jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anxiang Song | Xuanrui Yu | Nima Khodadadi | Yuan-Chen Guo | Antonio Nanni
Source: International Journal of Concrete Structures and Materials (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Explainable Machine Learning Framework with Experimental Validation for Strength Prediction of Magnesium Phosphate Cement Abstract Magnesium Phosphate Cement (MPC) is recognized as an effective rapid repair material, with compressive strength serving as a key mechanical property indicator for its mortar formulations. Nevertheless, due to MPC's complex composition and formulation, predicting its compressive strength remains a significant challenge. In this study, a comprehensive database was developed, incorporating four key input variables: the magnesium-to-phosphate (M/P) molar ratio, water-to-cement (W/C) mass ratio, sand-to-binder (S/B) weight ratio, and the borax-to-magnesia(B/M) weight ratio. This dataset was used to train and validate eight machine learning models, including the Lightweight Gradient Boosting (LGB) algorithm, Support Vector Machine (SVM), Decision Tree (DT), Extreme Gradient Boosting (XGB), Ridge Regression (RR), Random Forest (RF), Backpropagation Neural Network (BP), and Gradient Boosting (GB) models. The eight machine learning models were evaluated using performance metrics, including Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Correlation Coefficient, and Root Mean Square Error (RMSE), to identify the optimal model, which was then optimized via the Gray Wolf Optimizer (GWO). The most accurate prediction of MPC compressive strength was attained using the XGB model, with the GWO-optimized XGB model showing enhancement in MAPE, MAE, R2, and RMSE by 21.8%, 60.6%, 43.9%, and 55.3% respectively, relative to the unoptimized XGB model. Employing Shapley Additive exPlanations (SHAP) values and Partial Dependence Plots (PDP), this study facilitates the identification of the most influential input variables and quantifies their effects on MPC compressive strength. The optimized model was validated against experimental data, demonstrating robust and conservative prediction behavior. While the model is trained solely to predict compressive strength, its interpretability enables rational insights into how formulation variables influence strength, thereby supporting informed mix design decisions. This framework offers a reliable and transparent computational tool for preemptive strength assessment of MPC and guides the optimization of mechanical performance in structurally demanding applications. Compressive strength ; Mean squared error ; Extreme learning machine ; Artificial neural network ; Gradient boosting ; Random forest ; Support vector machine ; Boosting (machine learning) ; Backpropagation Magnesium Oxide Properties and Applications ; Magnesium Alloys: Properties and Applications ; Innovative concrete reinforcement materials
Authors: Haisong Xu | Tingting Du | Jiali Lin | Feiying You | Yingbo Shao | Qi Yang | Xin Li
Source: Angewandte Chemie International Edition (article, 2025-12-10)
Field: Chemistry — Organic Chemistry

Physicochemically Informed Axial Chirality Descriptors Enable Accurate Prediction of Atropisomeric Stability Atropisomers play a vital role in asymmetric synthesis, drug discovery, and the development of functional materials. However, the rational design of atropisomers is challenging due to the difficulty in predicting their configurational stability, which depends on the rotational barrier (ΔG<sup>‡</sup>). Here, we introduce ACSD-GAT, a deep learning framework that addresses this issue. Our approach comprises a newly curated benchmark dataset of 1015 experimentally measured rotational barrier, along with a physicochemically informed axial chirality structure descriptor (ACSD) that explicitly quantifies both static and dynamic steric repulsion during rotation. By integrating the ACSD with a graph attention network (GAT), our model accurately predicts the rotational barrier, achieving an R<sup>2</sup> of 0.91 and a RMSE of 2.02 kcal mol<sup>-1</sup> on test datasets. The robustness and real-world applicability of the model are also demonstrated through rigorous validation with complex pharmaceuticals, molecular switches, and newly synthesized atropisomers. Atropisomer ; Robustness (evolution) ; Chirality (physics) ; Steric effects ; Benchmark (surveying) ; Stability (learning theory) Axial and Atropisomeric Chirality Synthesis ; Supramolecular Chemistry and Complexes ; Synthesis and Properties of Aromatic Compounds
