jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Deepa Sreedev | Nandakumar Kalarikkal | Subila Kurukkal Balakrishnan
Source: ACS Applied Energy Materials (article, 2025-11-03)
Field: Materials Science — Materials Chemistry

Leveraging Machine Learning for Thermoelectric Material Design: Addressing Composition–Property Relations and Data Imbalance Challenges Thermoelectric (TE) technology has emerged as a promising and sustainable solution to address the growing global energy demand. While machine learning accelerates the discovery of high-performance thermoelectric materials, its effectiveness is frequently hampered by data imbalance and quality issues. This study addresses these challenges by utilizing a highly imbalanced data set comprising Germanium Telluride (GeTe) materials, including both pure GeTe and its doped or alloyed variants. A classification model was developed based on four key descriptors, such as temperature, Seebeck coefficient, electronegativity, and electron affinity, to categorize samples into low, medium, and high figure of merit (ZT) classes. To mitigate the effects of class imbalance, an ensemble learning approach was combined with the Adaptive Synthetic Sampling (ADASYN) oversampling technique. Among the models evaluated, the XGBoost classifier demonstrated superior performance, achieving a macro-average precision of 0.94, recall of 0.95, F1-score of 0.94, and an overall accuracy of 94%, making it the most effective model for identifying high-performance TE materials under imbalanced conditions. The XGBoost regression model performed well with an R2 of 0.97 and an RMSE of 0.07, allowing for effective screening of materials with high ZT values. To improve model interpretability, SHAP (SHapley Additive exPlanations) analysis was conducted, which revealed that temperature is the most significant factor for predicting the figure of merit. This work provides a solid and interpretable framework for accelerating the discovery of thermoelectric materials for next-generation energy conversion technologies. Oversampling ; Thermoelectric materials ; Thermoelectric effect ; Figure of merit ; Classifier (UML) ; Precision and recall ; Ensemble learning ; Thermoelectric generator Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Machine Learning and ELM
Authors: Yuxuan Zeng | Wei Cao | Yijing Zuo | Tan Peng | Yue Hou | Ling Miao | Ziyu Wang | Jing Shi
Source: Materials Futures (article, 2025-09-18)
Field: Materials Science — Materials Chemistry

Accelerating the discovery of materials with expected thermal conductivity via a synergistic strategy of DFT and interpretable deep learning Abstract Lattice thermal conductivity (LTC) is a critical parameter for thermal transport properties, playing a pivotal role in advancing thermoelectric materials and thermal management technologies. Traditional computational methods, such as density functional theory (DFT) and molecular dynamics (MD), are resource-intensive, limiting their applicability for high-throughput LTC prediction. While AI-driven approaches have made significant strides in material science, the trade-off between accuracy and interpretability remains a major bottleneck. In this study, we introduce an interpretable deep learning framework that enables rapid and accurate LTC prediction, effectively bridging the gap between interpretability and precision. Leveraging this framework, we identify and validate four promising thermal conductors/insulators using DFT and MD. Moreover, by combining sensitivity analysis with DFT calculations, we uncover novel insights into phonon thermal transport mechanisms, providing a deeper understanding of the underlying physics. This work not only accelerates the discovery of thermal materials but also sets a new benchmark for interpretable AI in material science. Interpretability ; Thermal conductivity ; Bridging (networking) ; Deep learning ; Limiting ; Thermal ; Benchmark (surveying) ; Thermal management of electronic devices and systems Machine Learning in Materials Science
Authors: Chen Sun | Yue Yuan | Meng Xu | Xin Li | Meifeng Li | Chungen Zhou
Source: Materials Today Communications (article, 2025-02-22)
Field: Materials Science — Metals and Alloys

A high-throughput molecular dynamics study with a machine-learning method on predicting diffusion coefficient of hydrogen in α-iron grain boundaries Materials science ; Throughput ; Grain boundary ; Diffusion ; Molecular dynamics ; Hydrogen ; Effective diffusion coefficient ; Grain boundary diffusion coefficient ; Dynamics (music) ; Chemical physics ; Metallurgy ; Thermodynamics ; Microstructure ; Computational chemistry ; Computer science Hydrogen embrittlement and corrosion behaviors in metals ; Nuclear Materials and Properties ; Material Properties and Failure Mechanisms
Authors: Kentaro Matsumoto | Nana Misawa | Shuhei Kanesato | Masataka Nagaoka
Source: Frontiers in Chemistry (review, 2025-06-10)
Field: Materials Science — Materials Chemistry

Atomistic simulation of olefin polymerization reaction by organometallic catalyst: significant role of microscopic structural dynamics of (pyridylamido) Hf(IV) complex in catalytic reactivity Understanding the microscopic catalytic mechanism of the olefin polymerization reaction is crucial for the rational design of next-generation catalysts. However, the dynamic nature of the active species, including the fluctuations of the ion pair structure and the orientation of substituents, presents significant challenges for theoretical approaches. In this paper, we present an overview of our recent computational studies on the role of the structural dynamics of the active species of olefin polymerization catalyst in determining reactivity, especially focusing on a novel olefin polymerization catalyst (pyridylamido) Hf(IV) complex. Utilizing the molecular dynamics method and our Red Moon method, a novel methodology we have developed for atomistic simulation of complex chemical reaction systems, we elucidate how the dynamic features, including anion coordination and steric interaction, govern the reactivity in key steps such as ligand modification and propagation reactions. In addition, we demonstrate how machine learning techniques can be applied to extract chemically meaningful descriptors from the structural ensemble obtained from atomistic simulation data of complex chemical reaction systems, thereby identifying the substituents that play an important role in propagation reactions. Our studies highlight the importance of incorporating molecular-level dynamic features of catalysts into mechanistic models. Reactivity (psychology) ; Catalysis ; Steric effects ; Polymerization ; Olefin fiber ; Chemistry ; Molecular dynamics ; Computational chemistry ; Combinatorial chemistry ; Ligand (biochemistry) ; Olefin polymerization ; Organic chemistry ; Polymer Machine Learning in Materials Science ; Organometallic Complex Synthesis and Catalysis ; Protein Structure and Dynamics
Authors: Reeturaj Tamuly | Salman Ansari | D. Ravi Kumar | S. Aravindan
Source: International Journal on Interactive Design and Manufacturing (IJIDeM) (article, 2025-07-01)
Field: Materials Science — Biomaterials

Prediction of peak load in warm extrusion of a biocompatible Mg–Zn–Ca alloy: a machine learning approach integrated with FE simulation Biocompatible material ; Extrusion ; Alloy ; Materials science ; Mechanical engineering ; Metallurgy ; Composite material ; Computer science ; Biomedical engineering ; Engineering Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Metal and Thin Film Mechanics
Authors: Lin‐Wang Wang
Source: ChemRxiv (preprint, 2025-08-05)
Field: Materials Science — Materials Chemistry

A density based machine learning force field for molecule-molecule nonbonded interactions A new machine learning force field is introduced. In this force field, the intermolecular interaction is calculated based on tted electron charge density, while the intramolecular energy is modeled using a traditional atomic-structure-based machine learning force field model. The electron density of the molecular charge is represented by spherical charges centered at atoms and bond centers. The intermolecular interaction energy is calculated through the Coulomb interaction, exchange correlation interaction and Thomas-Fermi van Weiszaker kinetic energy, all based on the electron charge density. A polarization model is also introduced based on the local electric potentials near the atoms calculated based on the charge density. These interaction energies are combined to form the density based machine learning force field (DB-ML-FF). We show that the DB-ML-FF can accurately represent the intermolecular interaction, and it is practical to use this linear scaling model to carry out large system molecular dynamics simulations despite the fact the charge density is evaluated on a numerical grid. Molecule ; Force field (fiction) ; Field (mathematics) ; Chemical physics ; Computer science ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics ; Pure mathematics Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Jisoo Nam | B. Chen | Miso Kim
Source: Advanced Materials (article, 2025-07-16)
Field: Materials Science — Polymers and Plastics

Machine Learning‐Driven Grayscale Digital Light Processing for Mechanically Robust 3D‐Printed Gradient Materials Grayscale digital light processing (g-DLP) is gaining recognition for its capability to create material property gradients within a single resin system, enabling programmable mechanical responses, enhanced shape accuracy, and improved toughness. However, research on the mechanical robustness of g-DLP is constrained by the limited range of tailorable properties in photocurable resins and insufficient exploration of structural optimization for complex geometries. This study presents a synergistic g-DLP strategy that integrates the synthesis of dynamic bond-controlled polyurethane acrylate (PUA) with a machine learning-based multi-objective optimization, enabling mechanically robust 3D-printed gradient materials. A PUA-based resin system is developed that expands the achievable elastic modulus from 8.3 MPa to 1.2 GPa, while maintaining superior damping performance, making it suitable for diverse applications. Furthermore, a multi-objective Bayesian optimization framework is constructed to efficiently identify optimal gradient structures, reducing strain concentrations and controlling effective stiffness. This approach is applicable to various 3D and arbitrary geometries, achieving a significant strain concentration reduction of up to 83% and demonstrating delayed crack initiation. By combining the developed material with this optimization framework, a versatile platform is established for creating mechanically robust g-DLP printed components, applicable in areas ranging from biomimetic artificial cartilage to automotive energy-absorbing structures. Digital Light Processing ; Materials science ; Robustness (evolution) ; Bayesian optimization ; 3D printing ; Grayscale ; Composite material ; Stiffness ; Digital image correlation ; Computer science ; Mechanical engineering ; Artificial intelligence Polymer composites and self-healing ; Additive Manufacturing and 3D Printing Technologies ; Photopolymerization techniques and applications
Authors: Jie Sun | Meng Han | Rulei Guo | Long Cheng | Yuan Shen | Xiaoliang Zhang
Source: Chinese Physics Letters (article, 2025-09-05)
Field: Materials Science — Materials Chemistry

Machine-Learning Accelerated Discovery of High-Performance Thermal Switch in Two-dimensional Materials Considering High-order Anharmonicity Abstract Heat dissipation and thermal switches are vital for adaptive cooling and extending the lifespan of electronic devices and batteries. In this work, we conducted high-throughput investigations on the thermal transport of 24 experimentally realized two-dimensional (2D) materials and their potential as thermal switches, leveraging machine-learning-assisted strain engineering and phonon transport simulations. We identified several high-performance thermal switches with ratios exceeding 2, with germanene (Ge) achieving an ultrahigh ratio of up to 9.64 within the reversible deformation range. The underlying mechanism is strain-induced bond softening, which sensitively affects anharmonicity represented by three- and four-phonon scattering. The widespread occurrence of four-phonon scattering was confirmed in the thermal transport of 2D materials. Opposite switching trends were discovered, with 2D transition metal dichalcogenide materials showing negative responses to tensile strain while buckled 2D elemental materials showed positive responses. We further proposed a screening descriptor based on strain-induced changes in the Grüneisen parameter for efficiently identifying new high-performance thermal switch materials. This work establishes a paradigm for thermal energy control in 2D materials through strain engineering, which may be experimentally realized in the future via bending, substrate mismatch, and related approaches, thereby laying a robust foundation for further developments and applications. Anharmonicity ; Thermal ; Order (exchange) ; Computer science ; Physics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Yunjun Zhang | Haoming Li | Yunfeng Mao | Zhongyi Zhang | Guan Wenlong | Zhenghao Wu | Xingying Lan | Chunming Xu | Tianhang Zhou
Source: Processes (article, 2025-03-17)
Field: Chemistry — Analytical Chemistry

