jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Olivia Clifton | A. E. Chávez | Antonio Madrigal | Annie Warren | Paige Yeung | Arnd Scheel
Source: Journal of Nonlinear Waves (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Vacuum bubble and fissure formation in collective motion with competing attractive and repulsive forces Abstract We study the continuum limit of the motion of agents in the plane driven by competing short-range repulsion and long-range attractive forces. At a critical parameter value, we find destabilization of a trivial branch of uniformly distributed solutions and analyse bifurcating solutions. Curiously, the bifurcating branch is vertical, leading to a reversible, non-hysteretic phase transition. Near the bifurcation point, we demonstrate scaling laws for the size of vacuum regions, which can form fissures or bubbles. We also study the effect of small noise and the eventual topological transition from vacuum bubbles to isolated particle clusters. Bifurcation ; Scaling ; Scaling law ; Bubble ; Noise (video) ; Collective motion ; Phase transition ; Inclined plane ; Limit (mathematics) Pickering emulsions and particle stabilization ; Fluid Dynamics and Mixing
Authors: Tomáš Gergeľ | O. Vacek | Miloš Gejdoš | Diana Zraková | Peter Balogh | Emil Ješko
Source: Forests (article, 2026-03-27)
Field: Chemistry — Organic Chemistry

Performance of Neural Networks in Automated Detection of Wood Features in CT Images Computed tomography (CT) enables non-destructive insight into internal log structure, yet fully automated interpretation of CT images remains limited by inconsistent annotations, boundary ambiguity, and insufficient spatial context in 2D slice-based analysis. These challenges restrict the industrial deployment of deep learning for wood quality assessment. This study applies artificial intelligence (AI) and deep learning to the automated analysis of computed tomography (CT) scans of wood logs for detecting internal qualitative features and segmenting bark. Using convolutional neural networks (CNNs), trained models accurately distinguish healthy and damaged regions and segment bark, including discontinuous parts. We introduce a novel pseudo-spatial representation by merging consecutive slices into red–green–blue (RGB) format, which improves prediction accuracy and model robustness across logs. To enhance interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) highlights regions contributing most to defect detection, particularly knots. Comprehensive evaluation using Sørensen–Dice similarity coefficients and confusion matrices confirms the effectiveness of the proposed approach under industrial conditions. These findings demonstrate that AI-driven CT image analysis can address key limitations of current log-grading workflows and enable more reliable, objective, and scalable quality assessment for timber-dependent economies. Convolutional neural network ; Robustness (evolution) ; Workflow ; Deep learning ; Pattern recognition (psychology) ; Context (archaeology) ; Artificial neural network ; Segmentation ; Scalability Wood and Agarwood Research ; Wood Treatment and Properties ; Industrial Vision Systems and Defect Detection
Authors: Maurício Silveira dos Santos | Luciano Pivoto Specht | Deividi da Silva Pereira | Lucas Dotto Bueno | Márcio Muniz de Farias | Luiz Guilherme Rodrigues de Mello | Lorenzo Chaves Pacheco | Paula Taiane Pascoal
Source: Transportes (article, 2026-03-16)
Field: Materials Science — General Materials Science

Desempenho de pavimentos asfálticos: comparação entre previsões MeDiNa, ensaios HVS e dados de tráfego real A previsão do desempenho de pavimentos asfálticos é uma tarefa complexa da Engenharia de Pavimentos, exigindo ensaios laboratoriais e bancos de dados de trechos experimentais capazes de captar a parcela empírica do comportamento estrutural. Assim, esta pesquisa avaliou três segmentos experimentais implantados na BR-116, no Rio Grande do Sul, denominados A, B e C, distintos pelo tipo de ligante asfáltico (CAP 50/70, AMP 55/75 e AMP 60/85) e pela espessura do revestimento (5 cm nos segmentos A e B e 14 cm no segmento C). Os segmentos foram solicitados pelo tráfego real e por um simulador do tipo Heavy Vehicle Simulator (HVS). Também foram realizados ensaios laboratoriais para determinação de parâmetros de rigidez e dano, permitindo a aplicação do Método de Dimensionamento Nacional (MeDiNa). A evolução da fadiga em campo foi comparada às curvas obtidas a partir da porcentagem de área trincada simulada no MeDiNa. O Segmento B apresentou melhor compatibilidade entre as metodologias. Nos segmentos A e C observaram-se divergências entre os resultados do HVS, tráfego real e MeDiNa. Assim, não houve compatibilidade global entre as abordagens, indicando a necessidade de aprimoramento do protocolo de utilização do simulador, possivelmente associada às condições climáticas locais e à natureza da solicitação. Engineering and Material Science Research ; Asphalt Pavement Performance Evaluation ; Infrastructure Maintenance and Monitoring
Source: International journal of intelligent engineering and systems (article, 2026-03-23)
Field: Chemistry — Inorganic Chemistry

JASS-Net: Joint Attention-selection Mapping for Local Coconut Seedling Classification JASS-Net is a proposed model for the classification of local coconut seedlings, designed to address the challenges of fine-grained classification with high computational efficiency.The model integrates a Lightweight CNN Backbone with three key modules: Lightweight Attention Extractor (LAE), Adaptive Feature Selector (AFS), and Joint Attention-Selection Mapping (JASM), enabling the extraction and interaction of relevant features for precise classification.Evaluation on an independent test set demonstrated outstanding performance, achieving an overall accuracy of 98.01%, Macro Precision, Recall, and F1-score of 98.01%, Top-2 Accuracy of 99.62%, and Top-3 Accuracy of 99.89%.All classes attained F1-scores above 0.96, with the "Immature" class achieving the highest F1 of 0.999, reflecting robust detection of subtle distinguishing features.JASS-Net also exhibits high computational efficiency, with a model size of 0.47 MB, 122,949 parameters, and an inference latency of 0.001346 s per sample, making it suitable for deployment on resource-limited devices.These results highlight JASS-Net ability to perform accurate, balanced, and efficient fine-grained classification, with potential applicability to other plant species and future improvements through transfer learning and larger datasets. Joint (building) ; Seedling ; Pattern recognition (psychology) Coconut Research and Applications ; Oil Palm Production and Sustainability ; Date Palm Research Studies
Authors: Srinithin Ganesh | Aditya Lele
Source: ChemRxiv (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Machine Learning Prediction of Transport Properties in Amorphous Polymer Electrolytes Using Chemically Informed Structural Descriptors Amorphous polymer electrolytes are promising candidates for next-generation lithium batteries due to their improved safety and mechanical stability compared to conventional liquid electrolytes. However, predicting their transport performance remains challenging because ionic conductivity and diffusivity depend on complex, coupled interactions between polymer structure, chemistry, and ion dynamics. In this work, we apply machine learning to predict four key transport properties-ionic conductivity, TFSI diffusivity, Li-ion diffusivity, and polymer diffusivity-using a molecular dynamics dataset of 6,286 polymer electrolyte systems. To enhance predictive capability, chemically and structurally meaningful features were engineered from SMILES representations, including elemental composition, functional group densities, polarity descriptors, and polymer flexibility metrics. Three modeling frameworks, Random Forest, Gradient Boosting, and Neural Networks were trained and evaluated, with Neural Networks achieving the highest overall performance. Feature importance analysis revealed that polymer backbone flexibility, polarity, and ionic environment dominate transport behavior. These results demonstrate that machine learning can effectively predict electrolyte performance while providing mechanistic insight, enabling accelerated screening and design of high-efficiency polymer electrolytes for future energy storage applications. Electrolyte ; Polymer ; Flexibility (engineering) ; Ionic conductivity ; Polymer electrolytes ; Artificial neural network ; Amorphous solid ; Ionic bonding ; Thermal diffusivity Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Chen Xu | Wenbo Du | Chuantian Zhai | Tong Wang | Chenchen Zhao | Hongxing Liang | Shubo Li
Source: Journal of Magnesium and Alloys (article, 2026-02-01)
Field: Materials Science — Biomaterials

Magnesium air batteries in terms of machine learning, anode alloying, electrolyte and air-cathode - A review • Latest research on magnesium-air battery: anode, electrolyte and catalyst. • The application of machine learning and density functional theory in battery design. • The importance of computational techniques in optimizing Mg-air battery components. Mg-air batteries, as a new energy storage solution, exhibit enormous potentials due to their high energy density and simple structure. However, traditional designs of Mg-air batteries still face theoretical, cost-related and time-consuming limitations. The integration of machine learning (ML) and density functional theory (DFT) presents a promising approach to optimize anode electrode and battery reaction kinetics. This review provided an overview of the fundamental principles of Mg-air batteries, focusing on aspects including ML/DFT-assisted design, anode alloying, electrolyte, and cathode catalysts. We reviewed recent research progress on each of these components, highlighted the primary challenges and summarized the directions of future developments for Mg-air batteries. Finally, we offered insights for improving the performance and commercial viability of Mg-air batteries. Anode ; Battery (electricity) ; Cathode ; Electrolyte ; Energy storage ; Energy density ; Density functional theory Magnesium Alloys: Properties and Applications ; Machine Learning in Materials Science ; Advancements in Battery Materials
Authors: Sathya Narayanan Jagadeesan | Mihir Kalvakaalva | Jessica Liu | J.S. Sharma Nanda | Xueli Zheng
Source: Advanced Materials Technologies (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Machine Learning‐Guided Design of Electrolytes for Rechargeable Batteries ABSTRACT Rising energy demand from electronics, electric transportation, and the grid is driving the development of safer, cheaper, scalable rechargeable batteries. Among all battery components, electrolytes play a pivotal role in governing energy density and cycle life, motivating intensive research into advanced methodologies for the rational design and selection of next‐generation electrolyte systems. Conventional trial‐and‐error electrolyte design is often time‐consuming and resource‐intensive, and struggles to efficiently navigate the vast chemical and materials spaces associated with organic, aqueous, and solid‐state electrolytes (SSEs). Machine learning (ML) strategies have emerged as powerful tools for guiding and accelerating the design of electrolytes. They can extract structure–property relationships, enable high‐throughput screening, and inform experimental decision‐making. Many ML methods are being applied to identify improved electrolyte chemistries that enhance battery performance. However, an in‐depth and critical understanding of how specific ML paradigms align with electrolyte classes, target properties, data quality, and validation practices has not yet been comprehensively addressed in the literature. In this review, we discuss established and emerging ML methods for electrolyte design by comparing them and task‐focused evaluations of ML strategies across different electrolyte systems. We highlight their effectiveness, electrolyte design, machine learning, model validation, rechargeable batteries, workflow limitations, and opportunities for integration into experimentally validated design workflows. Electrolyte ; Battery (electricity) ; Workflow ; Energy density ; Scalability ; Electrochemical energy storage Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advanced battery technologies research
Authors: Esther Heid
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-01-26)
Field: Materials Science — Materials Chemistry

Predictive and Generative Deep Learning Approaches for Chemical Reactions Machine learning is increasingly transforming our ability to predict, analyze, and generate chemical reactions, yet the development of robust reaction-centered models remains substantially more complex than molecular property prediction. This talk will introduce a set of recent advances aimed at addressing these challenges and moving toward a general reaction presentation. First, I will introduce ChemTorch [1], a general framework for reaction machine learning that unifies graph neural networks on reaction graphs, neural networks on reaction fingerprint, 3-dimensional graph convolutional architectures for coordinate-based representations, and string encoders for SMILES and related formats. I will also discuss a recent extension that augments graph representations with learned encodings of their 3D environments, enabling highly accurate predictions of barrier heights [2]. Building on these foundations, the talk will present GoFlow [3], a flow-matching generative model for transition-state structures, followed by a steering framework for flow matching [4] that allows fine-grained control over the generated geometries, including enforcement of correct transition-state chiralities. Together, these developments illustrate how predictive and generative deep learning can provide deeper insight into chemical reactivity if they are anchored in principled reaction representations and suitable inductive biases. Generative grammar ; Deep learning ; Graph ; Artificial neural network ; Convolutional neural network ; Set (abstract data type) ; Property (philosophy) ; Generative model Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced Graph Neural Networks
Authors: Anurag Shrivastava | Hassan M. Al-Jawahry | Poonam Singh | Ginni Nijhawan | Ravi Kant Prasad | Anshy Singh
Source:  (article, 2026-01-31)
Field: Materials Science — Materials Chemistry

Neural Network-Assisted Molecular Modelling for Computational Chemistry Applications Computational chemistry has long been reliant on a fundamental trade-off between the quantum-mechanical accuracy of ab initio methods and the computational tractability of classical force fields. The emergence of sophisticated machine learning, particularly deep neural networks (NNs), is rapidly dismantling this long-standing paradigm. This paper examines the transformative impact of neural network-assisted molecular modelling across diverse computational chemistry applications. We explore how NNs are being architected to learn intricate potential energy surfaces from high-fidelity quantum chemistry data, enabling near-ab initio accuracy at a fraction of the computational cost. Key methodologies, including neural network potentials (NNPs), graph neural networks (GNNs) for molecular property prediction, and generative models for de novo molecular design, are critically reviewed. The discourse extends to the application of these models in accelerating molecular dynamics simulations, guiding drug discovery pipelines, and optimizing catalytic materials. While significant challenges persist—such as data scarcity, transferability, and interpretability—the integration of neural networks into the computational chemistry workflow unequivocally heralds a new era of precision and efficiency in the molecular sciences. Computational model ; Artificial neural network ; Workflow ; Ab initio ; Molecular dynamics ; Quantum chemistry ; Transformative learning ; Molecular machine Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Xiangwen Wang | Muyang Chen | Gengyi Bao | Yan Lai | Jinghe Cao | Xinhua Liu | Rui Tan
Source: Advanced Science (article, 2026-01-04)
Field: Materials Science — Materials Chemistry

High‐Conductivity Electrolytes Screened Using Fragment‐ and Composition‐Aware Deep Learning Rising energy generation from renewables (e.g., wind, solar power) will drive global demand for >1.0 TWh of long-duration energy storage by 2030 to stabilise grids and balance supply. Rechargeable batteries are central to this transition, with their performance critically governed by the properties of active materials and supporting electrolytes. However, designing electrolyte formulations remains a major challenge, as their performance arises from complex, non-additive interactions among lithium salts and organic solvents, requiring elegant molecular design and selection. Conventional trial-and-error strategies still dominate electrolyte design, but they are slow and resource-intensive. Recent machine learning approaches have improved electrolyte screening, yet many rely on coarse molecular representations that neglect fragment-level chemistry and explicit ratios, limiting interpretability and their utility for guiding experiments. Here we introduce a deep learning framework that integrates intermolecular attributions across solvents with intramolecular attributions from functional units. The framework builds a hierarchical representation, decomposing formulations into molecules and their functional units, while integrating ratios, physicochemical descriptors, and salt identity to generate mixture-invariant embeddings for accurate and interpretable conductivity prediction. Applied to benchmark datasets of lithium battery electrolytes, the framework achieves high accuracy in predicting ionic conductivity and enables large-scale virtual screening. Crucially, it provides chemically interpretable insights: fragment-level attentions align with functional units; composition-aware attention reveals the impact of mixing ratios; and counterfactual perturbations confirm causal roles of key motifs. This framework paves the way for data-driven, interpretable electrolyte design and can be generalized to broader formulation challenges in materials science. Interpretability ; Electrolyte ; Deep learning ; Benchmark (surveying) ; Battery (electricity) ; Lithium (medication) ; Ranking (information retrieval) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Gunwook Nam | Junyoung Choi | K. I. Jang | Yousung Jung
Source: Bulletin of the Korean Chemical Society (article, 2026-01-18)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potentials for lithium battery electrolyte design Abstract Lithium‐ion batteries have dominated the energy storage landscape for decades, driven by their high energy density and operating voltage. However, increasing demands for wide operating temperature ranges and fast charging push current technologies to their limits, requiring next‐generation lithium batteries with superior safety and extended cycle life. In this context, the rational design of electrolytes remains a major challenge, as intricate bulk and interfacial mechanisms govern system viability. Specifically, solid‐state electrolytes struggle with low ionic conductivity, while liquid electrolytes are limited by complex chemical interactions. Machine learning interatomic potentials (MLIPs) address these issues by combining first‐principles accuracy with computational efficiency. These models allow us to simulate atomistic interactions and interfacial reactions that were previously impractical. We summarize recent advances in the application of MLIPs, focusing on bulk and interfacial phenomena in solid‐state and liquid electrolytes. Electrolyte ; Battery (electricity) ; Lithium (medication) ; Energy storage ; Ionic liquid ; Energy (signal processing) ; Energy density ; Ionic bonding Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Inorganic Chemistry and Materials
Authors: Shaghayegh Hamzehlou | Nicholas Ballard | José M. Asua
Source: Macromolecular Reaction Engineering (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Exploring the Strengths and Limitations of Polymer Chemistry Informed Neural Networks ABSTRACT Kinetic models are essential tools for providing a fundamental understanding of polymerization processes. However, they are constrained by the current level of understanding of the system under study and often rely on numerous unknown parameters that are difficult to measure experimentally or estimate reliably. To address these challenges, polymer chemistry‐informed neural networks (PCINNs) have been developed in which the domain knowledge embedded in these kinetic models is combined with data‐driven machine learning tools. Herein, we investigate the robustness and limitations of this methodology through an in‐silico investigation in which an imperfect kinetic model is combined with small datasets. As a case study, high‐temperature solution polymerization of methyl methacrylate was considered. It is demonstrated that PCINNs are able to utilize imperfect kinetic models in order to reach high levels of predictive performance, with reliable extrapolation at reaction temperatures significantly beyond the range of the original dataset. Integrating domain knowledge was found to substantially reduce variance in the outcomes of the final model when using a relatively small dataset. Finally, it is shown that PCINNs can be used to provide insights into the causes of poor predictive performance of kinetic models, offering the ability to use data‐driven learning to inform first‐principles approaches. Extrapolation ; Robustness (evolution) ; Artificial neural network ; Polymerization ; Imperfect ; Polymer ; Domain knowledge Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Carbon dioxide utilization in catalysis