jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Martine Petion | Matthew Shannon
Source: The Chemical Educator (article, 2026-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Investigation of Fluorinated β-Diketones by DFT and NMR Spectroscopy for Upper-Level Chemistry Laboratories In this study, various trifluoro β-diketones are explored using density functional theory and 19F Nuclear Magnetic Resonance (NMR) in four aprotic solvents with wide-ranging polarities. The ΔG° of tautomerization (diketone ⇌ enol) of each compound was calculated both experimentally and computationally. By examining how structural features of unique fluorinated β-diketones in different solvent environments affect diketone ⇌ enol equilibrium, students can develop a direct link between molecular structure and thermodynamic behavior. Students also evaluate the chemical accuracy of their computational results, which helps them understand both the power and limitations of computational chemistry. Furthermore, it challenges them to critically think about how model reliability may depend on molecular structure and solvent environment. This experiment is easily accessible and adaptable to a wide range of courses (organic chemistry, physical chemistry, computational chemistry, and advanced instrumental analysis) due to the simplicity, cost, and availability of the compounds. This laboratory module is pedagogically effective because it integrates hands-on spectroscopic analysis using straightforward 19F NMR data, applies thermodynamic analysis to real chemical systems, and uses readily available computational tools to interpret experimental results. Overall, the laboratory module promotes active learning, cross-disciplinary thinking, and deeper student understanding of tautomerization and thermodynamics. Tautomer ; Solvent ; Density functional theory ; Keto–enol tautomerism ; Enol ; Nuclear magnetic resonance spectroscopy ; Chemical shift ; Molecule Various Chemistry Research Topics ; Inorganic and Organometallic Chemistry ; Fluorine in Organic Chemistry
Authors: Zhongwei Yu | Rasul Tutunov | Alexandre Maraval | Zikai Xie | Zhenzhi Tan | Jiankang Wang | Zijing Li | Liangliang Xu | Qi Yang | Jun Jiang | Sanzhong Luo | Zhenxiao Guo | Haitham Bou-Ammar | Jun Wang
Source: ArXiv.org (article, 2026-04-01)
Field: Materials Science — Materials Chemistry

Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial Traditional scientific discovery relies on an iterative hypothesise-experiment-refine cycle that has driven progress for centuries, but its intuitive, ad-hoc implementation often wastes resources, yields inefficient designs, and misses critical insights. This tutorial presents Bayesian Optimisation (BO), a principled probability-driven framework that formalises and automates this core scientific cycle. BO uses surrogate models (e.g., Gaussian processes) to model empirical observations as evolving hypotheses, and acquisition functions to guide experiment selection, balancing exploitation of known knowledge and exploration of uncharted domains to eliminate guesswork and manual trial-and-error. We first frame scientific discovery as an optimisation problem, then unpack BO's core components, end-to-end workflows, and real-world efficacy via case studies in catalysis, materials science, organic synthesis, and molecule discovery. We also cover critical technical extensions for scientific applications, including batched experimentation, heteroscedasticity, contextual optimisation, and human-in-the-loop integration. Tailored for a broad audience, this tutorial bridges AI advances in BO with practical natural science applications, offering tiered content to empower cross-disciplinary researchers to design more efficient experiments and accelerate principled scientific discovery. Scientific discovery ; Frame (networking) ; Cover (algebra) ; Exploit ; Bayesian probability ; Core (optical fiber) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Gaussian Processes and Bayesian Inference
Authors: Zhongwei Yu | Rasul Tutunov | Alexandre Maraval | Zikai Xie | Zhenzhi Tan | Jiankang Wang | Zijing Li | Liangliang Xu | Qi Yang | Jun Jiang | Sanzhong Luo | Zhenxiao Guo | Haitham Bou-Ammar | Jun Wang | Bou-Ammar, Haitham | Wang, Jun
Source: arXiv (Cornell University) (preprint, 2026-04-01)
Field: Materials Science — Materials Chemistry

Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial Traditional scientific discovery relies on an iterative hypothesise-experiment-refine cycle that has driven progress for centuries, but its intuitive, ad-hoc implementation often wastes resources, yields inefficient designs, and misses critical insights. This tutorial presents Bayesian Optimisation (BO), a principled probability-driven framework that formalises and automates this core scientific cycle. BO uses surrogate models (e.g., Gaussian processes) to model empirical observations as evolving hypotheses, and acquisition functions to guide experiment selection, balancing exploitation of known knowledge and exploration of uncharted domains to eliminate guesswork and manual trial-and-error. We first frame scientific discovery as an optimisation problem, then unpack BO's core components, end-to-end workflows, and real-world efficacy via case studies in catalysis, materials science, organic synthesis, and molecule discovery. We also cover critical technical extensions for scientific applications, including batched experimentation, heteroscedasticity, contextual optimisation, and human-in-the-loop integration. Tailored for a broad audience, this tutorial bridges AI advances in BO with practical natural science applications, offering tiered content to empower cross-disciplinary researchers to design more efficient experiments and accelerate principled scientific discovery. Scientific discovery ; Frame (networking) ; Cover (algebra) ; Exploit ; Bayesian probability ; Core (optical fiber) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Gaussian Processes and Bayesian Inference
Authors: Eduardo F. F. Reis | João Felicidade | Fábio M. F. Santos
Source: Journal of Chemical Education (article, 2026-03-24)
Field: Chemistry — Physical and Theoretical Chemistry

Bright Multicomponent Chemistry: BASHY Synthesis This laboratory session introduces undergraduate students to the principles of multicomponent reactions (MCRs) and fluorescence through the one-pot four-component synthesis of a boronic acid-derived salicylidenehydrazone (BASHY) dye from nonfluorescent precursors. The proposed experiment exemplifies the atom economy, efficiency, and structural complexity of MCRs while showcasing its potential in the development of novel fluorescent architectures with applications in bioimaging and therapeutics. By combining synthetic design, mechanistic reasoning, and spectroscopic interpretation, this activity fosters active learning and strengthens students’ knowledge of fundamental chemical concepts, including the reactivity of carbonyl and amine functional groups, and structural characterization. Overall, the BASHY synthesis provides a versatile, engaging, and conceptually rich platform for teaching modern synthetic methodologies and the molecular principles of fluorescence. Fluorescence ; Reactivity (psychology) ; Amine gas treating ; Chemical synthesis ; Session (web analytics) ; Boronic acid Various Chemistry Research Topics ; Molecular Sensors and Ion Detection ; Multicomponent Synthesis of Heterocycles
Authors: Dononelli, Wilke
Source: Open MIND (preprint, 2026-02-02)
Field: Materials Science — Materials Chemistry

Integration of Variational Quantum Algorithms into Atomistic Simulation Workflows In this work, we present the integration of Qiskit Nature's quantum chemistry solvers into the Atomic Simulation Environment (ASE), enabling hybrid quantum-classical workflows for force-driven atomistic simulations. This coupling allows the use of the Variational Quantum Eigensolver (VQE) and its adaptive variant (ADAPT-VQE) not only for ground-state energy calculations, but also for geometry optimisation, vibrational frequency analysis, strain evaluation, and molecular dynamics, all managed through ASE's calculator interface. By applying ADAPT-VQE to multi-electron systems such as BeH2, we obtain vibrational and structural properties in close agreement with high-level classical CCSD calculations within the same minimal basis. These results demonstrate that adaptive variational quantum algorithms can deliver stable and chemically meaningful forces within an atomistic modelling workflow, enabling downstream applications such as molecular dynamics and active-learning accelerated simulations. Quantum ; Molecular dynamics ; Coupling (piping) ; Workflow ; Quantum algorithm ; Quantum chemistry ; Energy (signal processing) ; Quantum dynamics ; Quantum simulator Machine Learning in Materials Science ; Mechanical and Optical Resonators ; Quantum Computing Algorithms and Architecture
Authors: Dononelli, Wilke
Source: ArXiv.org (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Integration of Variational Quantum Algorithms into Atomistic Simulation Workflows In this work, we present the integration of Qiskit Nature's quantum chemistry solvers into the Atomic Simulation Environment (ASE), enabling hybrid quantum-classical workflows for force-driven atomistic simulations. This coupling allows the use of the Variational Quantum Eigensolver (VQE) and its adaptive variant (ADAPT-VQE) not only for ground-state energy calculations, but also for geometry optimisation, vibrational frequency analysis, strain evaluation, and molecular dynamics, all managed through ASE's calculator interface. By applying ADAPT-VQE to multi-electron systems such as BeH2, we obtain vibrational and structural properties in close agreement with high-level classical CCSD calculations within the same minimal basis. These results demonstrate that adaptive variational quantum algorithms can deliver stable and chemically meaningful forces within an atomistic modelling workflow, enabling downstream applications such as molecular dynamics and active-learning accelerated simulations. Quantum ; Molecular dynamics ; Coupling (piping) ; Workflow ; Quantum algorithm ; Quantum chemistry ; Energy (signal processing) ; Quantum dynamics ; Quantum simulator Machine Learning in Materials Science ; Mechanical and Optical Resonators ; Quantum Computing Algorithms and Architecture
Authors: Smile Bansal | Sapna Sharma
Source: Journal of Phase Equilibria and Diffusion (article, 2026-04-13)
Field: Materials Science — Materials Chemistry

Artificial Neural Network Enhanced Modeling of Convection Driven Heat Transfer During Volumetric Freezing of Binary Eutectic Systems Eutectic system ; Heat sink ; Heat transfer ; Heat flux ; Convection ; Thermal ; Alloy Solidification and crystal growth phenomena ; Phase Change Materials Research ; Machine Learning in Materials Science
Authors: Fan Xie | Fan Xia | Zhangyao Xu | Han Han | Ziqi Wang | Qingda Zhu | Haochuan Chen | Fuchao Yan | Yifan Lian | Jingyu Sun | Jincan Zhang
Source: Nanoscale (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Low-temperature growth of high-quality multilayer graphene films on porous CuNi alloys for enhanced corrosion resistance Multilayer graphene (MLG) is an excellent protective material for engineering alloy in aqueous environments, owing to its high crystallinity and nm-scale thickness. However, achieving conformal deposition of high-quality MLG films on porous alloys without damaging their intricate microstructures remains a challenge. Herein, utilizing CuNi alloy with multiscale microstructures as surrogate models, we achieve conformal growth of high-quality, continuous, MLG films (thickness <5 nm) without damaging the porous structures at 700 °C, which is the lowest temperature reported to date for defect-free graphene growth. This breakthrough is mainly attributed to the use of methanol as the carbon source and the favorable catalytic activity of the CuNi substrates. Electrochemical tests in a corrosive solution show that the graphene-skinned porous alloy exhibits a 14-fold reduction in corrosion current density and an 8-fold increase in charge transfer resistance compared to the bare substrate. Furthermore, complete conformal coverage of MLG films is successfully validated on bulk-porous CuNi alloy, along with comparable protective efficacy. This work thus provides a universal low-temperature growth strategy for MLG on alloys with diverse structural features, enabling their stable service in aqueous environments and thus holding promise for applications in marine engineering and energy-related fields. Graphene ; Alloy ; Microstructure ; Corrosion ; Porosity ; Crystallinity ; Electrochemistry ; Current density Nanoporous metals and alloys ; Nanomaterials and Printing Technologies ; Corrosion Behavior and Inhibition
Authors: Leehie Aridor | Yuval Yanushpolsky | Elisheva Michman | Ido Hadar
Source: Journal of Chemical Education (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

3D Model of Photoluminescence in Soft-Semiconductors The incorporation of modern teaching aids, such as videos, pictures, and demonstrations, rather than relying solely on textbooks, can increase student engagement and support understanding of the material. This is particularly important when dealing with abstract or complex concepts, such as the behavior of excited charge carriers, and specifically excitons, in semiconductors, where learning aids become essential tools for comprehension. In this educational article, we propose a working model of photoluminescence in semiconductors, with particular emphasis on semiconductors with a soft crystal lattice in which self-trapped excitons can form. The model can be easily replicated using a simple 3D printer and standard electronic components. The lesson is grounded in constructivist learning theory and implemented through the predict–observe–explain (POE) framework, which promotes active learning by encouraging students to compare their prior conceptions with experimental outcomes and refine their understanding accordingly, a particularly effective approach for illustrating photoluminescence. The proposed lesson plan is applicable from middle school classrooms to undergraduate chemistry courses. Photoluminescence ; 3d model ; 3d printer ; 3D modeling ; Quantum dot Advanced Materials and Semiconductor Technologies ; Semiconductor Quantum Structures and Devices ; Silicon Nanostructures and Photoluminescence
Authors: W. Tanner Yorgason | Andrea Jokisaari | Christopher L. Muhich
Source: Computational Materials Science (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

Accelerating vacancy diffusion calculations by a DFT informed modified gaussian process regression method: A case study of austenitic 316 stainless steel Austenite ; Vacancy defect ; Work (physics) ; Thermal diffusivity ; Diffusion ; Austenitic stainless steel ; Arrhenius equation ; Activation energy Nuclear Materials and Properties ; High Temperature Alloys and Creep ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Apurva Kalia | Vladimir Porokhin | Benjamin T. Blackburn | Robyn A. C. Alba | Caroline M. Ajo‐Franklin | Emily Mevers | Soha Hassoun
Source: ACS Omega (article, 2026-02-23)
Field: Chemistry — Organic Chemistry

Role of Structural, Pharmacokinetic, and Energy Properties in the High-Throughput Prediction of Redox Potentials for Organic Molecules with Experimental Calibration Small organic compounds' reduction/oxidation (redox) potential is a key property that drives innumerable chemical and biological electron transfer reactions. However, experimental measurement of redox potential is time-consuming and expensive, yielding few and small experimental measured data sets. Computational methods have previously been applied to create redox predictors applicable only to a specific data set. In this work, we investigate the effectiveness of various descriptors, including structural and functional properties, molecular energies, and drug-like properties, to predict redox potential. We use Gaussian Process Regression (GPR) as a model, as it is suitable for fitting small data sets and has shown promise in predicting redox potential. We train and test our redox predictor on three organic molecule data sets with increasing structural diversity. We demonstrate that a GPR-based redox predictor using individual features or a combination of molecular descriptors (DFT energies, molecular descriptors, and ADME properties works well across quinone-based and a general organic data set. Finally, we test the trained model against an experimental data set of quinones to assess if the model makes predictions well correlated with experimental data. Redox ; Gaussian ; Quantitative structure–activity relationship ; Organic molecules ; Molecule ; Molecular descriptor ; ADME ; Experimental data ; Test set ; Calibration Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Metal-Catalyzed Oxygenation Mechanisms
Authors: Zhiyang Ni | Min Guo | Zhanghua Chen | Jianxin Dong | He JIANG
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Derivative-free optimization strategies for crystal plasticity parameter calibration: a systematic comparison and applicability analysis Prior probability ; Identifiability ; Calibration ; Robustness (evolution) ; Bayesian optimization ; Estimation theory ; Bayesian probability ; Particle swarm optimization ; Uncertainty quantification Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Microstructure and mechanical properties
Authors: Liam Myhill | Enrique Martinez | Sez Atamturktur Russcher
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Bayesian Model Calibration with Integrated Discrepancy: Addressing Inexact Dislocation Dynamics Models Calibration ; Benchmark (surveying) ; Bayesian probability ; Process (computing) ; Gaussian process ; Dislocation ; Novelty ; Bayesian inference ; Gaussian Microstructure and mechanical properties ; Markov Chains and Monte Carlo Methods ; Model Reduction and Neural Networks
Authors: Robert Buarque de Macedo | Craig M. Hamel | Kyle Johnson
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

3D One-shot Geometry Agnostic Prediction of Non-linear Porous Microstructure Mechanics using Graph Neural Networks Artificial neural network ; Finite element method ; Convolutional neural network ; Microstructure ; Robustness (evolution) ; Complex geometry ; Porous medium ; Viscoplasticity ; Porosity Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Graph Neural Networks
Authors: Tianxiang Li | Ayat Hussein Adhab | Vicky Jain | Anurag Yadav | R Roopashree | Aditya Kashyap | Suman Saini | Pushpa Negi Bhakuni | Shaxnoza Saydaxmetova | Morug Salih Mahdi | Aseel Salah Mansoor | Usama Kadem Radi | Nasr Saadoun | Hojjat Abbasi
Source: Chemical Papers (article, 2026-01-30)
Field: Materials Science — Polymers and Plastics

Development of hybrid smart models to accurately model nano-polyethylene glycol composite viscosity Mean squared error ; Viscosity ; Bayesian optimization ; Correlation coefficient ; Nano- ; Shear rate ; Approximation error ; Mean absolute percentage error ; Sensitivity (control systems) Polymer Nanocomposites and Properties ; Composite Material Mechanics ; Machine Learning in Materials Science
Authors: María Isabel Galindo | Laura Lucia Luna | Paola Gómez Buitrago | Danny Arteaga
Source: Biochemistry and Molecular Biology Education (article, 2026-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Carb Detective: Developing a Compelling Board Game to Learn About the Classification of Monosaccharides The development of active learning strategies that position students as the main agents in generating their own knowledge has gained significant momentum in recent years. In particular, serious games have emerged as effective pedagogical tools in chemistry and related subjects. These games actively engage students with the material, increasing their interest and commitment to learning. Herein, we discuss the creation of a pedagogical board game and its implementation in the biochemistry class, as a playful tool for learning carbohydrates specifically monosaccharides. "Carb Detective" was designed to include the essential elements needed to create engaging and enjoyable learning experiences. It was tested with 89 students aged 18-20 years old who were enrolled in general biochemistry courses. The results of this quasi-experimental educational intervention indicated that the game enabled students to learn several key concepts, including the classification of monosaccharides based on their functional groups, configuration, and the number of carbon atoms. Furthermore, students gained exposure to identify epimers and anomeric carbons, while also studying their stereochemistry, which enhanced their ability to apply these concepts in practical contexts. The findings also suggest that the game promoted competence, motivated students, and improved their understanding of the topic. It provided immediate feedback on "yes" or "no" questions, helping students to correct errors and gain a deeper understanding of the material. Monosaccharide ; Key (lock) ; Active learning (machine learning) ; On board ; Intervention (counseling) ; Game based learning Various Chemistry Research Topics ; Science Education and Pedagogy ; Innovative Teaching Methods
Authors: Haoran Ma | Yuchen Zheng | L. Zhang | Xiaofei Chen | Dan Wang
Source: arXiv (Cornell University) (preprint, 2026-03-20)
Field: Materials Science — Materials Chemistry

Transformer-based prediction of two-dimensional material electronic properties under elastic strain engineering Strain engineering provides a powerful route for tuning the electronic properties of two-dimensional (2D) materials, but exploring the full multidimensional strain space with density functional theory (DFT) is computationally prohibitive due to the nonlinear coupling between normal and shear components. In this work, we introduce a Transformer-based, multi-target surrogate model framework that achieves DFT-level bandgap prediction accuracy, reaching a mean absolute error of 0.0103 eV while retaining full interpretability through attention-weight analysis. The learned self-attention map consistently identifies shear strain as the interaction center that influences both bandgap and phonon stability, an insight not readily captured by classical feature-importance metrics. This work establishes attention-based architectures as physically interpretable surrogate models for multi-property prediction, offering a generalizable strategy for accelerating deep elastic strain engineering in materials informatics. Interpretability ; Nonlinear system ; Coupling (piping) ; Work (physics) ; Strain (injury) ; Strain engineering ; Material properties ; Band gap 2D Materials and Applications ; Machine Learning in Materials Science ; Graphene research and applications