jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zicheng Wang | Zhenqing Li | Chaoyu He
Source: physica status solidi (RRL) - Rapid Research Letters (article, 2025-06-24)
Field: Materials Science — Materials Chemistry

Size Effects on the Electronic and Mechanical Properties of Diamond‐XII: A Machine Learning Potential Study Ultrahard carbon materials offer great potential for energy storage and advanced devices. Diamond‐XII, a cubic carbon allotrope with twelvefold diamond segments, stands out for its stability and hardness. This study refines a deep potential model for Diamond‐XII using potential fine‐tuning and first‐principles calculations. Structures with n = 1–11 are optimized, showing a turning point in energy and density at n = 9. All exhibit Vickers hardness above 50 GPa, with decreasing bandgap from n = 1 to 9. The results highlight the power of deep learning potentials in designing ultrahard carbon materials. Diamond ; Materials science ; Engineering physics ; Engineering ; Composite material Diamond and Carbon-based Materials Research ; Force Microscopy Techniques and Applications ; Advanced materials and composites
Authors: Ivan Vrban | Nenad Bolf | Josip Sacher
Source: Processes (article, 2025-06-12)
Field: Materials Science — Materials Chemistry

Data-Driven Prediction of Crystal Size Metrics Using LSTM Networks and In Situ Microscopy in Seeded Cooling Crystallization This work presents a data-driven modeling framework for predicting image-derived crystal size metrics in seeded cooling crystallization using Long Short-Term Memory (LSTM) neural networks. The model leverages in situ microscopy data to predict square-weighted D10, D50, D90, and particle counts based solely on seed loading and temperature profiles, without requiring real-time supersaturation measurements. To enhance predictive power, engineered process descriptors—including temperature derivatives and integrals—were incorporated as dynamic features. Experimental validation was performed using creatine monohydrate crystallization from aqueous solution, with LSTM models trained on a diverse dataset encompassing variable seed loadings and cooling profiles. The feature-engineered LSTM model consistently outperformed its non-engineered counterpart, particularly under nonlinear cooling conditions where crystallization dynamics were the most complex. This approach offers a practical alternative to mechanistic models and spectroscopic process analytical technology (PAT) tools by enabling accurate prediction of chord length distribution (CLD) metrics from routinely collected data. The framework is easily transferable to other crystallization systems and provides a low-complexity, high-accuracy tool for accelerating lab-scale crystallization development. Crystallization ; In situ ; Microscopy ; Seeding ; Materials science ; Crystal (programming language) ; Crystallography ; Computer science ; Optics ; Chemical engineering ; Chemistry ; Physics ; Thermodynamics ; Engineering ; Meteorology Crystallization and Solubility Studies ; Phase Equilibria and Thermodynamics ; Metallurgical Processes and Thermodynamics
Authors: Suisui Wang | Tianyong Zhang | Han Zhang | Wenxuan Zhu | Zhenghui Gu | Xufeng Huang | Hande Zhang | Bin Li | Jianhua Zhang
Source: Smart Molecules (article, 2025-10-30)
Field: Materials Science — Polymers and Plastics

Multifunctional polyimide performance prediction based on explainable machine learning Polyimides (PIs) are widely used in the microelectronics field due to their excellent comprehensive performance and the diversity and designability of their structures. In flexible substrate applications, designing the molecular structure to balance thermodynamic and optical properties is the most critical part of the PI design process. To accelerate the discovery of high-performance PIs, we established predictive models for glass transition temperature (T<sub>g</sub>), cut-off wavelength (CW), and coefficient of thermal expansion (CTE) using various machine learning algorithms. The optimal predictive models for the three properties demonstrated high accuracy and stability in both test set predictions and cross-validation results. Additionally, the interpretability of the three optimal models was analyzed using the SHAP method, and the accuracy and generalization ability of the models were validated using several novel PIs. By combining the three models, predictions were made for multiple PIs, leading to the selection and synthesis of PIs with excellent comprehensive performance. 135 novel PIs were designed and their key properties were obtained without the need for experimental verification. The predictive models established in this study can assist researchers in quickly determining the T<sub>g</sub>, CW and CTE of PIs, thereby facilitating the swift identification of promising candidates for further development. Interpretability ; Stability (learning theory) ; Field (mathematics) ; Performance prediction ; Predictive modelling ; Test set ; Generalization ; Set (abstract data type) Synthesis and properties of polymers ; Organic Electronics and Photovoltaics ; Epoxy Resin Curing Processes
Authors: Fulong Wang | Wei Liu | Bowen Hou | Yipu Sun | Bo Zhang | Hai Li | Longjun Chen | Jinhui Cui
Source: Anti-Corrosion Methods and Materials (article, 2025-12-13)
Field: Materials Science — Materials Chemistry

An explanatory study of factors influencing a corrosion prediction model for low-alloy steel in tropical marine atmospheric environments Purpose This study aims to address the urgent need for accurate prediction of the long-term corrosion performance of low-alloy steel in tropical marine atmospheric environments and to quantify the effects of environmental factors and steel composition on corrosion. Design/methodology/approach This study compared the predictive accuracy of four machine learning algorithms: Support Vector Regression, Multi-Layer Perceptron, Random Forest and Extreme Gradient Boosting (XGBoost). Subsequently, the best-performing XGBoost model was interpreted using SHapley Additive exPlanations (SHAP) and Accumulated Local Effects (ALE) to quantitatively assess the influence of features on corrosion. Findings The XGBoost model demonstrated the best predictive accuracy on the independent dataset and showed good generalization ability (R² = 0.841, MAE = 5.37 µm/a). SHAP and ALE quantified the influence of features and revealed the nonlinear threshold effects of features on corrosion. Originality/value This study provides insights into the long-term prediction of the corrosion performance of low-alloy steel and the formulation of targeted, condition-responsive corrosion prevention strategies. Corrosion ; Support vector machine ; Random forest ; Predictive modelling ; Generalization ; Nonlinear system Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Tom Pan | Evan Dramko | Mitchell D. Miller | Anastasios Kyrillidis | G.N. Phillips
Source: ArXiv.org (preprint, 2025-11-13)
Field: Materials Science — Materials Chemistry

Completion of partial structures using Patterson maps with the CrysFormer machine learning model Protein structure determination has long been one of the primary challenges of structural biology, to which deep machine learning (ML)-based approaches have increasingly been applied. However, these ML models generally do not incorporate the experimental measurements directly, such as X-ray crystallographic diffraction data. To this end, we explore an approach that more tightly couples these traditional crystallographic and recent ML-based methods, by training a hybrid 3-d vision transformer and convolutional network on inputs from both domains. We make use of two distinct input constructs / Patterson maps, which are directly obtainable from crystallographic data, and ``partial structure'' template maps derived from predicted structures deposited in the AlphaFold Protein Structure Database with subsequently omitted residues. With these, we predict electron density maps that are then post-processed into atomic models through standard crystallographic refinement processes. Introducing an initial dataset of small protein fragments taken from Protein Data Bank entries and placing them in hypothetical crystal settings, we demonstrate that our method is effective at both improving the phases of the crystallographic structure factors and completing the regions missing from partial structure templates, as well as improving the agreement of the electron density maps with the ground truth atomic structures. Ground truth ; Crystal structure ; Atomic model ; Electron density ; Protein Data Bank ; Diffraction ; Deep learning Enzyme Structure and Function ; Protein Structure and Dynamics ; Machine Learning in Materials Science
Authors: Xiaoliang Shi | Xiao Liu
Source: Research Review (article, 2025-02-20)
Field: Materials Science — Materials Chemistry

Research on Slight Decarburization Grinding Hardening based on the Gas-protecting Coupling Method This study advances the field of abrasive machining with a novel gas-protecting coupling method designed to control decarburization during the grinding hardening process.Traditional grinding hardening, while effective for imparting surface hardness, can cause decarburization, leading to a loss of material properties such as toughness and fatigue strength.To mitigate this, a method is proposed that employs a nitrogen gas shield to protect the workpiece surface from reactive atmospheric gases.An experimental platform is developed allowing for the application of nitrogen gas during grinding.The results from 45 steel workpiece demonstrated that the gas-protected grinding hardening process preserves carbon content at the surface.Computational fluid dynamics simulations were conducted to analyze the airflow-field interaction, providing insights that led to the optimization of the gas injection strategy.Additionally, a deep neural network model was trained to predict carbon content variations, supporting the precision control of the decarburization process.Collectively, these findings offer a significant contribution towards greener and more efficient manufacturing processes for carbon steel components. Decarburization ; Grinding ; Metallurgy ; Hardening (computing) ; Materials science ; Composite material Metal Alloys Wear and Properties ; Advanced Surface Polishing Techniques ; Advanced machining processes and optimization
Authors: Jingjing Wang | Yanpeng Zhao | Zhijiang Yang | Yao Ge | Penggang Han | Jiajia Liu | Chang Chen | Peng Zan | Xiukun Wan | Xiaochen Bo | Hui Jiang
Source: Research Square (preprint, 2025-03-19)
Field: Materials Science — Materials Chemistry

IECata: Interpretable bilinear attention network and evidential deep learning improve the catalytic efficiency prediction of enzymes Bilinear interpolation ; Artificial intelligence ; Computer science ; Machine learning Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Computational Drug Discovery Methods
Authors: Si‐Ping Gao | Haofan Zhang | Zhiqing Yang | Rongxue Wu | Min Chen | Dandan Zhai | Yang Yu | Yao Qin | Huawei Tao | Peng Li
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

A Dual-Branch Cnn-Encoder Model for Rapid Geographic Traceability of Astragalus Using Near-Infrared Spectroscopy Traceability ; Spectroscopy ; Encoder ; Dual (grammatical number) ; Infrared ; Near-infrared spectroscopy ; Computer science ; Infrared spectroscopy ; Artificial intelligence ; Remote sensing ; Chemistry ; Analytical Chemistry (journal) ; Physics ; Geography ; Optics ; Chromatography ; Linguistics ; Software engineering ; Organic chemistry ; Philosophy ; Operating system Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Ruihao Tan | Kaiwang Zhang | Yue-Wen Fang
Source: Journal of Materials Informatics (article, 2025-12-26)
Field: Materials Science — Materials Chemistry

Ultralow thermal conductivity via weak interactions in PbSe/PbTe monolayer heterostructure for thermoelectric design In this study, we systematically investigate the thermal and electronic transport properties of a two-dimensional (2D) PbSe/PbTe monolayer heterostructure by combining first-principles calculations, Boltzmann transport theory, and machine learning methods. The heterostructure exhibits a unique honeycomb-like corrugated and asymmetric configuration, which significantly enhances phonon scattering. Moreover, the relatively weak interatomic interactions in PbSe/PbTe lead to the formation of antibonding states, resulting in strong anharmonicity and ultimately yielding ultralow lattice thermal conductivity $$( {\kappa_{{\rm{L}}}} )$$ . In the four-phonon scattering model, the $$ {\kappa_{{\rm{L}}}} $$ values along the x and y directions are as low as 0.37 and 0.31 W · m−1 · K−1, respectively. Contrary to the conventional view that long mean free path acoustic phonons dominate heat transport, we find that optical phonons contribute approximately 59 % of the $$ {\kappa_{{\rm{L}}}} $$ in this heterostructure due to their larger group velocities than the acoustic phonons. Further analysis of thermoelectric performance shows that at a high temperature of 800 K, the heterostructure achieves an exceptional dimensionless figure of merit (ZT ) of 5.3 along the y direction, indicating outstanding thermoelectric conversion efficiency. These findings not only provide theoretical insights into the transport mechanisms of PbSe/PbTe monolayer heterostructure but also offer a practical design strategy for developing high-performance 2D layered thermoelectric materials. Heterojunction ; Thermoelectric effect ; Phonon ; Thermal conductivity ; Monolayer ; Anharmonicity ; Boltzmann equation ; Scattering Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Topological Materials and Phenomena
Authors: Rong Chang | Yu‐Cheng Lin | Po-Chao Hsu | Tsung‐Wei Huang | En-Jui Kuo
Source: ArXiv.org (preprint, 2025-05-16)
Field: Materials Science — Materials Chemistry

