineering) ; Stoichiometry ; Function (biology) ; Optimization problem ; Workflow Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Sina Dehghan-Toranposhti | Mohannad Alghamdi | Avery Smith | Martin Seifrid
Source: APL Machine Learning (article, 2026-02-25)
Field: Materials Science — Materials Chemistry

Robust learning from literature data: Model generalizability and uncertainty for predicting conjugated polymer solution conformation Predicting solution conformation and aggregation of conjugated polymers remains a bottleneck for translating solution processing into controlled film microstructures and for closing the loop in self-driving laboratories. We construct a cleaned, machine-readable dataset of 256 entries that links polymer chain length, polymer–solvent compatibility, formulation, and sample history to the radius of gyration, Rg. We evaluate molecular representations and common ML algorithms under both independent and identically distributed (IID) and out-of-distribution (OOD) regimes, including leave-one-polymer-out and polymer–solvent interaction splits. Under IID, models that combine size, chemistry, formulation, and history achieve strong accuracy (RMSE ≈ 0.18; R2 ≈ 0.90), while structure-only models add little beyond polymer chain length. Generalization is the central limit: OOD errors rise sharply for polar sidechain polymers and formulations with significant Hansen descriptor mismatches, and global distribution shift alone does not explain failure—alignment within key feature subspaces matters. Model uncertainty is reasonably calibrated overall, but degrades under strong distribution shift, limiting reliable experiment selection without targeted data. We identify concrete actions to improve robustness for future machine learning: standardized, machine-readable reporting of processing history (times, temperatures, and stimuli) and more deliberate sampling of chemical space. Together, these results highlight dataset design, robust generalization, and uncertainty-aware modeling as the foundations for accelerating discovery and control of conjugated polymer assemblies, capabilities that directly enable autonomous, data-driven experimentation in this field. Robustness (evolution) ; Generalizability theory ; Bottleneck ; Polymer ; Generalization ; Conjugated system ; Independent and identically distributed random variables ; Feature selection ; Feature (linguistics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Johann L. Rapp | Eric P. Weeda | Ryan Sayko | Nick Legaux | Foad Vashahi | Sergei S. Sheiko | Andrey V. Dobrynin | Frank A. Leibfarth
Source: Macromolecules (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

In-Line Molar Mass Determination in Semidilute Polymer Solutions Molar mass directly governs polymer properties, yet its routine measurement remains slow, offline, and largely confined to dilute solution conditions. This limitation restricts the rapid structure–property mapping needed for data-driven materials discovery and emerging self-driving laboratory workflows. Here we integrate semidilute polymer solution theory with practical in-line measurement to enable rapid determination of polymer weight-average molar mass (Mw) at concentrations typical of polymerization and processing (i.e., above the overlap concentration, c*). Building on modern scaling concepts that treat semidilute solutions as strings of correlation blobs, we establish a quantitative calibration linking solution specific viscosity to Mw for a concentration range c > c*. This framework allows Mw to be determined for unknown samples using a conventional rheometer in as little as 2 min. We implemented the same viscosity to Mw calibration under continuous flow by converting real-time pressure drop in a tubular module into solution viscosity and hence Mw. Together, the theory-grounded calibration and its flow-compatible implementation provide a practical route to rapid, Mw readout in a semidilute solution regime, enabling high-throughput synthesis and characterization workflows for materials development. Molar mass ; Rheometer ; Polymer ; Calibration ; Scaling ; Viscosity ; Intrinsic viscosity ; Characterization (materials science) ; Analytical Chemistry (journal) Machine Learning in Materials Science ; Rheology and Fluid Dynamics Studies ; Block Copolymer Self-Assembly
Authors: Harley Quinn | Gregory A. Robben | Zhaoyi Zheng | Jin Yan | Yuanzhi Li | Zhaoji Yang | Maria Politi | Nadya Peek | Lilo D. Pozzo | Jörg G. Werner | Keith A. Brown
Source: arXiv (Cornell University) (preprint, 2026-01-11)
Field: Materials Science — Materials Chemistry

PANDA-film: an automated system for electrodeposition of polymer thin films and their wetting analysis Thin polymer films are widely used as functional and protective coatings. However, determining the composition and processing conditions that produce a desired function is a tedious process due to the large number of factors that must be considered and the manual nature of most synthesis and characterization methods. Self-driving labs (SDLs), or robotic systems that prepare and test materials samples, are designed to overcome this bottleneck by enabling the efficient exploration of complex parameter spaces. In this paper, we report the development and testing of the polymer analysis and discovery array (PANDA)-film, a modular SDL for electrochemically synthesizing polymer films and then determining their water contact angle as a measure of surface energy. The system is designed to be highly modular and based upon a lowcost gantry platform to facilitate adoption. In addition to validating fluid handling and electrochemical tasks, we introduce two novel modular capabilities that enable PANDA-film to run sustained campaigns to study the wetting properties of films: (1) an electromagnetic capping/decapping system to mitigate fluid evaporation, and (2) a top-down optical method to determine water contact angle based upon reflectance. These capabilities are validated by depositing and characterizing a poly(allyl methacrylate) (PAMA) film using electrodeposition of polymer networks (EPoN). Comprehensive details for replicating the hardware and software of PANDA-film are included. Modular design ; Wetting ; Bottleneck ; Polymer ; Process (computing) ; Contact angle ; Characterization (materials science) Block Copolymer Self-Assembly ; Polymer Surface Interaction Studies ; Conducting polymers and applications
Authors: Harley Quinn | Gregory A. Robben | Zhaoyi Zheng | Jin Yan | Yuanzhi Li | Zhaoji Yang | Maria Politi | Nadya Peek | Lilo D. Pozzo | Jörg G. Werner | Keith A. Brown
Source: ArXiv.org (article, 2026-01-11)
Field: Materials Science — Materials Chemistry

PANDA-film: an automated system for electrodeposition of polymer thin films and their wetting analysis Thin polymer films are widely used as functional and protective coatings. However, determining the composition and processing conditions that produce a desired function is a tedious process due to the large number of factors that must be considered and the manual nature of most synthesis and characterization methods. Self-driving labs (SDLs), or robotic systems that prepare and test materials samples, are designed to overcome this bottleneck by enabling the efficient exploration of complex parameter spaces. In this paper, we report the development and testing of the polymer analysis and discovery array (PANDA)-film, a modular SDL for electrochemically synthesizing polymer films and then determining their water contact angle as a measure of surface energy. The system is designed to be highly modular and based upon a lowcost gantry platform to facilitate adoption. In addition to validating fluid handling and electrochemical tasks, we introduce two novel modular capabilities that enable PANDA-film to run sustained campaigns to study the wetting properties of films: (1) an electromagnetic capping/decapping system to mitigate fluid evaporation, and (2) a top-down optical method to determine water contact angle based upon reflectance. These capabilities are validated by depositing and characterizing a poly(allyl methacrylate) (PAMA) film using electrodeposition of polymer networks (EPoN). Comprehensive details for replicating the hardware and software of PANDA-film are included. Modular design ; Wetting ; Bottleneck ; Polymer ; Process (computing) ; Contact angle ; Characterization (materials science) Block Copolymer Self-Assembly ; Polymer Surface Interaction Studies ; Conducting polymers and applications
Authors: Harley Quinn | Gregory A. Robben | Zhaoyi Zheng | Jin Yan | Yuanzhi Li | Zhaoji Yang | Maria Politi | Nadya Peek | Lilo D. Pozzo | Jörg G. Werner | Keith A. Brown
Source: Review of Scientific Instruments (article, 2026-02-01)
Field: Materials Science — Surfaces, Coatings and Films

An automated system for electrodeposition of polymer thin films and their wetting analysis Thin polymer films are widely used as functional and protective coatings. However, determining the composition and processing conditions that produce a desired function is a tedious process due to the large number of factors that must be considered and the manual nature of most synthesis and characterization methods. Self-driving labs (SDLs), or robotic systems that prepare and test material samples, are designed to overcome this bottleneck by enabling the efficient exploration of complex parameter spaces. In this paper, we report the development and testing of the polymer analysis and discovery array (PANDA)-film, a modular SDL for electrochemically synthesizing polymer films and then determining their water contact angle as a measure of surface energy. The system is designed to be highly modular and based upon a low-cost gantry platform to facilitate adoption. In addition to validating fluid handling and electrochemical tasks, we introduce two novel modular capabilities that enable PANDA-film to run sustained campaigns to study the wetting properties of films: (1) an electromagnetic capping/decapping system to mitigate fluid evaporation and (2) a top-down optical method to determine water contact angle based upon reflectance. These capabilities are validated by depositing and characterizing a poly(allyl methacrylate) film using electrodeposition of polymer networks. Comprehensive details for replicating the hardware and software of PANDA-film are included. Modular design ; Wetting ; Polymer ; Contact angle ; Bottleneck ; Process (computing) ; Characterization (materials science) Polymer Surface Interaction Studies ; Nanomaterials and Printing Technologies ; Block Copolymer Self-Assembly
Authors: Boris N. Slautin | Sergei Kalinin
Source: Open MIND (preprint, 2026-02-08)
Field: Materials Science — Materials Chemistry

Sequential versus Manifold Bayesian Optimization under Realistic Experimental Time Constraints Bayesian optimization (BO) is widely used for autonomous materials discovery, yet its classical sequential formulation is insufficient for design of experimental workflows that often combine parallel or batch synthesis with inherently serial characterization. Methods such as combinatorial spread libraries and printed libraries sample a defined low-D manifold in the chemical space of the system. Here, we introduce a time-aware framework for comparing sequential and manifold BO under experimentally realistic constraints. By explicitly modeling synthesis and characterization times, we define an effective experimental time metric that enables fair, time-normalized benchmarking of optimization strategies. Using numerical experiments in ternary and quaternary compositional spaces, we show that sequential BO remains optimal for short-term experiments or when batching provides no effective time advantage, whereas manifold BO becomes favorable once multiplexed synthesis enables faster accumulation of measurements. We identify a small set of physically interpretable parameters that govern the transition between these regimes. These results establish a general, experimentally grounded framework for selecting optimization strategies in self-driving laboratories and autonomous materials discovery workflows. The accompanying analysis code is publicly available at https://github.com/Slautin/2025_GP_BO_Manifolds. Bayesian optimization ; Metric (unit) ; Workflow ; Manifold (fluid mechanics) ; Set (abstract data type) ; Benchmarking ; Code (set theory) ; Sequence (biology) ; Optimization problem Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods