jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Y.S. Wudil | M.A. Gondal | Mohammed A. Al‐Osta
Source: ACS Applied Materials & Interfaces (article, 2025-02-04)
Field: Materials Science — Materials Chemistry

High-Throughput Screening of 6858 Compounds for Zinc-Ion Battery Cathodes via Hybrid Machine Learning Optimization This work presents a machine-learning framework to explore cathode materials for zinc-ion batteries from a data set of 6858 zinc-containing compounds. Utilizing the extensive Materials Project (MP) database, we employed a two-step machine learning (ML) approach that uses transfer learning to compensate for missing electrochemical properties. Initially, a random forest regressor was used to fill in missing features in the zinc compounds, harnessing the full battery explorer in predictions. Two hybrid models were then developed: the sparrow search algorithm-light gradient boosting machine (SSA-LGBM), and Harris Hawk optimization-deep neural networks (HHO-DNN). The data set contains 107 feature vectors, which were minimized through principal component analysis. These features include descriptors related to structural, chemical, and electronic properties. Both models were trained using the 4351 known battery compounds from MP to predict key properties such as average voltage and gravimetric capacity. After initial prediction of 62 potential electrodes, further screening criteria were applied to identify 18 promising electrodes based on their voltage, specific capacity, electronic conductivity, safety, stability, cost, and abundance. The validation of our approach was carried out by applying the models to known cathode materials, verifying the accuracy of the predictions. This innovative approach significantly accelerates the discovery of efficient and stable cathode materials for zinc-ion batteries, paving the way for more sustainable and high-performance energy storage solutions. This method also provides a robust framework for future materials exploration across various battery technologies. Materials science ; Throughput ; Battery (electricity) ; Cathode ; Zinc ; Nanotechnology ; Computer science ; Metallurgy ; Electrical engineering ; Telecommunications ; Engineering ; Power (physics) Machine Learning in Materials Science ; Advanced battery technologies research ; Advanced Battery Technologies Research
Authors: Jiamin Zhong | Wei Zhu | Shuting Shen | Nan Zhou | Meiyang Xi | Kui Du | Dong Wang | Ben Zhong Tang
Source: Aggregate (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Machine Learning for Organic Fluorescent Materials ABSTRACT Organic fluorescent materials (OFMs), characterized by their unique molecular structures and exceptional optical properties, have demonstrated significant potential in diverse applications such as bioimaging, sensors, and display technologies. Nevertheless, the reliance on chemists' intuition and experience in the traditional design of OFMs, coupled with the high cost and lack of scalability of conventional methods such as fluorescence detection and Density Functional Theory (DFT) calculations, makes it difficult to keep up with the rapid development of the field. The advent of machine learning (ML) has introduced transformative possibilities, enabling data‐driven exploration of the intricate relationships between molecular structures and fluorescence properties. Herein, we review the applications of ML in the innovative design of OFMs with an emphasis on the workflow of modeling, optical property prediction, and OFM design. We also discuss the critical role of data curation and feature engineering in enhancing model performance. Our review provides an overview of commonly used models and assesses their efficacy. We critically examine key challenges such as database construction, model interpretability, and generalization ability, trying to provide a comprehensive framework that advances the integration of ML in the research of organic fluorescent materials, thereby facilitating the development of next‐generation materials. Fluorescence ; Computer science ; Environmental science ; Biochemical engineering ; Materials science ; Process engineering ; Artificial intelligence ; Engineering ; Physics ; Optics Luminescence and Fluorescent Materials ; Conducting polymers and applications ; Molecular Sensors and Ion Detection
Authors: Daniel Wines | Kamal Choudhary
Source: ACS Materials Letters (article, 2025-05-05)
Field: Materials Science — Materials Chemistry

CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties We introduce CHIPS-FF (Computational High-Performance Infrastructure for Predictive Simulation-based Force Fields), an open-source benchmarking platform for machine learning force fields (MLFFs). This platform focuses on complex properties including elastic constants, phonon spectra, defect formation energies, surface energies, and interfacial and amorphous phase properties. Utilizing 16 graph-based MLFFs including ALIGNN-FF, CHGNet, MatGL, MACE, SevenNet, ORB, MatterSim, and OMat24, CHIPS-FF integrates the Atomic Simulation Environment (ASE) with JARVIS-Tools to facilitate high-throughput simulations. Our framework is tested on a set of 104 materials, including metals, semiconductors, and insulators representative of those used in semiconductor components, with each MLFF evaluated for convergence, accuracy, and computational cost. Additionally, we evaluate the force-prediction accuracy of these models for close to 2 million atomic structures. By offering a streamlined, flexible benchmarking infrastructure, CHIPS-FF aims to guide the development and deployment of MLFFs for real-world semiconductor applications, bridging the gap between quantum mechanical simulations and large-scale device modeling. Computer science Machine Learning in Materials Science ; Advanced Materials Characterization Techniques ; Nuclear Materials and Properties
Authors: Woo Hyun Kim | Sungwoo Lee | Myeong Jin Jeon | Kwangyeol Lee | Jihyun Park | Dae Won Park | Sungho Park | Sang Jun Sim
Source: Advanced Science (article, 2025-02-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Rapid and Differential Diagnosis of Sepsis Stages Using an Advanced 3D Plasmonic Bimetallic Alloy Nanoarchitecture‐Based SERS Biosensor Combined with Machine Learning for Multiple Analyte Identification Rapid and accurate differential diagnosis of infections, sepsis, and septic shock is essential for preventing unnecessary antibiotic overuse and improving the chance of patient survival. To address this, a 3D gold nanogranule decorated gold-silver alloy nanopillar (AuNG@Au-AgNP) based surface-enhanced Raman scattering (SERS) biosensor is developed, capable of quantitatively profiling immune-related soluble proteins (interleukin three receptor, alpha chain: CD123, programmed cell death ligand 1: PD-L1, human leukocyte antigen-DR isotype: HLA-DR, and chitotriosidase: ChiT) in serum samples. The 3D bimetallic nanoarchitecture, fabricated using anodized aluminum oxide (AAO), features a uniform structure with densely packed nanogaps on the heads of Au-Ag alloy nanopillars, enabling fast, simple, and replicable production. The proposed biosensor achieves accurate results even with low detection limits (4-6 fM) and high signal consistency (relative standard deviation (RSD) = 1.79%) within a one-step multi-analytes identification chip with a directly loadable chamber. To enhance the diagnostic performance, a support vector machine (SVM) based machine learning algorithm is utilized, achieving 95.0% accuracy and 95.8% precision in classifying healthy controls, infections with and without sepsis, and septic shock. This advanced 3D plasmonic bimetallic alloy nanoarchitecture-based SERS biosensor demonstrates clinical usefulness for sepsis diagnosis and severity assessment, providing timely and personalized treatment. Bimetallic strip ; Materials science ; Biosensor ; Analyte ; Sepsis ; Plasmon ; Nanotechnology ; Biomedical engineering ; Medicine ; Optoelectronics ; Chemistry ; Immunology ; Chromatography ; Metallurgy Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Extracellular vesicles in disease
Authors: Leif Seute | Eric Hartmann | Jan Stühmer | Frauke Gräter
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Grappa – a machine learned molecular mechanics force field Simulating large molecular systems over long timescales requires force fields that are both accurate and efficient. In recent years, E(3) equivariant neural networks have lifted the tension between computational efficiency and accuracy of force fields, but they are still several orders of magnitude more expensive than established molecular mechanics (MM) force fields. Here, we propose Grappa, a machine learning framework to predict MM parameters from the molecular graph, employing a graph attentional neural network and a transformer with symmetry-preserving positional encoding. The resulting Grappa force field outperforms tabulated and machine-learned MM force fields in terms of accuracy at the same computational efficiency and can be used in existing Molecular Dynamics (MD) engines like GROMACS and OpenMM. It predicts energies and forces of small molecules, peptides, and RNA at state-of-the-art MM accuracy, while also reproducing experimentally measured values for <i>J</i>-couplings. With its simple input features and high data-efficiency, Grappa is well suited for extensions to uncharted regions of chemical space, which we show on the example of peptide radicals. We demonstrate Grappa's transferability to macromolecules in MD simulations from a small fast-folding protein up to a whole virus particle. Our force field sets the stage for biomolecular simulations closer to chemical accuracy, but with the same computational cost as established protein force fields. Molecular mechanics ; Force field (fiction) ; Field (mathematics) ; Computer science ; Classical mechanics ; Physics ; Artificial intelligence ; Molecular dynamics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Zhenyao Fang | Qimin Yan
Source: Chemistry of Materials (article, 2025-02-06)
Field: Materials Science — Materials Chemistry

Leveraging Persistent Homology Features for Accurate Defect Formation Energy Predictions via Graph Neural Networks In machine-learning-assisted high-throughput defect studies, a defect-aware latent representation of the supercell structure is crucial for the accurate prediction of defect properties. The performance of current graph neural network (GNN) models is limited due to the fact that defect properties depend strongly on the local atomic configurations near the defect sites and due to the oversmoothing problem of GNN. Herein, we demonstrate that persistent homology features, which encode the topological information on the local chemical environment around each atomic site, can characterize the structural information on defects. Using the dataset containing a wide spectrum of O-based perovskites with all available vacancies as an example, we show that incorporating the persistent homology features, along with proper choices of graph pooling operations, significantly increases the prediction accuracy, with the MAE reduced by 55%. Those features can be easily integrated into the state-of-the-art GNN models, including the graph Transformer network and the equivariant neural network, and universally improve their performance. Besides, our model also overcomes the convergence issue with respect to the supercell size that was present in previous GNN models. Furthermore, using the datasets of defective BaTiO<sub>3</sub> with multiple substitutions and multiple vacancies as examples, our GNN model can also predict the defect-defect interactions accurately. These results suggest that persistent homology features can effectively improve the performance of machine learning models and assist the accelerated discovery of functional defects for technological applications. Pooling ; Computer science ; Persistent homology ; Artificial neural network ; Graph ; Theoretical computer science ; Topology (electrical circuits) ; Algorithm ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Anupma Thakur | Yuan Zhang | Yury Gogotsi | Babak Anasori
Source: MRS Energy & Sustainability (article, 2025-03-31)
Field: Materials Science — Materials Chemistry

Electrochemistry of MXenes and their sustainable energy applications MXenes ; Sustainable energy ; Energy (signal processing) ; Electrochemistry ; Nanotechnology ; Biochemical engineering ; Environmental science ; Materials science ; Chemistry ; Engineering ; Physics ; Electrical engineering ; Renewable energy MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Advanced Photocatalysis Techniques
Authors: Yue Liu | Linhan Wu | Zhengwei Yang | Xinxin Zou | Zheyi Zou | Yuxiao Lin | Maxim Avdeev | Siqi Shi
Source: Advanced Functional Materials (article, 2025-02-11)
Field: Materials Science — Materials Chemistry

Descriptors Divide‐and‐Conquer Enables Multifaceted and Interpretable Materials Structure–Activity Relationship Analysis Abstract Machine learning (ML) is increasingly adopted to explore the dependence of properties on descriptors especially for materials with the complicated structure–activity relationships. However, most current ML modeling strategies typically depend on a single combination of descriptors, which leads to inaccurate and unilateral inferences. Here, a descriptors divide‐and‐conquer method is proposed for machine learning (descriptors‐DCML) in which rough set theory (RST) is integrated with materials domain knowledge to select multiple optimal sets of descriptors combinations and thus diverse rule extraction strategies are provided to dig out mechanisms latent in materials data. Its potential utility and applications using the sodium ion energy barrier prediction of NASICION‐type solid‐state electrolyte compounds with multifaceted influencing factors as an example are demonstrated. A total of 85 NASICION‐type samples with 45 descriptors derived from 72 published literature serve as the data foundation for ML modeling. Not only does descriptors‐DCML exhibit the energy barrier prediction accuracy of 93.8% but also extract 9 relations mapping essential factors to Na ion energy barrier in which 5 ones conform to existing understanding rule and the rest are waiting for validation. This work paves the way for reducing the complexity of analyzing materials structure–activity relationships and enhancing the interpretability of ML models. Divide and conquer algorithms ; Materials science ; Artificial intelligence ; Nanotechnology ; Pattern recognition (psychology) ; Computer science ; Algorithm Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Product Development and Customization
Authors: Aminu Abdullahi | Tarangini Korumilli | K. Jagajjanani Rao
Source: Food and Bioprocess Technology (article, 2025-05-08)
Field: Materials Science — Biomaterials

Revolutionizing Food Packaging with Biobased Polymers, Active and Intelligent Materials for Enhanced Food Safety and Sustainability: Review Sustainability ; Food packaging ; Active packaging ; Business ; Food safety ; Biochemical engineering ; Food science ; Chemistry ; Engineering Nanocomposite Films for Food Packaging ; biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution
Authors: Ali Rajabpour | Bohayra Mortazavi | Pedram Mirchi | Julien El Hajj | Yangyu Guo | Xiaoying Zhuang | Samy Mérabia
Source: International Journal of Thermal Sciences (article, 2025-03-18)
Field: Materials Science — Materials Chemistry

Accurate estimation of interfacial thermal conductance between silicon and diamond enabled by a machine learning interatomic potential Thermal management at silicon-diamond interface is critical for advancing high-performance electronic and optoelectronic devices. In this study, we calculate the interfacial thermal conductance between silicon and diamond using a computationally efficient machine learning (ML) interatomic potential trained on density functional theory (DFT) data. Using non-equilibrium molecular dynamics (NEMD) simulations, we compute the interfacial thermal conductance (ITC) for various system sizes. Our results reveal an extremely close agreement with experimental data than those obtained using traditional semi-empirical potentials such as Tersoff and Brenner which overestimate ITC. In addition, we analyze the frequency-dependent heat transfer spectrum, providing insights into the contributions of different phonon modes to the interfacial thermal conductance. The ML potential accurately captures the phonon dispersion relations and lifetimes, in good agreement with DFT calculations and experimental observations. It is shown that the Tersoff potential predicts higher phonon group velocities and phonon lifetimes compared to the DFT results. Furthermore, it predicts higher interfacial bonding strength, which is consistent with higher interfacial thermal conductance as compared to the ML potential. This study highlights the use of ML interatomic potentials to improve the accuracy and computational efficiency of thermal transport simulations of complex material interface systems. Diamond ; Silicon ; Interatomic potential ; Materials science ; Thermal ; Thermal conductivity ; Conductance ; Thermal resistance ; Diamond cubic ; Computer science ; Condensed matter physics ; Thermodynamics ; Composite material ; Optoelectronics ; Molecular dynamics ; Physics ; Quantum mechanics Thermal properties of materials ; Graphene research and applications ; Machine Learning in Materials Science
Authors: Muhammad Naeem Kiani | Hamza Khaliq | Muhammad Abubakar | Muhammad Rafique | Fazliddin Jalilov | Ghulam Abbas Ashraf | Amel Ayari‐Akkari | Ali Akremi
Source: Medical Oncology (review, 2025-06-04)
Field: Materials Science — Biomaterials

Advancing the potential of nanoparticles for cancer detection and precision therapeutics Hematology ; Cancer ; Nanoparticle ; Nanotechnology ; Medicine ; Internal medicine ; Materials science Nanoparticle-Based Drug Delivery ; Nanoplatforms for cancer theranostics ; Advanced biosensing and bioanalysis techniques
Authors: Oliver Lemke | Benjamin M. Heineike | Sandra Viknander | Nir Cohen | Feiran Li | Jacob L. Steenwyk | Leonard Spranger | Federica Agostini | Cory Thomas Lee | Simran Kaur Aulakh | Judith Berman | Antonis Rokas | Jens Nielsen | Toni I. Gossmann | Aleksej Zelezniak | Markus Ralser
Source: Nature (article, 2025-07-09)
Field: Materials Science — Materials Chemistry

The role of metabolism in shaping enzyme structures over 400 million years Advances in deep learning and AlphaFold2 have enabled the large-scale prediction of protein structures across species, opening avenues for studying protein function and evolution<sup>1</sup>. Here we analyse 11,269 predicted and experimentally determined enzyme structures that catalyse 361 metabolic reactions across 225 pathways to investigate metabolic evolution over 400 million years in the Saccharomycotina subphylum<sup>2</sup>. By linking sequence divergence in structurally conserved regions to a variety of metabolic properties of the enzymes, we reveal that metabolism shapes structural evolution across multiple scales, from species-wide metabolic specialization to network organization and the molecular properties of the enzymes. Although positively selected residues are distributed across various structural elements, enzyme evolution is constrained by reaction mechanisms, interactions with metal ions and inhibitors, metabolic flux variability and biosynthetic cost. Our findings uncover hierarchical patterns of structural evolution, in which structural context dictates amino acid substitution rates, with surface residues evolving most rapidly and small-molecule-binding sites evolving under selective constraints without cost optimization. By integrating structural biology with evolutionary genomics, we establish a model in which enzyme evolution is intrinsically governed by catalytic function and shaped by metabolic niche, network architecture, cost and molecular interactions. Metabolism ; Enzyme ; Biology ; Computational biology ; Biochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Microbial Metabolic Engineering and Bioproduction
Authors: Mikołaj Martyka | Lina Zhang | Fuchun Ge | Yi-Fan Hou | Joanna Jankowska | Mario Barbatti | Pavlo O. Dral
Source: npj Computational Materials (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

Charting electronic-state manifolds across molecules with multi-state learning and gap-driven dynamics via efficient and robust active learning We present a robust protocol for affordable learning of electronic states to accelerate photophysical and photochemical molecular simulations. The protocol solves several issues precluding the widespread use of machine learning (ML) in excited-state simulations. We introduce a novel physics-informed multi-state ML model that can learn an arbitrary number of excited states across molecules, with accuracy better or similar to the accuracy of learning ground-state energies, where information on excited-state energies improves the quality of ground-state predictions. We also present gap-driven dynamics for accelerated sampling of the small-gap regions, which proves crucial for stable surface-hopping dynamics. Together, multi-state learning and gap-driven dynamics enable efficient active learning, furnishing robust models for surface-hopping simulations and helping to uncover long-time-scale oscillations in <i>cis</i>-azobenzene photoisomerization. Our active-learning protocol includes sampling based on physics-informed uncertainty quantification, ensuring the quality of each adiabatic surface, low error in energy gaps, and precise calculation of the hopping probability. Surface hopping ; Excited state ; Molecular dynamics ; Computer science ; Statistical physics ; Ground state ; Photoisomerization ; Physics ; Chemistry ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Spectroscopy and Quantum Chemical Studies