jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhijie Lin | Chao Yang
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Artificial Intelligence Design of Sustainable Aluminum Alloys: A Review Sustainable aluminum alloys, renowned for their lower energy consumption and carbon emissions, present a critical path towards a circular materials economy. However, their design is fraught with challenges, including complex performance variability due to impurity elements and the time-consuming, cost-prohibitive nature of traditional trial-and-error methods. The high-dimensional parameter space in processing optimization and the reliance on human expertise for quality control further complicate their development. This paper provides a comprehensive review of Artificial Intelligence (AI) techniques applied to sustainable aluminum alloy design, analyzing their methodologies and identifying key challenges and optimization strategies. We review how AI methods such as knowledge graphs, evolutionary algorithms, and machine learning transform conventional processes into efficient, data-driven workflows, thereby enhancing development speed and precision. The review explicitly highlights existing bottlenecks, including insufficient data quality and standardization, the complexity of cross-scale modeling, and the need for industrial coordination. We conclude that AI holds immense potential to drive the recycled aluminum industry toward a more sustainable and intelligent future. Future research is poised to leverage generative AI, autonomous experimental platforms, and blockchain for improved life-cycle management, while also focusing on developing physics-informed models and establishing standardized data ecosystems. Leverage (statistics) ; Quality (philosophy) ; Sustainable development ; Energy consumption ; Key (lock) ; Applications of artificial intelligence Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Additive Manufacturing Materials and Processes
Authors: Filip Ciepiela | Szymon Wójcik | Małgorzata Jakubowska
Source: Expert Systems with Applications (article, 2025-03-04)
Field: Chemistry — Analytical Chemistry

Deep neural networks for separation of overlapping voltammetric signals Separation (statistics) ; Computer science ; Artificial neural network ; Artificial intelligence ; Deep neural networks ; Pattern recognition (psychology) ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Electrochemical Analysis and Applications
Authors: Xufeng Liu | Dongsheng Luo | Wenhan Gao | Yi Liu
Source:  (article, 2025-04-04)
Field: Materials Science — Materials Chemistry

3DGraphX: Explaining 3D Molecular Graph Models via Incorporating Chemical Priors We consider the explanation of 3D graph neural networks (GNNs) in the field of molecular learning. Recent studies have modeled molecules as 3D graphs, but there exist formidable challenges for 3D graph explanation. In this work, we propose a novel and principled paradigm, known as 3DGraphX, for 3D molecular graph explanation. Unlike existing 2D GNN explanation methods, 3DGraphX focuses on 3D motifs, which are subgraphs showing great occurrence and function significance in molecular activities. Once generated, 3D motifs are fixed in the explanation model; hence, 3DGraphX produces more accurate and chemically plausible explanations in an efficient manner. 3DGraphX contains two branches with several novel methods for instance-level and geometry-level explanations, respectively. Two novel components, known as the mask pooling component and mask unpooling component, are developed to discover important motifs for each 3D molecule as the instance-level explanation. Local spherical coordinate systems are built to investigate the relative positions among motifs for geometry-level explanation. Altogether, 3DGraphX sheds light on the characteristics of molecules as well as the behaviors of 3D GNNs in molecular learning. Experimental results show that 3DGraphX significantly outperforms baselines in instance-level explanation with various explanation budgets. Additional experiments show that 3DGraphX reveals the important geometries taken by 3D GNNs for accurate molecular learning. The code is publicly available at https://github.com/xufliu/3DGraphX. Prior probability ; Computer science ; Graph ; Theoretical computer science ; Artificial intelligence ; Bayesian probability Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Alberto Marbán‐González | José L. Medina‐Franco
Source: ChemRxiv (preprint, 2025-02-24)
Field: Chemistry — Organic Chemistry

Structure-Activity Relationships and Design of Focused Libraries Tailored for Staphylococcus aureus Inhibition Staphylococcus aureus is a bacterium classified among the ESKAPE pathogens, which are anticipated to pose a significant global health emergency in the coming decades. The FabI enzyme, present in both Gram-positive and Gram-negative bacteria, is a key enzyme involved in fatty acid synthesis II (FAS-II). In this study, we utilized transformation rules to expand the chemical space from the most potent S. aureus FabI inhibitors. Three newly generated focused libraries, named INDDS, DIADS, and PYRDS, encompassed 172,026 compounds. These compounds were ranked based on structural similarity and predicted pIC50 values obtained from machine learning models. This approach allowed to prioritize compounds in each focused library targeting S. aureus FabI. We analyzed the pharmacological properties and chemical space diversity of the S. aureus FabI inhibitors to gather relevant insights and support the prioritization of compounds for further study. The three newly generated libraries are freely available at https://github.com/DIFACQUIM/S.aureus_inhibitors Staphylococcus aureus ; Computational biology ; Chemistry ; Computer science ; Microbiology ; Biology ; Bacteria ; Genetics Synthesis and biological activity ; Chemical Synthesis and Analysis
Authors: Xinhang Li | Yongqiang Wang | Tianyu Jiao | Zhaoxin Liu | Cuihong Yang | Ri He | Liang Si
Source: Materials Genome Engineering Advances (article, 2025-05-06)
Field: Materials Science — Materials Chemistry

Finite‐temperature properties of NbO2 from a deep‐learning interatomic potential Abstract Using first‐principles‐based machine‐learning potential, molecular dynamics (MD) simulations are performed to investigate the micro‐mechanism in phase transition of . Treating the DFT results of the low‐ and intermediate‐temperature phases of as training data in the deep‐learning model, we successfully constructed an interatomic potential capable of accurately reproducing the phase transitions from low‐temperature (pressure) to high‐temperature (pressure) regimes. Notably, our simulations predict a high‐pressure monoclinic phase (&gt;14 GPa) without treating its information in the training set, consistent with previous experimental findings, demonstrating the reliability of the constructed interatomic potential. We identified the Nb‐dimers as the key structural motif governing the phase transitions. At low temperatures, the displacements of the Nb‐dimers drive the transition between the (‐) and (‐) phases, while at high temperatures, Nb ions are prone to being equally distributed and the disappearance of Nb‐dimers leads to the stabilization of a high‐symmetry phase. These findings elucidate the structural and dynamical mechanisms underlying the structural properties of and highlight the utility of combining DFT and deep potential MD methods for studying complex phase transitions in transition metal oxides. Natural bond orbital ; Condensed matter physics ; Interatomic potential ; Materials science ; Physics ; Molecular dynamics ; Quantum mechanics ; Density functional theory Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Catalysis and Oxidation Reactions
Authors: Chaohe Chen | Ye Dong | Mengjie Jiang | Lianbin Zhang
Source: International Journal of Energy Research (review, 2025-01-01)
Field: Materials Science — Materials Chemistry

Enhancing Hydrogen Storage Performance of Metal Hydrides in Ships: A Review Some metals and metal alloys can store gaseous hydrogen (GH 2 ), making the storage of hydrogen in metal hydrides (MHs) possible. The present work aims to explore the development of marine MHs and technologies for enhancing hydrogen storage performance while identifying future research priorities. First, the mechanism of MHs hydrogen storage is summarized and its applications in the maritime field are introduced. Subsequently, the technical and economic feasibility of utilizing MHs hydrogen storage technology in ships is analyzed along with the application scenarios and requirements of ship‐based MHs. Furthermore, to meet the target requirements for hydrogen storage properties in ship power systems, this review focuses on several aspects: the hydrogen storage materials, reactor structural parameters, hydrogen storage operating conditions, and thermal management optimization. The factors influencing the performance of MHs hydrogen storage systems and their impacts are summarized. Strategies to enhance the performance of MHs hydrogen storage are proposed and technologies for heat transfer enhancement in MHs hydrogen storage, along with the corresponding thermal management systems, are introduced. Finally, future research directions for ship‐based MHs hydrogen storage are outlined: developing new synthesis routes for novel MH hydrogen storage materials; creating microchannel hydrogen storage reactors with excellent hydrogen storage and heat transfer performance and conducting combined optimization analyses of multiple reactor structural parameters and operational parameters; coupling phase change materials (PCMs) to enhance the energy utilization efficiency of the hydrogen absorption and desorption process; reasonably regulating the kinetics of the hydrogen storage system to ensure the dynamic stability coupled with hydrogen fuel cells; and constructing intelligent hydrogen storage systems based on deep learning methods to improve the predictive and decision‐making capabilities for hydrogen storage performance and thermal management. Through exploring these research directions, MHs hydrogen storage technology is anticipated to achieve greater efficiency and wider applications in the future. Hydrogen storage ; Environmental science ; Metal ; Hydrogen ; Waste management ; Engineering ; Materials science ; Metallurgy ; Chemistry Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Spacecraft and Cryogenic Technologies
Authors: Z. Zhao | Haoran Zuo | Shenwen Fang | Ming Duan | Dan Zhao | Ting Chen
Source: Chemical Engineering Journal (article, 2025-11-10)
Field: Chemistry — Inorganic Chemistry

Hydrophobic metal–organic frameworks and membranes for enhanced carbon capture Membrane ; Adsorption ; Hydrophobic effect ; Metal-organic framework ; Rational design ; Surface modification Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Carbon dioxide utilization in catalysis
Authors: Enguang Fu | Zane R. Thornburg | Troy A. Brier | Rong Wei | Bo Yuan | Benjamin R. Gilbert | Shulei Wang | Zaida Luthey‐Schulten
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-07-05)
Field: Chemistry — Spectroscopy

