jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yanxiao Hu | Ye Sheng | Caichao Ye | Wenxing Qian | Xiaoxin Xu | Yabei Wu | Jiong Yang | William A. Goddard III | Wenqing Zhang
Source: arXiv (Cornell University) (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Material-Property-Field-based Deep Neural Network in Hopfield Framework The current Deep Neural Networks (DNNs) lack the necessary physical priors and a clear formulation specifically designed for material systems. making them non-analytical and non-interpretable 'black boxes'. In this work, we integrate Material Property Fields (MPF) with the Hopfield network architecture and propose an analytical DNN framework named mPFDNN. MPF provides a unified framework that represents physical properties of materials as an analytical field built upon pairwise interactions, rigorously respecting fundamental symmetries, while also enabling a physically legitimate decomposition of property distributions at the atomic level. Although the Hopfield model was initially developed for Ising-like systems, we prove that its dynamical evolution strategy for DNN design is equally well suited to MPF. By mathematically reformatting interatomic nonlinear interactions as 'hidden neurons', the MPF can be naturally evolved into a deep yet analytically tractable DNN architecture that approximates a fully connected interaction landscape. This framework also unifies nonlinear DNNs and linear approaches within a single cohesive model. Extensive validation across diverse systems (inorganic crystals, organic molecules, and aqueous solutions) and multiple properties (diffusion coefficients, adsorption energy, etc.) confirm that mPFDNN not only achieves accurate predictions but also provides a principled and universal framework for structure-property mapping for physical, chemical and materials science. Artificial neural network ; Nonlinear system ; Property (philosophy) ; Pairwise comparison ; Field (mathematics) ; Hopfield network ; Deep neural networks Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Inorganic Chemistry and Materials
Authors: Huicong Chen | Mingqiang Li | Zheyuan Ji | Yu Zou
Source: arXiv (Cornell University) (preprint, 2026-03-28)
Field: Materials Science — Materials Chemistry

Identification and Prediction of Photoplasticity in Semiconductors Using Feature Engineering and Machine learning Photoplasticity, the light-induced change in plastic deformation, plays a pivotal role in the mechanical durability and manufacturing of semiconductor materials. Yet, its governing mechanisms remain incompletely understood, owing to the interplay of coupled multiphysics factors. Here, we conduct high-throughput nanoindentation measurements to compile a dataset of paired hardness values in dark and light conditions. Then, we engineer physics-informed descriptors spanning electrical, mechanical, and optical properties, and identify the ten most informative features, including bandgap, breakdown field, and refractive index, to enable an interpretable machine learning framework that yields transferable design rules for light-tunable semiconductor mechanics. By identifying and predicting photoplasticity in semiconductors, this work provides a practical pathway for extracting mechanism-linked, transferable guidelines to engineer light-responsive mechanical behavior in semiconductor materials and devices. Multiphysics ; Identification (biology) ; Semiconductor industry ; Semiconductor ; Feature (linguistics) ; Feature engineering ; Semiconductor device fabrication ; Work (physics) Machine Learning in Materials Science ; Advanced Sensor and Energy Harvesting Materials ; Nanowire Synthesis and Applications
Authors: Raffaele Cheula | Mie Andersen | John R. Kitchin
Source: ArXiv.org (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

Fine-tuning universal machine learning potentials for transition state search in surface catalysis Determining transition states (TSs) of surface reactions is central to understanding and designing heterogeneous catalysts but remains computationally prohibitive with density functional theory (DFT). While machine learning potentials (MLPs) offer significant speedups, task-specific models have limited transferability across catalytic systems, and universal MLPs (uMLPs) lack the accuracy needed for reactive configurations. Here, we present a workflow based on active learning to iteratively fine-tune uMLPs for DFT-quality TS search. Using 250 TSs from the CO2 hydrogenation reaction network on metal and single-atom alloy surfaces, we first benchmark TS search algorithms, identifying the Sella algorithm as most robust, and propose a modification (Bond-Aware Sella) that substantially improves its success rate. We then explore sequential and batch active-learning strategies for fine-tuning and show that DFT-quality TS structures can be found using only 8 DFT single-point calculations on average per structure. This demonstrates the viability of fine-tuned uMLPs for high-throughput catalyst screening. Benchmark (surveying) ; Catalysis ; Transferability ; Density functional theory ; Workflow ; Surface (topology) ; Artificial neural network Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Alex Y. Liu | G. S. Kim | Hua Song
Source: Chemical Engineering Journal (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Agentic AI framework for accelerated catalyst design in biomass valorization via small language models and automated machine learning Interpretability ; Workflow ; Unstructured data ; Data-driven ; Identification (biology) ; Resource (disambiguation) Machine Learning in Materials Science ; Catalysis for Biomass Conversion ; Catalysis and Oxidation Reactions
Authors: Celia Massiel Hernández-Hernández | Víctor Alfredo Nolasco-Arizmendi | Sonia Hernández-González | L. Melo-Máximo
Source: Journal of Technology and Innovation (article, 2026-03-31)
Field: Materials Science — Biomaterials

Evaluation of mechanical properties of starch-gelatin-sargassum polymers based on neural networks In biodegradable polymer research, starch, gelatin, and Sargassum algae waste are gaining attention as a potential solution to mitigate the environmental impact of conventional plastics. This study, conducted at Universidad Tecnológica de Tula-Tepeji, evaluated the mechanical properties of a polymer composed of these components using a CT3 texture analyzer, with neural network models employed to predict such properties. Polymers were prepared with varying Sargassum concentrations, and their mechanical response under tensile loading was analyzed to estimate Young's modulus. The casting process effectively integrated Sargassum into the polymer, offering a potential eco-friendly alternative. Neural network predictions of the material's behavior proved accurate, demonstrating the usefulness of this approach in materials research. These findings support incorporating additional data and Sargassum variations in future work to further enhance predictive capability. Sargassum ; Artificial neural network ; Polymer ; Ultimate tensile strength ; Process (computing) ; Work (physics) biodegradable polymer synthesis and properties ; Nanocomposite Films for Food Packaging ; Collagen: Extraction and Characterization
Authors: Amitrajit Mukherjee | Robby Reynaerts | Bapi Pradhan | Sudipta Seth | Andreas T. Rösch | Tamali Banerjee | Lata Chouhan | Handong Jin | Christian Sternemann | Michael J. Paulus | Luca Leoncino | Kunal S. Mali | Steven De Feyter | Maarten B. J. Roeffaers | E. W. Meijer | Johan Hofkens | Elke Debroye
Source: Nature Communications (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Machine learning for microscopy data analytics targeting real-time optical characterization of semiconductor nanocrystals Semiconductor nanocrystals with uniform morphology and composition are expected to show consistent responses during light-matter interactions. However, microscopy reveals significant variations in their photoluminescence blinking patterns, even under identical experimental conditions. This discrepancy arises from differences in crystal defects and nonradiative trap states. As a result, heterogeneous blinking patterns serve as valuable indicator of material quality, uncovering several concealed features through statistical analysis of large datasets. Nonetheless, efficient segregation and analysis of numerous blinking trajectories remain a challenge due to laborious calculations, computational bottlenecks, and manual intervention. In this study, we introduce a robust unsupervised machine learning (UML) assisted module to cluster high-dimensional blinking patterns in near-real-time, while calculating category-wise power spectral densities (PSD) to investigate active traps. Furthermore, we explore the impact of data preprocessing on clustering performance. The 'clustering-segregation-analysis' (UML-PSD) methodology demonstrates versatility, paving a way to advance contemporary (micro)spectroscopy, specifically for rapid and cost-effective optical characterization of semiconductor nanocrystals. Characterization (materials science) ; Preprocessor ; Cluster analysis ; Semiconductor ; Cluster (spacecraft) ; Data analysis ; Analytics ; Nanocrystal Quantum Dots Synthesis And Properties ; Spectroscopy Techniques in Biomedical and Chemical Research ; Machine Learning in Materials Science
Authors: Raffaele Cheula | Mie Andersen | John R. Kitchin
Source: arXiv (Cornell University) (preprint, 2026-03-25)
Field: Materials Science — Materials Chemistry

Fine-tuning universal machine learning potentials for transition state search in surface catalysis Determining transition states (TSs) of surface reactions is central to understanding and designing heterogeneous catalysts but remains computationally prohibitive with density functional theory (DFT). While machine learning potentials (MLPs) offer significant speedups, task-specific models have limited transferability across catalytic systems, and universal MLPs (uMLPs) lack the accuracy needed for reactive configurations. Here, we present a workflow based on active learning to iteratively fine-tune uMLPs for DFT-quality TS search. Using 250 TSs from the CO2 hydrogenation reaction network on metal and single-atom alloy surfaces, we first benchmark TS search algorithms, identifying the Sella algorithm as most robust, and propose a modification (Bond-Aware Sella) that substantially improves its success rate. We then explore sequential and batch active-learning strategies for fine-tuning and show that DFT-quality TS structures can be found using only 8 DFT single-point calculations on average per structure. This demonstrates the viability of fine-tuned uMLPs for high-throughput catalyst screening. Benchmark (surveying) ; Catalysis ; Transferability ; Density functional theory ; Workflow ; Surface (topology) ; Artificial neural network Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Balachandra P. Shetty | Sudheer Reddy. J | Viraj G. R | Praveena B. A
Source: Springer Link (Chiba Institute of Technology) (article, 2026-01-07)
Field: Chemistry — Analytical Chemistry

Optimizing polymer classification through FTIR spectra and feature-scaled machine learning models The research is based on the characterization of polymer materials by Fourier Transform Infrared (FTIR) spectroscopy which is a method of characterizing polymers in terms of their characteristic molecular vibration patterns. Three widely used thermoplastics ABS (Acrylonitrile Butadiene Styrene), PP (Polypropylene) and Nylon-66 were selected and analyzed on the basis of FTIR transmittance spectra. Wavenumbers and the values of percent transmittance were obtained and converted into feature vectors. Gaussian noise was used to boost and clean the data. Machine learning methods that were used to classify the materials included Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), random forest, and logistic regression, where 80% of the data were used to train the machine learning model and 20% to test it. Accuracy measures and confusion matrices were used to determine the performance of the models. Random Forest was the best in terms of performance in Nylon-66 classification; however, the overall accuracy was influenced by the error in classifying ABS and PP. Consequently, k-NN (70) and the Logistic Regression (69) have shown a high performance compared to the Random Forest (57) in terms of overall accuracy. This study reveals that even though the Random Forest could be used to classify a particular set of polymers, polymers that have very similar spectral characteristics are still difficult to differentiate, indicating that the work should be improved by increasing the size of the data set and the number of extracted features. Random forest ; Support vector machine ; Pattern recognition (psychology) ; Fourier transform ; Fourier transform infrared spectroscopy ; Feature (linguistics) ; Test set ; Data set ; Confusion matrix Spectroscopy and Chemometric Analyses ; Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Mikael Takoutsin | Marta Campolucci | Nícolas de Andrade Ishiki | Nathan Mirgot | Frédéric Kanoufi | Jennifer Péron | Jean Charlety | Emiliano Fonda | Valérie Briois | Marco Faustini | Maria-Letizia de Marco | C. Goyhenex | Ovidiu Ersen | Hervé Bulou
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2026-03-09)
Field: Materials Science — Materials Chemistry

From Structure to Reactivity: A Multi-Scale Machine Learning Approach to High Entropy Alloys High-entropy alloys (HEA) are highly promising electrocatalysts, but the vastness of their configurational space severely limits traditional screening approaches. To overcome this challenge, this article proposes a paradigm shift towards "Inverse Design" ("propertyto-material") by developing REACT2COMPO, a neural network designed to deduce the optimal composition and atomic distribution of a nanocatalyst directly from targeted macroscopic catalytic properties. To address the lack of experimental 3D structural data required for its training, we introduce a second network, ATOMOD. This model leverages a multi-fidelity "Sim-to-Real" learning strategy: trained exclusively using in silico generated data, it implicitly reconstructs the 3D geometry of the nanoparticle, layer by layer, from a single 2D transmission electron microscopy (TEM) image. However, our results highlight the difficulty of using only TEM technique to chemically discriminate between elements with similar scattering cross-sections like Rh and Ir. To achieve full 3D chemical resolution, this study lays the foundation for a multimodal fusion integrating also complementary tools such as extended fine-structure X-ray absorption spectroscopy (EXAFS). This integration marks a major breakthrough by replacing uncertain iterative fitting methods with generative artificial intelligence. Ultimately, this multi-scale framework promises to drastically accelerate the discovery of new functional materials for energy applications. Artificial neural network ; Generative grammar ; Data-driven ; High entropy alloys ; Deep learning ; Supervised learning ; Chemical space ; Entropy (arrow of time) Machine Learning in Materials Science ; High Entropy Alloys Studies ; Electrocatalysts for Energy Conversion
Authors: Juhwan Kim | Jongseo Park | Gijoo Bang | Suji Gim | Youngtak Kim | Geun Ho Gu | Yousung Jung
Source: Journal of Industrial and Engineering Chemistry (article, 2026-03-01)
Field: Materials Science — Materials Chemistry

Integrating reaction enumeration and machine learning for microkinetic modeling of bond-exchange reactions Scalability ; Enumeration ; Chemical kinetics ; Chemical reaction ; Kinetics Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; N-Heterocyclic Carbenes in Organic and Inorganic Chemistry
Authors: Liulei Shao | Menglin Huang | J. H. Yang | Shiyou Chen
Source: Advanced Theory and Simulations (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Attention Mechanism in Line Graph Neural Network for Material Property Prediction ABSTRACT Graph Neural Networks (GNNs) models are intensively used for predicting the properties of molecular materials. To improve the extraction of structural features from molecular representations, the line graph representation introduced in Atomistic Line Graph Neural Network (ALIGNN) can explicitly incorporate bond angle information. However, in terms of the convolution process, ALIGNN still employs the edge‐gated graph convolution from previous GNNs, making it challenging to extract more effective information. In this work, combined with the line graph representation, we introduce multiple attention mechanisms into GNNs and propose two GNN models, N‐ALIGNN and T‐ALIGNN, which include the Node‐Attention Layer (NAL) and Self‐Attention Layer (SAL) respectively. These models can extract more structural features from both bond graph and line graph, thereby improving prediction accuracy. We conducted tests on three public datasets: JARVIS‐DFT, Materials Project, and QM9. The results demonstrate that the performance of the two models is improved in different prediction tasks, proving that the introduction of appropriate attention mechanisms can enhance the prediction accuracy of the models. Convolution (computer science) ; Bond graph ; Graph ; Artificial neural network ; Representation (politics) ; Property (philosophy) ; Line (geometry) ; Pattern recognition (psychology) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Zhiwei Shi | Miao Ma | Hanyang Ning | Bo Ram Yang | Liping Ding
Source: Physica A Statistical Mechanics and its Applications (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

A multi-level cross-molecular interaction neural network for mixture property prediction Predicting the properties of mixtures is crucial across chemical engineering, energy materials, environmental science, and biomedicine. It elucidates complex intercomponent interactions, improves experimental efficiency, and guides the design and optimization of new systems. However, traditional computational chemistry methods cannot keep pace with modern high-throughput workflows, and existing mixture prediction algorithms do not sufficiently capture intercomponent interactions and lack sufficient transferability, hindering practical adoption and broader dissemination. To address these challenges, we propose a multi-level cross-molecular interaction neural network (MCMINet), a novel native mixture modeling architecture with end-to-end component permutation invariance and generality. The model explicitly captures intercomponent interactions via cross-molecular interaction mechanisms and fuses structural information at both the molecular and mixture levels to construct mixture representations. This design preserves intrinsic information while enhancing interaction information, thereby improving predictive accuracy. Furthermore, inspired by the consistency between statistical mechanics and thermodynamics, we design a loss function ℒ mix for mixture modeling that embeds composition dependence into a regularization term to enforce physical consistency during training and improve generalization. MCMINet demonstrates state-of-the-art performance in predicting electrolyte ionic conductivity, activity coefficients of non-ideal solutions, and vapor–liquid equilibrium behavior. Experiments spanning diverse scales, mixing ratios, component counts, and physicochemical properties enable researchers to gain deeper insights into the non-additivity of intercomponent interactions, offering a new perspective on “structure–property” research in mixtures. This approach holds significant potential for downstream tasks such as process optimization, chemical screening, and drug formulation. Consistency (knowledge bases) ; Component (thermodynamics) ; Regularization (linguistics) ; Artificial neural network ; Property (philosophy) ; Construct (python library) ; Perspective (graphical) ; Mixture model ; Applicability domain Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Cem Oran | Riccarda Caputo | P. Villars | Adem Tekin
Source: Journal of Chemical Theory and Computation (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

PNcsp+: A Periodic Number-Based Crystal Structure Prediction Method Enhanced by Machine Learning Crystal structure prediction (CSP) is central to materials discovery, yet its efficiency and interpretability remain limited by the vast configurational space and reliance on costly local optimizations. Although template-based and machine-learning (ML) approaches have improved exploration, many approaches still require large data sets, complex similarity metrics, or opaque generative pipelines. In this work, we introduce PNcsp+, an enhanced and chemically interpretable CSP framework that uses the Mendeleev Periodic Number (PN) as a transparent descriptor of elemental similarity. PNcsp+ expands the original implementation through a larger prototype library, an improved data management strategy, and ML-assisted prototype scoring by combining cutting-edge neural network models such as MACE, M3GNet, and ALIGNN-FF. Despite its simplicity, PNcsp+ reaches state-of-the-art performance. In evaluations on the CSPBench data set─a curated set of 180 benchmark crystal structures for assessing CSP methods─our approach surpasses alternative methods by achieving 86.1% space group accuracy and 85.0% structure matching accuracy within the Top-5 predictions, all without structure relaxations. Moreover, our case study on several hybrid systems, including ammonium and methylammonium cations, demonstrated that molecular components emerge autonomously in the predicted lattices, guided solely by PN-derived similarity relationships. Overall, PNcsp+ shows that fundamental periodic trends, combined with targeted ML-based evaluation, offer an efficient, scalable, and interpretable CSP framework, enabling accelerated discovery across both inorganic and hybrid chemical spaces. Interpretability ; Chemical space ; Benchmark (surveying) ; Matching (statistics) ; Similarity (geometry) ; Set (abstract data type) ; Crystal structure prediction ; Artificial neural network ; Space (punctuation) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions