jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Philips Kumar | Akash Gutal | Pradeep Kumar
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Chemistry — Spectroscopy

The puzzle of high lifetime and low stabilization of HO<sub>3</sub>˙: rationalization and prediction One of the most important puzzles in atmospheric chemistry is the long-lifetime of HO<sub>3</sub>˙ in spite of its low-stabilization energy. In the present work, we have estimated the lifetime of HO<sub>3</sub>˙ using classical dynamics simulations by coupling an available neural-network analytical potential energy surface with a chemical dynamics program. The simulation results clearly indicate that at room temperature, the lifetime of HO<sub>3</sub>˙ can exceed 1 μs under collision-free conditions. In fact, at 200 K, the lifetime of HO<sub>3</sub>˙ can enter the millisecond timescale. This suggests that HO<sub>3</sub>˙ is indeed a stable enough intermediate that can affect the outcomes of crucial atmospheric processes. Rationalization (economics) ; Materials science ; Nanotechnology ; Chemistry ; Philosophy ; Epistemology Spectroscopy and Laser Applications ; Atmospheric Ozone and Climate ; Advanced Chemical Physics Studies
Authors: Zicong Marvin Wong | Dillon W. P. Tay | Yee Hwee Lim | Shi Jun Ang
Source: Research Square (preprint, 2025-08-11)
Field: Chemistry — Organic Chemistry

High-throughput antioxidant screening of flavan-3-ols via active learning Chemistry ; Density functional theory ; Antioxidant ; Quantitative structure–activity relationship ; Computational chemistry ; Stereochemistry ; Organic chemistry Free Radicals and Antioxidants ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Lei Zhang (38117) | Ben Ni (22225745) | Kaiyang Xu (20732625) | Yiru Huang (17117625) | Qingfang Li (4011788) | Lifeng Liu (164546)
Source: Figshare (article, 2025-09-10)
Field: Materials Science — Materials Chemistry

Leveraging Language Model, Crystal Structure Predictionand First-Principles Calculation for Material Design Large language models (LLMs) have demonstrated transformative potential for materials discovery in condensed matter systems, but their full utility requires both broader application scenarios and integration with ab initio crystal structure prediction (CSP), density functional theory (DFT) methods and domain knowledge to benefit future inverse material design. Here, we develop an integrated computational framework combining language model-guided materials screening with genetic algorithm (GA) and graph neural network (GNN)-based CSP methods to predict new photovoltaic material. This LLM + CSP + DFT approach successfully identifies a previously overlooked oxide material with unexpected photovoltaic potential. Through transformer-based vector similarity analysis coupled with unsupervised clustering and first-principles calculations, we demonstrate that this material exhibits a direct band gap and high theoretical efficiencies that are suitable for photovoltaic application. Our work highlights a hierarchical computational inverse design pipeline that can efficiently navigate the material space to identify nonintuitive functional materials with tailored optoelectronic properties. Photovoltaic system ; Cluster analysis ; Pipeline (software) ; Crystal structure prediction ; Ab initio ; Inverse ; Artificial neural network ; Density functional theory Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Heusler alloys: electronic and magnetic properties
Authors: Ruo-Fan Shen (21513044) | Ya-Chao Liu (14237726) | Wan-Li Jia (21513047) | Jia Shi (614042) | Yoshiyuki Kawazoe (1287717) | Vei Wang (8615328)
Source: Figshare (article, 2025-06-09)
Field: Materials Science — Materials Chemistry

Machine Learning Insights into Band Alignments ofvan der Waals Heterostructures The integration of two-dimensional (2D) materials into van der Waals heterostructures (vdWHs) enables the stacking of atomically thin layers through weak vdW interactions, offering tunable properties for next-generation optoelectronic and catalytic applications. In this study, we systematically predicted the band alignment types of over 32,000 vdWHs, constructed by pairing any two of the 256 semiconductor monolayers from the 2D Semiconductor Computational Database (2DSdb) [<i>J. Phys. Chem. Lett.</i> <b>2022</b> <i>13</i>, 11581], based on Anderson’s rule. Nearly 100 features were extracted from the physical properties of these vdWHs to establish a descriptor database for the triclassification of vdWHs using machine learning models, including extreme gradient boosting, random forest, and gradient boosting classifier, achieving accuracy rates between 85 and 87%. SHapley Additive exPlanations (SHAP) analysis identified electronegativity, oxygen fraction, valence electrons in the <i>p</i>-orbital, and lattice constants as the most influential features. The resulting database is expected to provide valuable guidance for experimentalists in designing nanodevices and photocatalysts. Stacking ; Heterojunction ; van der Waals force ; Semiconductor ; Pairing ; Gradient boosting ; Lattice (music) ; Boosting (machine learning) 2D Materials and Applications ; Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research
Authors: Tian, Zhihui | Suwandi, Ethan | Oppelstrup, Tomas | Bulatov, Vasily V. | Harley, Joel B. | Zhou, Fei
Source: arXiv (Cornell University) (preprint, 2025-11-22)
Field: Materials Science — Materials Chemistry

Scaling Kinetic Monte-Carlo Simulations of Grain Growth with Combined Convolutional and Graph Neural Networks Graph neural networks (GNN) have emerged as a promising machine learning method for microstructure simulations such as grain growth. However, accurate modeling of realistic grain boundary networks requires large simulation cells, which GNN has difficulty scaling up to. To alleviate the computational costs and memory footprint of GNN, we propose a hybrid architecture combining a convolutional neural network (CNN) based bijective autoencoder to compress the spatial dimensions, and a GNN that evolves the microstructure in the latent space of reduced spatial sizes. Our results demonstrate that the new design significantly reduces computational costs with using fewer message passing layer (from 12 down to 3) compared with GNN alone. The reduction in computational cost becomes more pronounced as the spatial size increases, indicating strong computational scalability. For the largest mesh evaluated (160^3), our method reduces memory usage and runtime in inference by 117x and 115x, respectively, compared with GNN-only baseline. More importantly, it shows higher accuracy and stronger spatiotemporal capability than the GNN-only baseline, especially in long-term testing. Such combination of scalability and accuracy is essential for simulating realistic material microstructures over extended time scales. The improvements can be attributed to the bijective autoencoder's ability to compress information losslessly from spatial domain into a high dimensional feature space, thereby producing more expressive latent features for the GNN to learn from, while also contributing its own spatiotemporal modeling capability. The training was optimized to learn from the stochastic Potts Monte Carlo method. Our findings provide a highly scalable approach for simulating grain growth. Autoencoder ; Scalability ; Convolutional neural network ; Scaling ; Memory footprint ; Inference ; Reduction (mathematics) ; Graph ; Bijection Machine Learning in Materials Science ; Microstructure and mechanical properties ; Quantum many-body systems
Authors: Seon Hwa Lee | Insoo Ye | Changhwan Lee | Jieun Kim | Sang Cheol Nam | Inchul Park
Source: ACS Energy Letters (article, 2025-10-11)
Field: Materials Science — Materials Chemistry

Machine-Learning-Accelerated Development of High-Nickel NCM Cathodes via Multivariable Co-optimization This study presents a machine-learning-based active-learning framework for optimizing high-nickel NCM cathode materials using a large-scale industrial dataset. Drawing from 3,019 pilot-scale experiments accumulated over two years, we utilized 706 high-quality samples for model development, capturing rich process variability under real manufacturing conditions. The framework was tested on a commercially important high-nickel NCM (LiNixCoyMn1-x-yO2, x ≥ 80%) cathode material containing 94% Ni, for which only a severely limited dataset of 18 samples was available. Using a Gradient Boosting model and iterative active learning, we achieved a discharge capacity of 228.3 mAh/g with only 38 experiments─reducing experimental effort by 94% compared to traditional methods. The model successfully leveraged human design biases to guide exploration beyond expert heuristics, discovering nonintuitive yet effective process conditions. By harnessing large, historically fragmented datasets, this work demonstrates a scalable approach for accelerating battery materials optimization in industrial environments. Cathode ; Scalability ; Process (computing) ; Boosting (machine learning) ; Process development ; Battery (electricity) ; Iterative and incremental development Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Memory and Neural Computing
Authors: Lei Zhang | Ben Ni | Kaiyang Xu | Yiru Huang | Qingfang Li | Lifeng Liu
Source: Journal of Chemical Information and Modeling (article, 2025-09-10)
Field: Materials Science — Materials Chemistry

Leveraging Language Model, Crystal Structure Prediction and First-Principles Calculation for Material Design Large language models (LLMs) have demonstrated transformative potential for materials discovery in condensed matter systems, but their full utility requires both broader application scenarios and integration with ab initio crystal structure prediction (CSP), density functional theory (DFT) methods and domain knowledge to benefit future inverse material design. Here, we develop an integrated computational framework combining language model-guided materials screening with genetic algorithm (GA) and graph neural network (GNN)-based CSP methods to predict new photovoltaic material. This LLM + CSP + DFT approach successfully identifies a previously overlooked oxide material with unexpected photovoltaic potential. Through transformer-based vector similarity analysis coupled with unsupervised clustering and first-principles calculations, we demonstrate that this material exhibits a direct band gap and high theoretical efficiencies that are suitable for photovoltaic application. Our work highlights a hierarchical computational inverse design pipeline that can efficiently navigate the material space to identify nonintuitive functional materials with tailored optoelectronic properties. Photovoltaic system ; Cluster analysis ; Pipeline (software) ; Inverse ; Ab initio ; Crystal structure prediction ; Artificial neural network ; Density functional theory ; Scratch Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Quantum Dots Synthesis And Properties
Authors: Han Zhong | Zhongying Xue | Pai Li | Xin Wei
Source: Small (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

Theoretical Study on the Thermodynamics of Si(001) Surface As one of the most widely used substrates in the integrated circuit industry, the surface structure of Si(001) is critical for the semiconductor manufacturing process. Due to the dimerization-related surface reconstruction and possible hydrogen saturation, the Si(001) surface exhibits structural diversity. However, the bonding mechanism and phase diagram of this surface have not been fully studied theoretically. Here, the thermodynamics of the Si(001) surface is systematically investigated using multiscale simulations based on density functional theory (DFT), grand canonical Monte Carlo (GCMC), and machine learning force fields (MLFF). A complete phase diagram of the Si(001) surface is obtained with respect to temperature and H<sub>2</sub> pressure, which reveals the isotropic premelting phenomenon on the Si(001) surface through MLFF-based molecular dynamics (MD) simulations. Finally, infrared (IR) adsorption spectra is presented of typical phases for comparison with experimental results. This study provides valuable insights into understanding surface bonding rules and contributes to elucidating the annealing mechanisms in silicon-based semiconductor fabrication. Materials science ; Phase diagram ; Annealing (glass) ; Thermodynamics ; Density functional theory ; Isotropy ; Adsorption ; Silicon ; Semiconductor ; Molecular dynamics ; Surface (topology) ; Surface reconstruction ; Chemical physics ; Condensed matter physics ; Physical chemistry ; Phase (matter) ; Computational chemistry ; Chemistry ; Physics ; Optics Machine Learning in Materials Science ; Semiconductor materials and devices ; Thermal properties of materials
Authors: Bingyu Lei (4437676) | Per H. Svensson (1720264) | Pavel Yushmanov (21163601) | Lars Kloo (1303731)
Source: Figshare (article, 2025-04-23)
Field: Materials Science — Materials Chemistry

AURORA - An AutomaticRobotic Platform for MaterialsDiscovery The urgent need for renewable energy solutions requires rapid advancements in materials discovery. In response, we present AURORA, an innovative robotic platform that enhances this process by integrating automated synthesis, characterization, and evaluation into a single unit, thereby improving efficiency and reducing errors. Its modular design allows for adaptable screening of diverse materials, including metal halide perovskites, and their application in solar cell devices. Our study demonstrates the ability of AURORA to autonomously synthesize and evaluate polycrystalline, mixed halide perovskites, including a novel mesoscopic solar cell array with improved data reliability and throughput. AURORA also conducts postsynthesis treatments and dynamic analyses under stress, setting it apart from traditional methods. These features make AURORA a transformative tool for the discovery of novel materials, with potential machine learning integration for optimization. Our results highlight the application of AURORA as a robust and adaptable platform for future developments in automated materials research. Modular design ; Process (computing) ; Renewable energy ; Efficient energy use ; Reliability (semiconductor) ; Key (lock) ; Robot Machine Learning in Materials Science ; Perovskite Materials and Applications ; TiO2 Photocatalysis and Solar Cells
Authors: Yu Shu (416227) | Naihua Miao (2627770) | Siyu Han (4614106) | Jian Zhou (28020) | Stephen R. Elliott (1571032) | Zhimei Sun (1665508)
Source: Figshare (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Ultralow Contact Resistancein Two-Dimensional SemiconductorTransistors Approaching the Quantum Limit The miniaturization of silicon-based integrated circuits is approaching its limits owing to strong quantum confinement and interfacial effects. Atomically thin, two-dimensional (2D) materials show great potential to break such a limit, but the realization of an ultralow contact resistance (<i>R</i><sub>C</sub>) remains a key challenge because of natural van der Waals gaps in 2D semiconductors and weak interlayer coupling at the metal–semiconductor junction (MSJ) interface. Herein, we develop efficient strategies to tune <i>R</i><sub>C</sub> and the Schottky barrier height (SBH) by combining high-throughput first-principles calculations and machine-learning techniques. Notably, we reveal that hydrogen-bonding interactions at the MSJ interface containing −OH functional groups significantly enhance metal/2D semiconductor coupling, leading to effective tunneling-barrier reduction and charge redistribution. Such interactions enable the formation of Ohmic contacts, yielding ultralow values of <i>R</i><sub>C</sub> approaching the quantum limit. Through symbolic regression, we establish robust physical models by linking <i>R</i><sub>C</sub> with critical properties, including SBH and the tunneling-specific resistivity. These findings provide critical insights and efficient approaches for designing next-generation transistor architectures with superior electrical-contact performance. Ohmic contact ; Semiconductor ; van der Waals force ; Schottky barrier ; Miniaturization ; Transistor ; Contact resistance ; Quantum ; Schottky diode 2D Materials and Applications ; Graphene research and applications ; Surface and Thin Film Phenomena
Authors: Ziyang Yu | Wenbing Huang | Yang Liu
Source: ArXiv.org (preprint, 2025-05-20)
Field: Materials Science — Materials Chemistry

UniSim: A Unified Simulator for Time-Coarsened Dynamics of Biomolecules Molecular Dynamics (MD) simulations are essential for understanding the atomic-level behavior of molecular systems, giving insights into their transitions and interactions. However, classical MD techniques are limited by the trade-off between accuracy and efficiency, while recent deep learning-based improvements have mostly focused on single-domain molecules, lacking transferability to unfamiliar molecular systems. Therefore, we propose \textbf{Uni}fied \textbf{Sim}ulator (UniSim), which leverages cross-domain knowledge to enhance the understanding of atomic interactions. First, we employ a multi-head pretraining approach to learn a unified atomic representation model from a large and diverse set of molecular data. Then, based on the stochastic interpolant framework, we learn the state transition patterns over long timesteps from MD trajectories, and introduce a force guidance module for rapidly adapting to different chemical environments. Our experiments demonstrate that UniSim achieves highly competitive performance across small molecules, peptides, and proteins. Molecular dynamics ; Set (abstract data type) ; Transferability ; Representation (politics) ; Dynamics (music) ; GRASP Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Mingyu Yuan | Zhihui Ni | Hui Ma | Zhixin Ren | Bo Zhao | Ke Gong
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Discovery of 2D Hypothetical Covalent Organic Frameworks for Methane Purification via Genetic Algorithm and Deep Learning Scalability ; Genetic algorithm ; Deep learning ; Encoding (memory) ; Deliverable ; Monte Carlo method ; Fraction (chemistry) Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Surface Chemistry and Catalysis
Authors: Wei, Ting-Ju | Su, Tung-Huan | Chen, Chuin-Shan
Source: arXiv (Cornell University) (article, 2025-12-07)
Field: Materials Science — Materials Chemistry

A Texture-Generalizable Deep Material Network via Orientation-Aware Interaction Learning for Polycrystal Modeling and Texture Evolution Machine learning surrogate models have emerged as a promising approach for accelerating multiscale materials simulations while preserving predictive fidelity. Among them, the Orientation-aware Interaction-based Deep Material Network (ODMN) provides a hierarchical homogenization framework in which material nodes encode crystallographic texture and interaction nodes enforce stress equilibrium under the Hill--Mandel condition. Trained solely on linear-elastic stiffness data, ODMN captures intrinsic microstructure--mechanics relationships, enabling accurate prediction of nonlinear mechanical responses and texture evolution. However, its applicability remains fundamentally limited by the absence of a parametric mapping from arbitrary microstructures to the ODMN parameter space. This limitation necessitates retraining for each new microstructure. To address this challenge, we reformulate ODMN generalization as a microstructure-to-parameter inference problem and propose the TACS--GNN--ODMN framework. The proposed framework combines a Texture-Adaptive Clustering and Sampling (TACS) scheme for texture representation with a Graph Neural Network (GNN) for inferring micromechanical equilibrium parameters. This strategy enables the construction of fully parameterized ODMNs for previously unseen microstructures without retraining. Numerical results demonstrate that the proposed framework accurately predicts nonlinear mechanical responses and texture evolution across diverse texture distributions. The predicted responses show close agreement with direct numerical simulations (DNS), highlighting the framework as a generalizable and physically interpretable surrogate model for microstructure-informed multiscale materials simulations. Initialization ; Nonlinear system ; Artificial neural network ; Texture (cosmology) ; Set (abstract data type) ; Generalization ; Cluster analysis ; Network model Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Advanced Graph Neural Networks
Authors: Hanyang Xu | Dongheng Qian | Jing Wang
Source: Chinese Physics Letters (article, 2025-05-20)
Field: Materials Science — Materials Chemistry

Predicting Many Crystal Properties via an Adaptive Transformer-Based Framework Abstract Machine learning has revolutionized many fields, including materials science. However, predicting the properties of crystalline materials using machine learning faces challenges in input encoding, output versatility, and interpretability. We introduce CrystalBERT, an adaptable transformer-based framework integrating space group, elemental, and unit cell information. This novel structure can seamlessly combine diverse features and accurately predict various physical properties, including topological properties, superconducting transition temperatures, dielectric constants, and more. CrystalBERT provides insightful interpretations of features influencing target properties. Our results indicate that space group and elemental information are crucial for predicting topological and superconducting properties, underscoring their intricate nature. By incorporating these features, we achieve 91% accuracy in topological classification, surpassing prior studies and identifying previously misclassified materials. This research demonstrates that integrating diverse material information enhances the prediction of complex material properties, paving the way for more accurate and interpretable machine learning models in materials science. Transformer ; Computer science ; Materials science ; Crystal (programming language) ; Electrical engineering ; Programming language ; Voltage Machine Learning in Materials Science