jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yunhao Yang | Ran Bai | Wenli Gao | Leitao Cao | Jing Ren | Zhengzhong Shao | Shengjie Ling
Source: Advanced Science (article, 2025-01-23)
Field: Materials Science — Biomaterials

Optimizing Biomimetic 3D Disordered Fibrous Network Structures for Lightweight, High‐Strength Materials via Deep Reinforcement Learning 3D disordered fibrous network structures (3D-DFNS), such as cytoskeletons, collagen matrices, and spider webs, exhibit remarkable material efficiency, lightweight properties, and mechanical adaptability. Despite their widespread in nature, the integration into engineered materials is limited by the lack of study on their complex architectures. This study addresses the challenge by investigating the structure-property relationships and stability of biomimetic 3D-DFNS using large datasets generated through procedural modeling, coarse-grained molecular dynamics simulations, and machine learning. Based on these datasets, a network deep reinforcement learning (N-DRL) framework is developed to optimize its stability, effectively balancing weight reduction with the maintenance of structural integrity. The results reveal a pronounced correlation between the total fiber length in 3D-DFNS and its mechanical properties, where longer fibers enhance stress distribution and stability. Additionally, fiber orientation is also considered as a potential factor influencing stress growth values. Furthermore, the N-DRL model demonstrates superior performance compared to traditional approaches in optimizing network stability while minimizing mass and computational cost. Structural integrity is significantly improved through the addition of triple junctions and the reduction of higher-order nodes. In summary, this study leverages machine learning to optimize biomimetic 3D-DFNS, providing novel insights into the design of lightweight, high-strength materials. Adaptability ; Stability (learning theory) ; Computer science ; Fiber ; Materials science ; Biomimetics ; Reinforcement learning ; Reduction (mathematics) ; Stress (linguistics) ; Nanotechnology ; Biological system ; Artificial intelligence ; Composite material ; Machine learning ; Mathematics Silk-based biomaterials and applications ; Electrospun Nanofibers in Biomedical Applications ; Cellular and Composite Structures
Authors: Yutao Liu | Tinghong Gao | Qingquan Xiao | Yunjun Ruan | Qian Chen | Bei Wang | Jin Huang
Source: Communications Physics (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Temperature-density dependent hidden order of amorphous carbon The microscopic structure of amorphous carbon is inherently complex, and its underlying topological order remains poorly understood. Here, we developed a high-precision machine learning potential and performed large-scale molecular dynamics simulations of the annealing process over a wide range of temperatures and densities. Nine representative amorphous carbon structures were identified and characterized through X-ray diffraction and structure factor analysis, enabling detailed comparison of their topological differences. From these results, we constructed a temperature–density phase diagram that reveals two distinct phase boundaries associated with discontinuous structural transitions, with the critical temperature increasing approximately linearly with density. The diagram further indicates two transformation pathways, where high-density disordered graphene networks and paracrystalline diamond structures act as intermediate states. Additional pressure-controlled simulations demonstrate asymmetric nucleation during diamond formation and decomposition. These findings provide a clearer understanding of hidden order in amorphous carbon and offer a theoretical framework to guide its controlled synthesis. Carbon exhibits several known allotropes and the amorphous equivalents exhibit an even wider range of structural variations that can be difficult to identify due to their lack of crystallinity. Here, the authors report large-scale machine-learned molecular dynamics simulations that maps the hidden topological orders of amorphous carbon as a function of temperature and density. Amorphous carbon ; Amorphous solid ; Phase diagram ; Molecular dynamics ; Graphene ; Nucleation ; Carbon fibers ; Annealing (glass) Machine Learning in Materials Science ; Diamond and Carbon-based Materials Research ; Electrocatalysts for Energy Conversion
Authors: Zetian Mao | Chuan-Shen Hu | Jiawen Li | Chen Liang | Diptesh Das | Masato Sumita | Kelin Xia | Koji Tsuda
Source: Journal of Chemical Theory and Computation (article, 2025-08-09)
Field: Materials Science — Materials Chemistry

Molecule Graph Networks with Many-Body Equivariant Interactions Message passing neural networks have demonstrated significant efficacy in predicting molecular interactions. Introducing equivariant vectorial representations augments expressivity by capturing geometric data symmetries, thereby improving model accuracy. However, two-body bond vectors in opposition may cancel each other out during message passing, leading to the loss of directional information on their shared node. In this study, we develop <b>E</b>quivariant <b>N</b>-body <b>I</b>nteraction <b>Net</b>works (ENINet) that explicitly integrates <i>l</i> = 1 equivariant many-body interactions to enhance directional symmetric information in the message passing scheme. We provided a mathematical analysis demonstrating the necessity of incorporating many-body equivariant interactions and generalized the formulation to <i>N</i>-body interactions. Experiments indicate that integrating many-body equivariant representations enhances prediction accuracy across diverse scalar and tensorial quantum chemical properties. Equivariant map ; Computer science ; Message passing ; Scalar (mathematics) ; Theoretical computer science ; Homogeneous space ; Graph ; Topology (electrical circuits) ; Distributed computing ; Mathematics ; Pure mathematics ; Combinatorics ; Geometry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Qingyue Zhang | Yixiao Wang | Jing Hu | Xiyue Zhang | Jun He | Xiaoting Xuan | Lufang Shi | Yonghua Sun
Source: Food Chemistry (article, 2025-06-06)
Field: Chemistry — Analytical Chemistry

An approach in developing graphical feature maps derived from machine learning and its application in loquat juice classification Fruit juice ; Feature (linguistics) ; Artificial intelligence ; Machine learning ; Computer science ; Pattern recognition (psychology) ; Food science ; Chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Yong‐Bin Zhuang | Chang Liu | Jiaxin Zhu | Jinyuan Hu | Jiabo Le | Jieqiong Li | Xiaojian Wen | Xue-Ting Fan | Mei Jia | Xiang-Ying Li | Ao Chen | Lang Li | Zhili Lin | Weihong Xu | Jun Cheng
Source: Scientific Data (article, 2025-06-13)
Field: Materials Science — Materials Chemistry

An artificial intelligence accelerated ab initio molecular dynamics dataset for electrochemical interfaces Understanding atomic-scale structures at electrochemical interfaces is essential for advancing research and applications in electrochemistry. While experiments can provide detailed microscopic insights, their complexity and inefficiency often limit the large-scale generation of data. Complementing experimental approaches, computational methods, such as ab initio molecular dynamics and machine learning-accelerated molecular dynamics, offer an efficient means of obtaining microscopic information. However, despite these advancements, computational studies of interfaces have typically shared research data in isolation, often through private repositories. This practice has led to fragmented knowledge, reduced data accessibility, and limited opportunities for cross-study comparisons or large-scale meta-analyses. To overcome these challenges, we introduce ElectroFace, an artificial intelligence-accelerated ab initio molecular dynamics dataset for electrochemical interfaces. ElectroFace is designed to compile, visualize, and provide open access to interface data, fostering collaboration and accelerating progress in the field. Ab initio ; Dynamics (music) ; Molecular dynamics ; Electrochemistry ; Computational chemistry ; Chemical physics ; Computer science ; Materials science ; Artificial intelligence ; Chemistry ; Physics ; Physical chemistry ; Electrode ; Organic chemistry Machine Learning in Materials Science ; Electrochemical Analysis and Applications ; Advanced Memory and Neural Computing
Authors: Jingjing Zhao | Yu Wang | Bing Liu
Source: Biosensors (review, 2025-04-03)
Field: Materials Science — Materials Chemistry

Doping Detection Based on the Nanoscale: Biosensing Mechanisms and Applications of Two-Dimensional Materials Doping undermines fairness in sports and threatens athlete health, while conventional detection methods like LC-MS and GC-MS face challenges such as complex procedures, matrix interferences, and lengthy processing times, limiting on-site applications. Two-dimensional (2D) materials, including graphene, MoS<sub>2</sub>, and metal-organic frameworks (MOFs), offer promising solutions due to their large surface areas, tunable electronic structures, and special interactions with doping agents, such as hydrogen bonding, π-π stacking, and electrostatic forces. These materials enable signal transduction through changes in conductivity or fluorescence quenching. This review highlights the use of 2D materials in doping detection. For example, reduced graphene oxide-MOF composites show high sensitivity for detecting anabolic steroids like testosterone, while NiO/NGO nanocomposites exhibit strong selectivity for stimulants like ephedrine. However, challenges such as environmental instability and high production costs hinder their widespread application. Future efforts should focus on improving material stability through chemical modifications, reducing production costs, and integrating these materials into advanced systems like machine learning. Such advancements could revolutionize doping detection, ensuring fairness in sports and protecting athlete health. Nanotechnology ; Doping ; Graphene ; Materials science ; Stacking ; Biosensor ; Limiting ; Oxide ; Computer science ; Chemistry ; Optoelectronics ; Mechanical engineering 2D Materials and Applications ; Luminescence and Fluorescent Materials ; Advanced biosensing and bioanalysis techniques
Authors: Etinosa Osaro | Ashiat Bakare | Yamil J. Colón
Source: Communications Materials (article, 2025-10-02)
Field: Materials Science — Materials Chemistry

Multi-method material selection for adsorption using Bayesian approaches Machine learning is reshaping the discovery of new materials, yet a persistent challenge is selecting the most informative training data from large and complex databases. Here we present a framework that integrates inducing points with diverse data acquisition strategies from active learning and Bayesian optimization to guide the selection of material training sets. We compare purely explorative selection, based on Gaussian process regression uncertainty, with exploitation-based approaches such as expected improvement and probability of improvement. Using methane uptake in metal–organic frameworks as a case study, we evaluate structures by properties such as void fraction, pore diameters, and accessible surface area. An intersection analysis across methods identifies a consensus set of 611 frameworks and key pressure points consistently chosen as informative. Training on this reduced subset yields a highly accurate predictive model, demonstrating that principled data selection accelerates adsorption modeling and enables more efficient materials screening. A major challenge in machine learning is selecting the most appropriate training data from large databases. Here, a framework that integrates inducing points with active learning and Bayesian optimization for selecting training sets is reported, and used to identify metal-organic frameworks for methane uptake. Bayesian optimization ; Gaussian process ; Training set ; Bayesian probability ; Selection (genetic algorithm) ; Set (abstract data type) ; Process (computing) ; Key (lock) ; Supervised learning Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Electrostatics and Colloid Interactions
Authors: Muhammad Hashami | Yerdos Ongarbayev | Yerbol Tileuberdi | Yerzhan Imanbayev | Ainur Zhambolova | Yernar Kanzharkan
Source: Applied Sciences (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

Technological Progress in Sulfur-Based Construction Materials: The Role of Modified Sulfur Cake in Concrete and Bitumen Modified sulfur cake is a by-product of sulfuric acid and hydrometallurgical processes, and presents an underutilized resource in sustainable infrastructure with significant potential. This review evaluates the current technological innovations as pertaining to the use of modified sulfur cake in the manufacture of sulfur concrete and sulfur-modified bitumen. The processing strategies (thermal, chemical, and mechano-chemical processing, and effects of organic and inorganic additives to promote mechanical, chemical, and thermal behaviors) are discussed systematically. The effect of the modified sulfur cake on the workability, compressive strength, corrosion resistance, and environmental resistance of construction materials, in particular, is tested, with compression strengths beyond 40 MPa being reported, alongside the improved rutting resistance up to 40%. The most critical limitations associated with phase instability, toxic gas release during processing, compositional variability, and the absence of standardization are identified. Correspondingly, to alleviate them, new developments such as blends with sulfur, nano-reinforcements (e.g., carbon nanotubes (CNT), nano-silica), and the incorporation of formulation optimization by machine-learning are considered. The review particularly focuses on the life cycle performance, reduction in volatile organic compounds (VOC) emissions, and circular economy advantages, highlighting modified sulfur cake as an economical and low-carbon alternative to conventional concrete and bitumen. This review mainly aims to bridge the gap between waste valorization and green construction technologies, offering a roadmap for future research and industrial implementation in line with global climate and sustainability goals. Sulfur ; Asphalt ; Engineering ; Materials science ; Environmental science ; Metallurgy ; Composite material Structural mechanics and materials ; Advanced Energy Technologies and Civil Engineering Innovations ; Structural Behavior of Reinforced Concrete
Authors: Xi Zeng | Chung‐Kang Peng | Wen Shi | Shengjie Hu | Yu Cao | Huan Wei | Pingan Chen | Jiangnan Xia | Jiaqi Ding | Liang Yu | Zhenqi Gong | Huajie Chen | Naiyan Lu | Rong Li | Yuanyuan Hu
Source: Small Methods (article, 2025-05-13)
Field: Materials Science — Polymers and Plastics

An Interlayer Strategy for Low‐Voltage Thin‐Film Organic Electrochemical Transistors Solid-state organic electrochemical transistors (SS-OECTs) are promising candidates for next-generation wearable and bioelectronic applications due to their high transconductance and low-voltage operation. However, conventional SS-OECTs rely on ion gels with high ionic liquid concentrations, which compromise mechanical robustness and scalability. This study addresses these limitations by developing thin-film OECTs (TF-OECTs) using solid electrolytes with significantly reduced ionic liquid concentrations and introducing a doped organic semiconductor film (DOSCF) as an interlayer between the gate and electrolyte. This strategy enables TF-OECTs to achieve film-like mechanical properties while maintaining high performance, including a maximum transconductance (g<sub>m</sub>) of 5.05 mS, operational voltages below 1 V, and exceptional stability over 1000 switching cycles. The devices also exhibit superior flexibility, enduring over 2000 bending cycles with minimal performance degradation. Their potential is demonstrated in ferric ion sensing, achieving an ultralow detection limit of 15 nm with a high selectivity of 0.7 mA dec<sup>-1</sup>, and in neuromorphic computing, where they emulate synaptic behaviors and achieve a 96.7% image recognition accuracy after training with artificial neural networks (ANN). These results highlight the transformative potential of TF-OECTs for integration into advanced, multifunctional electronic systems, combining high performance, mechanical robustness, and scalability. Materials science ; Neuromorphic engineering ; Transistor ; Transconductance ; Robustness (evolution) ; Optoelectronics ; Electrolyte ; Ionic liquid ; Nanotechnology ; Bioelectronics ; Voltage ; Scalability ; Computer science ; Electrode ; Electrical engineering ; Artificial neural network ; Artificial intelligence ; Chemistry ; Biosensor ; Engineering Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Advanced Memory and Neural Computing
Authors: Wataru Takahara | Ryuto Baba | Yosuke Harashima | Tomoaki Takayama | Shogo Takasuka | Yuichi Yamaguchi | Akihiko Kudo | Mikiya Fujii
Source: ACS Omega (article, 2025-04-10)
Field: Materials Science — Materials Chemistry

Data-Driven Approach Considering Imbalance in Data Sets and Experimental Conditions for Exploration of Photocatalysts In the field of data-driven material development, an imbalance in data sets where data points are concentrated in certain regions often causes difficulties in building regression models when machine learning methods are applied. One example of inorganic functional materials facing such difficulties is photocatalysts. Therefore, advanced data-driven approaches are expected to help efficiently develop novel photocatalytic materials even if an imbalance exists in data sets. We propose a two-stage machine learning model aimed at handling imbalanced data sets without data thinning. In this study, we used two types of data sets that exhibit the imbalance: the Materials Project data set (openly shared due to its public domain data) and the in-house metal-sulfide photocatalyst data set (not openly shared due to the confidentiality of experimental data). This two-stage machine learning model consists of the following two parts: the first regression model, which predicts the target quantitatively, and the second classification model, which determines the reliability of the values predicted by the first regression model. We also propose a search scheme for variables related to the experimental conditions based on the proposed two-stage machine learning model. This scheme is designed for photocatalyst exploration, taking experimental conditions into account as the optimal set of variables for these conditions is unknown. The proposed two-stage machine learning model improves the prediction accuracy of the target compared with that of the one-stage model. Computer science ; Data exploration ; Data science ; Data mining ; Visualization Machine Learning in Materials Science ; Advanced Chemical Sensor Technologies ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Giacomo Janson | Alexander Jussupow | Michael Feig
Source: Communications Chemistry (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Deep generative modeling of temperature-dependent structural ensembles of proteins Deep learning has revolutionized protein structure prediction, but capturing conformational ensembles remains a challenge. Molecular dynamics (MD) simulations can describe biomolecular dynamics, but they are computationally expensive. Alternatively, deep learning models trained on MD can generate structural ensembles at reduced cost. We present aSAM (atomistic structural autoencoder model), a latent diffusion model trained on MD to generate heavy atom protein ensembles. aSAM models atoms in a latent space to accurate sample side chain and backbone torsion angle distributions. Additionally, the aSAMt version generates ensembles conditioned on temperature. Trained on mdCATH, aSAMt captures temperature-dependent ensemble properties and demonstrates generalization beyond training temperatures. By comparing aSAMt ensembles to long MD simulations of fast-folding proteins, we find that high-temperature training enhances the ability of generators to explore energy landscapes. aSAMt can also capture experimentally observed thermal behavior of proteins. This work generalizes deep learning ensemble generation towards the inclusion of environmental conditions. Autoencoder ; Ensemble learning ; Deep learning ; Molecular dynamics ; Generalization ; Conformational ensembles ; Protein structure prediction ; Statistical ensemble Machine Learning in Materials Science ; Protein Structure and Dynamics ; Quantum many-body systems
Authors: Toshihiro Futazuka | Ryo Ishikawa | Tatsuya Yokoi | Katsuyuki Matsunaga | Naoya Shibata | Yuichi Ikuhara
Source: Nature Communications (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

Direct observation of substitutional and interstitial dopant diffusion in oxide grain boundary Grain boundaries (GBs) serve as fast diffusion paths for dopant atoms, and the segregated dopants can significantly alter the materials' properties. However, the exact mechanism of fast dopant diffusion along the GBs, particularly at atomic scale, is still unclear. Here we show direct observation of preferential GB diffusion of Hf dopant atoms along the Σ31 symmetric tilt GB in α-Al<sub>2</sub>O<sub>3</sub>, using time-resolved atomic-resolution scanning transmission electron microscopy and statistical tracking of Hf atom locations. Molecular dynamics simulations incorporating artificial neural network interatomic potentials reveal that Hf atoms preferentially diffuse along the GB by exchanging with co-segregated Al vacancies at the GB. Moreover, we demonstrate that GB interstitial diffusion can greatly enhance the diffusivity of Hf atoms along the GB, where shuffle motion plays a key role in lowering the activation energies for GB diffusion. Dopant ; Diffusion ; Grain boundary ; Atom (system on chip) ; Thermal diffusivity ; Tilt (camera) ; Oxide ; Transmission electron microscopy Electronic and Structural Properties of Oxides ; Semiconductor materials and devices ; Advanced materials and composites