jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rui-Hong He | Jingshuang Dang
Source: The Journal of Chemical Physics (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Machine learning potential-accelerated multiscale dynamical simulations of nanodiamond structural reconstruction Atomistic understanding of structural transformations in nanodiamonds (NDs) is vital for manipulating their physicochemical properties, yet remains limited due to the inherent trade-off between simulation accuracy and scale. Here, we develop a machine learning potential (MLP) with density functional theory accuracy and implement it within the deep potential molecular dynamics framework to enable large-scale simulations of NDs comprising 103-104 atoms over nanosecond timescales. Our simulations reveal that the transformation dynamics are governed by morphology, surface facets, particle size, and temperature. We identify a multistage transformation pathway, sequentially characterized by outward-in graphitization, inward-out atomic migration, and a subsequent self-healing process, driven by surface energy minimization and internal stress relaxation. These results provide atomistic insight into the evolution of NDs and demonstrate the power of MLP-based approaches for modeling complex, multiscale structural transformations in nanocarbon materials. Molecular dynamics ; Multiscale modeling ; Nanodiamond ; Transformation (genetics) ; Surface (topology) ; Particle (ecology) ; Energy minimization ; Minification ; Energy (signal processing) Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Force Microscopy Techniques and Applications
Authors: Alexandria Van Grouw | Markace A. Rainey | Olivia K. Reid | Molly E. Ogle | Samuel G. Moore | Johnna S. Temenoff | Facundo M. Fernández
Source: Journal of Chemical Information and Modeling (article, 2025-02-05)
Field: Chemistry — Spectroscopy

Toward Machine Learning Electrospray Ionization Sensitivity Prediction for Semiquantitative Lipidomics in Stem Cells Specificity, sensitivity, and high metabolite coverage make mass spectrometry (MS) one of the most valuable tools in metabolomics and lipidomics. However, translation of metabolomics MS methods to multiyear studies conducted across multiple batches is limited by variability in electrospray ionization response, making batch-to-batch comparisons challenging. This limitation creates an artificial divide between nontargeted discovery work that is broad in scope but limited in terms of absolute quantitation ability and targeted work that is highly accurate but limited in scope due to the need for matched isotopically labeled standards. These issues are often observed in stem cell studies using metabolomic and lipidomic MS approaches, where patient recruitment can be a years-long process and samples become available in discrete batches every few months. To bridge this gap, we developed a machine learning model that predicts electrospray ionization sensitivity for lipid classes that have shown correlation with stem cell potency. Molecular descriptors derived from these lipids' chemical structures are used as model input to predict electrospray response, enabling quantitation by MS with moderate accuracy (semiquantitation). Model performance was evaluated via internal and external validation using cultured cells from various stem cell donors, achieving global percent errors of 40% and 20% for positive and negative electrospray ion modes, respectively. Although this accuracy is typically insufficient for traditional targeted lipidomics experiments, it is sufficient for semiquantitative estimation of lipid marker concentrations across batches without the need for specific chemical standards that many times are unavailable. Furthermore, the precision for model-predicted concentrations was 16.9% for the positive mode and 7.5% for the negative mode, indicating promise for data harmonization across batches. The set of molecular descriptors used by the models described here was able to yield higher accuracy than those previously published in the literature, showing high promise toward semiquantitative lipidomics. Lipidomics ; Electrospray ionization ; Sensitivity (control systems) ; Electrospray ; Extractive electrospray ionization ; Chromatography ; Chemistry ; Mass spectrometry ; Engineering ; Biochemistry ; Protein mass spectrometry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Junchi Wu | Haoran Zhu | Xuanyi Li | Guoliang Yu | Hongyu Yu | Changsong Xu
Source: Physical review. B./Physical review. B (article, 2025-02-18)
Field: Materials Science — Materials Chemistry

Dynamics of ferroelectricity in monolayer <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:msub><mml:mi>AgCr</mml:mi><mml:mn>2</mml:mn></mml:msub><mml:msub><mml:mi mathvariant="normal">S</mml:mi><mml:mn>4</mml:mn></mml:msub></mml:mrow></mml:math> calculated with a machine learning potential Ferroelectric materials hold significant potential for high-density nonvolatile memory applications. Understanding the dynamic mechanism of ferroelectricity is crucial for maximizing the utilization of these materials. In this study, we investigate the ferroelectric properties of monolayer ${\mathrm{AgCr}}_{2}{\mathrm{S}}_{4}$ by combining density functional theory (DFT) calculations with molecular dynamics (MD) simulations that utilize machine learning potentials. Our investigation reveals that the off-center displacement of Ag ions primarily drives the ferroelectricity in monolayer ${\mathrm{AgCr}}_{2}{\mathrm{S}}_{4}$. Remarkably, the system exhibits a high ferroelectric-paraelectric phase transition temperature of 600 K. Under low temperatures, a distinctive domain structure emerges, characterized by domain walls that induce silver vacancy defects and interstitial atoms. Moreover, ferroelectric hysteresis loops indicate that the out-of-plane and in-plane polarizations of the ${\mathrm{AgCr}}_{2}{\mathrm{S}}_{4}$ monolayer are switchable by applying an external electric field. Thus, our findings suggest that monolayer ${\mathrm{AgCr}}_{2}{\mathrm{S}}_{4}$ retains ferroelectricity at room temperature, highlighting its promising potential for applications in ferroelectric devices. Ferroelectricity ; Computer science ; Physics ; Quantum mechanics 2D Materials and Applications ; Multiferroics and related materials ; Machine Learning in Materials Science
Authors: James A. Boulton | K. W. Kim
Source: Journal of Applied Physics (article, 2025-08-22)
Field: Materials Science — Materials Chemistry

Predicting 3D magnetic topological insulators and semimetals with machine learning Machine learning (ML) models are used to identify topologically non-trivial magnetic insulators and semimetals among a group of candidate materials. The search for these topological materials is critical in the development of spintronic devices due to their strong magnetoelectric responses enabling low-power operations. In a conventional approach, such a task usually requires an extensive use of time-intensive density functional theory (DFT) calculations combined with a Hamiltonian model using maximally localized Wannier functions (MLWFs). It is hypothesized that a ML model may make the process more efficient. As a specific example, our investigation focuses on AxByCz compounds with A = Ni, V, Co, or Eu; B = Bi or Sb; and C = Te or Se, which have received much attention recently. Of the three ML models trained (Decision Tree, Random Forest, and XGBoost), XGBoost appears to provide the highest level of accuracy in the three relevant tasks (e.g., 87.6% in metal vs non-metal; 95.7% in trivial vs topological insulators; 84.1% in trivial vs topological metals). Subsequent application of these models to the AxByCz compounds leads to the identification of seven topological insulators (verified with DFT and MLWFs). On the other hand, only one of the predicted topological semimetal candidates turns out to have the desired properties. This challenge in identifying the topological semimetals appears to be due to the sensitive dependence on the Fermi level position, which is rather difficult to predict a priori. Nevertheless, the results illustrate that the ML models can indeed significantly enhance the efficiency in the search for magnetic topological materials. Topological insulator ; Semimetal ; Condensed matter physics ; Materials science ; Physics ; Band gap Machine Learning in Materials Science ; Graphene research and applications ; Ferroelectric and Negative Capacitance Devices
Authors: Jinzhe Zeng | Xulong Peng | Yong‐Bin Zhuang | Haidi Wang | Fengbo Yuan | Duo Zhang | Renxi Liu | Yingze Wang | Ping Tuo | Yuzhi Zhang | Yixiao Chen | Yifan Li | Cao Thang Nguyen | Jiameng Huang | Anyang Peng | Marián Rynik | Weihong Xu | Zezhong Zhang | Xuyuan Zhou | Tao Chen | Jiahao Fan | Wanrun Jiang | Bowen Li | Denan Li | Haoxi Li | Wenshuo Liang | Rundong Liao | Liping Liu | Chenxing Luo | Logan Ward | Kaiwei Wan | Junjie Wang | Pan Xiang | Chengqian Zhang | Jinchao Zhang | R. Zhou | Jiaxin Zhu | Linfeng Zhang | Han Wang
Source: Journal of Chemical Information and Modeling (article, 2025-10-20)
Field: Materials Science — Materials Chemistry

dpdata: A Scalable Python Toolkit for Atomistic Machine Learning Data Sets Seamless management of atomistic data sets is a critical prerequisite for the successful development and deployment of machine learning potentials (MLPs). Here, we present dpdata, an open-source Python library designed to streamline every aspect of MLP data handling. Built upon a flexible, plugin-based architecture, dpdata supports reading, writing, and converting between a broad range of file formats─from popular quantum-chemistry packages and molecular-dynamics engines to specialized MLP frameworks. Users may define custom data types, formats, drivers, and minimizers, enabling effortless extension to emerging software. Key utilities include automated train-test splitting, coordinate perturbation for active learning, outlier-energy removal, Δ-learning data set generation, error-metric computation, and unit conversion. Through efficient NumPy-backed storage and system-level operations, dpdata achieves significant memory saving and inference speedups over configuration-by-configuration tools such as ASE. We also highlight practical impact, with dpdata used across published studies, for format conversion, data storage, coordinate perturbation, and utilization in other projects for data processing. Python (programming language) ; Scalability ; Inference ; Software deployment ; Data structure ; Key (lock) ; Data point ; Data set Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Guangyu Liu | Yuntao Li | Yanchang Qi | Chengyong Ma
Source: Materials Today Communications (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

Study on microstructure and mechanics of Cr/Nb on NiCrMo-based fusion clad metal based on BP artificial neural network Materials science ; Microstructure ; Artificial neural network ; Fusion ; Metal ; Metallurgy ; Artificial intelligence ; Computer science Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Advanced Sensor and Control Systems
Authors: Qinghe Gao | Daniël M. Miedema | Yidong Zhao | Jana M. Weber | Qian Tao | Artur M. Schweidtmann
Source: Systems and Control Transactions (article, 2025-06-30)
Field: Materials Science — Materials Chemistry

Bayesian uncertainty quantification of graph neural networks using stochastic gradient Hamiltonian Monte Carlo Graph neural networks (GNNs) have proven state-of-the-art performance in molecular property prediction tasks. However, a significant challenge with GNNs is the reliability of their predictions, particularly in critical domains where quantifying model confidence is essential. Therefore, assessing uncertainty in GNN predictions is crucial to improving their robustness. Existing uncertainty quantification methods, such as Deep ensembles and Monte Carlo Dropout, have been applied to GNNs with some success, but these methods are limited to approximate the full posterior distribution. In this work, we propose a novel approach for scalable uncertainty quantification in molecular property prediction using Stochastic Gradient Hamiltonian Monte Carlo (SGHMC). Additionally, we utilize a cyclical learning rate to facilitate sampling from multiple posterior modes which improves posterior exploration within a single training round. Moreover, we compare the proposed methods with Monte Carlo Dropout and Deep ensembles, focusing on error analysis, calibration, and sharpness, considering both epistemic and aleatoric uncertainties. Our experimental results demonstrate that the proposed parallel-SGHMC approach significantly outperforms Monte Carlo Dropout and Deep ensembles in terms of calibration and sharpness. Specifically, parallel-SGHMC reduces the sum of squared errors by 99.4% and 75%, respectively, when compared to Monte Carlo Dropout and Deep Ensembles. These findings suggest that parallel-SGHMC is a promising method for uncertainty quantification in GNN-based molecular property prediction. Hybrid Monte Carlo ; Monte Carlo method ; Computer science ; Bayesian probability ; Markov chain Monte Carlo ; Statistical physics ; Artificial intelligence ; Mathematics ; Physics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Electron Microscopy Techniques and Applications
Authors: Jenoff E. De Vrieze | Michiel Verswyvel | Kinza Yasmin Ghulam | Bart‐Jan Niebuur | Tobias Kraus | Markus Gallei | Norbert Nießner
Source: Macromolecules (article, 2025-07-30)
Field: Materials Science — Polymers and Plastics

Water-Induced Transparency Loss in Styrene Butadiene Block Copolymers: Mechanism, Morphology, and Predictive Modeling Water-induced transparency loss in styrene-butadiene block copolymers (SBCs) has been investigated under a variety of conditions. Consistent with earlier work on homopolymers, the opacity after prolonged water exposure is expected to be caused by water clustering, which results from stronger water-water than water-polymer interactions. The water clusters distort the surrounding polymer matrix, causing local changes in the refractive index. It was found that the hard phase has only a minor contribution to the transparency loss, while the rubbery phase appears to be the major contributor. However, the loss of transparency was found not to be directly proportional to the volume of the soft phase, and a significant effect of the block copolymer morphology was observed, which was confirmed by a series of transmission electron microscopy and SAXS measurements. This effect is particularly evident in the transition from a continuous hard phase through a co-continuous morphology to a continuous soft phase. The acquired insights were subsequently used to predict long-term optical performance in SBCs to provide a tool in product development. Loss of transparency predictions was proven to be adequate through a classical regression-extrapolation approach using a limited data set, accurately simulating performance beyond 2600 h exposure time using only 600 h of measurement time. Additionally, it was shown that artificial neural networks could provide a solid tool in predicting performance even prior to synthesis, granted that the selection of descriptors is complete and the appropriate amount of data is supplied with a proper spread over the descriptor space. Copolymer ; Morphology (biology) ; Styrene ; Transparency (behavior) ; Block (permutation group theory) ; Polymer chemistry ; Chemical engineering ; Materials science ; Chemistry ; Polymer science ; Computer science ; Organic chemistry ; Polymer ; Geology ; Engineering ; Mathematics Polymer crystallization and properties ; Advanced Polymer Synthesis and Characterization ; Polymer Nanocomposites and Properties
Authors: Daniel Willimetz | Joaquin Martínez-Ortigosa | Deborah A Brako-Amoafo | Lukáš Grajciar | Alejandro Vidal‐Moya | Carlos Bornes | Vincent Sarou‐Kanian | Andreas Erlebach | Fernando Rey | Teresa Blasco | Christopher J. Heard
Source: ChemRxiv (preprint, 2025-07-23)
Field: Chemistry — Inorganic Chemistry

Aluminum Siting in Zeolite RTH From a Combined Machine Learning - NMR Approach Determining the distribution of aluminum in zeolite frameworks remains a significant challenge, due to the limited sensitivity of conventional characterization techniques and the complexity of possible Al configurations. In this work, we have developed a procedure which combines experimental two-dimensional (2D) dipolar-based 27Al–29Si D-HMQC and MQMAS solid-state NMR spectroscopy with machine learning (ML) techniques to achieve a detailed and quantitative assignment of Al environments in zeolites. We applied this approach to investigate the Al distribution in RTH-type zeolites (Si/Al = 16 and 28), which contain four crystallographically distinct T-sites. A reactive ML potential was used to model framework dynamics under realistic conditions, including temperature and hydration, while kernel ridge regression enabled the accurate prediction of isotropic NMR chemical shifts. Comparison between theoretical and experimental spectra reveals that Al preferentially occupies the T2 (39 %) and T1 (27 %) sites, with lower populations in T3 (18 %) and T4 (16 %). The excellent agreement between computed and observed NMR data demonstrates that this ML- augmented spectroscopic approach is a powerful tool for quantitative elucidation of Al distributions in structurally complex zeolites via solid state NMR. Zeolite ; Aluminium ; Materials science ; Chemistry ; Metallurgy ; Organic chemistry ; Catalysis Zeolite Catalysis and Synthesis ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Shailendra P. Joshi | Ashley Bucsek | Darren C. Pagan | Samantha Daly | Suraj Ravindran | Jaime Marian | Miguel A. Bessa | Surya R. Kalidindi | Nikhil Chandra Admal | Celia Reina | Somnath Ghosh | James A. Warren | Jorge Viñals | Ellad B. Tadmor
Source: Mechanics of Materials (article, 2025-08-30)
Field: Materials Science — Materials Chemistry

Integrated experiment and simulation co-design: A key infrastructure for predictive mesoscale materials modeling Materials science ; Mesoscale meteorology ; Key (lock) ; Computer science ; Meteorology Microstructure and mechanical properties ; CO2 Sequestration and Geologic Interactions ; Machine Learning in Materials Science
Authors: Ismail A. Shittu | Mohamed A. Abou‐Khousa | Ibrahim M. Elfadel
Source:  (article, 2025-05-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-Resolution Recommender System for Metamaterial Synthesis The synthesis of metamaterials using the inverse design approach has revolutionized the field of metamaterials. It allows for efficient exploration of the vast design space and has the potential to accelerate the development of metamaterials for various applications. Machine learning methodologies within the inverse design framework present a viable strategy for metamaterial synthesis. One option in the machine-learning realm involves modeling the relationship between metamaterial layouts and performance parameters as complex nonlinear functions, which can be well approximated with deep generative models. However, deep generative models require enormous computational resources, careful hyperparameter tuning, and extensive training episodes for optimal performance. In addition, the residual training errors inhibit specific metamaterial reconstructions. Relying on the vast amount of data within a specified design space, this study transforms metamaterial synthesis into a retrieval process using a recommender system that outperforms deep generative models without enormous computations, meticulous hyperparameter optimization, and prolonged training sessions. The recommendation system, which uses a modified item-based filtering algorithm, operates on the premise that the metamaterial whose performance parameters most accurately correspond to a given query serves as a reliable approximation. This approach paves the way for a faster and more efficient metamaterial synthesis. The validity of this idea is further substantiated by the significant enhancements in reconstruction quality seen in experiments conducted on the corpus of stealth metamaterial designs, demonstrating the recommender system's ability to capture intricate low-level details. Compared to deep generative models, the algorithm significantly reduces computational time due to the absence of training while maintaining superior reconstruction accuracy, as there are no residual training errors. Metamaterial ; Hyperparameter ; Recommender system ; Inverse ; Deep learning ; Generative grammar ; Generative Design Metamaterials and Metasurfaces Applications ; Topology Optimization in Engineering ; Machine Learning in Materials Science
Authors: Takuma Higo | Koki Saegusa | S. Ishizaki | Sota Kakihara | Yoshihiro Yayama | Yuichiro Hirano | Yasushi Sekine
Source: The Journal of Physical Chemistry C (article, 2025-10-27)
Field: Materials Science — Materials Chemistry

Understanding the Role of Pr Doping in Redox Enhancement of CeO <sub>2</sub> for the Reverse Water–Gas Shift via Chemical Looping Fluorite-type CeO2 was examined as an oxygen carrier material for the chemical looping reverse water–gas shift (RWGS-CL) for effective conversion of CO2 to CO, and potential dopants to enhance its redox properties were explored through a combination of theoretical calculations and experimental investigations. A strong correlation was observed between the oxygen vacancy formation energy, calculated using neural network potential (NNP) calculations, and the experimentally determined reduction rate under a H2 atmosphere, leading to the identification of praseodymium (Pr) as a promising dopant. The redox performance of Pr-doped CeO2 improved with increasing doping ratio, with Ce0.67Pr0.33O2 exhibiting the highest CO2 splitting capacity. In situ X-ray absorption fine structure (XAFS) analysis confirmed that Pr doping promotes the Ce4+ ↔ Ce3+ redox cycle during the RWGS-CL process at 773 K. Furthermore, NNP-based calculations revealed that Pr doping induces a positive shift in the charge of Ce atoms, thereby enhancing the redox capacity of the Pr-doped system. Redox ; Chemical looping combustion ; Doping ; Dopant ; Oxygen ; Praseodymium ; Vacancy defect ; Charge carrier Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Chemical Looping and Thermochemical Processes