jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ziyu Zhang (3505355) | Xinzheng Yang (1392196) | Liang Yan (494300) | Sungwoo Jung (7460129) | Wei You (474340) | Ting Cao (633597) | Xiaosong Li (135207)
Source: Figshare (article, 2025-09-26)
Field: Materials Science — Polymers and Plastics

Fusion Deep Learningfor Predicting Conductivity inElectron-Doped Organic Polymers The development of efficient and air-stable n-type organic semiconductors suitable for molecular n-doping is critical for advancing high-performance, durable organic electronic devices, including transistors, thermoelectrics, and photovoltaics. Machine learning offers a powerful approach to uncovering hidden relationships between molecular structures and their electronic properties, thereby accelerating the discovery and design of promising materials. To support this effort, we constructed a curated database comprising 84 n-type conductive polymers, each characterized by experimental measurements under n-doping conditions with 4-(1,3-dimethyl-2,3-dihydro-1<i>H</i>-benzoimidazol-2-yl)phenyl dimethylamine (N-DMBI-H), and augmented with density functional theory calculations to provide complementary molecular descriptors. After constructing the database, we developed a fusion deep learning model that integrates convolutional neural networks with fully connected artificial neural networks to capture both structural and property-based features of the polymers. The model was trained on the data set and evaluated using leave-one-out cross-validation. The model was further applied to a test set of n-type polymers bearing oligoethylene glycol (OEG) side chains, enabling the identification of key physical factors that influence their conductivity when doped with N-DMBI-H. Finally, a double-blind experiment was conducted to validate the model’s practical utility by predicting the conductivity of four BDPPV-type polymers and one N2200-type polymer doped with N-DMBI-H. For the N2200-type polymer and two of the BDPPV-type polymers, the predicted conductivities agreed with experimental values within the same order of magnitude. These results demonstrate the fusion model’s reliability and establish a strong foundation for data-driven property prediction and the design of high-conductivity n-type polymers. Deep learning ; Convolutional neural network ; Artificial neural network ; Reliability (semiconductor) ; Polymer ; Conductive polymer ; Organic semiconductor ; Conductivity ; Set (abstract data type) Conducting polymers and applications ; Machine Learning in Materials Science ; Organic Electronics and Photovoltaics
Authors: Jinwen Zeng | Minjie Qin | M.M. Jiang | Shaochi Wang | Xinyue Xie | Lijia Li | Ren Wei | Penghui Shao | Xiao Xiao | Xubiao Luo
Source: Environmental Functional Materials (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Tailor-made adsorbent materials on machine learning instructions for efficient purification of heavy metal contaminants in wastewater Heavy metal pollution is becoming an increasingly serious environmental and public health concern. As a result, developing efficient, cost-effective, and sustainable wastewater treatment technologies has become a pressing challenge. Adsorption technology has emerged as a promising solution due to its flexibility, simplicity, and economic advantages. However, conventional adsorbent design methods rely heavily on empirical approaches and trial-and-error experimentation, leading to inefficiencies and limitations. In recent years, machine learning (ML) has emerged as a powerful tool for accelerating material discovery and optimizing adsorption processes. This review provides a comprehensive analysis of ML applications in adsorbent material design, focusing on key algorithms, data-driven methodologies, and their role in improving adsorption performance. It explores innovative ML applications in developing high-performance adsorbents, emphasizing relevant data sources, feature engineering techniques, and preprocessing strategies critical for model accuracy. Additionally, we discuss the major challenges and limitations of integrating ML into adsorption studies, including data scarcity, model interpretability, and computational costs. Finally, we outline future research directions to advance the tailored design of adsorbent materials and foster the sustainable development of wastewater treatment technologies. • ML enables efficient adsorbent design for heavy metal removal from wastewater. • Feature engineering and preprocessing improve ML accuracy in adsorption studies. • Hybrid experimental-simulation datasets address data scarcity in material discovery. • ML-driven optimization enhances cost-efficiency in adsorption-based water treatment. • Interpretable ML models and open data promote sustainable wastewater solutions. Adsorption ; Wastewater ; Data pre-processing ; Scarcity ; Sewage treatment ; Sustainable development Machine Learning in Materials Science ; Adsorption and biosorption for pollutant removal ; Hydrological Forecasting Using AI
Authors: Kazutaka Nishiguchi | Ryota Yamamoto | Meguru Yamazaki | Naoki Matsumura | Yuta Yoshimoto | Seiichiro Ten‐no | Yasufumi Sakai
Source: ArXiv.org (preprint, 2025-06-11)
Field: Materials Science — Materials Chemistry

Molecular Dynamics Simulations of SrTiO$_3$ with Oxygen Vacancies using Neural Network Potentials A precise analysis of point defects in solids requires accurate molecular dynamics (MD) simulations of large-scale systems. However, ab initio MD simulations based on density functional theory (DFT) incur high computational cost, while classical MD simulations lack accuracy. We perform MD simulations using a neural network potential (NNP) model (NNP-MD) to predict the physical quantities of both pristine SrTiO$_3$ and SrTiO$_3$ in the presence of oxygen vacancies (V$_{\text{O}}$). To verify the accuracy of the NNP models trained on different data sets, their NNP-MD predictions are compared with the results obtained from DFT calculations. The predictions of the total energy show good agreement with the DFT results for all these NNP models, and the NNP models can also predict the formation energy once SrTiO$_3$:V$_{\text{O}}$ data are included in the training data sets. Even for larger supercell sizes that are difficult to calculate using first-principles calculations, the formation energies evaluated from the NNP-MD simulations well reproduce the extrapolated DFT values. This study offer important knowledge for constructing accurate NNP models to describe point-defect systems including SrTiO$_3$:V$_{\text{O}}$. Molecular dynamics ; Density functional theory ; Supercell ; Artificial neural network ; Ab initio ; Energy (signal processing) ; Potential energy Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Electron Microscopy Techniques and Applications
Authors: Yongxian Wu (16948698) | Qiang Zhu (188733) | Ray Luo (141237)
Source: Figshare (article, 2025-10-06)
Field: Materials Science — Materials Chemistry

End-to-End Modelingof Reaction Field Energy UsingData-Driven Geometric Graph Neural Networks Electrostatic interactions are fundamental to the structure, dynamics, and function of biomolecules, with broad applications in protein–ligand binding, enzymatic catalysis, and nucleic acid regulation. The Poisson–Boltzmann (PB) equation provides a physically grounded framework for modeling these interactions. However, solving the PB equation for large and complex biomolecular systems remains computationally expensive as traditional numerical solvers scale poorly with system size. While the Generalized Born (GB) model offers a more computationally efficient approximation, it does so at the cost of reduced accuracy relative to full PB solutions. To overcome these limitations, we propose PBGNN, a novel end-to-end framework that uses data-driven geometric graph neural networks to directly approximate PB electrostatic energies without relying on the GB approximation. PBGNN incorporates sinusoidal embeddings of atomic charges and a message-passing architecture to efficiently capture long-range interactions in large biomolecules. To address training instability caused by high variance in atomic electrostatic potentials, we introduce a charge-weighted mean squared error (CMSE) optimization objective that improves convergence. We benchmark PBGNN on the AMBER PBSA suite and PBSMALL, a new dataset designed for rapid evaluation of small-molecule electrostatics in drug discovery contexts. The results demonstrate that PBGNN consistently achieves high accuracy in predicting the PB energy with linear computational complexity. Furthermore, it provides reliable and precise PB free energy predictions for both large biomolecular complexes and small-molecule datasets, showcasing its strong generalizability, scalability, and potential utility in drug discovery tasks requiring accurate electrostatic modeling of small molecules. Comprehensive ablation studies further reveal the impact of architectural components, such as geometric representation, objective design, and cutoff strategy, informing future research directions. Finally, we release PBGNN as an open-source, self-contained codebase, along with preprocessed datasets and a complete training and evaluation pipeline, to support scalable and accurate electrostatic analysis that facilitates future research in associated areas. Electrostatics ; Benchmark (surveying) ; Suite ; Artificial neural network ; Graph ; Field (mathematics) ; Energy (signal processing) ; Energy minimization ; Function (biology) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Mariano Forti | Alesya Malakhova | Yury Lysogorskiy | Wenhao Zhang | Jean‐Claude Crivello | Jean‐Marc Joubert | Ralf Drautz | Thomas Hammerschmidt
Source: ArXiv.org (preprint, 2025-07-25)
Field: Materials Science — Materials Chemistry

Data-efficient machine-learning of complex Fe-Mo intermetallics using domain knowledge of chemistry and crystallography Atomistic simulations of multi-component systems require accurate descriptions of interatomic interactions to resolve details in the energy of competing phases. A particularly challenging case are topologically close-packed (TCP) phases with close energetic competition of numerous different site occupations even in binary systems like Fe-Mo. In this work, machine learning (ML) models are presented that overcome this challenge by using features with domain knowledge of chemistry and crystallography. The resulting data-efficient ML models need only a small set of training data of simple TCP phases $A$15, $σ$, $χ$, $μ$, $C$14, $C$15, $C$36 with 2-5 WS to reach robust and accurate predictions for the complex TCP phases $R$, $M$, $P$, $δ$ with 11-14 WS. Several ML models with kernel-ridge regression, multi-layer perceptrons, and random forests, are trained on less than 300 DFT calculations for the simple TCP phases in Fe-Mo. The performance of these ML models is shown to improve systematically with increased utilization of domain knowledge. The convex hulls of the $R$, $M$, $P$ and $δ$ phase in the Fe-Mo system are predicted with uncertainties of 20-25 meV/atom and show very good agreement with DFT verification. Complementary X-ray diffraction experiments and Rietveld analysis are carried out for an Fe-Mo R-phase sample. The measured WS occupancy is in excellent agreement with the predictions of our ML model using the Bragg Williams approximation at the same temperature. Domain (mathematical analysis) ; Interatomic potential ; Intermetallic ; Simple (philosophy) ; Set (abstract data type) ; Phase (matter) ; Binary number ; Diffraction Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Omoriwhovo Jude Oghenekome | Asare Godfred Kesse
Source: Physical Science International Journal (article, 2025-03-24)
Field: Materials Science — Materials Chemistry

Integrating the Asymptotic Iteration Method with Machine Learning for Predicting Vibrational Energy Levels of Diatomic Molecules Aim: This study integrates the Asymptotic Iteration Method (AIM) with Machine Learning (ML) models to enhance the prediction of vibrational energy levels in diatomic molecules. Traditional quantum mechanical methods, while accurate, are computationally demanding. This study aims to determine whether ML models can approximate these calculations efficiently while maintaining high accuracy. Methodology: The vibrational energy levels of Li₂, CN, and CO molecules were computed using AIM within the Morse potential framework. Three ML models—Random Forest (RF), Gradient Boosting (GB), and Support Vector Regression (SVR)—were trained using AIM-derived datasets. The models were evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R² score to assess their predictive performance. Results: SVR demonstrated the highest predictive accuracy, achieving a R² score of 0.999650, the lowest MAE and RMSE values of 0.124391 and 0.158412 respectively, outperforming RF and GB. The results indicate that ML models, particularly SVR, can effectively approximate AIM calculations with minimal error. Furthermore, 3D potential energy surface visualizations confirmed the strong agreement between ML and AIM predictions, validating the reliability of ML-based approaches. Conclusions: This study demonstrates that ML can serve as an efficient and scalable alternative to traditional quantum mechanical methods for predicting vibrational energy levels. The findings have implications for computational chemistry, spectroscopy, and materials science by reducing reliance on computationally intensive calculations. However, the study is limited by data generalization, as accuracy depends on the diversity of the training dataset. Future work should focus on expanding datasets, integrating deep learning techniques, and exploring hybrid AIM-ML models to improve generalizability and predictive robustness. Mean squared error ; Diatomic molecule ; Support vector machine ; Random forest ; Gradient boosting ; Computer science ; Mean absolute percentage error ; Artificial intelligence ; Quantum ; Machine learning ; Algorithm ; Mathematics ; Artificial neural network ; Physics ; Statistics ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Text Analysis Techniques
Authors: Martín Otero-Lema | Raúl Lois-Cuns | Ángel Míguez-Roel | Trinidad Méndez‐Morales | Luis M. Varela | Hadrián Montes‐Campos
Source: ChemRxiv (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

Neural Network-based Simulations of Solid Electrolyte Interfaces Electrolyte ; Solvation ; Molecular dynamics ; Electrochemistry ; Diffusion ; Chemical reaction ; Molecule ; Component (thermodynamics) ; Interface (matter) Machine Learning in Materials Science ; Electrostatics and Colloid Interactions ; Spectroscopy and Quantum Chemical Studies
Authors: Haris Suhendar
Source: Journal of Physics Conference Series (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Comparative Machine Learning Approaches for Band Gap Prediction in Perovskite Materials Using an ABX <sub>3</sub> Dataset Abstract Accurate prediction of electronic properties such as band gaps is crucial for accelerating the discovery of functional perovskite materials. In this study, we employed a dataset of 45,570 perovskite compounds, with structural and electronic properties derived from density functional theory (DFT) calculations. The dataset exhibits a highly skewed distribution of band gap values, dominated by metallic or nearly metallic compounds with zero band gaps, posing a challenge for machine learning (ML) modeling. To address this, we benchmarked four ML algorithms XGBoost, Random Forest (RF), LightGBM (LGBM), and Support Vector Regression (SVR) for band gap prediction. Performance was evaluated using mean absolute error (MAE), root mean squared error (RMSE), and the coefficient of determination (R 2 ) across training and testing sets. Ensemble-based methods (XGBoost, RF, LGBM) consistently outperformed SVR, with LGBM achieving the best testing performance (MAE = 0.413 eV, RMSE = 0.478 eV, R 2 = 0.791), closely followed by XGBoost (MAE = 0.429 eV, RMSE = 0.491 eV, R 2 = 0.784). Correlation plots confirmed the superior predictive accuracy of ensemble models, while SVR showed substantial deviations and systematic underestimation of higher band gaps. Further analysis of XGBoost revealed that predictive performance saturates after incorporating ∼20 features, with testing R 2 stabilizing around 0.75–0.80, indicating that a compact set of descriptors captures most of the predictive information. These findings highlight the effectiveness of gradient boosting methods, particularly XGBoost and LGBM, for robust band gap prediction in complex perovskite systems, thereby providing a reliable framework to accelerate materials discovery. Random forest ; Mean squared error ; Gradient boosting ; Support vector machine ; Boosting (machine learning) ; Perovskite (structure) ; Ensemble learning ; Regression ; Predictive modelling Machine Learning in Materials Science ; Heusler alloys: electronic and magnetic properties ; Perovskite Materials and Applications
Authors: Zixiao Yang | Hanyu Gao | Xian Kong
Source: ChemRxiv (article, 2025-08-19)
Field: Materials Science — Materials Chemistry

Integrating Equivariant Architectures and Charge Supervision for Data-Efficient Molecular Property Prediction Understanding and predicting molecular properties remains a central challenge in scientific machine learning, especially when training data are limited or task-specific supervision is scarce. We introduce the Molecular Equivariant Transformer (MET), a symmetry-aware pretraining framework that leverages quantum-derived atomic charge distributions to guide molecular representation learning. MET combines an Equivariant Graph Neural Network (EGNN) with a Transformer architecture to extract physically meaningful features from three-dimensional molecular geometries. Unlike previous models that rely purely on structural inputs or handcrafted descriptors, MET is pretrained to predict atomic partial charges, which are quantities grounded in quantum chemistry. This enables MET to capture essential electronic information without requiring downstream labels. We show that this pretraining scheme improves performance across diverse molecular property prediction tasks, particularly in low-data regimes. Analyses of the learned representations reveal chemically interpretable structure–property relationships, including the emergence of functional group patterns and smooth alignment with molecular dipoles. Ablation studies confirm that the EGNN encoder plays a crucial role in capturing transferable spatial features, while the Transformer layers adapt these features to specific prediction tasks. This architecture draws direct analogies to quantum mechanical basis transformations, where MET learns to transition from coordinate-based to electron-based representations in a symmetry-preserving manner. By integrating domain knowledge with modern deep learning techniques, MET offers a unified and interpretable framework for data-efficient molecular modeling, with broad applications in computational chemistry, drug discovery, and materials science. Equivariant map ; Charge (physics) ; Property (philosophy) ; Computer science ; Distributed computing ; Physics ; Mathematics ; Pure mathematics ; Particle physics ; Epistemology ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Noah Felis | Wilke Dononelli
Source: npj Computational Materials (article, 2025-12-12)
Field: Materials Science — Materials Chemistry

FALCON: fast active learning for machine learning potentials in atomistic and ab initio molecular dynamics simulations Abstract Machine Learning (ML) potentials can be used for the construction of potential energy surfaces (PES) to avoid computationally expensive ab initio methods. However, many such applications still require a significant number of first-principles calculations to train the ML model, prior to use. Active learning methods can address this issue by performing these calculations and trainings “on-the-fly” (OTF), based on the ML model’s uncertainty estimation. Nevertheless, current active learning approaches suffer from problems in complex simulations were frequent retraining is required, since repeated training of a large ML model increases training times substantially. This work presents a solution to this limitation by introducing the FALCON ( F ast A ctive L earning for C omputational ab initio m O lecular dy N amics) calculator. Instead of relying on a single large ML model, FALCON clusters the training data into subsets of similar structures and distributes them across multiple smaller ML models. This approach significantly increases the efficiency of the OTF training, drastically reducing the computational cost of training-intensive simulations. The use of FALCON is demonstrated on various molecular dynamics (MD) simulations of bulk metals, metal clusters, cathode materials and water diffusion in a carbon nanotube. However, the FALCON calculator is highly flexible and could be easily adapted for various applications and different ML models. Ab initio ; Molecular dynamics ; Active learning (machine learning) ; Calculator ; Work (physics) ; Energy (signal processing) ; Ab initio quantum chemistry methods Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Electron Microscopy Techniques and Applications