jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jonas Hänseroth | Christian Dreßler
Source: ArXiv.org (preprint, 2025-05-12)
Field: Materials Science — Materials Chemistry

Optimizing Machine Learning Potentials for Hydroxide Transport: Surprising Efficiency of Single-Concentration Training We investigate the transferability of machine learning interatomic potentials across concentration variations in chemically similar systems, using aqueous potassium hydroxide solutions as a case study. Despite containing identical chemical species (K$^+$, OH$^-$, H$_2$O) across all concentrations, models fine-tuned on specific KOH concentrations exhibit surprisingly poor transferability to others, with force prediction errors increasing dramatically from ~30 meV/A (at training concentration) to ~90 meV/A (at very different concentrations). This reveals a critical limitation when applying such models beyond their training domain, even within chemically homogeneous systems. We demonstrate that strategic selection of training data can substantially overcome these limitations without requiring extensive computational resources. Models fine-tuned on intermediate concentrations (6.26 mol/L) exhibit remarkable transferability across the entire concentration spectrum (0.56-17.98 mol/L), often outperforming more computationally expensive models trained on multiple concentration datasets. This approach enables accurate simulation of hydroxide transport dynamics across varying electrolyte conditions while maintaining near-quantum accuracy. Our simulations further reveal the emergence of hydroxide-hydroxide hydrogen bonding at high concentrations - a phenomenon not explicitly represented in dilute training data but successfully captured by our intermediate-concentration model. This work establishes practical guidelines for developing broadly applicable machine learning force fields with optimal transferability, challenging the assumption that diverse training datasets are always necessary for robust performance in similar chemical environments. Transferability ; Training (meteorology) ; Potassium hydroxide ; Work (physics) ; Training set ; Hydroxide ; Homogeneous ; Field (mathematics) Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Quantum many-body systems
Authors: Jingqiong Zhang | Nicholas T. H. Farr | James Nohl | Yufeng Lai | Kerry J. Abrams | Kate Black | Jon R. Willmott | Cornelia Rodenburg | Lyudmila Mihaylova
Source: IEEE Access (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Toward Automated Chemical Analysis of Materials Using Secondary Electron Hyperspectral Imaging and Unsupervised Learning Advancements in materials science have significantly transformed materials discovery and advanced manufacturing. This, along with the rapid development of sensing and instrumentation, results in a continuous increase in data volumes. To address the limitations of conventional manual analysis, this paper introduces an AI-driven framework for high-throughput chemical analysis of material surfaces at the micro- and nano-scale. The framework integrates unsupervised machine learning with secondary electron hyperspectral imaging (SEHI). It consists of four stages: hyperspectral image processing via tiling, spectral peak extraction, peak categorisation by probabilistic clustering, and chemical analysis. Tiling enables the capture of local spatial-spectral information and generation of a large number of training samples from a single SEHI image stack. After tile-wise spectral peak extraction, the distribution of the peak positions is accurately represented by probabilistic clustering with a Gaussian mixture model (GMM) or a Dirichlet process Gaussian mixture model (DPGMM). Each peak corresponds to a specific chemical bond or element in a material, reflecting the unique spectral characteristics. The performance of the GMM and GPGMM approaches is validated over a case study for identifying chemical elements or bonds of complex metal alloy and carbon films. The results demonstrate accurate chemical analysis, yielding relative errors within ±15% compared to the theoretical model of the valence band density of states. This work is a step forward towards automated material analysis across different tasks such as identifying chemical elements and bonds, visualizing surface (in)homogeneity in metal alloy films for guiding film printing, and supporting digital twins integration for advanced manufacturing. Hyperspectral imaging ; Mixture model ; Cluster analysis ; Unsupervised learning ; Probabilistic logic ; Pattern recognition (psychology) ; Gaussian process ; Chemical imaging ; Gaussian ; Chemical bond Machine Learning in Materials Science
Authors: Zakiya Shireen
Source: Macromolecular Theory and Simulations (article, 2025-10-27)
Field: Materials Science — Materials Chemistry

Bayesian Optimization in Polymer Modeling: From Coarse‐Graining Foundations to Autonomous Inverse Design ABSTRACT Coarse‐graining (CG) is crucial for simulating polymers at extended scales, addressing the computational limitations of atomistic molecular dynamics. However, developing accurate and transferable CG force fields remains a formidable challenge, due to high‐dimensional parameter spaces, conflicting objectives, and inherent noise. This review highlights Bayesian Optimization (BO) as a transformative, data‐driven framework for automating the parameterization of CG force fields. BO leverages probabilistic Gaussian process surrogates and acquisition functions to navigate complex landscapes efficiently, minimizing expensive simulations while quantifying uncertainty. We survey BO's application in CG model development, from single‐ to multi‐objective optimization for achieving structural, thermodynamic, and dynamic fidelity, and enhancing transferability across conditions. Key examples include CG models for electrolytes, block copolymers, and epoxy resins, often integrated with advanced machine learning techniques for learning potentials, optimal mappings, and active data acquisition. We also discuss emerging autonomous pipelines like SPACIER, RAPSIDY, CAMELOT, and PAL 2.0, which streamline the inverse design. Finally, we outline persistent challenges such as surrogate scalability, handling nonstationarity, and extending to reactive/multiscale systems, and envision BO as a cornerstone of future automated materials discovery, accelerating the design of novel polymeric materials. Bayesian optimization ; Surrogate model ; Gaussian process ; Probabilistic logic ; Uncertainty quantification ; Process (computing) ; Bayesian probability ; Key (lock) ; Inverse problem Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Multi-Objective Optimization Algorithms
Authors: Vinita Sangwan | Rashmi Bhardwaj | Marco Alfaro | Andrés Soto-Bubert | Roberto Acevedo
Source: Chemical Thermodynamics and Thermal Analysis (article, 2025-10-01)
Field: Chemistry — Analytical Chemistry

Non-linear AI based viscosity prediction for binary brine data A semi-empirical framework is developed to estimate the viscosity of binary brine systems (m-Pas) over temperatures from 293 to 323 K and molal concentrations between 0 and 4, using a continuous viscosity function η [ m , T ] constructed from experimental salt-water data. The modeling employs Machine Learning (ML) algorithms – Artificial Neural Network (ANN), Random Forest Regressor (RFR), and Gaussian Process Regressor (GPR) to optimize prediction accuracy with fewer fitting parameters. Comparative assessment shows that the AI-based approach achieves an average absolute deviation (%AAD) below 1%, outperforming conventional models while utilizing fewer parameters. Specifically, the GPR model yielded the best results with a %AAD of 0.96%, root mean square error (RMSE) of 0.0001, and an R 2 score of 0.9979, surpassing RFR (%AAD 6.65%) and ANN (%AAD 18.67%). Robustness and model validity are confirmed through rigorous cross-validation and confidence intervals. These findings demonstrate that machine learning, particularly GPR, provides a highly accurate and practical approach for predicting physicochemical properties in aqueous electrolyte environments, enabling effective industrial applications in process optimization and resource management. The results underscore machine learning as a practical approach for modelling physicochemical properties within aqueous electrolyte environments. Artificial neural network ; Mean squared error ; Viscosity ; Absolute deviation ; Binary number ; Brine ; Random forest ; Robustness (evolution) ; Root mean square Spectroscopy and Chemometric Analyses ; NMR spectroscopy and applications ; Machine Learning in Materials Science
Authors: Zefeng Liu | Chenxuan Zhang | Yilin Zhang
Source: Applied and Computational Engineering (article, 2025-11-05)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Application of MOF Materials in Electrochemical Energy Storage Devices Metal-organic frameworks (MOFs), due to their highly ordered structure, ultra-high specific surface area, and adjustable porosity, have become highly promising new energy storage materials. This article reviews the applications of MOFs and their derivatives in energy storage devices such as supercapacitors and lithium/sodium-ion batteries, and discusses the design principles for enhancing their conductivity, optimizing their porous structures, and strengthening their structural stability. Although MOFs can be directly used as electrode materials due to their advantages such as adjustable structure and large specific surface area, their inherent poor conductivity and the decrease in ion transmission efficiency with increasing thickness limit their practical applications. Through carbonization treatment, the conductivity can be significantly enhanced and a stable porous carbon framework can be formed, such as the C-ZIF-67 material; while when combined with carbon materials, conductive polymers or metal oxides (such as PANI/MIL-101), multiple advantages can be integrated, significantly improving the electrochemical performance, mechanical strength and cycle life, thus becoming an effective strategy to break through the practical application bottleneck of MOFs. This article summarizes the current challenges in the industrialization process of MOF materials, such as costs, structural control, and processing difficulties. It proposes solutions including green synthesis, machine learning-assisted design, and the construction of conductive composite structures. Supercapacitor ; Energy storage ; Carbonization ; Electrical conductor ; Carbon fibers ; Electrochemical energy storage ; Anode ; Composite number Supercapacitor Materials and Fabrication ; Metal-Organic Frameworks: Synthesis and Applications ; Advancements in Battery Materials
Authors: Djalma Ribeiro da Silva | Reinaldo Giudici
Source: Industrial & Engineering Chemistry Research (article, 2025-09-17)
Field: Chemistry — Analytical Chemistry

Relationship between NIR Spectrum and Polymer Particle Size for Monitoring Emulsion Polymerization Processes Emulsion polymerization is widely used in industrial applications, such as paints and resins, due to its versatility and ability to create materials with varied properties. To enhance these processes, in-line and online monitoring techniques are crucial, particularly for tracking the polymer particle size during reactions. Near-infrared (NIR) spectroscopy combined with fiber optic sensors has demonstrated potential for this purpose; however, direct measurement is not feasible, which requires the development of calibration models to correlate spectral data with desired properties. The present study includes experimental data from the synthesis of 11 homopolymers and 3 copolymers of methyl methacrylate (MMA) and styrene (St) and analyzes their NIR spectra. The spectral region between 9000 and 14,000 cm–1 showed high sensitivity to particle size changes during emulsion polymerization. Calibration models using Principal Component Regression (PCR), Partial Least Squares regression (PLS), and Artificial Neural Networks (ANN) with spectral ranges of 14,000–4000 cm–1 and 14,000–9000 cm–1 were developed. Results indicated that the ANN model with the 14,000–4000 cm–1 range performed best, demonstrating that a robust calibration model for monitoring particle size across various formulations and conditions was achievable. Particle size ; Calibration ; Partial least squares regression ; Emulsion polymerization ; Polymer ; Particle (ecology) ; Emulsion ; Principal component analysis ; Methyl methacrylate Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Hossein Najaf-Zadeh | Zahra Raissi | Shole Golmohammady | Parivash Safari Kaji | Mahdad Esmaeili
Source: Scientific Reports (article, 2025-11-22)
Field: Materials Science — Materials Chemistry

Deep learning for classifying quantum emission signals in WS2 monolayers using wavelet transform This study aimed to develop and evaluate deep learning approaches for the classification of quantum emission signals from WS<sub>2</sub> monolayer nanobubbles across multiple spectral bands, addressing challenges in quantum materials characterization and spectral distinguishability assessment. We utilized a dataset of quantum emission signals ranging from 604 to 629 nm, emitted from WS₂ monolayer nanobubbles on gold substrates, categorized into four spectral bands (604.06-608.24 nm, 611.07-616.34 nm, 617.42-623.35 nm, and 624.16-636.57 nm). Our methodology involved signal preprocessing through normalization and moving average smoothing, followed by transformation into 128 × 128 RGB images using Continuous Wavelet Transform (CWT) with Complex Morlet wavelet. Three convolutional neural network architectures (ResNet50, VGG16, and Xception) were implemented and evaluated using fivefold cross-validation across six possible band pair combinations. All models demonstrated exceptional classification performance, with VGG16 achieving the highest overall mean accuracy of 99.4%, followed by Xception (99.1%) and ResNet50 (98.2%). Perfect classification accuracy (100%) was consistently achieved for spectrally distant band pairs, particularly Band 1 versus Band 4 (20.5 nm separation), while the most challenging classification involved adjacent bands (Band 2 vs. Band 3, 6.27 nm separation) with VGG16 achieving 96.5% accuracy. Statistical analysis using Friedman tests confirmed significant performance differences among models (χ<sup>2</sup> = 8.67, p = 0.013). Xception demonstrated remarkable computational efficiency, achieving optimal convergence in as few as 2 epochs for certain band combinations while maintaining ultralow training loss values (8.23 × 10⁻<sup>6</sup>). Deep learning models, particularly when combined with CWT preprocessing, provide a robust framework for quantum emission signal classification with significant implications for quantum photonics, quantum cryptography, and quantum sensing applications. Our approach bridges the gap between classical machine learning and quantum materials characterization, establishing quantifiable metrics for evaluating spectral distinguishability in quantum information systems. The demonstrated ability to achieve high classification accuracy with minimal training through transfer learning addresses data scarcity challenges inherent to quantum systems, offering a promising direction for future quantum technology development. Deep learning ; Pattern recognition (psychology) ; Convolutional neural network ; Normalization (sociology) ; Quantum ; Wavelet ; Preprocessor ; Transformation (genetics) 2D Materials and Applications ; Mechanical and Optical Resonators ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Yisheng Xu | Wanxun Feng | Liang Gao | Liquan Wang | Jiaping Lin | Xiangfei Ye | Lei Liang | Lei Du
Source: Advanced Materials (article, 2025-09-22)
Field: Materials Science — Polymers and Plastics

<i>De Novo</i> Design of Polyimides Leveraging Deep Reinforcement Learning Agent Designing molecular structures has long been a central pursuit in organic films with super properties. However, the vast chemical space of candidate molecules poses a challenge in screening optimal materials with exceptional performance. Herein, a multi-objective performance-oriented strategy driven by deep reinforcement learning and train an agent, DAPiGen is proposed, for a de novo template-free polyimide creation. The agent combines the property predictors identified from four machine learning models and a fragment-based generation architecture with active fragments extracted from polyimides as fundamental building blocks. Its successful use is demonstrated to create several polyimides for flexible display scenarios, i.e., with specific properties such as higher transparency, lower coefficient of linear thermal expansion, superior tensile strength, and elevated glass transition temperature. Experiment validation and structural importance analysis demonstrate the efficacy and reliability of the proposed research approach. The scalable strategy presented in this work stands as a paradigm for the inverse design of a spectrum of polymeric materials, offering guidance for other structural engineering endeavors. Reinforcement learning ; Scalability ; Polyimide ; Reliability (semiconductor) ; Molecular engineering ; Inverse ; Reinforcement ; Space (punctuation) ; Glass transition Synthesis and properties of polymers ; Advanced Sensor and Energy Harvesting Materials ; Organic Electronics and Photovoltaics
Authors: Daniel Willimetz | Lukáš Grajciar
Source: The Journal of Physical Chemistry Letters (article, 2025-10-16)
Field: Materials Science — Materials Chemistry

A Simple and Scalable Kernel Density Approach for Reliable Uncertainty Quantification in Atomistic Machine Learning Machine learning models are increasingly used to predict material properties and accelerate atomistic simulations, but the reliability of their predictions depends on the representativeness of the training data. We present a scalable, GPU-accelerated uncertainty quantification framework based on <i>k</i>-nearest-neighbor kernel density estimation (KDE) in a PCA-reduced descriptor space. This method efficiently detects sparsely sampled regions in large, high-dimensional data sets and provides a transferable, model-agnostic uncertainty metric without requiring retraining costly model ensembles. The framework is validated across diverse case studies varying in (i) chemistry, (ii) prediction models (including foundational neural network), (iii) descriptors used for KDE estimation, and (iv) properties whose uncertainty is sought. In all cases, the KDE-based score reliably flags extrapolative configurations, correlates well with conventional ensemble-based uncertainties, and highlights regions of reduced prediction trustworthiness. The approach offers a practical route for improving the interpretability, robustness, and deployment readiness of ML models in materials science. Uncertainty quantification ; Metric (unit) ; Reliability (semiconductor) ; Kernel density estimation ; Kernel (algebra) ; Probabilistic logic ; Scalability ; Representativeness heuristic ; Artificial neural network ; Density estimation Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Materials Characterization Techniques
Authors: Daisuke Goto | Hirotoshi Mori
Source: Chemical Communications (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Reconciling color purity and redox potential in electrochromic materials <i>via</i> electronic-state-informed molecular design Reconciling vivid coloration and low oxidation potential in electrochromic materials-essential for functional devices such as displays, sensors, and energy systems-remains a key challenge due to intrinsic electronic trade-offs. Here we establish a generalizable design strategy based on electronic-state informatics, integrating quantum chemical calculations with machine learning, to identify and experimentally validate a triphenylamine-based compound that exhibits pure yellow color (<i>h</i> = 93.6°) and low oxidation potential (<i>E</i><sub>ox</sub> = 0.12 V). While demonstrated here for electrochromic materials, our interpretable, data-driven framework is broadly applicable to the multi-objective design of π-conjugated molecules, including emitters, absorbers, and charge-transporting materials. This approach moves beyond empirical trial-and-error, offering a rational and generalizable methodology for property-driven molecular engineering. Electrochromism ; Rational design ; Electrochromic devices ; Redox ; Key (lock) ; Quantum chemical ; Design elements and principles Conducting polymers and applications ; Transition Metal Oxide Nanomaterials ; Polyoxometalates: Synthesis and Applications
Authors: Gerfried Millner | Gabriel Kronberger | Manfred Mücke | Lorenz Romaner | Daniel Scheiber
Source: International Journal of Engineering Science (article, 2025-04-03)
Field: Materials Science — Metals and Alloys

Comparison of semi-empirical models, symbolic regression, and machine learning approaches for prediction of tensile strength in steels Symbolic regression ; Ultimate tensile strength ; Regression analysis ; Regression ; Empirical modelling ; Computer science ; Machine learning ; Artificial intelligence ; Materials science ; Mathematics ; Metallurgy ; Statistics ; Simulation Hydrogen embrittlement and corrosion behaviors in metals ; Non-Destructive Testing Techniques ; Material Properties and Failure Mechanisms
Authors: Yaru Shi | Yiyang Li | Jihang Zhai | Yueqing Zhang | Baochuan Hu | Yu‐Cheng Gu | Xinmeng Chen | Lianrui Hu | Xiao He
Source: Chemical Physics Reviews (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

AI-driven advances in the design of RTP and TADF luminescent material The design of room-temperature phosphorescence (RTP) and thermally activated delayed fluorescence (TADF) materials is crucial for advancing organic light-emitting diodes (OLEDs) and other optoelectronic devices. However, traditional experimental methods are inefficient. This review discusses the application of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), in optimizing RTP and TADF materials. AI-driven approaches have revolutionized the discovery and design process by efficiently predicting material properties and performance. We highlight challenges in RTP and TADF material design, including optimizing singlet-triplet energy gaps and minimizing non-radiative decay. Additionally, we explore how ML models, combined with quantum chemical calculations, accelerate the identification of promising materials. The integration of AI allows for rapid screening and optimization of luminescent materials, improving quantum yield, fluorescence efficiency, and stability. With the rapid growth of AI applications in materials science, this review aims to provide insights and guide future research toward leveraging AI for the development of next-generation luminescent materials for OLED technologies. Phosphorescence ; OLED ; Luminescence ; Fluorescence ; Quantum dot ; Light-emitting diode Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Conducting polymers and applications