Interpretable Analysis of the Viscosity of Digital Oil Using a Combination of Molecular Dynamics Simulation and Machine Learning Although heavy oil remains a crucial energy source, its high viscosity makes its utilization challenging. We have performed an interpretable analysis of the relationship between the molecular structure of digital oil and its viscosity using molecular dynamics simulations combined with machine learning. In this study, we developed three “digital oils” to represent light, medium, and heavy oils in consideration of their composition and molecular structure. Using molecular dynamics (MD) simulations, we calculated the density, self-diffusion coefficient, and viscosity of these digital oils at various temperatures (323–453 K). The accuracy of the simulation results was demonstrated by their good fit to the experimental data. We further explored the correlation between interaction energy and viscosity. As interaction energy increased, molecular attraction strengthened, resulting in greater friction between molecules and a higher viscosity of the digital oil. Cluster analysis revealed that, compared with the other two oils, the heavy oil contained rod-shaped molecular aggregates in greater quantity and larger clusters. Additionally, we computed the radial distribution functions of the SARA (saturates, aromatics, resins, and asphaltenes) components; among molecular pairs, aromatics and resins showed the largest interaction energy and were the most tightly bound, contributing to increased viscosity. To more effectively predict the viscosity of digital oils, we integrated four machine learning (ML) techniques: linear regression, random forest, extra trees, and gradient boosting. Post-hoc analysis coupled with SHapley Additive exPlanations (SHAP) was applied to interpret how macroscopic and microscopic features influence the viscosity and to identify the contributions of individual molecules. This work presents a novel and efficient method for estimating the viscosity of digital oils by combining MD simulations with ML approaches, offering a valuable tool for quick and cost-effective analysis. Viscosity ; Molecular dynamics ; Dynamics (music) ; Computer science ; Artificial intelligence ; Biological system ; Machine learning ; Statistical physics ; Materials science ; Chemistry ; Psychology ; Computational chemistry ; Physics ; Composite material ; Biology Petroleum Processing and Analysis ; Phase Equilibria and Thermodynamics ; Enhanced Oil Recovery Techniques
Authors: Cheng Wei | Z. Cui | Haokun Li | Jingyuan Guo | Linzhuang Xing | Yihang Li | Hongyu Yang | Zhimin Li | Yue Hao
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

An interpretable machine learning model for Mn-based cathode development: mapping synthesis parameters to cycling stability An efficient and transparent machine learning model is proposed and verified for predicting manganese-based cathode materials with high cycling stability. Cycling ; Stability (learning theory) ; Cathode ; Computer science ; Materials science ; Artificial intelligence ; Machine learning ; Engineering ; Geography ; Electrical engineering Machine Learning in Materials Science
Authors: J. M. Rickman | Katayun Barmak | Matthew J. Patrick | Godfred Adomako Mensah
Source: Entropy (article, 2025-05-17)
Field: Materials Science — Materials Chemistry

Using Nearest-Neighbor Distributions to Quantify Machine Learning of Materials’ Microstructures Machine learning strategies for the semantic segmentation of materials' micrographs, such as U-Net, have been employed in recent years to enable the automated identification of grain-boundary networks in polycrystals. For example, most recently, this architecture has allowed researchers to address the long-standing problem of automated image segmentation of thin-film microstructures in bright-field TEM micrographs. Such approaches are typically based on the minimization of a binary cross-entropy loss function that compares constructed images to a ground truth at the pixel level over many epochs. In this work, we quantify the rate at which the underlying microstructural features embodied in the grain-boundary network, as described stereologically, are also learned in this process. In particular, we assess the rate of microstructural learning in terms of the moments of the <i>k</i>-th nearest-neighbor pixel distributions and associated metrics, including a microstructural cross-entropy, that embody the spatial correlations among the pixels through a hierarchy of <i>n</i>-point correlation functions. From the moments of these distributions, we obtain so-called learning functions that highlight the rate at which the important topological features of a grain-boundary network appear. It is found that the salient features of network structure emerge after relatively few epochs, suggesting that grain size, network topology, etc., are learned early (as measured in epochs) during the segmentation process. Artificial intelligence ; Computer science ; Segmentation ; Pixel ; Ground truth ; k-nearest neighbors algorithm ; Pattern recognition (psychology) ; Algorithm ; Topology (electrical circuits) ; Statistical physics ; Machine learning ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Electron and X-Ray Spectroscopy Techniques
Authors: Ritesh Kumar (145044) | Minh Canh Vu (8807621) | Peiyuan Ma (11090557) | Chibueze V. Amanchukwu (1331781)
Source: Figshare (article, 2025-04-01)
Field: Materials Science — Materials Chemistry

Electrolytomics:A Unified Big Data Approach for ElectrolyteDesign and Discovery Electrolyte discovery is the bottleneck for developing next-generation batteries. For example, lithium metal batteries (LMBs) promise to double the energy density of current Li-ion batteries (LIBs), while next-generation LIBs are desired for operations at extreme temperature conditions and with high voltage cathodes. However, there are no suitable electrolytes to support these battery chemistries. Electrolyte requirements are complex (conductivity, stability, safety), and the chemical design space (salts, solvents, additives, concentration) is practically infinite; hence, discovery is primarily guided through trial and error, which slows the deployment of such next-generation battery chemistries. Inspired by artificial intelligence (AI)-enabled drug discovery, we adapt these machine learning (ML) approaches to electrolyte discovery. We assemble the largest small molecule experimental liquid electrolyte ionic conductivity data set and build highly accurate ML and deep learning models to predict ionic conductivity across a wide range of electrolyte classes. The developed models yield results similar to those of molecular dynamics (MD) simulations and are interpretable without explicit encoding of ionic solvation. While most ML-based approaches target a single property, we build additional models of oxidative stability and Coulombic efficiency and develop a metric called the electrolyte score (<i>eScore</i>) to unify the predicted disparate electrolyte properties. Deploying these models on large unlabeled data sets, we discover distinct electrolyte solvents, experimentally validate that the electrolyte is conductive (>1 mS cm<sup>–1</sup>), stable up to 6 V, supports efficient anode-free LMB, and even LIB cycling at extreme temperatures. Our work marks a significant step toward efficient electrolyte design, accelerating the development and deployment of next-generation battery technologies. Electrolyte ; Battery (electricity) ; Bottleneck ; Ionic conductivity ; Faraday efficiency ; PubChem ; Software deployment ; Metric (unit) ; Conductivity Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Yuan Jiang | Shupan Zhang | Ming Li | Kaiqiang Liu | Yuan Wu
Source: Journal of Building Engineering (article, 2025-05-27)
Field: Materials Science — Materials Chemistry

Multi-isolating mechanism and neural network-based design of shape memory alloy-integrated isolators with friction-constrained device Shape-memory alloy ; Mechanism (biology) ; Artificial neural network ; Alloy ; Mechanism design ; Materials science ; Computer science ; Mechanical engineering ; Structural engineering ; Engineering ; Artificial intelligence ; Composite material ; Physics ; Mathematics Shape Memory Alloy Transformations ; Vibration Control and Rheological Fluids
Authors: Robin Strickstrock | Alexander Hagg | Dirk Reith | Karl N. Kirschner
Source: ChemPhysChem (article, 2025-09-05)
Field: Materials Science — Materials Chemistry

Speed up Multi‐Scale Force‐Field Parameter Optimization by Substituting Molecular Dynamics Calculations with a Machine Learning Surrogate Model Molecular modeling plays a vital role in many scientific fields, ranging from material science to drug design. To predict and investigate the properties of those systems, a suitable force field (FF) is required. Improving the accuracy or expanding the applicability of the FFs is an ongoing process, referred to as force-field parameter (FFParam) optimization. In recent years, data-driven machine learning (ML) algorithms have become increasingly relevant in computational sciences and elevated the capability of many molecular modeling methods. Herein, time-consuming molecular dynamic simulations, used during a multiscale FFParam optimization, are substituted by a ML surrogate model to speed-up the optimization process. Subject to this multiscale optimization are the Lennard-Jones parameters for carbon and hydrogen that are used to reproduce the target properties: n-octane's relative conformational energies and its bulk-phase density. By substituting the most time-consuming element of this optimization, the required time is reduced by a factor of ≈20, while retaining FFs with similar quality. Furthermore, the workflow used to obtain the surrogate model (i.e., training data acquisition, data preparation, model selection, and training) for such substitution is presented. Force field (fiction) ; Molecular dynamics ; Workflow ; Computer science ; Field (mathematics) ; Process optimization ; Surrogate model ; Process (computing) ; Machine learning ; Artificial intelligence ; Chemistry ; Computational chemistry ; Mathematics ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics