jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ahmad Nawaz | Muhammad Nawaz | Leyang Lv | Yingwu Zhou
Source: Case Studies in Construction Materials (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Machine learning based predictive insights into compressive strength and strength retention of magnesium oxychloride cement Magnesium oxychloride (MOC) cement is a sustainable alternative to Portland cement (PC) due to its excellent mechanical properties, but the poor water resistance limits its usage. Various supplementary cementitious materials (SCMs) and chemical additives can enhance MOC's strength retention (SR) after exposure to water. However, MOC cement design to attain desirable compressive strength (CS) and SR requires a precise selection of input materials, mix proportions, and additives, which is quite challenging. Therefore, employing machine learning techniques could facilitate and accelerate the development of MOC binders with the desired properties. So, this study employed four machine learning (ML) models to predict the CS and SR of MOC using two literature-derived databases. This study evaluates the performance of various ML models for accurate CS and SR prediction of MOC cement. The GBR and XGB models emerged as the most effective predictors for both CS and SR datasets, achieving the best prediction accuracy (coefficient of determination; R 2 ≥0.90) on testing data. In addition, Shapely Additive Explanation (SHAP) is used to interpret the effect of various parameters on CS and SR and is discussed in detail. SHAP-derived feature contributions and experimental observations from existing literature are consistent. The interpretable ML strategy used in this study will aid in the production and performance tuning of CS and SR of sustainable MOC cement for widespread applications. • SVR, RF, GBR, and XGB ML models were used to develop prediction models for CS and SR of MOC. • GBR and XGB models emerged as the most effective predictors based on statistical evaluation measures. • SHAP-derived feature contributions and experimental observations from existing literature are consistent. Compressive strength ; Cement ; Magnesium ; Materials science ; Composite material ; Metallurgy Magnesium Oxide Properties and Applications ; Concrete and Cement Materials Research ; Microwave Dielectric Ceramics Synthesis
Authors: Ramin Bairami Habashi | Mohammad Najafi | Reza Zarghami | Alireza Sabzevari
Source: Journal of Polymer Research (article, 2025-04-01)
Field: Chemistry — Spectroscopy

Decoding the predesigned HDPE synthesis recipe: utilizing the power of ANN and Monte Carlo for tailored molecular weight distribution Recipe ; Materials science ; Monte Carlo method ; Power (physics) ; High-density polyethylene ; Decoding methods ; Molar mass distribution ; Statistical physics ; Composite material ; Computer science ; Thermodynamics ; Polyethylene ; Algorithm ; Mathematics ; Polymer ; Statistics ; Physics Mass Spectrometry Techniques and Applications ; Machine Learning in Materials Science ; Analytical Chemistry and Chromatography
Authors: Xu Pan | Liu Yang | Li Song
Source: Solid State Communications (article, 2025-10-07)
Field: Materials Science — Materials Chemistry

Machine learning incorporating stability features and Bayesian Optimization for perovskite structure prediction Feature selection ; Stability (learning theory) ; Feature (linguistics) ; Perovskite (structure) ; Interpretability ; Feature extraction ; Pattern recognition (psychology) ; Outlier Machine Learning in Materials Science ; Machine Learning and ELM ; Perovskite Materials and Applications
Authors: Fang Wu | Bin Hu | Stan Z. Li
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

Generalized Implicit Neural Representations for Dynamic Molecular Surface Modeling Molecular dynamics (MD) has long been the de facto choice for simulating intricate physical systems from first principles. Recent efforts utilize the implicit neural representation (INR) to directly learn surface point clouds' signed distance function (SDF) with promising outcomes. However, INR's temporal generalization to unexplored molecular systems remains limited, which poses a significant barrier to applying INR to a broader range of real-world scenarios. This study introduces MoE-DSR, an enhanced version of dynamic surface representations (DSR) that effectively integrates the mixture-of-experts (MoE) strategy. Specifically, the router employs a novel geometric surface cloud network to extract the structural information from the initial static protein conformation as the prior knowledge. Meanwhile, experts compromising a team of equivariant implicit neural networks (E-INNs), each responsible for distinct protein families, ensure precise SDF estimation across varied protein data landscapes. We showcase the ability of MoE-DSR to model dynamic protein surface shapes using ensembles from ATLAS, the largest available protein MD simulations database. Extensive experiments validate its effectiveness in analyzing complex molecular systems across continuous space and time domains. Surface (topology) ; Computer science ; Mathematics ; Geometry Machine Learning in Materials Science ; Neural Networks and Applications
Authors: Timofei Miryashkin | Olga Klimanova | Alexander V. Shapeev
Source: Computational Materials Science (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

Clarifying the Ti–V phase diagram using first-principles calculations and Bayesian learning Phase diagram ; CALPHAD ; Spinodal decomposition ; Ab initio ; Miscibility ; Inference ; Ab initio quantum chemistry methods ; Bayesian probability Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Metal and Thin Film Mechanics
Authors: Cesare Roncaglia | Fábio Lopes | Nick Goossens | Michael Stuer | Daniele Passerone
Source: The Journal of Physical Chemistry C (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Machine Learning Lattice Parameters of M<sub>2</sub>AX Phases MAX phases, with a general composition of Mn+1AXn, are layered materials with hexagonal symmetry that have increasingly captivated a lot of attention because of their unique way of combining ceramic and metallic properties into a homogeneous bulk material. We developed a machine learning approach to predict the lattice parameters a and c of M2AX phases. This approach consists of training an ensemble model on a data set collecting all experimentally synthesized M2AX phases’ lattice parameters. Our approach combines a data augmentation scheme with state-of-the-art regression models and hyperparameter optimization tools. We tested our model on newly synthesized compositionally complex high-entropy M2AX phases with positive results. Finally, we also show that our machine learning predictions of lattice parameters are useful as initial values for variable-cell relaxations of M2AX structures with the density functional theory. Lattice (music) ; Materials science ; Statistical physics ; Computer science ; Physics Machine Learning in Materials Science ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Ryan Elwood-Clarke | David M. Wilkins
Source: Journal of Physics Condensed Matter (article, 2025-03-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Symmetry-adapted models for the hyperpolarizability of water Accurately modeling nonlinear optical experiments such as second-harmonic scattering and hyper-Raman spectroscopy requires the hyperpolarizability<b><i>β</i></b>, a nonlinear response to an applied electric field. The hyperpolarizability tensor is a computationally expensive quantity to calculate, making it a natural target for machine-learning methods. We test a family of recently developed models for the hyperpolarizability of water, trained on small clusters containing up to 8 water molecules. These models are able to predict<b><i>β</i></b>for larger clusters, with more complex structures than those observed in the training set. For configurations of bulk water, the agreement is not so straightforward: while the total hyperpolarizability is quite well described, the predicted<i>molecular</i><b><i>β</i></b>tensors vary wildly between models. This means that while experiments whose outputs depend on total hyperpolarizability can be accurately modeled, those that require molecular quantities will require improved models. Hyperpolarizability ; Symmetry (geometry) ; Nonlinear system ; Raman scattering ; Scattering ; Tensor (intrinsic definition) ; Physics ; Field (mathematics) ; Statistical physics ; Chemistry ; Molecular physics ; Computational chemistry ; Raman spectroscopy ; Nonlinear optical ; Quantum mechanics ; Mathematics ; Geometry Nonlinear Optical Materials Research ; Spectroscopy and Quantum Chemical Studies ; Advanced NMR Techniques and Applications
Authors: Ameer Hamza | Muhammad Arshad Javid | Sadaqat Hayyat | Mohammed Najeeb Ahmed Abdualjaleel Abduallah
Source: EJSMT (article, 2025-06-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Energy Management in Lithium-Ion Battery and Supercapacitor Hybrid Systems: Techniques and Approaches The lithium-ion battery/supercapacitor hybrid energy storage system has become the most widely used hybrid energy storage system due to its excellent performance, low cost, and strong versatility. Energy management technology is one of the core technologies in hybrid energy storage systems and remains a significant research focus. To systematically review the energy management methods for hybrid energy storage systems, this article first introduces the topology, energy management architecture, and power allocation control of lithium-ion battery/supercapacitor hybrid systems. Next, the article categorizes existing energy management methods for hybrid energy storage systems into five main types: experience-based, optimization-based, operating condition pattern recognition-based, and machine learning-based. A detailed comparative analysis is provided, with a focus on evaluating the effectiveness of various energy management methods under both regular and random operating conditions. The robustness and computational complexity of each method are also discussed. Finally, the article summarizes current energy management methods and outlines future research directions and development trends in this field. The comprehensive analysis suggests that improving the prediction accuracy of future operating conditions for random loads, establishing more precise hybrid energy storage system models, and enhancing the real-time performance of energy management methods through cloud collaboration will be key areas of focus for future research in hybrid energy storage systems. Supercapacitor ; Battery (electricity) ; Lithium (medication) ; Ion ; Lithium-ion battery ; Energy (signal processing) ; Materials science ; Computer science ; Physics ; Power (physics) ; Medicine ; Electrode ; Electrochemistry ; Thermodynamics ; Internal medicine Supercapacitor Materials and Fabrication ; Electric and Hybrid Vehicle Technologies ; Advanced Battery Technologies Research
Authors: Aparna Ashok | Neeru Bhagat
Source: Applied Mechanics and Materials (article, 2025-04-07)
Field: Materials Science — Materials Chemistry

Predictive Modeling of Half-Metallicity in Heusler Compound through Machine Learning Modeling Heusler alloys are intermetallic compounds formed in two combinations: Full-Heusler (X 2 YZ) and Half-Heusler (XYZ). X and Y can be any transition element, and Z belongs to the main group. This shows that there can be a huge variation in the combinations, leading to various properties and applications. We aimed at predicting the combination leading to shape memory properties using machine learning tools and then synthesizing the same. The predictions are done by training the tool with input data. We employed the lattice strain, valence electron concentration ratio, mechanical stress, difference in entropy, and saturation magnetization as input features. The correlation between the martensitic and austenitic temperature was evaluated in terms of regression metrics. The random forest and decision tree modeling were executed. Test scores were obtained using frequency ordering, PCA, linear regression, and correlation matrix to forecast magnetically controlled shape memory effect. The silhouette score matched the transition temperature at which the material showed shape memory behavior. Additionally, from 70% of the training data, a combination of Iron (Fe), Nickel (Ni), and Aluminum (Al) as Full Heusler alloys stimulated the algorithms in gaining the accuracy of predictive modeling by minimizing the error. Through DFT-based bandgap and density of states calculations, the Fe 2 NiAl Heusler compound is hypothesized to behave as a half-metallic ferromagnet by considering the atomic number, the number of valence electrons, and the local magnetic moment. The experimental validation will be done along with magnetization studies, magneto-transport, and magneto-caloric measurements. Materials science ; Metallicity ; Computer science ; Computer vision Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; X-ray Diffraction in Crystallography
Authors: Brenda de Souza Ferrari | Zohreh Fallah | Maya Khatun | Hannu Häkkinen
Source: Aggregate (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

Development of an Interaction Model of the Protein–Nanocluster Interface by Machine Learning–Assisted Clustering of Amino Acids ABSTRACT The interactions that occur in the interface of proteins and ligand‐stabilised metal nanoclusters are crucial to understand the adsorption process of biomolecules on the surface of these nanomaterials. Despite the relevance of the adsorption phenomena for biological applications, such as bioimaging, biosensing and targeted drug delivery, efforts to model the interactions observed in the interface of those systems are still scarce in the literature. In this work, a model of the interactions observed in the peptide–Au 38 (p‐MBA) 24 interface was developed, employing clustering analysis, an unsupervised machine learning technique. The accuracy of this model was evaluated by simulating the peptide–Au 38 (p‐MBA) 24 interaction using molecular dynamics simulations and density functional theory calculations. The insights derived from this model can also be applied to the context of protein–AuNC interactions, given that the model was developed to provide a generalisable approach. The developed model was able to predict the amino acids that could interact well or poorly with the gold nanoclusters (AuNC), defining the specific chemical groups responsible for the effect. The results obtained in this study can lead efforts to accelerate discoveries in the fields that rely on the understanding of the interaction observed in the protein–AuNC interface. Nanoclusters ; Cluster analysis ; Biomolecule ; Interface (matter) ; Context (archaeology) ; Interaction model ; Protein–protein interaction ; Biosensor Nanocluster Synthesis and Applications ; Diatoms and Algae Research ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Zhijian He | Chihou Lei | Dongliang Shan | Zhanpeng Zhang | Hongyi Luo | Shuhong Xie | Yunya Liu
Source: International Communications in Heat and Mass Transfer (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Discovery of perovskite oxides with different thermal conductivities on demand by interpretable machine learning Perovskite (structure) ; Materials science ; Thermal conductivity ; Thermal ; Computer science ; Artificial intelligence ; Thermodynamics ; Chemical engineering ; Composite material ; Physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Magnetic and transport properties of perovskites and related materials
Authors: Aaron M. Havens | Benjamin Kurt Miller | Bing Yan | Carles Domingo-Enrich | Anuroop Sriram | Brandon M. Wood | Daniel S. Levine | Bin Hu | Brandon Amos | Brian Karrer | Xiang Fu | Guan-Horng Liu | Ricky T. Q. Chen
Source: ArXiv.org (preprint, 2025-04-16)
Field: Materials Science — Materials Chemistry

Adjoint Sampling: Highly Scalable Diffusion Samplers via Adjoint Matching We introduce Adjoint Sampling, a highly scalable and efficient algorithm for learning diffusion processes that sample from unnormalized densities, or energy functions. It is the first on-policy approach that allows significantly more gradient updates than the number of energy evaluations and model samples, allowing us to scale to much larger problem settings than previously explored by similar methods. Our framework is theoretically grounded in stochastic optimal control and shares the same theoretical guarantees as Adjoint Matching, being able to train without the need for corrective measures that push samples towards the target distribution. We show how to incorporate key symmetries, as well as periodic boundary conditions, for modeling molecules in both cartesian and torsional coordinates. We demonstrate the effectiveness of our approach through extensive experiments on classical energy functions, and further scale up to neural network-based energy models where we perform amortized conformer generation across many molecular systems. To encourage further research in developing highly scalable sampling methods, we plan to open source these challenging benchmarks, where successful methods can directly impact progress in computational chemistry. Scalability ; Key (lock) ; Scale (ratio) ; Sampling (signal processing) ; Energy (signal processing) ; Boundary (topology) ; Matching (statistics) ; Cartesian coordinate system ; Diffusion Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Gaussian Processes and Bayesian Inference
Authors: Md. Moinul Hasan Sadik | Arijit Baral | Maruf Ahmed | Md. Khalid Al Zuhanee | Md. Ashraful Islam | Md.Abdul Wakil | Md.Abdul Hasib
Source: Results in Materials (article, 2025-12-04)
Field: Materials Science — Polymers and Plastics

Integrating experiments, finite element analysis, and explainable machine learning for natural fiber based hybrid composites The reliable prediction of mechanical properties in natural fiber–based composites is crucial for their advancement as sustainable structural materials. The development of natural fiber–based hybrid composites is often hindered by costly, resource-intensive, and time-consuming experimental iterations. While Machine Learning (ML) offers a powerful alternative for accelerating property prediction, standard models often function as 'black boxes,' which limits their trustworthiness and fails to provide the scientific insights needed for true material design. This study addresses this critical gap by developing and validating an integrated framework that combines experiments, Finite Element Analysis, and Explainable Machine learning to predict and interpret mechanical properties of banana fiber reinforced composites. Four supervised machine learning (ML) models—Random Forest, XGBoost, Gradient Boosting, and k-Nearest Neighbor—were developed and trained on a combined literature–experimental dataset. Among them, the XGBoost model exhibited the highest predictive accuracy, achieving R 2 values of 0.71 for tensile strength and 0.76 for flexural strength in validation. To address the “black box” challenge of ML, SHapley Additive exPlanations (SHAP) analysis was employed, revealing fiber length and filler percentage as the most influential features governing strength predictions. The integration of ML with experimental and numerical validation demonstrates a powerful framework for reducing trial-and-error in composite design, while providing interpretable insights into the role of key material parameters. This work establishes explainable ML as an effective and sustainable tool for optimizing natural fiber–based hybrid composites. Finite element method ; Natural fiber ; Ultimate tensile strength ; Trustworthiness ; Fiber ; Composite number ; Function (biology) ; Flexural strength ; Work (physics) Natural Fiber Reinforced Composites ; Composite Material Mechanics ; Mechanical Behavior of Composites
Authors: Huan Pei | Jing Li | Jiale Zhang | Chong Zhao | Jianzhuo Zhu | Yingjie Liu | Yong Wei
Source: Nanotechnology (article, 2025-10-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Single-molecule enhanced Raman spectroscopy in nanocavities: mechanisms and recent advances Surface-enhanced Raman spectroscopy (SERS) refers to the phenomenon where the Raman signals of molecules are significantly enhanced when they are adsorbed onto or located near the surface of substrates with specific nanostructures. By integrating with scanning probe microscopy techniques, SERS effectively overcomes the low sensitivity of conventional Raman spectroscopy and has been widely applied in surface science, biological sciences, and other fields. This review provides a comprehensive overview of recent theoretical advances in understanding the substrate-induced enhancement mechanisms of molecular Raman signals, as well as progress in the design and optimization of surface plasmonic configurations. The primary objective is to explore effective strategies for achieving high-resolution Raman signal enhancement. We elaborate in detail on the individual mechanisms of electromagnetic and chemical enhancement, as well as their synergistic interplay. Several key factors influencing surface plasmon-enhanced effects are systematically discussed, including charge transfer, external electric fields, and the role of different substrate materials in enhancing single-molecule Raman responses. In addition, we briefly highlight recent developments in first-principles studies using dispersion-corrected density functional theory (DFT), which incorporate long-range corrections to describe weak interactions between molecules and substrates within van der Waals radii. Finally, we offer perspectives on the future theoretical and experimental directions of surface plasmon and tip-enhanced techniques in the single-molecule regime. We also discuss the potential of integrating single-molecule junction sensors with machine learning and DFT for deeper spectroscopic insights, aiming to further advance the field of single-molecule spectroscopy. Raman spectroscopy ; van der Waals force ; Plasmon ; Surface-enhanced Raman spectroscopy ; Raman scattering ; Density functional theory ; Molecule ; Substrate (aquarium) Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Strong Light-Matter Interactions