jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Uriel Rivera-González | Iván Antonio Nieves-Quiñones
Source: Journal of Chemical Education (article, 2025-12-30)
Field: Chemistry — Physical and Theoretical Chemistry

Active Learning to Reinforce the Use of Quality Parameters in Quantitative Chemical Analysis While active learning has shown benefits in chemistry education, there is limited application of these strategies specifically aimed at strengthening students’ understanding of quality parameters in laboratory settings. This study explores the effectiveness of an educational activity based on an active learning approach to reinforce the use of quality parameters in quantitative chemical analysis. The activity involved undergraduate students in the context of the Laboratory of Quantitative Analytical Chemistry. The students worked in collaborative groups, making decisions about the design of their procedures and the implemented analysis. In the laboratory experiment, the students evaluated two methods to determine ascorbic acid: implementing the use of electrodes modified or not with gold nanoparticles and applying the square wave voltammetry technique. The learning outcomes were evaluated through pre- and post-tests, resulting in a gain in subject knowledge with the intervention. Also, the participants expressed their satisfaction with the experiment, recognizing different attributes of the experience, such as the application of prior knowledge, the integration of an electroanalytical method, the opportunity to decide their implemented methodology, and teamwork. The integration of active learning in the laboratory curriculum not only achieved a gain of subject content among the participants but also resulted in a rewarding experience for them. This study serves as a model for curricular upgrades to integrate experimental laboratories based on an active learning approach to improve the academic experience and preparation of future chemists. Active learning (machine learning) ; Quality (philosophy) ; Context (archaeology) ; Curriculum ; Teaching method ; Ascorbic acid Various Chemistry Research Topics ; Innovative Teaching Methods ; Science Education and Pedagogy
Authors: Hyun-Jong Kim | Suyoung Chi | Yongkwi Lee
Source:  (article, 2025-10-27)
Field: Materials Science — Materials Chemistry

Excel-in-the-Loop Active Learning on KoMaP for Consumer Electronics Materials, with Extension to Battery Electrode AI Autonomous Manufacturing We present Excel-in-the-Loop Active Learning on KoMaP, a researcher-in-the-loop platform for consumer-electronics (CE) materials that operationalizes active learning and Bayesian optimization (AL–BO) through a familiar Excel-based workflow. KoMaP’s next-experiment recommendation engine uses a Gaussian-process surrogate with calibrated uncertainty to propose feasible batches under domain constraints (e.g., ternary compositional sum-to-100%, low-temperature processing windows) and constrained acquisitions (EI/UCB; c-EHVI for multi-objective runs). Practical features—spreadsheet I/O, auditable logs, and SHAP-based interpretability—lower adoption barriers while preserving transparency and traceability. We prototype the service in Streamlit and demonstrate it on a 10-round perovskite stability optimization, where the loop rapidly concentrates on low-instability regions and reduces experiment count versus grid/random baselines, all while honoring CE-relevant limits. Beyond single-objective stability, KoMaP supports multi-objective optimization (e.g., stability–efficiency–Tmax–cycle-time–cost), exposing Pareto candidates and user-steerable preferences for CE trade-off decisions. Finally, we outline an extension from lab to line toward battery electrode AI autonomous manufacturing: KoMaP’s recommendations and uncertainty metrics feed quality-prediction and productivity objectives on the shop floor, enabling factory-in-the-loop optimization and compatibility with future robotic execution. Together, KoMaP bridges advanced AL–BO methods and everyday experimental practice, empowering CE-oriented materials R&D to "learn more from fewer experiments" without disrupting existing workflows. Bayesian optimization ; Electronics ; Active learning (machine learning) ; Pareto principle ; Transparency (behavior) ; Battery (electricity) ; Multi-objective optimization Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Gaussian Processes and Bayesian Inference
Authors: Akhil S. Nair | Lucas Foppa | Matthias Scheffler
Source: ChemRxiv (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

Interpretable Bayesian Optimization for Catalyst Discovery Bayesian optimization ; Key (lock) ; Probabilistic logic ; Property (philosophy) ; Bayesian probability ; Selection (genetic algorithm) ; Reduction (mathematics) ; Independence (probability theory) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Natalia BAZAN | Diana Chisca
Source:  (article, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Chemical experiment: the key to active learning and student motivation for the study of chemistry Learning real sciences, especially chemistry, can be challenging for many students. Curiosity is the inner drive for learning, essential in acquiring and exploring knowledge. Students learn chemitry more effectively through direct experiences, measuring, touching, observing, and recording data. These practical activities are fundamental in teaching chemistry, stimulating students' interest and motivation. The teacher organizes classroom instruction, including clear objectives and discussions that develop students' curiosity towards chemistry. Chemical experiments in school are not only a teaching method but also a valuable means of forming cognitive and practical skills, contributing to a deep understanding of chemical phenomena and the application of knowledge in daily life. Curiosity ; Key (lock) ; Active learning (machine learning) ; Chemistry education ; Cognition ; Discovery learning Various Chemistry Research Topics
Authors: Armin
Shayesteh Zadeh (22313613) | Alexander J. Winton (22313616) | Joseph M. Palomba (1752169) | Andrew L. Ferguson (491758)
Source: Figshare (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

High-ThroughputVirtual Screening of Protein-CatalyzedCapture Agents for Novel Hydrogel-Nanoparticle Fentanyl Sensors Fast, portable, and reliable detection of chemical and biological compounds is an important challenge in many safety and healthcare applications. Chemical sensors based on photonic crystals that use protein-catalyzed capture (PCC) agents as molecular sensors are promising tools for the portable detection of chemical and biological compounds. We present the development and deployment of a high-throughput virtual screening protocol to computationally identify PCC candidates that maximize the binding sensitivity and selectivity for fentanyl. The approach integrates enhanced sampling molecular dynamics free-energy calculations, Gaussian process regression surrogate models, and Bayesian optimization to efficiently navigate the design space of over 1 million PCC candidates, resolve the sensitivity–selectivity Pareto frontier, and identify the top-performing PCC candidates. We analyze the molecular interactions between our top candidates and fentanyl target to propose design rules for high-performance PCC agents to be used for experimental testing and, ultimately, incorporation into next-generation hydrogel–nanoparticle-based chemical sensing devices. Bayesian optimization ; Virtual screening ; Gaussian process ; Chemical space ; Bayesian probability ; Protocol (science) ; Chemical process ; Sensitivity (control systems) Machine Learning in Materials Science ; Advanced Biosensing Techniques and Applications ; Data Visualization and Analytics
Authors: Pranoy Ray | Asoma Yuichiro | Nikhith Vankireddy | Adam P. Generale | Nakauchi Masataka | Lee Haein | Katsuhisa Yoshida | Surya R. Kalidindi | Okuno Yoshishige
Source: ChemRxiv (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Assessing the accuracy of Bayesian-optimized CGMD in predicting polymer miscibility Miscibility ; Radius of gyration ; Polymer ; Polystyrene ; Molecular dynamics ; Polymer blend ; Force field (fiction) ; Work (physics) ; Hildebrand solubility parameter Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Subhadeep Pal
Source: Open MIND (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Overcoming Strength-Toughness Trade-off in Polymer Nanocomposites through Active Learning-guided Molecular Simulations Polymer ; Nanoparticle ; Polymer nanocomposite ; Process (computing) ; Work (physics) Polymer Nanocomposites and Properties ; Nanopore and Nanochannel Transport Studies ; Block Copolymer Self-Assembly
Authors: Muhammad Malik Nawaz Khan | Joo-Sung Lee | Chul‐Jin Lee
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Surrogate Modeling of Ammonia Decomposition Reactor and Catalyst Loading Optimization with Genetic Algorithm Decomposition ; Catalysis ; Ammonia ; Ammonia production ; Genetic algorithm ; Surrogate model ; Computer science ; Chemical engineering ; Algorithm ; Mathematical optimization ; Chemistry ; Materials science ; Mathematics ; Engineering ; Organic chemistry Catalytic Processes in Materials Science
Authors: Ndenga Lumbu Barack
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Spectrally-Driven Active Learning Enables Femtojoule-Efficient Discovery of Photocatalysts in Under One Hour: The LuminaFemto AI Platform Workflow ; Energy consumption ; Energy (signal processing) ; Efficient energy use ; Function (biology) ; Active learning (machine learning) ; Nonlinear system Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; Catalysis and Oxidation Reactions
Authors: Sylvain Le Tonquesse (7870901) | Laura Agnarelli (11327113) | Swathi Sakthivel (21739221)
Source: Figshare (article, 2025-07-18)
Field: Materials Science — Materials Chemistry

Bayesian Optimizationof the Experimental Parametersof Material Synthesis: Application to the Magnesioreduction of Rare-Earth-Free(Mn<sub>1–<i>x</i></sub>Fe<sub><i>x</i></sub>)<sub>5</sub>Si<sub>3</sub> Magnetocalorics Optimizing complex synthesis processes remains a significant challenge in materials science, particularly when high phase purity must be achieved in complex multivariable systems with restricted experimental throughput. Here, we demonstrate the application of a fully experimental Bayesian Optimization (BO) framework to efficiently optimize the synthesis conditions of (Mn<sub>1–<i>x</i></sub>Fe<sub><i>x</i></sub>)<sub>5</sub>Si<sub>3</sub>, a rare-earth-free magnetocaloric material. Using magnesioreduction as the synthesis route, seven experimental parameters were iteratively refined via active learning to maximize phase purity. Phase-pure samples, including the challenging peritectoid-forming Fe<sub>5</sub>Si<sub>3</sub>, were obtained within five active learning iterations. The approach generalizes to unseen compositions and yields chemically meaningful insights via SHAP analysis of the trained Gaussian Process model, identifying maximum temperature and SiO<sub>2</sub> excess as key drivers of phase purity. X-ray diffraction and scanning electron microscopy confirmed high crystallinity, uniform submicron grain size, and accurate compositional control in the optimized samples. Magnetic measurements revealed a near-room-temperature magnetocaloric effect in ferromagnetic Mn<sub>1.25</sub>Fe<sub>3.75</sub>Si<sub>3</sub>, with a peak −Δ<i>S</i><sub>mag</sub> of 1.06 J kg<sup>–1</sup> K<sup>–1</sup> at 275 K under a 2 T magnetic field change. This study establishes BO as a powerful and data-efficient framework for accelerating the synthesis of functional materials from small experimental data sets. Magnetic refrigeration ; Bayesian optimization ; Ferromagnetism ; Phase (matter) ; Gaussian process ; Process (computing) ; Bayesian probability ; Gaussian Machine Learning in Materials Science ; Magnetic and transport properties of perovskites and related materials ; Heusler alloys: electronic and magnetic properties
Authors: Vinson Liao (17310753) | Arthi Jayaraman (629537)
Source: Figshare (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

Inverse Design ofBlock Polymer Materials with DesiredNanoscale Structure and Macroscale Properties The rational design of novel polymers with tailored material properties has been a long-standing challenge in the field due to the large number of possible polymer design variables. To accelerate this design process, there is a critical need to develop novel tools to aid in the inverse design process and to efficiently explore the high-dimensional polymer design space. Optimizing macroscale material properties for polymeric systems is even more challenging than inorganics and small molecules as these properties are dictated by features on a multitude of length scales, ranging from the chosen monomer chemistries to the chain level design to larger-scale (nm to microns) domain structures. In this work, we present an efficient high-throughput in-silico based framework to effectively design high-performance polymers (blends, copolymers) with desired multiscale nanostructure and macroscale properties which we call RAPSIDY 2.0 - Rapid Analysis of Polymer Structure and Inverse Design strategY 2.0. This new version of RAPSIDY builds upon our previous work, RAPSIDY 1.0, which focused purely on identifying polymer designs that stabilized a desired nanoscale morphology. In RAPSIDY 2.0 we use a combination of molecular dynamics (MD) simulations and Bayesian optimization driven active learning to optimally query high-dimensional polymer design spaces and propose promising design candidates that simultaneously stabilize a selected nanoscale morphology and exhibit desired macroscale material properties (e.g., tensile strength, thermal conductivity). We utilize MD simulations with polymer chains preplaced into selected nanoscale morphologies and perform virtual experiments to determine the stability of the chosen polymer design within the target morphology and calculate the desired macroscale material properties. Our methodology directly addresses the unique challenge associated with copolymers whose macroscale properties are a function of both their chain design and mesoscale morphology, which are coupled. We showcase the efficacy of our methodology in engineering high-performance blends of block copolymers that exhibit (1) high thermal conductivity and (2) high tensile strength. We also discuss the impact of our work in accelerating the design of novel polymeric materials for targeted applications. Material Design ; Nanoscopic scale ; Rational design ; Polymer ; Nanostructure ; Inverse ; Block (permutation group theory) ; Multiscale modeling ; Bayesian optimization Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Qi Chen | Fábio Ramos | Alán Aspuru-Guzik | Florian Shkurti
Source: ArXiv.org (preprint, 2025-12-15)
Field: Materials Science — Materials Chemistry

Informing Acquisition Functions via Foundation Models for Molecular Discovery Bayesian Optimization (BO) is a key methodology for accelerating molecular discovery by estimating the mapping from molecules to their properties while seeking the optimal candidate. Typically, BO iteratively updates a probabilistic surrogate model of this mapping and optimizes acquisition functions derived from the model to guide molecule selection. However, its performance is limited in low-data regimes with insufficient prior knowledge and vast candidate spaces. Large language models (LLMs) and chemistry foundation models offer rich priors to enhance BO, but high-dimensional features, costly in-context learning, and the computational burden of deep Bayesian surrogates hinder their full utilization. To address these challenges, we propose a likelihood-free BO method that bypasses explicit surrogate modeling and directly leverages priors from general LLMs and chemistry-specific foundation models to inform acquisition functions. Our method also learns a tree-structured partition of the molecular search space with local acquisition functions, enabling efficient candidate selection via Monte Carlo Tree Search. By further incorporating coarse-grained LLM-based clustering, it substantially improves scalability to large candidate sets by restricting acquisition function evaluations to clusters with statistically higher property values. We show through extensive experiments and ablations that the proposed method substantially improves scalability, robustness, and sample efficiency in LLM-guided BO for molecular discovery. Scalability ; Prior probability ; Probabilistic logic ; Bayesian probability ; Tree (set theory) ; Knowledge acquisition ; Selection (genetic algorithm) ; Function (biology) ; Key (lock) Machine Learning in Materials Science ; Machine Learning and Data Classification ; Computational Drug Discovery Methods
Authors: Ramsey Issa | Said Hamad | Ricardo Grau-Crespo | Emad Awad | Taylor D. Sparks
Source: ChemRxiv (article, 2025-07-07)
Field: Materials Science — Materials Chemistry

Improving Model Generalizability in Materials Informatics via Active Learning with Integrated Posterior Variance Sampling Generalizability theory ; Active learning (machine learning) ; Sampling (signal processing) ; Variance (accounting) ; Informatics Machine Learning in Materials Science ; Machine Learning and Algorithms ; Model Reduction and Neural Networks
Authors: Matthew S. Johnson | Judit Zádor
Source: ChemRxiv (article, 2025-09-23)
Field: Chemistry — Physical and Theoretical Chemistry

Automatic Calculation of Kinetic Coverage Dependence using Pynta: Combining Active Learning and Automated Quantum Chemistry Quantum chemistry ; Quantum ; Dissociation (chemistry) ; Transition state ; Decomposition ; Kinetic energy ; Software ; Node (physics) ; Work (physics) Various Chemistry Research Topics
Authors: Timothy Quah (4899439) | Charles Li (3396221) | Noah P. Wamble (13615653) | Elizabeth A. Murphy (841367) | Christopher M. Bates (1657204) | Craig J. Hawker (675327) | Benny D. Freeman (1419337) | M. Scott Shell (267372) | Glenn H. Fredrickson (1436974)
Source: Figshare (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

Data-EfficientMethods for Determining Flory–Hugginsχ Parameters in Multicomponent Polymer Formulations Polymer formulations are essential in diverse applications including personal care products, coatings, paints, adhesives, and plastic materials. Designing these formulations requires navigating large, complex design spaces, where phase and self-assembly behavior critically impact performance. The Flory–Huggins χ parameter, which quantifies segmental miscibility, is widely used to parametrize the excess free energy of mixing in formulation models. In this work, we introduce two data-efficient, top-down methods for estimating χ parameters using the Random Phase Approximation (RPA): (i) Boundary Nonlinear Regression (Boundary-NLR), which fits theoretical spinodal boundaries to experimental phase boundaries, and (ii) Surrogate Model Inverse Parameter Estimation (SMIPE), which uses a Gaussian Process Classifier to fit sparse phase maps via a surrogate model. Both methods allow rapid parametrization of polymer field-theoretic models without the need for additional experiments. We evaluate these approaches on data sets involving polymer–solvent–nonsolvent ternary mixtures and block copolymer–solvent systems, demonstrating their robustness to experimental noise and their relevance for real-world formulation design. Spinodal ; Robustness (evolution) ; Nonlinear system ; Parametrization (atmospheric modeling) ; Estimation theory ; Inverse problem ; Phase boundary ; Tikhonov regularization ; Gaussian ; Regularization (linguistics) Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Model Reduction and Neural Networks
Authors: Timothy Quah | Charles Li | Noah P. Wamble | Elizabeth Murphy | Christopher M. Bates | Craig J. Hawker | Benny D. Freeman | M. Scott Shell | Glenn H. Fredrickson
Source: Macromolecules (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

Data-Efficient Methods for Determining Flory–Huggins χ Parameters in Multicomponent Polymer Formulations Polymer formulations are essential in diverse applications including personal care products, coatings, paints, adhesives, and plastic materials. Designing these formulations requires navigating large, complex design spaces, where phase and self-assembly behavior critically impact performance. The Flory–Huggins χ parameter, which quantifies segmental miscibility, is widely used to parametrize the excess free energy of mixing in formulation models. In this work, we introduce two data-efficient, top-down methods for estimating χ parameters using the Random Phase Approximation (RPA): (i) Boundary Nonlinear Regression (Boundary-NLR), which fits theoretical spinodal boundaries to experimental phase boundaries, and (ii) Surrogate Model Inverse Parameter Estimation (SMIPE), which uses a Gaussian Process Classifier to fit sparse phase maps via a surrogate model. Both methods allow rapid parametrization of polymer field-theoretic models without the need for additional experiments. We evaluate these approaches on data sets involving polymer–solvent–nonsolvent ternary mixtures and block copolymer–solvent systems, demonstrating their robustness to experimental noise and their relevance for real-world formulation design. Spinodal ; Parametrization (atmospheric modeling) ; Phase boundary ; Nonlinear system ; Robustness (evolution) ; Ternary operation ; Polymer ; Inverse problem ; Gaussian ; Estimation theory Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Model Reduction and Neural Networks
Authors: Bo Song | Tao Liu | Mingyan Zhao | Yan Cui | Yuanjun Li
Source: IFAC-PapersOnLine (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Surrogate modeling and control optimization of batch crystallization process of β form LGA To describe a quantitative relationship between the operating conditions of cooling crystallization process and product crystal size distribution (CSD), a surrogate modelling method based on the Gaussian process regression (GPR) is proposed by using only experimental data of batch crystallization process of β form L-glutamic acid (LGA). A modified design of experiments (DoE) is presented to reduce the number of batch crystallization experiments. Based on the surrogate model, an objective function reflecting the concentration of product CSD and desired yield is introduced to optimize these operating conditions. Experiments on the seeded cooling crystallization process of β -LGA are conducted to verify the effectiveness and advantage of the proposed method. Crystallization ; Process (computing) ; Surrogate model ; Computer science ; Mathematical optimization ; Mathematics ; Engineering ; Chemical engineering ; Programming language Crystallization and Solubility Studies ; Advanced Control Systems Optimization ; Mineral Processing and Grinding