jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hou Wu | Sirui Lin | Yifan Zhou | Miao Yang | Hao Pan | Chenxu Yao | José Blanchet | Z. Suo | Tongqing Lu
Source: Advanced Functional Materials (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Constructing Structure‐Property Maps using High‐Throughput Experiments and Machine Learning ABSTRACT A fundamental challenge in materials science is to map structures to properties. Physics‐based models are limited by theoretical idealizations, whereas data‐driven models are limited by the size of available experimental datasets. Here, we demonstrate an approach to construct structure‐property maps through a combination of a high‐throughput experiment and machine learning. We print thousands of samples, the structure of each being defined by a pixel arrangement. We then measure the stress–strain curves of these samples by developing a high‐throughput experiment. From these, we derived stiffness, fracture strain, fracture stress, and work of fracture, forming a dataset linking pixelated structures to properties. A convolutional neural network, initialized on ImageNet and fine‐tuned by transfer learning, learned maps that generalize: out‐of‐sample test errors were 2.44%, 7.05%, 4.65%, and 12.16% for the four properties, despite the design space (∼10 35 ) far exceeding the fabricated set (∼10 3 ). Embedding the learned map within an active learning closed loop to optimize fracture stress and fracture strain. Within a few iterations, it discovered a bar‐like topology for fracture stress (94% improvement) and a zig‐zag topology for fracture strain (282% improvement). These results demonstrate that high‐throughput experiments combined with machine learning can construct reliable structure–property maps and effectively optimize fracture properties. Construct (python library) ; Fracture (geology) ; Embedding ; Convolutional neural network ; Set (abstract data type) ; Stress (linguistics) ; Transfer of learning ; Topology (electrical circuits) ; Pixel Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Topology Optimization in Engineering
Authors: W.J. Wang | Isabel Cooley | Morgan R. Alexander | Ricky D. Wildman | Anna K. Croft | B. Johnston
Source: Digital Discovery (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A case study on hybrid machine learning and quantum-informed modelling for solubility prediction of drug compounds in organic solvents Machine learning pipeline integrates COSMO-RS and multiple molecular descriptors to predict and interpret solubility across diverse solute–solvent systems. Solubility ; Pipeline (software) ; Stability (learning theory) ; Feature (linguistics) ; Artificial neural network ; Drug ; Active learning (machine learning) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Shun Wang | Shun‐Li Shang | Zi‐Kui Liu | Wenrui Hao
Source: Proceedings of the National Academy of Sciences (article, 2026-01-02)
Field: Materials Science — Materials Chemistry

ZENN: A thermodynamics-inspired computational framework for heterogeneous data–driven modeling Traditional entropy-based methods-such as cross-entropy loss in classification problems-have long been essential tools for representing the information uncertainty and physical disorder in data and for developing artificial intelligence algorithms. However, the rapid growth of data across various domains has introduced new challenges, particularly the integration of heterogeneous datasets with intrinsic disparities. To address this, we introduce a zentropy-enhanced neural network (ZENN), extending zentropy theory into the data science domain via intrinsic entropy, enabling more effective learning from heterogeneous data sources. ZENN simultaneously learns both energy and intrinsic entropy components, capturing the underlying structure of multisource data. To support this, we redesign the neural network architecture to better reflect the intrinsic properties and variability inherent in diverse datasets. We demonstrate the effectiveness of ZENN on classification tasks and energy landscape reconstructions, showing its superior generalization capabilities and robustness-particularly in predicting high-order derivatives. In image and text classification tasks, ZENN demonstrates superior generalization by introducing a learnable temperature variable that models latent multisource heterogeneity, allowing it to surpass state-of-the-art models on CIFAR-10/100, BBC News, and AG News. As a practical application in materials science, we employ ZENN to reconstruct the Helmholtz energy landscape of Fe<sub>3</sub>Pt using data generated from density functional theory and capture key material behaviors, including negative thermal expansion and the critical point in the temperature-pressure space. Overall, this work presents a zentropy-grounded framework for data-driven machine learning, positioning ZENN as a versatile and robust approach for scientific problems involving complex, heterogeneous datasets. Generalization ; Artificial neural network ; Entropy (arrow of time) ; Domain (mathematical analysis) ; Helmholtz free energy ; Key (lock) ; Information theory ; Data modeling Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Model Reduction and Neural Networks
Authors: Binquan Luan | C. A. C. dos Santos | Bowen Zheng | Felipe Lopes Oliveira | R. Ferreira | Michael Steiner | Grace X. Gu
Source: The Journal of Physical Chemistry C (article, 2026-01-13)
Field: Chemistry — Inorganic Chemistry

Combining Machine-Learning and Classical Potentials for Grand Canonical Monte Carlo Simulation of Chemisorption Chemisorption, characterized by the strong and highly selective chemical bonding formation between a gas molecule and a solid sorbent, plays a critical role in various applications, such as surface functionalization, carbon capture, and hydrogen storage. Sorption processes are typically modeled by means of a Grand Canonical Monte Carlo (GCMC) simulation. However, GCMC methods are limited to physisorption processes described by molecular-mechanics or classical interatomic potentials. Therefore, a computationally efficient GCMC method that accounts for chemisorption is needed. Here, we report an accurate and efficient modeling method for GCMC simulations of chemisorption using hybrid interatomic potentials. Namely, the sorbate–sorbate interaction is described by classical potentials, and the sorbate–sorbent interaction is rendered by a machine-learning interatomic potential (MLIP) trained on simulation data extracted from quantum calculations. To prove the principal, we have applied the method to the simulation of CO2 chemisorption in the porous metal–organic framework Mg-MOF-74. By comparing the GCMC simulated CO2 adsorption isotherms and isobars, respectively, with the experimental data, we obtain better agreement compared with other classical and quantum-chemical simulation methods while keeping computational cost at a minimum. With the rise of transferable MLIP, we expect that the method could enable computationally efficient and highly accurate sorption studies in reticular materials. Chemisorption ; Physisorption ; Monte Carlo method ; Adsorption ; Hydrogen ; Molecular dynamics ; Molecule ; Sorption Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Carbon Dioxide Capture Technologies
Authors: Donya Esmaeilpour | Michael R. Hamblin | Jianlin Cheng | Arezoo Khosravi | Jian Liu | Atefeh Zarepour | Ali Zarrabi | Mika Sillanpaa | Ehsan Nazarzadeh Zare‬ | Jianliang Shen | Hassan Karimi-Maleh
Source: Bioactive Materials (article, 2026-01-30)
Field: Materials Science — Biomaterials

Artificial intelligence driven protein design and sustainable nanomedicine for advanced theranostics The integration of artificial intelligence, protein engineering, and sustainable nanomedicine is driving a paradigm shift in theranostics by enabling highly precise disease diagnosis and targeted therapy. AI-driven methodologies, including machine learning and deep learning, facilitate the rapid analysis of complex biological and chemical datasets, accelerating protein structure prediction, molecular docking, and structure-activity relationship modeling. These capabilities support the rational design of proteins and peptides with enhanced specificity, therapeutic efficacy, and safety, while enabling personalized treatment strategies tailored to individual molecular profiles. In parallel, sustainable nanomedicine focuses on the development of biodegradable, biocompatible, and environmentally benign nanomaterials to improve drug bioavailability, stability, and controlled release. AI-assisted optimization further refines nanocarrier design by balancing therapeutic performance with safety and environmental impact. Advanced intelligent nanocarriers capable of real-time monitoring, adaptive drug release, and degradation into non-toxic by-products represent a significant advancement over conventional static systems. The theranostic paradigm has become central to precision medicine, particularly in oncology, especially where AI-designed nanoplatforms enable targeted delivery of imaging agents and therapeutics to tumors, while allowing continuous treatment monitoring and minimizing off-target effects. Emerging applications in neurological, infectious, and cardiovascular diseases further highlight the broad clinical potential of this approach. Accordingly, this review summarizes AI-driven protein design strategies, sustainable nanocarrier engineering, and their convergence in next-generation theranostic systems, critically discussing mechanistic insights, translational challenges, and design principles required for developing safe, scalable, and clinically adaptable intelligent nanomedicines. Nanocarriers ; Nanomedicine ; Targeted drug delivery ; Rational design ; Drug delivery ; Paradigm shift Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Graphene and Nanomaterials Applications
Authors: Deepali Jagga | Savita | Vijay Verma
Source: Materials Research Bulletin (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Graphene and its derivatives: From synthesis pathways to emerging technological frontiers Graphene ; Oxide ; Carbon nanotube ; Scope (computer science) ; Carbon fibers Graphene research and applications ; Graphene and Nanomaterials Applications ; Supercapacitor Materials and Fabrication
Authors: Qun Su | Kai Zhu | Qiaolin Gou | Jintu Zhang | Renling Hu | Y Li | Yongze Wang | Haitao Zhang | Ziyi You | Linlong Jiang | Yu Kang | Jike Wang | Chang-Yu Hsieh | Tingjun Hou
Source: Nature Communications (article, 2026-03-07)
Field: Materials Science — Materials Chemistry

A scalable and quantum-accurate foundation model for biomolecular force fields via linearly tensorized quadrangle attention Accurate atomistic biomolecular simulations are vital for understanding disease mechanisms and drug discovery, yet existing methods struggle to balance quantum-mechanical accuracy with computational scalability. Classical force fields often lack precision, while quantum methods are computationally prohibitive for complex biological systems. Here we show that LiTEN, a scalable equivariant neural network, resolves this dilemma by efficiently modeling complex three- and four-body interactions with linear complexity via Linearly Tensorized Quadrangle Attention. We introduce LiTEN-FF, a foundation model pre-trained on extensive datasets to ensure broad chemical generalization across diverse molecular spaces. We demonstrate that LiTEN achieves state-of-the-art accuracy on standard benchmarks, consistently outperforming leading approaches in both precision and speed. Furthermore, LiTEN-FF enables comprehensive modeling tasks, ranging from geometry optimization to free energy surface construction, with high computational efficiency for large biomolecules. This framework provides a physically grounded, versatile foundation for advanced biomolecular modeling and drug design applications. Scalability ; Generalization ; Foundation (evidence) ; Quantum ; Equivariant map ; Quadrangle ; Ranging ; Energy (signal processing) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Physical and Chemical Molecular Interactions
Authors: Véronique Charbonnier | Matthew D. Witman | Keita Shinzato | Hyunjeong Kim | Jinseok Koh | Hyun Cho | Mark D. Allendorf | Kohta Asano | Eun Seon Cho | Kouji Sakaki | Vitalie Stavila
Source: Journal of Alloys and Compounds (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

Performance assessment of AB2 alloys (A = Zr, Ti; B = Mn, Ni, Cr, V, Fe, Al) for hydrogen storage and separation in the presence of carbon dioxide Hydrogen storage ; Hydride ; Hydrogen ; Alloy ; Metal ; Chemical stability ; Phase (matter) ; Enthalpy ; Volume (thermodynamics) Hydrogen Storage and Materials ; Catalysts for Methane Reforming ; Metalloenzymes and iron-sulfur proteins
Authors: Yingmei Yin | Liangqi Tang | Gongfa Chen | Dingwen Zhong
Source: Journal of Materials in Civil Engineering (article, 2026-01-22)
Field: Chemistry — Analytical Chemistry

Asphalt Quality Evaluation and Intelligent Classification via ATR-FTIR Functional Group Analysis Conventional asphalt testing methods inadequately and slowly represent the inherent chemical composition variations among materials. This study utilizes attenuated total reflectance Fourier transform infrared spectroscopy (ATR-FTIR) to identify subtle changes in functional groups within asphalt binders. Additionally, it integrates machine learning technology to develop a rapid and intelligent framework for quality evaluation and brand recognition based on chemical fingerprints. This paper examines four distinct brands of No. 70 needle penetration grade (PEN 70) base asphalt binders, with spectral information acquired through ATR-FTIR analysis. This work innovatively links the characteristic differences of functional groups, as captured by FTIR, to asphalt brand affiliation and quality characteristics, enabling intelligent and high-precision classification via machine learning. Principal component analysis (PCA) indicated that various asphalt brands exhibit notable differences in spectral characteristics within the fingerprint region (1,300–400 cm−1) and in the relative content of aromatic and aliphatic functional groups, which is essential for brand differentiation. Characteristic functional group peaks at 1,376, 813, 865, and 722 cm−1 were identified as essential for brand differentiation. The differences in functional groups serve to differentiate brands and correlate with performance indicators, including ductility. Random forest (RF) is the most effective machine learning algorithm for learning patterns of functional group changes. The optimized RF model attained an overall classification accuracy of 98.4% on an independent test set, as determined by fingerprint region spectral data. This framework offers a dependable technical method for the rapid quality assurance of asphalt in location. Asphalt ; Principal component analysis ; Quality assurance ; Fingerprint (computing) ; Pattern recognition (psychology) ; Random forest ; Quality (philosophy) ; Functional near-infrared spectroscopy Spectroscopy and Chemometric Analyses ; Asphalt Pavement Performance Evaluation ; Infrastructure Maintenance and Monitoring
Authors: Sergio Contreras Arredondo | Chenyu Tang | Radu A. Talmazan | Alberto Megías | Cheng Giuseppe Chen | Christophe Chipot
Source: Nature Computational Science (article, 2026-02-17)
Field: Materials Science — Materials Chemistry

Learning the committor without collective variables Key (lock) ; Identification (biology) ; Function (biology) ; Artificial neural network ; Perceptron ; Graph Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Guangwu Zhang | X. Q. Xiang | Zhengfang Qian | Yixin Xu | Shengying Yue | Hyejin Jang | Lin Yang | Yanguang Zhou | Xinyu Wang | Qiye Zheng
Source: Acta Materialia (article, 2026-01-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Strain-gradient-driven decoupling of thermal suppression from anisotropy in β-Ga2O3 Anisotropy ; Decoupling (probability) ; Thermal conductivity ; Thermal ; Scattering ; Silicon ; Temperature gradient ; Semiconductor Ga2O3 and related materials ; Photoacoustic and Ultrasonic Imaging ; Transition Metal Oxide Nanomaterials
Authors: Guoquan Li | X. Pan | BinQiang LUO | GuiJi WANG | Xuemiao Chen | Rongjie Shui | Chao Xu | Fuli Tan Fuli Tan | Jianheng Zhao | Chengwei Sun
Source: Journal of Applied Physics (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Compression and yield characteristics of CuCrZr alloy under high-pressure and high-strain-rate loading CuCrZr alloy is a promising candidate for electromagnetic railgun rails owing to its combination of high strength and hardness with excellent electrical and thermal conductivity. Investigating its dynamic behavior under high-pressure (&amp;gt;10 GPa) and high-strain-rate (&amp;gt;105/s) conditions is of great significance, as it not only elucidates the material's mechanical response in extreme service environments but also informs the design and performance evaluation of rail materials. In this study, shock and ramp loading experiments were conducted using magnetically driven compression devices. Key properties of CuCrZr alloys, including its high-pressure equation of state, dynamic yield strength, and spall strength, were determined in these experiments, where the dynamic yield strength was specifically calculated by combining the self-consistent method with a long short-term memory neural network inversion approach. The results showed that the sound velocity of CuCrZr alloys under both shock and ramp loading exhibits good consistency, and the corresponding pressure–specific volume (P–V) curves are in good agreement at pressures below 20 GPa with relative deviation less than 2.23%. Under shock compression, the relation between shock wave velocity and particle velocity of CuCrZr alloys is very close to that of oxygen-free copper (OFHC), indicating that the high-pressure compression characteristics of CuCrZr alloys is similar to those of OFHC. The measured dynamic yield strength of CuCrZr alloys ranged from 375 to 691 MPa, consistently exceeding that of OFHC, with the discrepancy diminishing as pressure rises. The spall strength was found to be 1.65–1.88 GPa and increases with peak loading pressure, significantly higher than that of OFHC (1.0–1.5 GPa). This study provides critical experimental data for the application of CuCrZr alloy under extreme dynamic loading. Alloy ; Spall ; Yield (engineering) ; Compression (physics) ; Dynamic range compression ; Shock wave ; Shock (circulatory) ; Particle velocity High-Velocity Impact and Material Behavior ; Electromagnetic Launch and Propulsion Technology ; High-pressure geophysics and materials
Authors: Chao Gong | Mengru Li | Jing Tian | Binghui Cai | Jiahang Wu | Bingyan Zhang | Xiaoyan Zhu | Shuai He | Pei Liu
Source: Results in Chemistry (article, 2026-01-03)
Field: Materials Science — Materials Chemistry

Green synthesis of zinc oxide nanoparticles: Advances, applications, and AI-driven innovations for sustainability The green synthesis of Zinc Oxide Nanoparticles (ZnO NPs) has garnered significant attention due to its eco-friendly nature and the versatile applications of ZnO NPs in various fields. This review delves into the latest advancements in green synthesis methods, emphasizing plant extracts, microorganisms, and biomacromolecules (notably proteins and peptides). By integrating bibliometric analysis of Web of Science data (2015–2025), it pinpoints global research hotspots like “green synthesis,” “antibacterial activity,” and “photocatalysis.” Key insights include: plant-mediated synthesis offers cost-effectiveness but is affected by seasonal variability; microorganism-driven approaches allow for scalability yet require strict sterility; and biomacromolecules-mediated methods. Critically, we highlight how artificial intelligence (AI), particularly machine learning (ML), is being integrated across these methods—from predicting optimal plant extract compositions and microbial culture conditions to designing peptide templates—to enhance reproducibility, yield, and functionality of green-synthesized ZnO NPs. The bibliometric analysis reveals India and China as research hubs, with “Good Health and Well-Being” being the dominant sustainable development goal (4720 publications). The synthesis of sustainable chemistry and AI promises future breakthroughs in large-scale, tailored ZnO NPs production for applications in biomedicine and environmental protection. This review provides a comprehensive analysis integrating green synthesis techniques with scientometric insights, offering a critical assessment of ZnO NPs sustainability and its role in advancing global health solutions. Sustainability ; Biomedicine ; Human health ; Sustainable development ; Scalability ; Sustainable production Nanoparticles: synthesis and applications ; Antimicrobial Peptides and Activities ; Peptidase Inhibition and Analysis
Authors: Z. T. Sun | Zijun Qi | Yunfei Song | Lijie Li | Sheng Liu | Wei Shen | Gai Wu
Source: npj Computational Materials (article, 2026-02-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Multiscale investigation of thermal transport in β-Ga2O3-based heterointerfaces enabled by machine learning potential: cross-scale parameter The rising power density of advanced electronics demands improved thermal management, while traditional single-scale methods are unable to fully reveal the complex heat transfer mechanisms in heterostructures. This work establishes a multiscale simulation framework by constructing a machine learning potential, enabling accurate cross-scale parameter transfer from atomic to mesoscopic and then to macroscopic levels. Results show that the thermal boundary resistance (TBR) at the β-Ga2O3/diamond interface is higher than that at the β-Ga2O3/Si and β-Ga2O3/SiC interfaces, and that the TBR decreases with increasing temperature, which contradicts conventional understanding. Vibrational density of states and interface conductance modal analysis elucidate the underlying mechanisms. These mesoscale insights are incorporated into macroscopic simulations, showing the β-Ga2O3/diamond heterostructure’s peak power capability reaches 226% of that of β-Ga2O3/Si. Further analysis reveals that although the thermal conductivity of the heat-spreading substrate remains the dominant factor in overall thermal performance, the thermal bottleneck gradually shifts toward the interface as both substrate conductivity and operating temperature rise. Moreover, crystal orientation significantly influences thermal performance and thermal stress distribution, necessitating careful trade-offs. This study not only provides effective strategies for optimizing β-Ga2O3-based devices but also establishes a generalizable paradigm for cross-scale thermal management research in heterogeneous material systems. Thermal conductivity ; Mesoscopic physics ; Thermal resistance ; Thermal contact conductance ; Thermal ; Interfacial thermal resistance ; Thermal grease ; Heat transfer ; Bottleneck Ga2O3 and related materials ; Thermal properties of materials ; Transition Metal Oxide Nanomaterials
Authors: Ziyan Li | Haopeng Xu | Zhihui Guo | Xuming Cai | enyan Wang | Huliang Chen | Zhinian Li | Wei Chen
Source: International Journal of Hydrogen Energy (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Research progress on BCC solid solution type high entropy hydrogen storage alloys Hydrogen storage ; Vanadium ; Hydrogen ; High entropy alloys ; Solid solution ; Phase diagram ; Valence electron ; Solid hydrogen Hydrogen Storage and Materials ; High Entropy Alloys Studies ; Electrocatalysts for Energy Conversion