jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bejjipurapu, Akhil | Strachan, Alejandro | Sandhage, Kenneth H. | Titus, Michael S.
Source: arXiv (Cornell University) (preprint, 2025-11-02)
Field: Materials Science — Materials Chemistry

Machine learning descriptors for predicting the high temperature oxidation of refractory complex concentrated alloys Refractory Complex Concentrated Alloys (RCCAs) can exhibit exceptional high-temperature strength, making such alloys promising candidates for high-temperature structural applications. However, current RCCAs do not possess the high-temperature oxidation resistance required to survive in oxidizing environments for more than a few hours at or above 1000$^\circ$C, without relying primarily on an environmental barrier coating. Here, we present a machine-learning framework designed to predict the oxidation-induced specific mass changes of RCCAs exposed for 24 h at 1000$^\circ$C in air, in order to support the search for oxidation-resistant alloys over a wide range of compositions. A database was constructed of experimental specific mass change data, upon oxidation at 900-1000$^\circ$C for 24 h in air, for 77 compositions comprised of simple elements, binary alloys, and higher-order elemental systems. We then developed a Gaussian Process Regression (GPR) model with physics-informed descriptors based on oxidation products, capturing the fundamental chemistry of oxide formation and stability. Application of this GPR model to the database yielded a MAE (mean absolute error) test score of 5.78 mg/cm$^2$, which was a significant improvement in accuracy relative to models only utilizing traditional alloy-based descriptors. Our model was used to screen over 5,100 quaternary RCCAs, revealing compositions with significantly lower predicted specific mass changes compared to existing literature sources. Overall, this work establishes a versatile and efficient strategy to accelerate the discovery of next-generation RCCAs with enhanced resistance to extreme environments. Complex oxide ; Binary number ; Oxide ; Oxidizing agent ; Refractory (planetary science) ; Work (physics) ; Support vector machine ; Kriging Machine Learning in Materials Science ; High Entropy Alloys Studies ; High-Temperature Coating Behaviors
Authors: Zeng, Yuxuan | Cao, Wei | Zuo, Yijing | Lyu, Fang | Xie, Wenhao | Peng, Tan | Hou, Yue | Miao, Ling | Wang, Ziyu | Shi, Jing
Source: arXiv (Cornell University) (article, 2025-12-07)
Field: Materials Science — Materials Chemistry

Learning Thermoelectric Transport from Crystal Structures via Multiscale Graph Neural Network Graph neural networks (GNNs) are designed to extract latent patterns from graph-structured data, making them particularly well suited for crystal representation learning. Here, we propose a GNN model tailored for estimating electronic transport coefficients in inorganic thermoelectric crystals. The model encodes crystal structures and physicochemical properties in a multiscale manner, encompassing global, atomic, bond, and angular levels. It achieves state-of-the-art performance on benchmark datasets with remarkable extrapolative capability. By combining the proposed GNN with \textit{ab initio} calculations, we successfully identify compounds exhibiting outstanding electronic transport properties and further perform interpretability analyses from both global and atomic perspectives, tracing the origins of their distinct transport behaviors. Interestingly, the decision process of the model naturally reveals underlying physical patterns, offering new insights into computer-assisted materials design. Interpretability ; Representation (politics) ; Artificial neural network ; Graph ; Benchmark (surveying) ; Multiscale modeling ; Process (computing) Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Advanced Graph Neural Networks
Authors: Zeng, Yuxuan | Cao, Wei | Zuo, Yijing | Lyu, Fang | Xie, Wenhao | Peng, Tan | Hou, Yue | Miao, Ling | Wang, Ziyu | Shi, Jing
Source: arXiv (Cornell University) (preprint, 2025-12-07)
Field: Materials Science — Materials Chemistry

Learning Thermoelectric Transport from Crystal Structures via Multiscale Graph Neural Network Graph neural networks (GNNs) are designed to extract latent patterns from graph-structured data, making them particularly well suited for crystal representation learning. Here, we propose a GNN model tailored for estimating electronic transport coefficients in inorganic thermoelectric crystals. The model encodes crystal structures and physicochemical properties in a multiscale manner, encompassing global, atomic, bond, and angular levels. It achieves state-of-the-art performance on benchmark datasets with remarkable extrapolative capability. By combining the proposed GNN with \textit{ab initio} calculations, we successfully identify compounds exhibiting outstanding electronic transport properties and further perform interpretability analyses from both global and atomic perspectives, tracing the origins of their distinct transport behaviors. Interestingly, the decision process of the model naturally reveals underlying physical patterns, offering new insights into computer-assisted materials design. Interpretability ; Representation (politics) ; Artificial neural network ; Graph ; Benchmark (surveying) ; Multiscale modeling ; Process (computing) Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Advanced Graph Neural Networks
Authors: Khowshik Dey | Murat Barisik | Yu Liang
Source: UTC Scholar (University of Tennessee at Chattanooga) (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Machine Learning for Reparameterization of Molecular Dynamics Water Model to Accurately Capture Electrical and Thermal Behavior Water is an important component in many scientific and engineering applications, but precisely modeling its thermal, electrical, and physical properties remains difficult in molecular dynamics (MD) simulations. The widely used TIP4P water model, while useful in many circumstances, requires parameterization to better agreement with experimental results under various conditions. In this study, interpretable machine learning techniques were used to systematically tune the TIP4P model parameters where mathematical relationship between features and targets has been developed using deep symbolic optimization (DSO), resulting in a more accurate depiction of water behavior in molecular dynamics simulation. A data-driven approach was created to optimize critical model parameters while maintaining physical interpretability by combining molecular dynamics simulations and neural network-based modeling. A new optimized TIP4P water model has been developed to reproduce the thermal conductivity, diffusion coefficient, density and dielectric constant with accurate dipole calculations. Interpretability ; Water model ; Molecular dynamics ; Component (thermodynamics) ; Artificial neural network ; Thermal ; Constant (computer programming) ; Dipole Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Protein Structure and Dynamics
Authors: Zebin Li | Shijie Deng | Yijin Liu | Jia Hu
Source: ArXiv.org (preprint, 2025-12-18)
Field: Materials Science — Materials Chemistry

Machine Learning Enabled Graph Analysis of Particulate Composites: Application to Solid-state Battery Cathodes Particulate composites underpin many solid-state chemical and electrochemical systems, where microstructural features such as multiphase boundaries and inter-particle connections strongly influence system performance. Advances in X-ray microscopy enable capturing large-scale, multimodal images of these complex microstructures with an unprecedentedly high throughput. However, harnessing these datasets to discover new physical insights and guide microstructure optimization remains a major challenge. Here, we develop a machine learning (ML) enabled framework that enables automated transformation of experimental multimodal X-ray images of multiphase particulate composites into scalable, topology-aware graphs for extracting physical insights and establishing local microstructure-property relationships at both the particle and network level. Using the multiphase particulate cathode of solid-state lithium batteries as an example, our ML-enabled graph analysis corroborates the critical role of triple phase junctions and concurrent ion/electron conduction channels in realizing desirable local electrochemical activity. Our work establishes graph-based microstructure representation as a powerful paradigm for bridging multimodal experimental imaging and functional understanding, and facilitating microstructure-aware data-driven materials design in a broad range of particulate composites. Particulates ; Microstructure ; Bridging (networking) ; Cathode ; Representation (politics) ; Graph ; Battery (electricity) Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Michael Jacobs | Ryan Sayko | Andrey V. Dobrynin | Marissa Dominijanni
Source: UNC Libraries (article, 2025-02-04)
Field: Chemistry — Spectroscopy