Accelerating Parameter Initialization in Quantum Chemical Simulations via LSTM-FC-VQE We present a meta-learning framework that leverages Long Short-Term Memory (LSTM) neural networks to accelerate parameter initialization in quantum chemical simulations using the Variational Quantum Eigensolver (VQE). By training the LSTM on optimized parameters from small molecules, the model learns to predict high-quality initializations for larger systems, reducing the number of required VQE iterations. Our enhanced LSTM-FC-VQE architecture introduces a fully connected layer, improving adaptability across molecules with varying parameter sizes. Experimental results show that our approach achieves faster convergence and lower energy errors than traditional initialization, demonstrating its practical potential for efficient quantum simulations in the NISQ era. Initialization ; Convergence (economics) ; Quantum ; Quantum chemical ; Artificial neural network ; Energy (signal processing) ; Quantum computer ; Adaptability Machine Learning in Materials Science ; Quantum Computing Algorithms and Architecture ; Quantum many-body systems
Authors: Chethana K | Baishali Garai
Source:  (article, 2025-12-10)
Field: Materials Science — Materials Chemistry

Role of AI in Solar Cell Materials Research: A Review One of the sustainability goals provided by the United Nations is "Affordable and clean energy", which emphasizes ensuring access to reliable, sustainable and modern energy for all. Among various renewable energy sources, solar energy holds significant promise as a result of its abundance and scalability. However, widespread adoption of photovoltaic (PV) technologies remains a challenge because of material efficiency, cost, and long-term stability. In this context, materials innovation plays a crucial role in enhancing solar energy conversion and reducing overall system costs. This review explores the emerging role of Artificial Intelligence (AI) in accelerating research and development of advanced materials for solar cells. The review covers various AI technologies like machine learning (ML), predictive modeling, and data mining which are used for faster and more accurate material screening and performance prediction in the context of solar cells and highlights the limitations associated with AI usage for material research. This paper highlights the intersection of AI and materials science as a pivotal approach in the transition toward clean energy solutions. Renewable energy ; Sustainability ; Photovoltaic system ; Context (archaeology) ; Solar energy ; Clean energy ; Efficient energy use ; Applications of artificial intelligence Machine Learning in Materials Science ; Nanowire Synthesis and Applications ; Silicon and Solar Cell Technologies
Authors: Varun Rishi (10493034) | Ali Abou Taka (11643393) | Hrant P. Hratchian (1665103) | Laura M. McCaslin (8725662)
Source: Figshare (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

QuantifyingDesign Principles for Light-Emitting Materialswith Inverted Singlet–Triplet Energy Gaps Molecular engineering of organic emitter molecules with inverted singlet–triplet energy gaps (INVEST) has emerged as a powerful approach for enhancing fluorescence efficiency through triplet harvesting. In these unique materials, the first excited singlet state (S<sub>1</sub>) lies below the lowest triplet state (T<sub>1</sub>), enabling efficient reverse intersystem crossing. Previous computational studies have focused on accurately calculating the inverted energy gap and establishing qualitative structure–property relationships. Here, we present quantitative relationships that link the molecular structure to the S<sub>1</sub>–T<sub>1</sub> energy gap, Δ<i>E</i><sub>ST</sub>, by introducing a benchmark set of 15 heptazine-based INVEST molecules (HEPTA-INVEST15). We identify a strong linear correlation (<i>R</i><sup>2</sup> > 0.94) between Δ<i>E</i><sub>ST</sub> and both the degree of intramolecular charge transfer and the deviation from a single-excitation character, as quantified by %<i>R</i><sub>1</sub> values and transition density matrix norms. These trends persist across our expanded set of 44 mono-, di-, and tri-substituted heptazines (HEPTA-INVEST44), underscoring the generality of our findings. Notably, strongly electron-donating groups, such as −NH<sub>2</sub>, minimize the magnitude of inverted gaps in mono-substituted heptazines yet produce the most negative Δ<i>E</i><sub>ST</sub> in certain tri-substituted derivatives, a result arising from competing resonance effects and excited-state aromaticity. Although Δ<i>E</i><sub>ST</sub> shows no clear correlation with Hammett parameters, our results reveal that physically meaningful, computable descriptors offer a mechanistic foundation for the future data-driven design of INVEST emitters. These findings pave the way for machine-learning approaches that connect the molecular structure to Δ<i>E</i><sub>ST</sub> without requiring high-level excited-state calculations. Intersystem crossing ; Benchmark (surveying) ; Singlet state ; Excited state ; Intramolecular force ; Singlet fission ; Set (abstract data type) ; Energy (signal processing) Luminescence and Fluorescent Materials ; Organic Light-Emitting Diodes Research ; Synthesis and Properties of Aromatic Compounds
Authors: Yūsuke Nanba | Michihisa Koyama
Source: Materials Today Communications (article, 2025-09-23)
Field: Materials Science — Materials Chemistry

Scalable density of states prediction in nanoalloys via machine learning Scalability ; Density functional theory ; Gaussian ; Gaussian process ; Rendering (computer graphics) ; Position (finance) ; Nanoparticle ; Key (lock) Machine Learning in Materials Science ; Nanowire Synthesis and Applications ; 2D Materials and Applications
Authors: Zizhuo Wang
Source:  (article, 2025-06-13)
Field: Chemistry — Organic Chemistry

Predicting and Editing Perceptual Glossiness of Translucent Materials in a Single Image The material editing of a single image leverages generative neural networks to modify pixel-by-pixel at the image level, allowing for the alteration of material properties in the target object. Current methods for perceptual glossiness editing of a single image are limited to opaque objects. This paper introduces a novel perceptual glossiness prediction and editing approach for translucent objects using generative adversarial networks. Specifically, we utilize StyleGAN and the pixel2style2pixel encoder to extract the latent code from material images. Building upon this latent code, we propose a method to predict and edit the perceptual glossiness of translucent materials. To establish the relationship between latent code and perceptual glossiness, we incorporate an autoencoder based on a multi-layer perceptron (MLP). Additionally, we create a dataset of translucent materials with perceptual glossiness labels for training. The StyleGAN, pixel2style2pixel series networks, and MLP are trained in two stages. Our experimental results, tested across different sample sets, demonstrate that the proposed network architecture effectively extracts perceptual information from material images, enabling accurate prediction and editing of the perceptual glossiness in single translucent material images. Autoencoder ; Encoder ; Perception ; Code (set theory) ; Artificial neural network ; Image (mathematics) ; Encoding (memory) ; Image editing Photopolymerization techniques and applications ; Additive Manufacturing and 3D Printing Technologies
Authors: Cristina F. Bazzano | Luiz F. G. Alves | Guilherme P. Telles | Daniela Barretto Barbosa Trivella
Source: Scientific Data (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Labeled dataset of X-ray protein ligand images in 3D point cloud and validated deep learning models LigPCDS (Ligand Point Cloud Data Set) is the first dataset of chemically labeled 3D point clouds of protein ligands. 3D images and structures of ligands were derived from X-ray protein crystallography experimental datasets deposited at the Protein Data Bank. The 3D point cloud format allowed for a computer-comprehensive representation of the ligand's experimental data, enabling the interpretation of the ligand's chemical structure using a building block-like labeling approach. For constructing LigPCDS, the images of the ligands were interpolated from their difference electron density map into a 3D grid-like structure, filtered around their atomic spheres, and stored in point clouds. The density value was used as a single feature. Chemical vocabularies, based on atoms and their cyclic structural arrangements, were designed and used to pointwise label these 3D representations of the ligands. The proposed imaging and labeling approaches were validated by training semantic segmentation deep learning models on a stratified dataset from LigPCDS, which could recover the protein ligand's chemical structure with good performance. LigPCDS can be used to achieve solutions for building known and yet unknown protein ligands (small organic molecules) from experimental X-ray protein crystallography, in silico ligand screening, drug design, and to understand protein function in basic biology. Point cloud ; Pointwise ; Deep learning ; Protein ligand ; Pattern recognition (psychology) ; Segmentation ; Representation (politics) ; Protein structure Enzyme Structure and Function ; Machine Learning in Materials Science ; Protein Structure and Dynamics