jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Övgü Ceyda Yelgel | Celal Yelgel
Source: Advances in Physics X (review, 2025-07-27)
Field: Materials Science — Materials Chemistry

A review of machine learning approaches for the discovery of thermoelectric materials Thermoelectric (TE) materials have garnered significant interest due to their capacity to convert heat directly into electrical energy and vice versa, offering a sustainable route for energy harvesting and waste heat recovery. Nevertheless, many of the high-performance TE materials reported to date rely on elements that are scarce, costly, or environmentally hazardous, thereby limiting their large-scale deployment. To overcome these challenges, the development of efficient, earth-abundant, and environmentally benign alternatives is essential. Although first-principles methods provide valuable insights into the transport behavior of potential TE materials, their high computational cost restricts their utility in large-scale material screening. Recent progress in computational infrastructure, along with the advent of data-centric approaches such as machine learning (ML), has transformed the landscape of thermoelectric research. ML algorithms, trained on comprehensive datasets including experimental measurements, crystallographic data, and density functional theory (DFT) results can predict key TE metrics, such as the figure of merit (ZT), with remarkable speed and accuracy. This review explores the integration of ML into TE materials discovery, emphasizing its role in property prediction, descriptor engineering, and structural optimization. A systematic examination of ML-driven strategies promises to accelerate the discovery process and improve the efficiency of next-generation thermoelectric systems. Thermoelectric effect ; Thermoelectric materials ; Data science ; Computer science ; Engineering physics ; Materials science ; Engineering ethics ; Engineering ; Physics ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermography and Photoacoustic Techniques
Authors: Shyue Ping Ong | Chad Risko | Osvaldo N. Oliveira
Source: Chemistry of Materials (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

Machine Learning for Materials Chemistry: New Frontiers and Emerging Paradigms Field (mathematics) ; Deep learning ; Key (lock) ; Active learning (machine learning) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Takuya Shibayama | Hideaki Imamura | Katsuhiko Nishimra | Kohei Shinohara | Chikashi Shinagawa | So Takamoto | Ju Li
Source: ArXiv.org (preprint, 2025-03-27)
Field: Materials Science — Materials Chemistry

Efficient Crystal Structure Prediction Using Universal Neural Network Potential with Diversity Preservation in Genetic Algorithms Crystal structure prediction (CSP) is crucial for identifying stable crystal structures in given systems and is a prerequisite for computational atomistic simulations. Recent advances in neural network potentials (NNPs) have reduced the computational cost of CSP. However, searching for stable crystal structures across the entire composition space in multicomponent systems remains a significant challenge. Here, we propose an improvement of genetic algorithm (GA) -based CSP method using a universal NNP. Our GA-based methods are designed to efficiently expand convex hull volumes while preserving the diversity of crystal structures. Our hull-informed filtering and elitist-selection procedures incorporate an aging mechanism that prioritizes recently improved compositions. We also employ niching to prevent convergence to a small set of stoichiometries, thereby preserving a diverse, high-quality population. Our evaluation shows that the present method outperforms the symmetry-aware random structure generation and existing CSP methods, achieving a larger convex hull with fewer trials. We demonstrated that our approach, combined with the developed universal NNP (PFP), can accurately reproduce and explore phase diagrams obtained through DFT calculations; this indicates the validity of PFP across a wide range of crystal structures and element combinations. This study, which integrates a universal NNP with a GA-based CSP method, highlights the promise of these methods in materials discovery. Convex hull ; Crystal structure prediction ; Artificial neural network ; Similarity (geometry) ; Genetic algorithm ; Range (aeronautics) ; Multi-objective optimization ; Crystal (programming language) Crystallization and Solubility Studies
Authors: Sicheng Yi | Zuan Li | Yuntao Li | Guopeng Wang | Xiaolong Zhang | Quan Zhang
Source: Journal of Physics Conference Series (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Strong nonlinear hysteresis modeling of shape memory alloy actuator with parallel neural network Abstract A dual-input single-output neural network system with parallel hidden layers is constructed to characterize the complex hysteresis of shape memory alloy (SMA) wire actuators. By constructing a piecewise function, an activation function capable of reflecting the loading and unloading path differences of the SMA actuator is designed. The asymmetric hysteresis behaviour is simulated successfully. Moreover, the model accuracy can be enhanced through the introduction of the derived input-space features. The network weights are identified based on the Levenberg-Marquardt (LM) algorithm. Experimental results demonstrate that the hysteresis modeling accuracy of the proposed approach is consistently superior to that of the traditional Preisach model. Under a sine wave excitation signal with an amplitude of 5-8 V and a frequency of 0.025 Hz, the mean squared error is 4.819×10 −5 mm with the proposed model, compared to 0.32918 mm with the Preisach model. The proposed neural network-based modeling method can be utilized in the future for closed-loop control of systems such as shape memory alloy wire-driven artificial muscles. Shape-memory alloy ; Hysteresis ; Actuator ; Artificial neural network ; Piecewise ; Control theory (sociology) ; Nonlinear system ; Amplitude ; Piecewise linear function Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control ; Iterative Learning Control Systems
Authors: Benarbia, Achouak
Source: UND Scholarly Commons (University of North Dakota) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Guided Discovery Of Metal-Ligand Catalysts For Affordable Green Hydrogen Produced Via Proton Exchange Membrane Electrolyzers The rising demand for clean and sustainable energy sources has underscored the critical role of hydrogen in the global energy transition. However, the widespread adoption of green hydrogen remains constrained by the high cost and scarcity of platinum-based electrocatalysts used in Proton Exchange Membrane (PEM) electrolyzers. This dependence inflates production costs and poses long-term risks related to supply security and scalability. While numerous studies have explored alternative materials, the discovery of efficient and stable non-platinum catalysts remains a major bottleneck due to the vastness of chemical space and the resource-intensive nature of experimental screening. To address this challenge, this study applied a machine learning approach to systematically explore and classify transition metal–ligand complexes based on electronic and structural features relevant to Hydrogen Evolution Reaction (HER) performance. Using the tmQM dataset, which contains over 108,000 transition metal complexes from the Cambridge Structural Database (CSD), we selected five key descriptors: HOMO–LUMO gap, dipole moment, molecular size, metal node degree, and total charge, to represent catalytic activity and structural robustness. Three unsupervised clustering algorithms: K-Means, DBSCAN, and Gaussian Mixture Models (GMM), were applied to partition the dataset into chemically and electronically distinct classes. By comparing their clustering performance, the most robust model was selected to guide the identification of promising candidates that align with desirable HER-relevant features, including low HOMO–LUMO gaps, high dipole moments, and moderate coordination numbers. The resulting clusters revealed multiple classes of metal–ligand complexes with favorable catalytic and structural properties. One cluster was enriched with candidates exhibiting strong electronic reactivity and solvation potential, coupled with neutral charge and manageable size, indicative of both catalytic efficiency and synthetic feasibility. Importantly, all shortlisted complexes are structurally documented in crystallographic databases, which enables direct experimental validation and minimizes trial-and-error in material design. Bottleneck ; Cluster analysis ; Catalysis ; Hydrogen production ; Transition metal ; Proton exchange membrane fuel cell ; Cluster (spacecraft) ; Hydrogen storage ; Chemical space Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Electrocatalysts for Energy Conversion
Authors: Yujia Chen | Zhaoxu Zhou | Qiying Liu | Siyu He | Feixiang Tang | Yueyun Weng
Source:  (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

An Approach for Extracting Material Design Criteria Based on Neural Networks and SHAP Analysis Material selection is a critical factor affecting the core performance and reliability of engineering structures, whereas traditional approaches often struggle to effectively address the relationship of such complex coupling of multiple factors. In order to overcome this challenge, this study proposes a modeling and interpretation framework that integrates Backpropagation Neural Networks (BPNN) with Shapley Additive exPlanations (SHAP), aiming to reveal the influence mechanisms of key features of automotive chassis materials on performance indicators. The study first constructs a Backpropagation Neural Network (BPNN) model based on experimental data, and then employs SHAP to analyze the importance of input features, thereby enhancing model interpretability. The model achieved a high prediction accuracy of 0.9444 with a computation time of just 10.001 seconds. Finally, the Kruskal–Wallis H test is applied to statistically validate feature differences. The results demonstrate that the proposed method, while maintaining high predictive accuracy, successfully identifies the elastic modulus (E) and shear modulus (G) as the core discriminative criteria for chassis material selection. This study not only provides a scientific basis for material optimization under complex operating conditions but also highlights the potential of artificial intelligence methods in the field of engineering materials. Artificial neural network ; Backpropagation ; Chassis ; Field (mathematics) ; Reliability (semiconductor) ; Automotive industry ; Property (philosophy) ; Discriminative model ; Computation Machine Learning in Materials Science ; Topology Optimization in Engineering ; Mechanical Engineering and Vibrations Research
Authors: Shyue Ping Ong | Chad Risko | Osvaldo N. Oliveira
Source: ACS Materials Letters (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Machine Learning for Materials Chemistry: New Frontiers and Emerging Paradigms Feature (linguistics) ; Field (mathematics) ; Key (lock) ; Focus (optics) ; Task (project management) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Anubhav Paul (22877873) | Kumar Rishav (22877876) | Guus Klootwijk (22877879) | Mitradeep Sarkar (1653787) | Onima Bisht (22877882) | Zizheng Li (6703103) | Sonia Conesa-Boj (1352106) | Georgia T. Papadakis (14361330) | Silvania F. Pereira (7023236)
Source: Figshare (article, 2025-12-17)
Field: Materials Science — Polymers and Plastics

Characterizationof the 3D-Optical Properties of vander Waals Materials with Deep Learning-Based Coherent Fourier Scatterometry The characterization of optical anisotropy in thin van der Waals (vdW) materials is crucial for both fundamental studies and nanophotonic applications. However, conventional techniques such as spectroscopic ellipsometry face significant limitations in measuring out-of-plane anisotropy and require large-area, uniform films. In this work, we present a novel framework based on coherent Fourier scatterometry (CFS) combined with deep learning for the rapid, label-free characterization of in-plane and out-of-plane refractive indices of anisotropic thin films. We designed a specialized deep neural network, AnisoVision, and trained it on simulated far-field angular spectra from multilayer stacks using the 4 × 4 Berreman matrix formalism. To efficiently capture the directional dependence of anisotropy, we utilize radially polarized light and extract only three far-field azimuthal cross sections (0, 45, 90°), enabling robust retrieval while minimizing data requirements. Our method demonstrates accurate index retrieval for both isotropic and anisotropic materials, including uniaxial h-BN and biaxial α-MoO<sub>3</sub> flakes of varying thickness. We further validate the model’s stability by testing multiple flakes of the same material across a range of thicknesses, yielding consistent optical constants. Our approach is single-shot, nondestructive, and applicable to localized sample regions, making it suitable for heterogeneous or exfoliated samples. Additionally, the technique can be readily extended to broadband operation for spectroscopic analysis. Our work establishes CFS coupled with deep learning as a powerful platform for high-throughput optical metrology of low-dimensional materials. Anisotropy ; Characterization (materials science) ; Isotropy ; Refractive index ; van der Waals force ; Metrology ; Coherence (philosophical gambling strategy) ; Ellipsometry ; Mueller calculus Transition Metal Oxide Nanomaterials ; Optical Polarization and Ellipsometry ; Optical Coatings and Gratings
Authors: Yu Xie | M. J. Wang | Senja Ramakers | F. Spaepen | Boris Kozinsky
Source: ArXiv.org (preprint, 2025-02-25)
Field: Materials Science — Materials Chemistry

Incongruent Melting and Phase Diagram of SiC from Machine Learning Molecular Dynamics Silicon carbide (SiC) is an important technological material, but its high-temperature phase diagram has remained unclear due to conflicting experimental results about congruent versus incongruent melting. Here, we employ large-scale machine learning molecular dynamics (MLMD) simulations to gain insights into SiC decomposition and phase transitions. Our approach relies on a Bayesian active learning workflow to efficiently train an accurate machine learning force field on density functional theory data. Our large-scale simulations provide direct indication that melting of SiC proceeds incongruently via decomposition into silicon-rich and carbon phases at high temperature and pressure. During cooling at high pressures, carbon nanoclusters nucleate and grow within the homogeneous molten liquid. During heating, the decomposed mixture reversibly transitions back into a homogeneous SiC liquid. The full pressure-temperature phase diagram of SiC is systematically constructed using MLMD simulations, providing new understanding of the nature of phases, resolving long-standing inconsistencies from previous experiments and yielding technologically relevant implications for processing and deposition of this material. Phase diagram ; Nanoclusters ; Molecular dynamics ; Nucleation ; Silicon carbide ; Homogeneous ; Decomposition ; Phase (matter) ; Deposition (geology) Machine Learning in Materials Science ; Silicon Carbide Semiconductor Technologies ; Boron and Carbon Nanomaterials Research
Authors: Elyar Tourani | Brian J. Edwards | Bamin Khomami
Source: ArXiv.org (preprint, 2025-07-23)
Field: Materials Science — Materials Chemistry

Machine Learning Workflow for Analysis of High-Dimensional Order Parameter Space: A Case Study of Polymer Crystallization from Molecular Dynamics Simulations Currently, identification of crystallization pathways in polymers is being carried out using molecular simulation-based data on a preset cut-off point on a single order parameter (OP) to define nucleated or crystallized regions. Aside from sensitivity to cut-off, each of these OPs introduces its own systematic biases. In this study, an integrated machine learning workflow is presented to accurately quantify crystallinity in polymeric systems using atomistic molecular dynamics data. Each atom is represented by a high-dimensional feature vector that combines geometric, thermodynamic-like, and symmetry-based descriptors. Low dimensional embeddings are employed to expose latent structural fingerprints within atomic environments. Subsequently, unsupervised clustering on the embeddings identified crystalline and amorphous atoms with high fidelity. After generating high quality labels with multidimensional data, we use supervised learning techniques to identify a minimal set of order parameters that can fully capture this label. Various tests were conducted to reduce the feature set, demonstrating that using only three order parameters is sufficient to recreate the crystallization labels. Based on these observed OPs, the crystallinity index (C-index) is defined as the logistic regression model's probability of crystallinity, remaining bimodal throughout the process and achieving over 0.98 classification performance (AUC). Notably, a model trained on one or a few snapshots enables efficient on-the-fly computation of crystallinity. Lastly, we demonstrate how the optimal C-index fit evolves during various stages of crystallization, supporting the hypothesis that entropy dominates early nucleation, while symmetry gains relevance later. This workflow provides a data-driven strategy for OP selection and a metric to monitor structural transformations in large-scale polymer simulations. Workflow ; Cluster analysis ; Supervised learning ; Unsupervised learning ; Molecular dynamics ; Crystallinity ; Crystallization of polymers ; Feature (linguistics) ; Crystallization Machine Learning in Materials Science
Authors: Pouya Pishkar | Saeed Sheibani | Mika Sillanpää
Source: Emergent Materials (article, 2025-11-18)
Field: Materials Science — Materials Chemistry

A review of morphological studies on CuO nanostructures: comparative analysis of synthesis methods and predictions by machine learning Thin film ; Particle (ecology) ; Nanoparticle ; Substrate (aquarium) ; Nanostructure ; Deposition (geology) ; Bridge (graph theory) ; Pulmonary surfactant Copper-based nanomaterials and applications ; Machine Learning in Materials Science ; Laser-Ablation Synthesis of Nanoparticles
Authors: Yuze Ji | Peng Wang | Shiqing Wang | Jia-kuan Ren | Yong‐Gang Zhao | Lijuan Zhu | Xinyan Liu
Source: Journal of Physics Conference Series (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Machine Learning-Driven Design Optimization of High-Strength Titanium Alloys for Petroleum Industry Applications High-strength titanium alloys (HSTAs) have great application potential in ultra deep drilling due to their exceptional strength-to-weight ratio and good fatigue resistance. However, the contradiction in strength and toughness control remains the bottleneck restricting its application. This study proposes an innovative approach combining machine learning and genetic algorithms to optimize the composition and heat-treating process design of HSTAs, achieving a balance of superior yield strength (YS) and impact toughness (αk). A modified greedy algorithm was employed for feature selection, efficiently identifying key variables from high-dimensional datasets. Through 200 random partitions of the data set, 121 performance prediction models were screened out, and were further used to comprehensively calculate the predicted results. Coupled with a dual-objective genetic algorithm, the HSTAs with the YS&gt;950MPa and the αk&gt;70J/cm 2 was designed and verified by experiment successfully, exhibiting a great alloy cost advantage. This data-driven material design framework provides an efficient pathway for accelerating the design of titanium alloys. Titanium alloy ; Toughness ; Genetic algorithm ; Titanium ; Bottleneck ; Engineering design process ; Multi-objective optimization ; Process (computing) Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Welding Techniques and Residual Stresses
Authors: Sehyeok Oh | Jaimyun Jung | Junyong Choi | Yoon-Ho Lee | Young-Hee Cho | Mingi Oh | Ho Won Lee
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Prediction of elastic modulus, Poisson’s ratio, and phase volume fraction from XRD pattern in high-elasticity aluminum alloys for casting using deep learning Volume fraction ; Elastic modulus ; Casting ; Phase (matter) ; Alloy ; Aluminium ; Volume (thermodynamics) ; Similarity (geometry) Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Titanium Alloys Microstructure and Properties
Authors: R. Edwin Garcı́a
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Machine Learning Enhanced Phase Field Modeling of Battery Materials Historically, the properties of cathode materials for rechargeable lithium-ion batteries (LIBs) have been investigated by predicting the properties by starting from atomistics, or by self-consistently averaging the effect of an individual representative material feature and incorporating it into coarse grained porous electrode theory descriptions that capture the time-dependent electronic and ionic transport. Such approaches are based simplified representations of grains, pores, or inclusions, and averaging them into models that aim to understand or engineer the macroscopic properties of the device or component at hand. However, if effects due to phase transformation kinetics, or the impact of defects such as vacancies or interstitials, dislocations, or grain boundaries are to be engineered, currently used averaging approaches will not capture the underlying multiphysical and microstructural richness of behavior of the device. In general, the modern development of advanced technology for LIBs demands: 1) the formulation of a methodology that provides an accurate description of the materials that integrate these devices at each length scale; 2) the systematic and mindful coarse-graining of lower length scale methodologies into higher length scale descriptions, and 3) the establishment of meaningful databases that enable the development of insight to understand and then engineer advanced, reliable, next generation devices. Here, by defining a thermodynamically consistent representation of materials that spatially resolves the multiphysical fields that results from formally considering microstructural features such as grain size, crystallographic texture, grain boundaries, particle size, and porosity, the time-dependent behavior is analyzed in materials for lithium-ion battery applications. Progress towards integrating the physical contributions of each individual phase and its processing-induced spatial distribution into advanced models is presented. The effect on the performance and degradation is analyzed. Reaches and limitations of well-known and emerging theories will be reviewed and compared, and efforts to accelerate to the limit of real time performance and degradation computations will be presented in an effort to explore the space of what is physically possible. Battery (electricity) ; Representation (politics) ; Field (mathematics) ; Phase (matter) ; Scale (ratio) ; Cathode ; Component (thermodynamics) ; Grain boundary Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advancements in Battery Materials