Deep Learning for Determination of Properties of Semidilute Polymer Solutions The ability to predict polymer solution viscosity is essential for polymer characterization and processing. Here, we use a synergistic approach combining the scaling theory of polymer solutions and convolutional neural network (CNN) models to obtain system-specific parameters and describe semidilute solution viscosity in the unentangled and entangled solution regimes. The scaling approach relies on the existence of a characteristic microscopic length scalethe solution correlation length (correlation blob size) &xi;which uniquely defines macroscopic solution properties. It is based on a relationship between the solution correlation length &xi; = lg &nu;/B and the number of monomers g per correlation volume for polymers with the monomer projection length l. The system-specific set of parameters B g, B th, and 1 in the corresponding solution regime with the scaling exponent &nu; = 0.588, 0.5, and 1, respectively. Applying two CNN models, we obtained the sets B g and B th from the solution specific viscosity, &eta;sp, as a function of concentration, c, and weight-average degree of polymerization, N w. The CNN was trained on theoretically generated datasets converted to sparse images representing the normalized specific viscosity &eta;sp/N w (cl 3)1/(3&nu;&ndash;1) in the unentangled Rouse regime. The trained CNN was utilized in automated data analysis of the solution viscosity of polystyrene, poly&shy;(ethylene oxide), poly&shy;(methyl methacrylate), poly&shy;(acrylonitrile-co-itaconic acid), cellulose, sodium hyaluronate, hydroxypropyl methyl cellulose, methyl cellulose, hydroxypropyl cellulose, cellulose tris&shy;(phenyl carbamate), xanthan gum, galactomannan, and sodium &kappa;-carrageenan in water, organic solvents, and ionic liquids. This approach produced values of the B-parameters with mean absolute percentage differences of less than 6% from the corresponding values determined by the manual data analysis. The B-parameters are then used to obtain the packing number P e defining the onset of entanglements in polymer solutions and to describe semidilute solution viscosity as a function of concentration and N w. Polymer ; Polymer science ; Computer science ; Materials science ; Composite material Analytical Chemistry and Chromatography
Authors: Matheus de Oliveira Bispo
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Full Integration of Machine Learning Techniques with Nonadiabatic Molecular Dynamics Simulations Nonadiabatic Molecular Dynamics (NAMD) simulations are widely used for exploring the time evolution of photoexcited molecular systems, which are able to access multiple potential energy surfaces during dynamics. Recent advancements in Machine Learning (ML) enabled the acceleration of NAMD using supervised learning methods to predict the necessary energies and forces for driving these simulations. This, in turn, can be exploited for improving statistical descriptions of photochemical reactions by increasing the number of trajectories being propagated. One significant challenge facing the adoption of ML techniques in NAMD simulations is the lack of clear protocols and software for training and deploying ML potentials, which hinders their widespread adoption. To address this challenge, I present a comprehensive end-to-end workflow for NAMD simulations with ML potentials, from geometry sampling and model training using active learning to the analysis of trajectories and properties using unsupervised learning, allowing the characterization of reaction mechanisms and photoproducts. This workflow is designed to be flexible and robust, applicable to different molecular systems and capable of handling large and complex datasets. It is implemented in two free and open source Python packages: MELTS for active learning and ULaMDyn for unsupervised learning, both integrated within the Newton-X software suite. Workflow ; Python (programming language) ; Molecular dynamics ; Software ; Unsupervised learning ; Acceleration ; Supervised learning Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Chemical Physics Studies
Authors: Moon Hyung Jang | K.-H. Park | Yong‐Heum Lee | Joon Hyung Shim | Kwangnam Kim | Seungho Yu
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Mechanistic insights into superionic thioarsenate argyrodite solid electrolytes <i>via</i> machine learning interatomic potentials Machine learning interatomic potentials reveal that optimal anion disorder boosts Li-ion conductivity beyond 20 mS cm −1 in thioarsenate argyrodite solid electrolytes. Fast ion conductor ; Electrolyte ; Materials science ; Interatomic potential ; Chemical physics ; Chemistry ; Physical chemistry ; Computational chemistry ; Molecular dynamics ; Electrode Machine Learning in Materials Science ; Polyoxometalates: Synthesis and Applications ; Inorganic Chemistry and Materials
Authors: Mahshid Ataei | M. Modarresi | Mahmood Rezaee Roknabadi | A. Mogulkoc
Source: Physica Scripta (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

Molecular dynamics study of corrugation in low-defect graphene using machine learning potential Abstract Machine learning has significant promise to improve inter-atomic potentials for accurate, efficient, and more general atomic simulations. This study investigates the effect of low vacancy concentrations (0.05%–0.8%) on graphene corrugation using molecular dynamics simulations enhanced by a neural network potential. Our results demonstrate that the presence and quantity of vacancy defects significantly affect the formation of corrugation patterns on graphene surfaces. Specifically, we found that the type and concentration of defects play crucial roles; for instance, double vacancies result in more pronounced corrugation than single vacancies. Furthermore, maintaining an appropriate distance between the vacancy defects can reduce lattice corrugation. Interestingly, while it is generally expected that an increase in defect concentration correlates with heightened corrugation, our results indicate a counterintuitive trend at low defect concentrations (0.05%–0.2%). Specifically, within this range, an increase in defect concentration leads to a decrease in corrugation formation on graphene layers. This reduction may explain experimental observations, such as the increased Young’s modulus and altered Thermal Expansion Coefficient (TEC) reported in graphene with similar defect densities. However, at higher defect concentrations, a subsequent increase in corrugation is observed. These findings offer valuable insights for designing and enhancing graphene-based materials across various technological applications. Graphene ; Molecular dynamics ; Dynamics (music) ; Materials science ; Chemical physics ; Statistical physics ; Nanotechnology ; Physics ; Quantum mechanics ; Acoustics Graphene research and applications ; Semiconductor materials and devices ; Advancements in Semiconductor Devices and Circuit Design
Authors: Tianran Ding | Gustavo Larrea‐Gallegos | Federico Busio | Antonino Marvuglia | Thomas Schaubroeck
Source: ChemRxiv (preprint, 2025-05-19)
Field: Chemistry — Analytical Chemistry

Characterizing Chemical Toxicity for Life Cycle Assessment Using Machine Learning Models Based on Environmental Footprint – Illustrated importance through a textile case study To support the integration of emerging chemicals into life cycle assessment (LCA), we developed a machine learning (ML) workflow to predict characterization factors (CFs) for both human toxicity and ecotoxicity, aligned with the EU Environmental Footprint (EF) methodology. Using extended chemicals from EF version 3 database and molecular descripotrs derived from SMILES (Simplified Molecular-Input Line-Entry System) strings, we trained and evaluated three ML models: extreme gradient boosting (XGBoost), gaussian process (GP) regression, and deep neural networks (NN). A clustering step was used to guide model selection for new compounds. XGBoost consistently performed best, achieving R² values up to 0.65 and 0.61 for ecotoxicity and human toxicity (seas water, continent). New chemicals should first be assigned to clusters for which the best tailored ML model is then selected. An LCA case study in the textile sector illustrates the importance of CF prediction, as the total human toxicity score is at least 4 orders of magnitudes higher when including predicted CFs that were originally missing. Overall, this end-to-end ML approach offers a robust and efficient alternative to traditional methods, enabling immediate integration of predicted CFs into LCA studies, which could facilitate safe and sustainable by design implementation. Textile ; Footprint ; Life-cycle assessment ; Ecological footprint ; Chemical toxicity ; Environmental science ; Engineering ; Computer science ; Sustainability ; Toxicity ; Chemistry ; Economics ; Materials science ; Geography ; Ecology ; Biology Dye analysis and toxicity ; Chemistry and Chemical Engineering
Authors: Shouhong Ji | Gia Jeong | Hanvit Kim | Jeong Won Park | Chul Huh | Jun Chang Yang | Joo Yong Sim
Source: IEEE Sensors Journal (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Harnessing Machine Learning for Intelligent Control of Shape Memory Alloy Actuators in Versatile Environments Shape memory alloy (SMA) actuators, known for their high force output despite being lightweight and compact, are increasingly attracting attention in fields such as robotics, medical devices, and wearable devices. Due to these advantages, many studies have been conducted on SMA actuators driven by Joule heating under static air conditions. However, ensuring precise and responsive control of these actuators in diverse and dynamic environmental conditions remains a significant challenge. In particular, the implementation of efficient cooling mechanisms—such as fan-based or liquid-based cooling—for rapid operation is critical for practical applications but still underexplored. Herein, we introduce a durable and adaptive SMA actuation system that operates under randomly generated current conditions. This system enables reliable prediction of SMA output force using machine learning models, even as environmental conditions change. A Long Short-Term Memory (LSTM) neural network is trained to predict the output force of SMA coils using time-series inputs of current, electrical resistance, and environmental temperature. This model is validated under both static air and flowing water conditions with varying temperatures and flow rates. Furthermore, we demonstrate a closed-loop feedback control strategy for the SMA actuator, highlighting its potential for precise and robust operation across diverse environments. SMA* ; Shape-memory alloy ; Actuator ; Artificial neural network ; Wearable computer ; Control system ; Joule effect ; Smart material Shape Memory Alloy Transformations ; Musicians’ Health and Performance ; Aeroelasticity and Vibration Control
Authors: Yi Jin | Hao Zhang
Source:  (article, 2025-01-31)
Field: Materials Science — Materials Chemistry

Explainable machine learning for predicting the formation energies and bandgaps of the bulk and monolayer materials Materials with van der Waals (vdW) bonding in one, two, or three dimensions have attracted much attention due to their possibility to reduced dimensionality by mechanical exfoliation. In this work, by using the XGBoost algorithm, we successfully built the machine-learning (ML) models for formation energies and bandgaps for materials with vdW bonding, and the performances of the built ML models, i.e. R2 and MAE values, reach 0.98/0.02 eV/atom and 0.89/0.56 eV, respectively. Based on the analysis of feature importance and SHAP values, 20 important features are identified and further reduced by the correlation analysis. Our work not only demonstrates the effectiveness of the XGBoost method in modeling key properties of materials with vdW bonding, but also contributes to the understanding of the critical properties in these materials. Monolayer ; Computer science ; Materials science ; Nanotechnology Machine Learning in Materials Science
Authors: Xiaoyu Wu | Rui Zheng | Quan Liu | Jianwen Jiang
Source: Advanced Functional Materials (article, 2025-11-17)
Field: Chemistry — Inorganic Chemistry

Digital Discovery of Synthesizable Metal−Organic Frameworks via Molecular Dynamics‑Informed, High‑Fidelity Deep Learning Abstract Metal–organic frameworks (MOFs) are celebrated for their chemical and structural versatility, and in‑silico screening has significantly accelerated their discovery; yet most hypothetical MOFs (hMOFs) never reach the bench because their synthetic feasibility remains unknown. Herein, a deep‑learning proxy based on the Tabular Prior‐Data Fitted Network (TabPFN) is introduced that predicts free energy with high fidelity. The fine‐tuned surrogate attains a coefficient of determination ( R 2 ) of 0.96 and a mean absolute error (MAE) of 0.67 on the hold‑out test set, preserves strong temporal generalization ( R 2 = 0.89 and MAE = 1.35), as well as robust extrapolation on a chemically diverse blind set. Applying the surrogate to a well‐established hMOF library, we rapidly flag prime hMOF candidates that fall within the synthetic‑likelihood window observed for experimentally realized MOFs. This study delivers a practical bridge between digital reticular design and laboratory synthesis, transforming synthetic‑likelihood assessment from a computational bottleneck into a routine, high‑throughput step. Beyond MOFs, the approach provides a general framework for accelerating free‑energy‑driven materials discovery with minimal loss of accuracy, opening a new avenue to data‑driven exploration of complex chemical spaces. Extrapolation ; Bottleneck ; Deep learning ; Generalization ; Hardware acceleration ; Synthetic data ; Preprocessor ; Bridge (graph theory) Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Nanocluster Synthesis and Applications
Authors: Shyue Ping Ong | Chad Risko | Osvaldo N. Oliveira
Source: ACS Applied Materials & Interfaces (article, 2025-12-03)
Field: Materials Science — Materials Chemistry

Machine Learning for Materials Chemistry: New Frontiers and Emerging Paradigms Field (mathematics) ; Deep learning ; Work (physics) ; Key (lock) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods