jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kamyar Barakati | Haochen Zhu | C Charlotte Buchanan | Dustin A. Gilbert | Rack, Philip | Sergei V. Kalinin
Source: arXiv (Cornell University) (preprint, 2026-01-09)
Field: Materials Science — Materials Chemistry

Autonomous Probe Microscopy with Robust Bag-of-Features Multi-Objective Bayesian Optimization: Pareto-Front Mapping of Nanoscale Structure-Property Trade-Offs Combinatorial materials libraries are an efficient route to generate large families of candidate compositions, but their impact is often limited by the speed and depth of characterization and by the difficulty of extracting actionable structure-property relations from complex characterization data. Here we develop an autonomous scanning probe microscopy (SPM) framework that integrates automated atomic force and magnetic force microscopy (AFM/MFM) to rapidly explore magnetic and structural properties across combinatorial spread libraries. To enable automated exploration of systems without a clear optimization target, we introduce a combination of a static physics-informed bag-of-features (BoF) representation of measured surface morphology and magnetic structure with multi-objective Bayesian optimization (MOBO) to discover the relative significance and robustness of features. The resulting closed-loop workflow selectively samples the compositional gradient and reconstructs feature landscapes consistent with dense grid "ground truth" measurements. The resulting Pareto structure reveals where multiple nanoscale objectives are simultaneously optimized, where trade-offs between roughness, coherence, and magnetic contrast are unavoidable, and how families of compositions cluster into distinct functional regimes, thereby turning multi-feature imaging data into interpretable maps of competing structure-property trends. While demonstrated for Au-Co-Ni and AFM/MFM, the approach is general and can be extended to other combinatorial systems, imaging modalities, and feature sets, illustrating how feature-based MOBO and autonomous SPM can transform microscopy images from static data products into active feedback for real-time, multi-objective materials discovery. Robustness (evolution) ; Nanoscopic scale ; Scanning probe microscopy ; Bayesian optimization ; Characterization (materials science) ; Microscopy ; Workflow ; Grid Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Topological and Geometric Data Analysis
Authors: Kamyar Barakati | Haochen Zhu | C Charlotte Buchanan | Dustin A. Gilbert | Rack, Philip | Sergei V. Kalinin
Source: ArXiv.org (article, 2026-01-09)
Field: Materials Science — Materials Chemistry

Autonomous Probe Microscopy with Robust Bag-of-Features Multi-Objective Bayesian Optimization: Pareto-Front Mapping of Nanoscale Structure-Property Trade-Offs Combinatorial materials libraries are an efficient route to generate large families of candidate compositions, but their impact is often limited by the speed and depth of characterization and by the difficulty of extracting actionable structure-property relations from complex characterization data. Here we develop an autonomous scanning probe microscopy (SPM) framework that integrates automated atomic force and magnetic force microscopy (AFM/MFM) to rapidly explore magnetic and structural properties across combinatorial spread libraries. To enable automated exploration of systems without a clear optimization target, we introduce a combination of a static physics-informed bag-of-features (BoF) representation of measured surface morphology and magnetic structure with multi-objective Bayesian optimization (MOBO) to discover the relative significance and robustness of features. The resulting closed-loop workflow selectively samples the compositional gradient and reconstructs feature landscapes consistent with dense grid "ground truth" measurements. The resulting Pareto structure reveals where multiple nanoscale objectives are simultaneously optimized, where trade-offs between roughness, coherence, and magnetic contrast are unavoidable, and how families of compositions cluster into distinct functional regimes, thereby turning multi-feature imaging data into interpretable maps of competing structure-property trends. While demonstrated for Au-Co-Ni and AFM/MFM, the approach is general and can be extended to other combinatorial systems, imaging modalities, and feature sets, illustrating how feature-based MOBO and autonomous SPM can transform microscopy images from static data products into active feedback for real-time, multi-objective materials discovery. Robustness (evolution) ; Nanoscopic scale ; Scanning probe microscopy ; Bayesian optimization ; Characterization (materials science) ; Microscopy ; Workflow ; Grid Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Topological and Geometric Data Analysis
Authors: Brian Ignacio Machorro-Martínez | Ramsés H. Mena | Gustavo A. Chapela | Jacqueline Quintana-H
Source: Journal of Computational Science (article, 2026-03-18)
Field: Materials Science — Materials Chemistry

Self-assembling of polygons via machine learning Bayesian optimization (BO) is employed to determine the optimal parameters for a two-dimensional molecular building block designed to self-assemble into specific polygonal structures. The building block is a simplified model consisting of a central soft repulsive disk, decorated with two attractive patches near its perimeter, separated by an angle. The soft repulsion of the central disk using the Weeks-Chandler-Andersen (WCA) potential, while the patch attractions are modeled via the Lennard-Jones potential (LJ). The optimization seeks parameters that maximize an objective function defining the abundance of the target polygon while suppressing competing species. We introduce an inverse-design workflow that couples BO with molecular-dynamics (MD) simulations for polygon selection. A selectivity-driven objective function and polygon classifier guide the search toward high-yield conditions. Computationally, the BO–MD coupling identifies promising regions of parameter space with O ( 1 0 1 − 1 0 2 ) MD evaluations rather than a combinatorial scan. The workflow is transferable to other targets by redefining the objective/classifier and adjusting parameter bounds. The parameters optimized by BO are the patch separation angle, the radial distance from the disk center to the patch centers, and the cutoff radius of the attractive interactions; all other parameters (e.g., disk diameter and LJ well depth) are held constant. After optimization, canonical MD simulations are run for the optimized parameter sets, followed by density scans at fixed temperature. Results are presented as phase-diagram color maps for each target polygon as a function of its number of sides. Polygon (computer graphics) ; Workflow ; Bayesian optimization ; Block (permutation group theory) ; Parameter space ; Function (biology) ; Classifier (UML) ; Block size Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Supramolecular Self-Assembly in Materials
Authors: Brenden W. Hamilton | Travis E. Jones | Timothy C. Germann | Benjamin Nebgen
Source: Open MIND (preprint, 2026-02-18)
Field: Materials Science — Materials Chemistry

Stoichiometry Dependent Properties of Cerium Hydride: An Active Learning Developed Interatomic Potential Study Cerium hydride has a variety of interesting properties, including a known lattice contraction and densification with increasing hydrogen content. However, precise stoichiometric control is not experimentally straightforward and {\it ab initio} approaches are not computationally feasible for many properties such as melting and low temperature diffusion. Therefore, we develop a machine-learned interatomic potential for cerium hydride that is valid for H to Ce ratios from 2.0 to 3.0. A query-by-committee active learning approach is used to develop the training set. Leveraging classical molecular dynamics simulations, we assess a range of properties and provide fundamental mechanisms for the trends with stoichiometry. A majority of the properties follow the trend of lattice contraction, being governed by the stronger lattice binding induced by adding octahedral atoms. Stoichiometry ; Cerium ; Interatomic potential ; Lattice (music) ; Octahedron ; Hydride ; Hydrogen Machine Learning in Materials Science ; Hydrogen Storage and Materials ; Rare-earth and actinide compounds
Authors: Ping Lan | Minhui Dai | Fangming Zhao | Linlin Zeng | Rui Zhao | Weihua Tang | Meng Zhou
Source: Advanced Optical Materials (article, 2026-01-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Carrier Dynamics of <i>β</i> ‐Ga <sub>2</sub> O <sub>3</sub> Single Crystal Abstract β ‐Ga 2 O 3 is a promising ultra‐wide bandgap semiconductor with substantial potential for optoelectronic applications. A deeper understanding of its carrier dynamics is essential for optimizing device performance. In this study, the carrier dynamics in unintentionally doped β ‐Ga 2 O 3 (100) single crystals across a broad range of timescales, from sub‐picosecond to microseconds, are investigated. The sub‐picosecond dynamics are resolved into three distinct kinetic components: two Gaussian processes, which are attributed to nondegenerate two‐photon absorption, and a single‐exponential decay corresponding to hot carrier relaxation. Additionally, defect‐state‐mediated carrier recombination processes that occur on timescales ranging from picoseconds to microseconds are explored. These findings offer valuable insights into the photophysical properties of β ‐Ga 2 O 3 , providing a foundation for optimizing its performance in future optoelectronic devices. Picosecond ; Microsecond ; Free carrier ; Doping ; Single crystal ; Carrier lifetime ; Semiconductor ; Band gap ; Recombination Ga2O3 and related materials ; Copper-based nanomaterials and applications ; Advanced Photocatalysis Techniques
Authors: Soumya Ranjan Parimanik | Debadutta Mishra | Trupti Ranjan Mahapatra | Sumitkumar Rathor | Ravi Kant
Source: Engineering Computations (article, 2026-02-13)
Field: Materials Science — Materials Chemistry

NiTi wire welding using Fe, Cu and Ni fillers: multi-objective modelling and AHP–entropy approach to sustainability evaluation Purpose This research introduces a comprehensive framework for optimising fibre laser butt welding of NiTi shape memory alloy (SMA) wires, integrating experimental studies, predictive modelling and sustainability evaluation, with particular focus on the tensile strength–corrosion trade-off. Design/methodology/approach A 43-run central composite design (CCD) experiment was conducted to assess how laser power, pulse duration, frequency, wire diameter and filler powder types (Fe/Cu/Ni) influence tensile strength and corrosion rate. The resulting response patterns were examined using ANOVA and Pareto charts, and three modelling methods – adaptive neuro-fuzzy inference system (ANFIS), particle swarm optimised ANFIS (PSO-ANFIS) and Gaussian process regression (GPR) – were compared. A hybrid AHP–entropy approach was used to benchmark the sustainability of different welding techniques. Findings ANOVA and Pareto analysis confirmed pulse time, frequency and power as dominant factors for both responses. Fe filler produced tensile strengths over 400 MPa, while Ni filler had the lowest corrosion rate (∼0.001298 mm/year) and more uniform weld microstructures, showing a strength–corrosion trade-off. GPR model outperformed others with high accuracy for tensile strength (R2 = 0.8752; RMSE = 23.9706 MPa) and corrosion rate (R2 = 0.9565; RMSE = 0.0001 mm/year). Confirmatory tests supported predictions, and fibre laser welding was ranked most sustainable. Practical implications The integration of predictive modelling with sustainability evaluation offers a strong tool for optimising technical performance and environmental impact in NiTi SMA welding. This study's insights are vital for industrial engineering, where high-performance materials and sustainable manufacturing improve reliability, cut costs and ensure environmental compliance. Originality/value The proposed methodology offers a novel comparative perspective on modelling strategies, supporting informed decision-making in advanced manufacturing using a relatively small dataset. Welding ; Nickel titanium ; Ultimate tensile strength ; Laser beam welding ; Kriging ; Adaptive neuro fuzzy inference system ; Corrosion ; Particle swarm optimization ; Tensile testing Shape Memory Alloy Transformations ; Advanced Welding Techniques Analysis ; Welding Techniques and Residual Stresses
Authors: Tina M. Slowan-Pomeroy | Christine B. Emmons | David T. Crowther | Li Ke
Source: The American Biology Teacher (article, 2026-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

Teaching About Operons Using a Modified Version of Bybee’s 5E Learning Cycle The regulation of gene expression through operons is a core concept taught in the undergraduate microbiology classroom. However, it is one of the most difficult topics for students to understand, and teaching challenging subject matter using a traditional lecture methodology can overwhelm and frustrate students. Operon structure and function are not easily relatable for students; providing a real-world experience whereby students participate in hands-on operon activities is essential when teaching this material effectively. By modifying Bybee’s 5E learning cycle to fit the college classroom, students can engage by visualizing the phenomenon through picture observations. They then receive an explanation in a mini-lecture before exploring the concept by acting out the processes of inducible and repressible operons. Students then discuss the material with their peers to elaborate on and reinforce these ideas. Throughout the entire activity, the instructor provides immediate feedback to ensure accuracy of the information being learned. When surveyed, 78.9% of students found the experience to be a good use of classroom time. Teaching gene expression and regulation in this active learning manner fosters a more collaborative classroom student experience while also enhancing instructor communication and technique. Expression (computer science) ; Function (biology) ; Operon ; Subject (documents) ; Active learning (machine learning) ; Phenomenon ; Subject matter Various Chemistry Research Topics ; Science Education and Pedagogy ; Genetics, Bioinformatics, and Biomedical Research
Authors: Zihan Yan | Denan Li | Xin Wu | Zhoulin Liu | Chen Hua | Boyi Situ | Hao Yang | Shengjie Tang | Benrui Tang | Ziyang Wang | Shangzhao Yi | Huan Wang | Dian Huang | Ke Li | Qilin Guo | Zherui Chen | Ke Xu | Yanzhou Wang | Ziliang Wang | Gang Tang | Shi Liu | Zheyong Fan | Yizhou Zhu
Source: arXiv (Cornell University) (preprint, 2026-03-18)
Field: Materials Science — Materials Chemistry

GPUMDkit: A User-Friendly Toolkit for GPUMD and NEP Machine-learned interatomic potentials have revolutionized molecular dynamics simulations by providing quantum-mechanical accuracy at empirical-potential speeds. The graphics processing unit molecular dynamics (GPUMD) package, featuring the highly efficient neuroevolution potential (NEP) framework, has emerged as a powerful tool in this domain. However, the complexity of force field development, active learning, and trajectory post-processing often requires extensive manual scripting, imposing a steep learning curve on new users. To address this, we present GPUMDkit, a comprehensive and user-friendly toolkit that streamlines the entire simulation workflow for GPUMD and NEP. GPUMDkit integrates a suite of essential functionalities, including format conversion, structure sampling, property calculation, and data visualization, accessible through both interactive and command-line interfaces. Its modular, extensible architecture ensures accessibility for users of all experience levels while allowing seamless integration of new features. By automating complex tasks and enhancing productivity, GPUMDkit substantially lowers the barrier to using GPUMD and NEP programs. This article describes the program architecture and demonstrates its capabilities through practical applications. Workflow ; Suite ; Field (mathematics) ; Architecture ; Property (philosophy) ; Trajectory ; Molecular graphics ; Graphics ; Computer graphics Machine Learning in Materials Science ; Quantum many-body systems ; Protein Structure and Dynamics
Authors: Andre  Paul Ruybalid | Andrea Rovinelli | Ryan Sweet | Kyle Gamble | Laurent Capolungo
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Multi-Scale Modeling of Zircaloy Cladding: From Mechanistic Crystal Plasticity to Finite Element Surrogates with Scenario-Based Validation Creep ; Viscoplasticity ; Finite element method ; Cladding (metalworking) ; Dislocation ; Plasticity ; Zirconium alloy ; Solver ; Multiphysics Nuclear Materials and Properties ; High Temperature Alloys and Creep ; Nuclear reactor physics and engineering
Authors: Andre  Paul Ruybalid | Andrea Rovinelli | Ryan Sweet | Kyle Gamble | Laurent Capolungo
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Multi-Scale Modeling of Zircaloy Cladding: From Mechanistic Crystal Plasticity to Finite Element Surrogates with Scenario-Based Validation Viscoplasticity ; Creep ; Finite element method ; Cladding (metalworking) ; Plasticity ; Dislocation ; Solver ; Zirconium alloy ; Multiphysics Nuclear Materials and Properties ; High Temperature Alloys and Creep ; Nuclear reactor physics and engineering
Authors: Walid Khalid AL-Hussein | Laila A. Jaragh-Alhadad
Source: Kuwait Journal of Science (article, 2026-02-19)
Field: Chemistry — Organic Chemistry

In vitro experimental and computational study of 1,2,4-triazole derivatives as antimicrobial and anticancer agents The 1,2,4-triazole core poses many applications in different fields, especially medicine. Based on this information, 1,2,4-triazole derivatives were studied for the antimicrobial and anticancer activities. First, the antimicrobial activity of the agents was evaluated against Gram-positive and Gram-negative bacterial strains, as well as fungal species at very low concentrations. The data revealed that 1,2,4- triazole derivatives substituted with alkyl moieties showed potency more than the aryl moieties, substituted on ortho, meta, and para positions. The agent, 4-amino-5-methyl-4H- 2-(4-amino-5-mercapto-4H-1,2,4-triazol-3-yl) acetonitrile ( 4h ), proved to have antimicrobial activity against Candida albicans at (1μg/mL). Moreover, the binding score and pose of agent ( 4h ) were inspected towards C. albicans utilizing AutoDock4.2.6 software, the docking computations results demonstrated competing binding scores for compound ( 4h ) and the co-crystallized 4C7 ligand with values of −5.5 and −5.3 kcal/mol, respectively. Secondly, for better drug delivery of the agents into the cancer cells, low density lipoprotein, polyethylene glycol, and chitosan were used to encapsulate the agents to target tubulin in liver (HEPG2) and breast (MDA-MB-453) cancer cell lines as surrogate models. The cell proliferation results demonstrated that the 1,2,4-triazole derivatives exhibited potent antiproliferative activity, effectively inhibiting cancer cell growth in both tested cell lines. All derivatives benefited from the encapsulation and drug delivery strategy and the IC 50 values were lowered after encapsulation strategy. In sum, the results confirmed that the 1,2,4-triazole derivatives displayed a potent apoptotic effect and is considered as antifungal and anticancer agent. Antimicrobial ; Candida albicans ; In vitro ; Corpus albicans ; Docking (animal) ; Drug ; Potency ; Triazole ; Cytotoxicity Click Chemistry and Applications ; Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Rezvan Joshaghani | Alessandra Winters | Yanyan Zong | Steve Cutchin
Source: IEEE Transactions on Visualization and Computer Graphics (article, 2026-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

XR Module for Enhancing Glove Hygiene Skills in First-Year Chemistry Laboratories Extended Reality (XR) technologies offer innovative opportunities to address inequities in laboratory preparation among first-year undergraduate chemistry students, particularly benefiting students from under-served communities. This study presents the development and evaluation of an immersive XR module specifically designed to teach glove hygiene, a critical laboratory safety skill frequently overlooked in traditional high school curricula due to resource constraints. Involving 176 student test cases across diverse institutions, this module integrates active learning and inclusive, equity-driven design principles. A previously piloted VR glove hygiene module revealed significant student enthusiasm but encountered challenges related to nausea during immersive experiences. Addressing this, our refined XR module employs practices for nausea reduction, providing an accessible virtual lab environment with interactive glove hygiene simulations. Preliminary evaluations indicate substantial improvements in students' confidence, reduction in laboratory-related anxiety, and increased engagement, highlighting XR's potential to close preparation gaps. This work underscores the broader impact of XR interventions in STEM education, fostering greater retention and success for underrepresented and first-generation college students. Curriculum ; Enthusiasm ; Hygiene ; Psychological intervention ; Work (physics) ; Virtual reality ; Resource (disambiguation) Various Chemistry Research Topics ; Innovative Teaching Methods ; Chemical Safety and Risk Management
Authors: Giyeok Lee | Catherine Stampfl | Aloysius Soon
Source: AI Agent (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Robust global optimization of atomic structures via a learning loss-informed on-the-fly firefly algorithm In computational materials science, global optimization is pivotal for bridging theory and experiment but can fail when the theoretical treatment defining the potential energy surface does not accurately predict stability trends. Conventional approaches to address this rely on statistical sampling over numerous independent calculations or the use of more expensive theories throughout the global optimization process, both of which substantially increase computational cost. To overcome this, we present nature-inspired algorithm for robust atomic structure search (NARA), a framework that combines a firefly algorithm-based multimodal search with uncertainty-aware active learning. Instead of converging to a single structure, NARA simultaneously explores multiple distinct configurations, thereby mitigating sensitivity to potential limitations. For the “8” surface oxide on Cu(111), it achieves higher efficiency than the widely used basin-hopping algorithm. For gold clusters, a single run recovers both planar and non-planar structures, resolving stability reversals induced by different theoretical treatments. NARA thus achieves both efficiency and robustness for reliable atomic-structure identification. Robustness (evolution) ; Global optimization ; Firefly algorithm ; Stability (learning theory) ; Optimization problem ; Optimization algorithm ; Sensitivity (control systems) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Quantum Computing Algorithms and Architecture