Assembly of Macromolecular Complexes in the Whole-Cell Model of a Minimal Cell Abstract Macromolecular complexes in the genetically minimized bacterium, JCVI-syn3A, support gene expression (RNA polymerase, ribosome, degradosome), metabolism (ABC transporters, ATP synthase) and chromosome dynamics. In this work, we further incorporate the assembly of 21 unique macromolecular complexes into the existing whole-cell kinetic model of Syn3A. The synthesis and translocation of protein subunits in membrane complexes occur through distinct pathways. A range of 2D association rates for membrane complexes were considered to guarantee a high yield of assembly given the existing time scales of gene expression. By alleviating the undesired kinetically trapped intermediates in ATP synthase assembly, the efficiency was improved. The assembly of RNA polymerase, ribosome, and degradosome influence the speed and efficiency of protein synthesis. Collectively, this model predicted time-dependent cellular behaviors consistent with experiments. A machine learning analysis of the time-dependent metabolomics and metabolic fluxes highlighted the effect of introducing complex assembly into our whole-cell model. Macromolecule ; Cell ; Cell biology ; Chemistry ; Computational biology ; Biophysics ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Genetics, Bioinformatics, and Biomedical Research ; Chemical Reactions and Isotopes
Authors: Ziyuan Li | Shan Gao | Yunhan Wang | Yueyu Zhang | Weichao Wang | Xiangmei Duan
Source: Advanced Science (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Hierarchical Embedded Sphere Model: An Interpretable ML‐Guided Multiscale Descriptor Engineering Decodes OER Activity on TM@MO <sub>2</sub> Catalysts Understanding and predicting catalytic activity in transition-metal oxides remains challenging due to the multiscale interplay between geometric and electronic structure. Here, we propose a Hierarchical Embedded Sphere Model (HESM) integrating density functional theory (DFT) with interpretable machine learning (IML) to establish a generalizable descriptor framework for oxide electrocatalysis. By analyzing transition metal-doped anatase MO2 (101) surfaces, HESM disentangles catalytic activity into three hierarchical contributions: Global electronic structure (G-class), atomic-site intrinsic properties (A-class), and local coordination effect (L-class). The combined effect of these three classes leads to the emergence of two catalytic activation paradigms: Rh@MO<sub>2</sub> optimizes activity via dopant-induced electronic modulation (η<sub>TD</sub> = 0.29-0.36 V), while Fe@ZrO<sub>2</sub> enhances performance (η<sub>TD</sub> = 0.49 V) by host-site coordination field tuning. The hierarchical framework explains the competitive adsorption of OH<sup>*</sup> across doped/host sites, reconciles the well-known V-shaped activity trends with site-dependent deviations, and SHapley Additive exPlanation (SHAP) analysis identifies G-class as a critical feature that governs the model's prediction. This work demonstrates that HESM bridges adsorption energetics with multiscale geometric-electronic couplings, offering a generalizable methodology for descriptor discovery and catalyst design in complex systems. Catalysis ; Work (physics) ; Density functional theory ; Multiscale modeling ; Field (mathematics) ; Feature (linguistics) ; Anatase ; Property (philosophy) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: B. Narasimha Rao | Y. Kavya | P. Vanaja | Ramachandran Balaji | Samia Afzal | Vikram Suresh | N. Raghavendra Naveen
Source: Current Nanomedicine (article, 2025-07-15)
Field: Materials Science — Materials Chemistry

Recent Advances in Nanobiotechnology: Applications in Medicine, Environmental Science, and Industry Abstract: Nanobiotechnology is an interdisciplinary field that integrates nanotechnology with biological sciences to develop nanoscale tools and systems with transformative applications in medicine, environmental science, and industry. By leveraging the unique properties of nanomaterials- such as high stability, enhanced reactivity, tunable surface chemistry, and precise functionalization- nanobiotechnology facilitates groundbreaking innovations in targeted drug delivery, diagnostics, regenerative medicine, and biosensing. In medicine, nanocarriers, including liposomes, dendrimers, and polymeric nanoparticles, have revolutionized drug delivery by enhancing bioavailability, reducing systemic toxicity, and enabling controlled drug release, particularly in cancer therapy and gene therapy. Moreover, nanomaterials such as quantum dots and gold nanoparticles (AuNPs) contribute to early disease detection through advanced imaging and biosensing techniques. In regenerative medicine, nanostructured scaffolds mimic the extracellular matrix, promoting tissue repair and wound healing. Beyond healthcare, nanobiotechnology plays a pivotal role in environmental applications, including water purification, soil remediation, and pollution control, through nano-bioremediation strategies that employ nanocatalysts and biopolymer-based nanomaterials. Industrial applications range from the development of nanobiosensors for food safety monitoring to the synthesis of high-performance nanocomposites for sustainable energy solutions. This review provides a comprehensive overview of recent advancements in nanobiotechnology, discussing its impact on healthcare, environmental science, and industry. It also addresses the challenges of biocompatibility, large-scale production, and regulatory hurdles, which must be overcome to unlock the full potential of nanobiotechnology. As research continues to progress, the integration of artificial intelligence and machine learning with nanobiotechnology is expected to further enhance precision, efficiency, and applicability in diverse scientific and technological domains. Nanobiotechnology ; Nanotechnology ; Nanomedicine ; Regenerative medicine ; Drug delivery ; Materials science ; Biotechnology ; Chemistry ; Nanoparticle Nanoparticles: synthesis and applications ; Molecular Communication and Nanonetworks ; Nanotechnology research and applications
Authors: R. L. Kushwaha | Ankur Chauhan
Source: Engineering Fracture Mechanics (article, 2025-12-08)
Field: Materials Science — Materials Chemistry

Predicting fracture toughness of hydrogen-exposed Zr-2.5Nb using physics-informed neural network Fracture toughness ; Hydride ; Toughness ; Hydrogen embrittlement ; Embrittlement ; Artificial neural network ; Hydrogen ; Fracture (geology) Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science
Authors: Max Eissler | Tim Korjakow | Stefan Ganscha | Oliver T. Unke | Klaus‐Robert Müller | Stefan Gugler
Source: ArXiv.org (preprint, 2025-03-03)
Field: Materials Science — Materials Chemistry

How simple can you go? An off-the-shelf transformer approach to molecular dynamics Most current neural networks for molecular dynamics (MD) include physical inductive biases, resulting in specialized and complex architectures. This is in contrast to most other machine learning domains, where specialist approaches are increasingly replaced by general-purpose architectures trained on vast datasets. In line with this trend, several recent studies have questioned the necessity of architectural features commonly found in MD models, such as built-in rotational equivariance or energy conservation. In this work, we contribute to the ongoing discussion by evaluating the performance of an MD model with as few specialized architectural features as possible. We present a recipe for MD using an Edge Transformer, an ``off-the-shelf'' transformer architecture that has been minimally modified for the MD domain, termed MD-ET. Our model implements neither built-in equivariance nor energy conservation. We use a simple supervised pre-training scheme on $\sim$30 million molecular structures from the QCML database. Using this ``off-the-shelf'' approach, we show state-of-the-art results on several benchmarks after fine-tuning for a small number of steps. Additionally, we examine the effects of being only approximately equivariant and energy conserving for MD simulations, proposing a novel method for distinguishing the errors resulting from non-equivariance from other sources of inaccuracies like numerical rounding errors. While our model exhibits runaway energy increases on larger structures, we show approximately energy-conserving NVE simulations for a range of small structures. Rounding ; Transformer ; Simple (philosophy) ; Molecular dynamics ; Architecture ; Energy (signal processing) ; Potential energy ; Artificial neural network Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Graph Neural Networks
Authors: Thea Denell | Lauri Himanen | Markus Scheidgen | Claudia Draxl
Source: npj Computational Materials (article, 2025-02-06)
Field: Materials Science — Materials Chemistry

Automated identification of bulk structures, two-dimensional materials, and interfaces using symmetry-based clustering Abstract With the rapidly increasing amount of materials data being generated in a variety of projects, efficient and accurate classification of atomistic structures is essential. A current barrier to effective database queries lies in the often ambiguous, inconsistent, or completely missing classification of existing data, highlighting the need for standardized, automated, and verifiable classification methods. This work proposes a robust solution for identifying and classifying a wide spectrum of materials through an iterative technique, called symmetry-based clustering (SBC). Because SBC is not a machine learning-based method, it requires no prior training. Instead, it identifies clusters in atomistic systems by automatically recognizing common unit cells. We demonstrate the potential of SBC to provide automated, reliable classification and to reveal well-known symmetry properties of various materials. Even noisy systems are shown to be classifiable, showing the suitability of our algorithm for real-world data applications. The software implementation is provided in the open-source Python package, MatID, exploiting synergies with popular atomic-structure manipulation libraries and extending the accessibility of those libraries through the NOMAD platform. Cluster analysis ; Identification (biology) ; Symmetry (geometry) ; Computer science ; Materials science ; Artificial intelligence ; Mathematics ; Geometry ; Biology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Bokai Liao | Shiquan Ma | Hao-Lan Huang | Huizhen Li
Source: Journal of Materials Engineering and Performance (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

Machine Learning Approach to Predict Corrosion Inhibition Performance of Amino Acid-Derivate Corrosion Inhibitor for Carbon Steel in Neutral Medium Materials science ; Corrosion ; Carbon steel ; Corrosion inhibitor ; Metallurgy ; Chemical engineering ; Engineering Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability