jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pranoy Ray | Sayan Bhowmik | Phanish Suryanarayana | Surya R. Kalidindi | Andrew J. Medford
Source: Open MIND (preprint, 2026-03-07)
Field: Materials Science — Materials Chemistry

Universal electronic manifolds for extrapolative alloy discovery This study presents a computationally efficient framework for accelerated alloy discovery that uses the non-interacting electron density to capture intrinsic structure-property relationships in refractory high-entropy alloys (HEAs). Unlike state-of-the-art approaches relying on expensive, self-consistent density functional theory calculations, our method employs the non-interacting electron density as the primary structural descriptor. By extracting physical features through directionally resolved two-point spatial correlations and compressing them via Principal Component Analysis, we efficiently map the design space. Coupling these descriptors with Bayesian active learning, we achieve a normalized mean absolute error (NMAE) of &lt;2% for the bulk modulus of Al-Nb-Ti-Zr alloys using only 10 training samples. Furthermore, we demonstrate that the model learns an electronic packing manifold that is transferable across distinct chemical species within refractory HEAs. Validated on a distinct 7-component refractory system (Mo-Nb-Ta-Ti-V-W-Zr) containing four elements entirely absent from the training data, the framework enables rigorous zero-shot extrapolation. Moreover, by augmenting the base model with just 20 samples from the target domain, we achieve high-fidelity predictions (NMAE &lt; 3%) for 7-component alloys, reducing data acquisition costs by orders of magnitude compared to standard workflows. These results establish the non-interacting electron density as a rigorous, extrapolative descriptor for vast compositional landscapes. Coupling (piping) ; Nonlinear dimensionality reduction ; Density functional theory ; Principal component analysis ; Alloy ; Bayesian probability ; Ranking (information retrieval) ; Manifold (fluid mechanics) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Pranoy Ray | Sayan Bhowmik | Phanish Suryanarayana | Surya R. Kalidindi | Andrew J. Medford
Source: ArXiv.org (article, 2026-03-07)
Field: Materials Science — Materials Chemistry

Universal electronic manifolds for extrapolative alloy discovery This study presents a computationally efficient framework for accelerated alloy discovery that uses the non-interacting electron density to capture intrinsic structure-property relationships in refractory high-entropy alloys (HEAs). Unlike state-of-the-art approaches relying on expensive, self-consistent density functional theory calculations, our method employs the non-interacting electron density as the primary structural descriptor. By extracting physical features through directionally resolved two-point spatial correlations and compressing them via Principal Component Analysis, we efficiently map the design space. Coupling these descriptors with Bayesian active learning, we achieve a normalized mean absolute error (NMAE) of <2% for the bulk modulus of Al-Nb-Ti-Zr alloys using only 10 training samples. Furthermore, we demonstrate that the model learns an electronic packing manifold that is transferable across distinct chemical species within refractory HEAs. Validated on a distinct 7-component refractory system (Mo-Nb-Ta-Ti-V-W-Zr) containing four elements entirely absent from the training data, the framework enables rigorous zero-shot extrapolation. Moreover, by augmenting the base model with just 20 samples from the target domain, we achieve high-fidelity predictions (NMAE < 3%) for 7-component alloys, reducing data acquisition costs by orders of magnitude compared to standard workflows. These results establish the non-interacting electron density as a rigorous, extrapolative descriptor for vast compositional landscapes. Coupling (piping) ; Nonlinear dimensionality reduction ; Density functional theory ; Principal component analysis ; Alloy ; Bayesian probability ; Ranking (information retrieval) ; Manifold (fluid mechanics) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Additive Manufacturing Materials and Processes
Authors: Hanhwi Jang | W. Lee | Hwa‐Jung Kim | Sohyang Cha | Hosun Shin | won bo Lee | Min-Wook Oh | Yeon Sik Jung | Y. Kim
Source: Advanced Materials (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐Entropy Thermoelectrics (Adv. Mater. 10/2026) Active Learning In their Research Article (DOI: 10.1002/adma.202515054), Min-Wook Oh, Yeon Sik Jung, YongJoo Kim, and co-workers accelerate the discovery of high-entropy chalcogenides with zT > 2 via active learning. The framework pinpoints top materials in only 80 experiments out of 16 206 candidates (< 0.5% of the design space). Thermoelectric materials ; Germanium compounds ; Thermoelectric effect ; Angstrom ; Science, technology and society Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; High Entropy Alloys Studies
Authors: Aswin Karkadakattil
Source: Figshare (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

<b>Physics-informed surrogate modelling of finite-size scaling and Curie temperature suppression in ferroelectric perovskite nanostructures</b> Finite-size suppression of the Curie temperature (<i>T</i><sub><em>c</em></sub>) in ferroelectric perovskite nanostructures remains an important yet insufficiently resolved problem, with reported scaling exponents varying considerably across experimental and theoretical studies. Although density functional theory provides atomistic insight into size-dependent behaviour, its high computational cost limits systematic exploration across broad size ranges. Conversely, purely empirical fitting approaches often lack physical interpretability and formal uncertainty quantification. In this work, a physics-informed surrogate modelling framework is developed to investigate finite-size scaling in BaTiO₃ and KNbO₃ nanostructures using a structured dataset compiled from the literature. The model is based on thermodynamically motivated scaling behaviour, enabling extraction of physically meaningful size-dependent parameters. Bootstrap resampling is employed to quantify statistical robustness, yielding scaling exponents of 1.59 (95% confidence interval: 1.43–1.72) for BaTiO₃ and 1.40 (95% confidence interval: 1.31–1.52) for KNbO₃. Gaussian Process regression is further integrated to provide uncertainty-aware predictions across the nanoscale domain. In addition to forward prediction, the framework enables inverse estimation of the minimum particle size required to preserve ferroelectric stability at a specified operating temperature. For a threshold of 300 K, the predicted critical sizes are approximately 4.96 nm for BaTiO₃ and 2.89 nm for KNbO₃. Extension to a coupled size–strain formulation produces a two-dimensional stability map, demonstrating tunable interactions between confinement and strain. Overall, the proposed methodology provides a transparent, statistically rigorous, and computationally efficient framework for predictive analysis and rational design of nanoscale ferroelectric materials. Scaling ; Curie temperature ; Perovskite (structure) ; Ferroelectricity ; Stability (learning theory) ; Interpretability ; Inverse Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Ferroelectric and Piezoelectric Materials
Authors: Aswin Karkadakattil
Source: Figshare (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

<b>Physics-informed surrogate modelling of finite-size scaling and Curie temperature suppression in ferroelectric perovskite nanostructures</b> Finite-size suppression of the Curie temperature (<i>T</i><sub><em>c</em></sub>) in ferroelectric perovskite nanostructures remains an important yet insufficiently resolved problem, with reported scaling exponents varying considerably across experimental and theoretical studies. Although density functional theory provides atomistic insight into size-dependent behaviour, its high computational cost limits systematic exploration across broad size ranges. Conversely, purely empirical fitting approaches often lack physical interpretability and formal uncertainty quantification. In this work, a physics-informed surrogate modelling framework is developed to investigate finite-size scaling in BaTiO₃ and KNbO₃ nanostructures using a structured dataset compiled from the literature. The model is based on thermodynamically motivated scaling behaviour, enabling extraction of physically meaningful size-dependent parameters. Bootstrap resampling is employed to quantify statistical robustness, yielding scaling exponents of 1.59 (95% confidence interval: 1.43–1.72) for BaTiO₃ and 1.40 (95% confidence interval: 1.31–1.52) for KNbO₃. Gaussian Process regression is further integrated to provide uncertainty-aware predictions across the nanoscale domain. In addition to forward prediction, the framework enables inverse estimation of the minimum particle size required to preserve ferroelectric stability at a specified operating temperature. For a threshold of 300 K, the predicted critical sizes are approximately 4.96 nm for BaTiO₃ and 2.89 nm for KNbO₃. Extension to a coupled size–strain formulation produces a two-dimensional stability map, demonstrating tunable interactions between confinement and strain. Overall, the proposed methodology provides a transparent, statistically rigorous, and computationally efficient framework for predictive analysis and rational design of nanoscale ferroelectric materials. Scaling ; Curie temperature ; Perovskite (structure) ; Ferroelectricity ; Stability (learning theory) ; Interpretability ; Inverse Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Ferroelectric and Piezoelectric Materials
Authors: Etinosa Osaro | Yamil J. Colón
Source: ACS Engineering Au (article, 2026-04-07)
Field: Chemistry — Inorganic Chemistry

PPO-GPR: A Custom Proximal Policy Optimization Tool for Active Reinforcement Learning Efficient data selection is critical in domains where data acquisition is expensive and time-consuming, such as material science. In this work, we introduce a novel active learning framework that integrates proximal policy optimization (PPO) with Gaussian process regression (GPR) to strategically select informative data points and thereby enhance predictive modeling. Leveraging the inherent stability and sample efficiency of PPO, achieved through a clipped surrogate objective, the framework guides data acquisition via a custom-designed Gymnasium environment tailored for GPR. In this environment, the PPO agent dynamically chooses data points based on their potential to improve the GPR’s performance, as measured by the R2 score, while preventing redundancy through an action masking mechanism. We apply the proposed methodology to predict the selectivity of methane (CH4) over higher alkanes in metal–organic frameworks (MOFs), focusing on CuBTC and IRMOF-1. The framework is evaluated using both ternary and quaternary gas mixtures, where the performance of the GPR is assessed through metrics such as R2, mean absolute error (MAE), and root mean squared error (RMSE). Across CuBTC and IRMOF-1 in ternary and quaternary hydrocarbon mixtures, PPO-guided acquisition achieves 77–86% data savings relative to full GCMC grids, typically querying only ∼14–23% of the candidate pool while the clipped-update PPO policy converges stably by focusing selections in the pressure–temperature–composition regions where selectivity changes most rapidly. This work shows the potential of combining advanced reinforcement learning techniques with regression models to accelerate material discovery and optimize gas separation processes. Reinforcement learning ; Redundancy (engineering) ; Regression ; Data acquisition ; Gaussian process ; Process (computing) ; Stability (learning theory) ; Kriging Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics
Authors: Dongchao Zhao | Yanhui Zhang | Xilin Zhang | Rui Xu | Maochao Lv | Jingchao Li | Jianli Wang | Dazhi Hou
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Parallel Sampling Bayesian Optimization for In-Plane Thermal Diffusivity Inversion of Non-Uniform Graphene-Bamboo Fiber Composite Films Thermal diffusivity ; Composite number ; Thermal ; Bayesian optimization ; Inversion (geology) ; Bayesian probability ; Sampling (signal processing) ; Thermal conductivity ; Inverse problem Thermal properties of materials ; Thermography and Photoacoustic Techniques ; Thermal Radiation and Cooling Technologies
Authors: Stephan Simon | Nitin Kaistha | Vishal Agarwal
Source: Journal of Chemical Theory and Computation (article, 2026-03-13)
Field: Materials Science — Materials Chemistry

An Active Learning Algorithm for Identifying Transition States on a Potential Energy Surface Mapping reaction pathways on complex potential energy surfaces (PESs) and locating transition states (TSs) is often used for understanding chemical reaction mechanism(s). The nudged elastic band (NEB) method is widely used for this purpose, but it becomes computationally expensive for large systems due to the repeated evaluation of energies and forces. We present an active learning algorithm coupled with the nudged elastic band, AL-NEB, for efficient convergence to the TS. AL-NEB constructs a surrogate PES and actively selects training points in two phases: (a) Exploration-Exploitation and (b) Renunciation. Strategies have been introduced for making the algorithm efficient and stable. We show the efficacy of the algorithm on several 2D analytical potentials, HCN isomerization, keto-enol tautomerization, and high-dimensional heptamer island diffusion (up to 525 degrees of freedom). In all cases, AL-NEB locates the "exact" TS on the chosen model chemistry with an order-of-magnitude fewer force evaluations than the standard NEB, demonstrating its scalability and efficiency. Scalability ; Convergence (economics) ; Energy (signal processing) ; Potential energy ; Potential energy surface ; Transition state ; Active learning (machine learning) ; Energy profile Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Physical and Chemical Molecular Interactions
Authors: Haixia Cheng | Yanzhou Wang | Keke Song | Minhui Song | Yaqing Hou | Zhongnan Bi
Source: Materials & Design (article, 2026-02-02)
Field: Materials Science — Materials Chemistry

Machine-learned interatomic potential for Fe–Co–X (X = Re, W) alloys: Ductility–brittleness behavior at finite temperatures Fe–Co alloys combine high strength with poor ductility, limiting their structural applications at elevated temperatures. To overcome this limitation, we developed a machine-learned interatomic potential within the Neuroevolution Potential (NEP) framework for the Fe–Co–Re–W system, trained via active learning on ab initio and molecular dynamics datasets. The model reproduces DFT-level energies, forces, and elastic properties with high fidelity, enabling large-scale finite-temperature simulations. Across 300–1500 K, the elastic response follows a hierarchy of for stiffness and the inverse for ductility, with all phases satisfying the Born–Huang stability criteria. At 300 K, Re and W doping enhances ductility by increasing and , while FeCoW shows a pronounced increase in the elastic ductility indicators and approaches the empirical ductile–brittle thresholds around 1000 K. W induces stronger ductility in B2 and A1, whereas Re improves toughness in A2. This work provides an accurate and efficient framework for exploring and designing ductile Fe–Co–X (X = Re, W) high-temperature structural alloys within the tested conditions. • A machine-learned NEP interatomic potential is developed for the Fe-Co-Re-W alloy system. • The model achieves DFT-level accuracy for energies, forces, virials, and elastic constants. • Temperature-composition-structure coupling is mapped across B2, A2, and A1 phases up to 1500 K. • Re and W additives systematically enhance Fe-Co ductility, with Re improving A2 toughness most. • W induces stronger softening in B2 and A1 phases, enabling early ductile-brittle transition at high temperature. Ductility (Earth science) ; Interatomic potential ; Softening ; Toughness ; Alloy ; Molecular dynamics ; Work (physics) ; Ab initio Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Microstructure and mechanical properties
Authors: Sunidhi Garg | Jishnu J. Bhattacharyya | Vineet V. Joshi | Sean R. Agnew | Prasanna V Balachandran
Source: Research Square (preprint, 2026-04-13)
Field: Materials Science — Materials Chemistry

A Physics-Regularized Machine Learning Approach for Predicting Time-Temperature-Transformation Curves in Alloys: Application to Uranium-Based Alloys Mean squared error ; Gaussian process ; Stability (learning theory) ; Ternary operation ; Kriging ; Isothermal process ; Function (biology) ; Gaussian function ; Regularization (linguistics) Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear reactor physics and engineering
Authors: Haibo Deng | Rongwei Fang | Ting Zhang | Xuetao Yan | Zeyu Li | Yuze Ren | Zhenzhen Li | Lingyan Feng
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Closed-loop active learning for low-budget multi-objective optimization of narrowband-emissive carbon dots Photoluminescence ; Laser linewidth ; Full width at half maximum ; Carbon fibers ; Nanomaterials ; Quantum dot ; Quantum yield ; Workflow Carbon and Quantum Dots Applications ; Carbon Nanotubes in Composites ; Supercapacitor Materials and Fabrication
Authors: Yihan Qin | Ruize Qi | Haowen Ma | Tianyu Cui | Pingyang Zhang
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Biomaterials

Mechanistic Insights into CaO Hydration at the Atomic Scale from Machine-Learned Molecular Dynamics Molecular dynamics ; Density functional theory ; Kinetics ; Hydrogen ; Relaxation (psychology) ; Mechanism (biology) ; Atomic units ; Calcium oxide Calcium Carbonate Crystallization and Inhibition ; Chemical Looping and Thermochemical Processes ; Machine Learning in Materials Science
Authors: Jiaxuan Che | Yaxin Zhang | Junbang Yao | Xiaojing Li | Xing Liu | Bo Liu | Tao Yang
Source: Buildings (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Numerical Simulation and Experimental Research of the Hot-Wire Method for Thermal Insulation Materials The thermal conductivity of thermal insulation materials is a critical parameter for assessing energy efficiency and performance in building, industrial, and aerospace applications. This study combined numerical simulation, parameter inversion optimization and experimental measurement to evaluate the transient hot-wire method for measuring the thermal conductivity of expanded polystyrene (EPS) foam. Using a nickel wire as the hot wire, the effects of various parameters—including wire length and width, heating power, Kapton film thickness and end effect—were systematically analyzed through finite element analysis and Bayesian optimization algorithm. Following the simulation and inversion conclusions, a series of hot-wire sensors with a fixed length of 30 mm and widths of 25 μm, 50 μm, 100 μm, 150 μm, and 200 μm were fabricated for experimental validation. Measurement results were compared against a reference value obtained by the guarded hot plate method. It was found that the sensor with a length of 30 mm and a width of 100 μm demonstrated optimal performance among the configurations tested, with deviations between the experimental measurements and the reference value remaining within approximately ±1.5%. Thermal conductivity ; Kapton ; Thermal ; Finite element method ; Computer simulation ; Thermal insulation ; Expanded polystyrene ; Aerospace Thermal properties of materials ; Aerogels and thermal insulation ; Heat Transfer and Numerical Methods
Authors: Alexey Leontyev | Justin B. Houseknecht | Jennifer L. Muzyka | Catherine O. Welder
Source: Journal of Chemical Education (article, 2026-03-11)
Field: Chemistry — Physical and Theoretical Chemistry

OrganicERs: Fostering a Community of Practice for Organic Chemistry Instructors through Workshops and Online Faculty Learning Communities This paper presents recent developments within the Organic Education Resources (OrganicERs) community of practice, focused on transforming how organic chemistry is taught through evidence-based instructional practices. From 2021 to 2024, OrganicERs expanded its impact by organizing national workshops and facilitating online faculty learning communities that support instructors in implementing active learning strategies. Building on more than a decade of prior work, this new phase emphasized virtual engagement and sustained peer support, enabling educators to create and share resources, reflect on teaching practices, and collaborate on course transformations. We also implemented a new leadership model to develop a more sustainable leadership team. The paper highlights the design of professional development activities, participant outcomes, community growth, and implications for future professional development efforts in chemistry education. Faculty development ; Professional development ; Professional learning community ; Community of practice ; Learning community ; Educational technology ; Community engagement ; Sustainability ; Sustainable development Various Chemistry Research Topics ; Innovative Teaching Methods ; Student Assessment and Feedback
Authors: Aashwin Mishra | Matt Seaberg | Ryan Roussel | Daniel Ratner | Apurva Mehta
Source: Open MIND (preprint, 2026-02-11)
Field: Materials Science — Materials Chemistry

Domain Knowledge Guided Bayesian Optimization For Autonomous Alignment Of Complex Scientific Instruments Bayesian Optimization (BO) is a powerful tool for optimizing complex non-linear systems. However, its performance degrades in high-dimensional problems with tightly coupled parameters and highly asymmetric objective landscapes, where rewards are sparse. In such needle-in-a-haystack scenarios, even advanced methods like trust-region BO (TurBO) often lead to unsatisfactory results. We propose a domain knowledge guided Bayesian Optimization approach, which leverages physical insight to fundamentally simplify the search problem by transforming coordinates to decouple input features and align the active subspaces with the primary search axes. We demonstrate this approach's efficacy on a challenging 12-dimensional, 6-crystal Split-and-Delay optical system, where conventional approaches, including standard BO, TuRBO and multi-objective BO, consistently led to unsatisfactory results. When combined with an reverse annealing exploration strategy, this approach reliably converges to the global optimum. The coordinate transformation itself is the key to this success, significantly accelerating the search by aligning input co-ordinate axes with the problem's active subspaces. As increasingly complex scientific instruments, from large telescopes to new spectrometers at X-ray Free Electron Lasers are deployed, the demand for robust high-dimensional optimization grows. Our results demonstrate a generalizable paradigm: leveraging physical insight to transform high-dimensional, coupled optimization problems into simpler representations can enable rapid and robust automated tuning for consistent high performance while still retaining current optimization algorithms. Bayesian optimization ; Bayesian probability ; Domain (mathematical analysis) ; Key (lock) ; Optimization problem ; Simulated annealing ; Transformation (genetics) Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Advanced Bandit Algorithms Research