Authors: Vivin Vinod | Peter Zaspel
Source: arXiv (Cornell University) (preprint, 2025-08-21)
Field: Materials Science — Materials Chemistry

LFaB: Low fidelity as Bias for Active Learning in the chemical configuration space Active learning promises to provide an optimal training sample selection procedure in the construction of machine learning models. It often relies on minimizing the model's variance, which is assumed to decrease the prediction error. Still, it is frequently even less efficient than pure random sampling. Motivated by the bias-variance decomposition, we propose to minimize the model's bias instead of its variance. By doing so, we are able to almost exactly match the best-case error over all possible greedy sample selection procedures for a relevant application. Our bias approximation is based on using cheap to calculate low fidelity data as known from $Δ$-ML or multifidelity machine learning. We exemplify our approach for a wider class of applications in quantum chemistry including predicting excitation energies and ab initio potential energy surfaces. Here, the proposed method reduces training data consumption by up to an order of magnitude compared to standard active learning. Active learning (machine learning) ; Fidelity ; Energy consumption ; Sample (material) ; Class (philosophy) ; Selection (genetic algorithm) ; Configuration space ; Ab initio ; High fidelity Machine Learning in Materials Science ; Machine Learning and Algorithms ; Quantum Computing Algorithms and Architecture
Authors: Yang Liu | Chen Li | Milan Ončák | Hua Guo
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Two-dimensional ring polymer molecular dynamics determination of the MnO<sup>+</sup> + H<sub>2</sub>/D<sub>2</sub> reaction rates on a Δ-machine learned potential energy surface In this work, we investigate the impact of nuclear quantum effects in the kinetics of the MnO<sup>+</sup> + H<sub>2</sub> reaction, a prototypical system for gas-phase H<sub>2</sub> activation by transition metal oxide ions. The DFT based potential energy surfaces (PESs) for the lowest-lying quintet and septet spin states reported in our previous work [Y. Liu <i>et al.</i>, <i>J. Phys. Chem. A</i>, 2025, <b>129</b>, 6306-6314] are improved by 2953 newly calculated CCSD(T)/AVDZ points using a delta-machine learning (Δ-ML) method. To examine nuclear quantum effects, the rate coefficients are computed using ring-polymer molecular dynamics (RPMD). Due to the mechanistic complexity of the reaction, two reaction coordinates are necessary to map out the free-energy surface and an extended RPMD rate theory is developed. The calculated RPMD rate coefficients on the new PES are in better agreement with experimental data. The calculated kinetic isotope effects (KIEs) range from 1.6 to 1.8, also in good agreement with the experimental values. In addition, the converged RPMD rate coefficients are 2.3 to 3.1 times higher than their classical counterparts across the studied temperature range, suggesting the presence of moderate nuclear quantum effects in this reaction. Molecular dynamics ; Kinetic energy ; Potential energy surface ; Work (physics) ; Potential energy ; Quantum ; Kinetic isotope effect ; Reaction rate ; Range (aeronautics) ; Activation energy Inorganic and Organometallic Chemistry ; Analytical Chemistry and Sensors ; Conducting polymers and applications
Authors: Konstantinos Kalamatianos | Olga N. Flenga
Source: Journal of Computational Chemistry (article, 2025-03-20)
Field: Materials Science — Materials Chemistry

Prediction of Activation Energies of Organic Molecules With at Most Seven Non‐Hydrogen Atoms Using Quantum‐Chemically Assisted <scp>ML</scp> In this study, a hybrid machine learning (ML) approach is presented for accurately predicting activation energies (E<sub>a</sub>) of gas-phase elementary reactions involving organic compounds with up to seven non-hydrogen atoms. Given the importance of activation energies in reaction studies and modeling, ML composite models were created that effectively integrate molecular descriptors with semi-empirical and single energy density functional theory (DFT) calculations. The dataset, containing 300 randomly selected elementary gas-phase reactions, was assembled using accurate DFT (ωB97X-D3/def2-TZVP) values for activation energies E<sub>a</sub> from a database alongside semi-empirical computations. For accurate predictions, this approach required the inclusion of both physical organic and geometric/empirical descriptors in the training procedure. The best two ML models demonstrated efficient E<sub>a</sub> prediction capability, achieving a mean absolute error (MAE) of 1.314 kcal mol<sup>-1</sup> and R<sup>2</sup> of 0.992 (Model 3) and (MAE) of 1.949 kcal mol<sup>-1</sup> and R<sup>2</sup> of 0.979 (Model 2) in validation tests. Notably, this performance approaches the threshold of "chemical accuracy" of 1 kcal mol<sup>-1</sup>. Model's 3 robustness was tested across the reaction types present in the dataset, demonstrating its ability in properly predicting activation energies, which is critical for the study and optimization of chemical processes. Molecule ; Hydrogen ; Quantum ; Quantum chemical ; Hydrogen molecule ; Computational chemistry ; Chemistry ; Activation energy ; Materials science ; Chemical physics ; Physical chemistry ; Organic chemistry ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Various Chemistry Research Topics
Authors: Y. Kitamura | Yuki Namiuchi | Hiroaki Imai | Yasuhiko Igarashi | Yuya Oaki
Source: Nanoscale Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-scientific validation of prediction models for the controlled syntheses of exfoliated nanosheets Exfoliated nanosheets have attracted considerable interest as two-dimensional (2D) building blocks. In general, the yield, size, and size distribution of the exfoliated nanosheets cannot be easily controlled or predicted because of the complexity in the processes. Our group studied the prediction models of the yield, size, and size distribution based on the small experimental data available. Sparse modeling for small data (SpM-S) combining machine learning (ML) and chemical insight was used for the construction of predictors. In SpM-S, the weight diagram visualizing the significance of explanatory variables plays an important role in variable selection to construct the models. However, the processes of variable selection were not validated in a data-scientific manner. In the present work, the significance of data size, visualization method, and chemical insight for variable selection was studied to validate the processes of model construction. The data size had a lower limit to extract appropriate descriptors. The weight diagram had an appropriate visualizing range for variable selection. Chemical insight as domain knowledge supplemented the limitation caused by the data size. These studies indicated that SpM-S can be applied to construct predictors, straightforward linear regression models, for the controlled syntheses of other 2D materials, even based on small data. Model validation ; Materials science ; Computer science ; Nanotechnology ; Data science Machine Learning in Materials Science ; Semiconductor materials and interfaces ; Graphene research and applications
Authors: Yuanyuan Chen | Zihao Song | Shilong Lv | Li-Bin Shi | Ping Qian
Source: Computational Materials Science (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

Phase diagram and thermoelectric performance of lead-free perovskite using machine learning potentials and density functional theory Density functional theory ; Phase diagram ; Thermoelectric effect ; Perovskite (structure) ; Materials science ; Lead (geology) ; Condensed matter physics ; Phase (matter) ; Thermodynamics ; Chemistry ; Physics ; Crystallography ; Computational chemistry ; Geology ; Quantum mechanics Advanced Thermoelectric Materials and Devices ; Perovskite Materials and Applications ; Machine Learning in Materials Science
Authors: Carlos A. Huang-Zhu | Reid C. Van Lehn
Source: Materials Horizons (review, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Engineering the nano-bio interface: challenges and opportunities for predicting the surface properties of monolayer-protected nanoparticles The surface properties of biologically active nanoparticles (NPs) are often dictated by synthetic ligands that are grafted to the NP core to form a protecting monolayer. Ligand selection is thus critical in determining NP surface properties and corresponding interactions at the nano-bio interface, which are relevant to numerous applications including drug delivery and biosensing. However, chemically specific structure-property relationships for rationally selecting ligands to achieve desired biointeractions are largely lacking. In this Focus Article, we review the challenges associated with relating ligand chemical properties to monolayer-protected NP surface properties due to the interplay of ligand-ligand, ligand-solvent, and ligand-biomolecule interactions that are difficult to anticipate. In particular, we highlight unexpected spatially varying properties that emerge even for uniformly functionalized NPs due to the fluctuations of ligands at the nanoscale. We further review the capability of physics-based molecular simulations to reveal these unexpected behaviors, providing powerful computational methods to predict NP properties. Finally, we discuss the opportunity for such simulations to be combined with machine-learning methods to guide the computational design of monolayer-protected NPs prior to synthesis. Monolayer ; Biomolecule ; Ligand (biochemistry) ; Nanotechnology ; Nanoparticle ; Materials science ; Biosensor ; Interface (matter) ; Drug delivery ; Molecule ; Chemistry ; Organic chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Quantum Dots Synthesis And Properties
Authors: Samad Hajinazar | Eva Zurek
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Xtalopt Version 14: Variable-Composition Crystal Structure Search for Functional Materials Through Pareto Optimization Composition (language) ; Pareto principle ; Variable (mathematics) ; Computer science ; Mathematical optimization ; Mathematics ; Art Machine Learning in Materials Science ; Thermal and Kinetic Analysis ; X-ray Diffraction in Crystallography
Authors: Ahmad Avatar Tulsi | Abdul Momin | Virginia M. Ayres
Source: Smart Agricultural Technology (article, 2025-05-30)
Field: Chemistry — Analytical Chemistry

Moisture prediction in chicken litter using hyperspectral data and machine learning • HSI provides a fast, non-invasive alternative to traditional moisture measurement. • XGBoost outperformed PLSR with raw data, achieving an R² of 0.97. • Preprocessing improved PLSR accuracy but reduced XGBoost performance. • Deep learning and data fusion can further enhance moisture prediction accuracy. Efficient moisture monitoring in chicken litter is essential for sustainable poultry waste management and its application as an organic fertilizer. Conventional methods like oven drying are destructive, time-consuming, and unsuitable for real-time assessment. While hyperspectral imaging (HSI) has been widely applied in agricultural sensing, its use for non-destructive moisture estimation in chicken litter remains largely unexplored. This study addresses that gap by evaluating the feasibility of combining HSI with machine learning models to predict moisture content in chicken litter. We employed two modeling approaches: Partial Least Squares Regression (PLSR) and Extreme Gradient Boosting (XGBoost), to assess performance using raw spectral data and under three preprocessing techniques designed to enhance data quality: Moving Average (MA), Multiplicative Scatter Correction (MSC), and Standard Normal Variate (SNV). XGBoost performed best on raw data (R² = 0.97, RMSE = 1.01), while PLSR improved notably with preprocessing, especially MA (R² = 0.92, RMSE = 1.56). These results reveal a key insight: preprocessing enhances linear model accuracy but may suppress critical non-linear patterns needed by tree-based models like XGBoost. Additionally, spatial visualization of moisture distribution across composting stages demonstrated the added value of HSI in characterizing heterogeneous organic materials. This study introduces a novel framework integrating HSI and machine learning for fast, non-destructive moisture assessment in poultry litter. It establishes a baseline for future work exploring deep learning, feature importance analysis, and multi-modal fusion to further improve accuracy and generalizability across diverse agricultural waste conditions. Hyperspectral imaging ; Moisture ; Litter ; Environmental science ; Water content ; Artificial intelligence ; Machine learning ; Remote sensing ; Computer science ; Meteorology ; Geology ; Geography ; Ecology ; Geotechnical engineering ; Biology Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Effects of Environmental Stressors on Livestock
Authors: Yalun Zheng | Yang Zhou | Yiling Zhu | Yuan Zhuang | ChiYung Yam | Zi‐Hao Chen | Zipeng An | Xiao Zheng | Ziyang Hu | GuanHua Chen
Source: The Journal of Chemical Physics (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

Machine-learning-enhanced density functional theory calculations Machine learning has been widely applied to improve accuracy in computational chemistry. Here, we present a simple yet efficient machine-learning post-correction model that can calibrate the total energy from density functional theory's (DFT) value to the coupled cluster's one by training on energy differences between them across 56 small molecules from the G2 dataset. Our approach has significantly reduced the error of absolute energy from 358.7 kcal/mol of DFT calculations to 1.3 kcal/mol on that dataset. Moreover, a reduction in errors of relative energies, including atomization energies, ionization potentials, electron affinities, noncovalent interactions, reaction energies, and barrier heights, on dozens of other datasets demonstrates the strong transferability and applicability of our model. In addition, our method only performs a single post-processing correction step following standard DFT calculations, thus incurring a minor additional time consumption of 0.69 s on average for G2 molecules. This study thus elucidates a systematic and efficient approach for enhancing the accuracy of DFT energy-related calculations. Density functional theory ; Transferability ; Reduction (mathematics) ; Energy (signal processing) ; Simple (philosophy) ; Approximation error ; Ionization ; Energy consumption Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Kristen A. Fichthorn | Huaizhong Zhang
Source: The Journal of Physical Chemistry C (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

Identifying Useful Nanocrystal Morphologies Using Advanced Sampling Techniques and Machine Learning We applied dimensionality reduction, together with both supervised and unsupervised machine learning (ML), to classify the temperature-dependent equilibrium shapes of Ag nanoparticles containing 100–200 atoms in 1–2 nm size range. The Ag nanocrystal shapes were generated using parallel tempering molecular dynamics. Using ML techniques, we identified five unique particle shape classes with 14 underlying subclasses. We considered the ramifications of our results for catalysis by characterizing the strain and coordination of the surface atoms for different particle subclasses. These studies revealed that icosahedra and hybrid decahedra-icosahedra possess the widest variety of under-coordinated surface atoms and the highest strain. This work helps to forge the link between structure and function and identify processing strategies for achieving beneficial nanocrystal morphologies. Nanocrystal ; Curse of dimensionality ; Particle (ecology) ; Surface (topology) ; Nanoparticle ; Function (biology) ; Variety (cybernetics) ; Sampling (signal processing) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Block Copolymer Self-Assembly
Authors: Xin Zhang | Ying‐Mei Liang | Jiachen Wang | Pan Chen | Pei Li
Source: Science Progress (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Prediction of piezoelectric properties of NBT-based ceramics based on deep neural networks In recent years, the rapid advancement of materials science has underscored the significance and complexity of understanding and optimizing the properties of piezoelectric materials. Traditional analytical methods have encountered difficulties in addressing the challenges posed by variable material compositions and intricate processing conditions. However, deep neural networks (DNNs) present new opportunities in this field due to their robust capabilities in nonlinear data processing and pattern recognition. This study employs DNN to model the relationship between the composition and processing conditions of piezoelectric materials and their electrical properties. Through quenching experiments, it was observed that the electrical properties of Na<sub>0.5</sub>Bi<sub>0.5</sub>TiO<sub>3</sub>-based ceramics are positively correlated with local polar heterogeneity. This phenomenon was further validated by manipulating the local polar heterogeneity through Bi or Sr nonstoichiometric modifications. Subsequently, a DNN model characterized by local polarity heterogeneity was developed. After training on extensive high-quality experimental data, the DNN model demonstrated exceptional prediction accuracy and effectively elucidated the underlying complex mechanisms governing material properties. The findings indicate that DNN can accelerate the development and optimization of new piezoelectric materials, provide guidance for experimental design, and significantly enhance research and development efficiency. The successful application of this research strongly supports the expansion of deep learning technology in materials science. Piezoelectricity ; Artificial neural network ; Computer science ; Materials science ; Nonlinear system ; Ceramic ; Field (mathematics) ; Polarity (international relations) ; Deep learning ; Artificial intelligence ; Physics ; Mathematics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Acoustic Wave Resonator Technologies