jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: K E Vipin | Prahallad Padhan
Source: ACS Applied Energy Materials (article, 2025-07-17)
Field: Materials Science — Materials Chemistry

Thermoelectric Properties in Skutterudite Materials: Integrating Experimental Data, Density Functional Theory, and Machine Learning An XGBoost regression model has been utilized to accurately predict the temperature-dependent thermal conductivity (κ), electrical conductivity (σ), Seebeck coefficient (S), and figure of merit (ZT) of skutterudite materials from 50 to 1000 K. Shapley values, derived from cooperative game theory, were employed to quantify the contributions of various material descriptors, providing valuable insights into the underlying correlations and trade-offs between different features and thermoelectric properties. The analysis revealed crucial factors influencing the thermoelectric performance of skutterudites, including temperature, compositional attributes, electronic configurations, and structural properties. The model predicts a room-temperature ZT of approximately 0.45 for Nd(CoP3)4, a promising thermoelectric material that has not yet been experimentally investigated. In addition, the prediction of half-metallic ferromagnetic behavior in Nd(CoP3)4 by first-principles density functional theory (DFT) with Hubbard corrections has led to the investigation of its thermoelectric properties. At elevated temperatures, a strong correlation was observed between XGBoost predictions and DFT calculations for the σ and S of Nd(CoP3)4. The agreement between predicted and calculated values was less pronounced, though still reasonable, for κ and ZT. By integrating machine learning with fundamental materials science principles, this study paves the way for accelerated discovery and optimization of high-performance thermoelectric materials, contributing to the advancement of sustainable energy technologies and the pursuit of a more energy-efficient future. Skutterudite ; Thermoelectric effect ; Density functional theory ; Materials science ; Thermoelectric materials ; Computer science ; Physics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Neural Networks and Applications
Authors: Benouali Mohamed Elamine | Mohammed Beldjilali | Smaïn Bousalem | M’hamed Guezzoul | Drai Ikram | Alejandro Jiménez
Source: Applied Organometallic Chemistry (article, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Green Synthesis of Sep‐NiO Nanocomposite Using <scp><i>Mentha aquatica</i></scp> Leaf: Exploring Catalytic Efficiency, Kinetics, Thermodynamics, and Neural Network Prediction in Methylene Blue Degradation ABSTRACT In present research, small‐sized Sep‐NiO nanocomposites were synthesized using Mentha aquatica leaf extract as a reducing and capping agent. The nanocomposites were systematically characterized to determine their crystallographic structure, chemical composition, morphological features, thermal stability, and luminescence properties. X‐ray diffraction (XRD) was employed to assess the crystal structure and phase purity, while FTIR spectroscopy and X‐ray photoelectron spectroscopy (XPS) provided insights into the chemical bonding and surface states. Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) coupled with energy‐dispersive X‐ray spectroscopy (EDX) were used to examine the nanoscale morphology and bulk elemental distribution. Atomic force microscopy (AFM) offered additional topographical information, and thermogravimetric analysis (TGA) evaluated the thermal stability. Zetasizer Nano and zeta potential measurements were conducted to assess particle size distribution and colloidal stability, respectively. Photoluminescence (PL) studies were performed to explore the optical and electronic properties of the nanocomposites. The nanocomposite was applied to the catalytic reduction of methylene blue, with a deep neural network model predicting degradation efficiency based on variables including catalyst mass, NaBH4 concentration, MB concentration, and reaction time. The model demonstrated excellent predictive accuracy ( R 2 = 0.99), with RMSE, MAE, and MSE values of 1.95, 1.71, and 3.83, respectively. Kinetic studies showed that methylene blue degradation increased with catalyst mass and NaBH4 concentration but decreased at higher MB concentrations. Thermodynamic analysis indicated that the process was endothermic, involving physical adsorption on the catalyst surface, and led to increased system order. Chemistry ; Thermogravimetric analysis ; Nanocomposite ; X-ray photoelectron spectroscopy ; Chemical engineering ; Thermal stability ; Analytical Chemistry (journal) ; Nuclear chemistry ; Scanning electron microscope ; Materials science ; Organic chemistry ; Composite material Supercapacitor Materials and Fabrication ; Advanced Photocatalysis Techniques ; Nanomaterials for catalytic reactions
Authors: Fu‐Ming Pan | Zhoulin Liu | Guangyao Li | Yuhui Zhang | Zepeng Fan | Gang Yang | Dawei Wang
Source: The Journal of Physical Chemistry C (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

Hydration Behavior of Quartz Surfaces Revealed by Molecular Dynamics Simulations with a Novel Machine Learning Potential Understanding the hydration behavior of quartz surfaces is critical for elucidating interfacial properties in geochemical and engineering systems. However, capturing these processes via simulation poses a significant challenge, as the high computational cost of conventional density functional theory (DFT) methods renders them unsuitable for simulating large interfacial systems over long time scales. In this work, a machine learning potential (MLP) was developed using an active learning strategy and a high-quality data set to enable large-scale molecular dynamics simulations of the quartz–water interface at ab initio accuracy. The trained potential achieves high accuracy in energy and force predictions across diverse structures and significantly outperforms empirical potentials in both precision and generalization. Driven by this potential, nanosecond-scale machine learning molecular dynamics (ML-MD) simulations have been performed to elucidate the surface reconstruction behavior of cleaved α-quartz (101) and the temporal evolution of surface hydroxylation. The results show that surface reconstruction induces a pronounced structural response that varies with depth in bulk atoms, and hydroxylation proceeds via water dissociation and proton transfer mechanisms, yielding a final silanol density of 5.169 ± 0.416 nm–2, which is consistent with the thermogravimetric analysis (TGA) results. Additionally, we characterize the layered structure and hydrogen-bonding network of interfacial water, revealing a hierarchical transition from surface-anchored to bulk-like water. This study demonstrates the potential of ML-MD as a powerful and efficient framework for simulation of complex mineral–water interfacial processes with ab initio accuracy at reduced computational cost. Quartz ; Dynamics (music) ; Molecular dynamics ; Chemical physics ; Materials science ; Nanotechnology ; Statistical physics ; Computer science ; Chemistry ; Physics ; Composite material ; Computational chemistry ; Acoustics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; X-ray Diffraction in Crystallography
Authors: Jameel Ahemd Bhutto | Zia-ur Rahman | Muhammad Aamir | Yurong Guan | Zhihua Hu
Source: Chemical Engineering Science (article, 2025-04-29)
Field: Materials Science — Materials Chemistry

Machine learning-driven discovery of high-performance hole-conducting organic materials for solar cells and synthetic accessibility assessment Organic solar cell ; Materials science ; Engineering physics ; Nanotechnology ; Environmental science ; Process engineering ; Biochemical engineering ; Computer science ; Engineering ; Photovoltaic system ; Electrical engineering Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Aarthi Sai Meghana Munnangi | Hara krishna Reddy Koppolu | S. M. Abdul Nayeem | Sowjanya Prathipati | M. Srinivasa Reddy
Source: Journal of Molecular Liquids (article, 2025-06-18)
Field: Chemistry — Spectroscopy

Integrating experimental data, molecular dynamics, and machine learning for physicochemical insights into γ-butyrolactone and isopropyl acetate binary system Isopropyl ; Binary number ; Molecular dynamics ; Dynamics (music) ; Chemistry ; Ethyl acetate ; Computer science ; Computational chemistry ; Organic chemistry ; Physics ; Mathematics Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods
Authors: Yuanhui Sun | Austin Ellis | Xin Chen | Maosheng Miao
Source: Journal of the American Chemical Society (article, 2025-10-22)
Field: Materials Science — Materials Chemistry

Batch Discovery of Complex Metal Superhydrides via an Effective Machine Learning Method Structured by Chemical Template Concept Metal superhydrides, known for their high hydrogen content and polyhedral hydrogen cages, are promising candidates for high-temperature superconductivity. Despite extensive studies on binary metal superhydrides, there remains a large, unexplored chemical space, particularly regarding noninteger hydrogen-to-metal ratios. By integrating the "chemical template effect" with machine learning algorithms, we developed a specialized structure discovery workflow that significantly enhances the efficiency of predicting stable superhydrides. Within the compiled data set used in this work, our method led to the identification of 13 new structural prototypes and 31 stable metal superhydrides, representing a 23% increase in discoveries. The 3D hydrogen clathrates in these compounds are significantly correlated with high superconducting transition temperatures (<i>T</i><sub>c</sub>), and our approach achieves a remarkable 65% increase. Most of these structures contain over 50 atoms per primitive cell, with the <i>I</i>4/<i>m</i> M<sub>10</sub>H<sub>84</sub> prototype having the largest unit cell, containing 94 atoms. Additionally, 19 of the newly identified superhydrides exhibit <i>T</i><sub>c</sub> > 100 K, highlighting the potential for higher <i>T</i><sub>c</sub> materials within the 3D hydrogen clathrate structures. The method also shows good potential to search for ternary superhydrides on a large scale. Workflow ; Hydrogen ; Ternary operation ; Metal ; Set (abstract data type) ; Binary number ; Identification (biology) ; Transition metal Machine Learning in Materials Science ; Nuclear Materials and Properties ; X-ray Diffraction in Crystallography
Authors: Minhyeok Lee | Jinyoung Jeong | Islambek Ashyrmamatov | Umit V. Ucaxk | S. B. Kim | Juyong Lee | Eunji Sim
Source: Scientific Data (article, 2025-10-20)
Field: Materials Science — Materials Chemistry

A dataset of chemical reaction pathways incorporating halogen chemistry Machine learning interatomic potentials (MLIPs) promise to revolutionize computational chemistry; however, their performance depends critically on the quality and diversity of the training data. Existing quantum chemical datasets predominantly focus on equilibrium structures and exhibit limited halogen coverage, despite halogens being present in approximately 25% of pharmaceuticals and numerous materials. We present Halo8, a comprehensive dataset that addresses this gap by systematically incorporating fluorine, chlorine, and bromine chemistry into reaction pathway sampling. Using our efficient multi-level computational workflow, which achieves a 110-fold speedup over pure DFT approaches, Halo8 comprises approximately 20 million quantum chemical calculations from 19,000 unique reaction pathways. The dataset combines recalculated Transition1x reactions with new halogen-containing molecules from GDB-13, employing systematic halogen substitution to maximize chemical diversity. All calculations were performed at the ωB97X-3c level, providing accurate energies, forces, dipole moments, and partial charges. Validation demonstrates that Halo8 captures diverse structural distortions and chemical environments essential for reactive systems, serving as a valuable resource for training MLIPs applicable to pharmaceutical discovery, materials design, and catalysis. Halogen ; Quantum chemical ; Molecule ; Bromine ; Chemical reaction ; Quantum chemistry ; Speedup Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Kiwoong Lee | Minhi Han | Seung Jin Lee | Yunchang Son | D. -J. Kim | Yujin Jeong | Byeongyoon Kim | Kwangyeol Lee | S. H. Park
Source: Small Methods (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Standardization and Machine Learning Prediction of Tafel Slope of Pt‐Based Nanocatalysts for High‐Performance HER Catalyst Development The hydrogen evolution reaction (HER) is a key process for sustainable hydrogen production, with Pt-based nanocatalysts widely used for their superior activity. However, inconsistent reporting of electrochemical properties hinders reliable evaluation and comparison of catalytic activity across diverse studies. In this study, a standardization method is established in which Tafel slopes were systematically extracted from polarization curves, enabling consistent electrochemical kinetic analysis across diverse studies. A comprehensive database of 127 Pt-based nanocatalysts from 42 published articles was constructed. Machine learning (ML) models trained on this dataset demonstrated strong predictive ability for Tafel slopes. To evaluate model generalizability, six Pt-based nanocatalysts were synthesized, where ML predictions closely matched experimentally measured Tafel slopes. Feature importance analysis revealed key structural and compositional parameters, such as substructure size and hetero-element ratio, that strongly influenced catalytic activity. Guided by these insights, an ML-guided design map for spherical Pt-based nanocatalysts was constructed. As a proof of concept, Pt<sub>34</sub>Ni<sub>66</sub> was synthesized and exhibited a Tafel slope of 88.9 mV dec<sup>-1</sup> which corresponded to enhanced catalytic activity compared to prior reports. This work demonstrates an ML-assisted strategy to accelerate discovery of high-performance HER catalysts, integrating data curation, ML model development, ML-guided design, and experimental validation. Tafel equation ; Nanomaterial-based catalyst ; Catalysis ; Standardization ; Polarization (electrochemistry) ; Hydrogen Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Oxidation Reactions
Authors: Rafaqat Ali Khan | Saad Javed | Shahzad Anwar | Hina Ali | Hasnain Ahmad | Farwa Nurjis | Ribqa Akhtar | Muhammad Saleem | Babar Manzoor Atta
Source: BioNanoScience (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

ZnO Nanoflowers as Photosensitive Materials for Antibacterial Photodynamic Therapy: Experimental and Neural Network Modeling Approaches Nanomaterials ; Fluorescence ; Zinc ; Nanoparticle ; Nanostructure ; Dynamic light scattering ; Scanning electron microscope ; Fluorescence microscope ZnO doping and properties ; Nanoparticles: synthesis and applications ; Magnesium Oxide Properties and Applications
Authors: Nguyễn Thiên Phúc Từ | A. Knoll | Jörg Behler | Christopher N. Rowley
Source: The Journal of Physical Chemistry B (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

Long-Range Interactions in High-Dimensional Neural Network Potentials: A Benchmark Study for Small Organic Molecules Many machine learning potentials (MLPs) rely on representations of the total energy in terms of the positions of the atoms in their local environment, using either a cutoff radius or a limited number of message-passing layers. This restricts their ability to model long-range intermolecular interactions accurately. This limitation can be addressed by explicitly incorporating long-range electrostatic and dispersion interactions into the MLP framework. In this paper, we investigate the impact of augmenting high-dimensional neural network potentials (HDNNPs) with both electrostatic and dispersion corrections on the prediction of gas-phase intermolecular interactions between small organic molecules. We employ a machine learning-based charge equilibration (QEq) scheme to model electrostatics and the Machine-Learning eXchange-hole Dipole-Moment (MLXDM) model to account for dispersion. The resulting model, CombineNet, integrates these long-range terms with short-range atomic energies trained on density functional theory (DFT) data and achieves a low mean absolute error (MAE) of 0.59 kcal/mol (root-mean-square error (RMSE) of 3.38 meV/atom) against CCSD(T)/CBS benchmarks on the DES370K test set. Notably, electrostatic interactions derived from Hirshfeld charges tend to underestimate long-range effects, whereas the minimal basis iterative stockholder (MBIS) charges yield more accurate interaction trends. For reliable modeling of molecular dimers, the training set must capture both the dissociation limit and the transition region near the cutoff radius. Cutoff ; Artificial neural network ; Electrostatics ; Intermolecular force ; Dispersion (optics) ; Limit (mathematics) ; Dissociation (chemistry) ; Charge (physics) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Md Mahmudul Hasan | Deep Choudhuri
Source: The Journal of Chemical Physics (article, 2025-02-25)
Field: Materials Science — Materials Chemistry

Metastable states assisted homogeneous nucleation in supercooled liquid aluminum alloys: Insights from a phenomenologically coupled atomistic, phase-field, and machine learning investigation Crystallization due to liquid → solid transformation is observed in many natural and engineering processes. Extant literature indicates that crystallization in supercooled liquids is initiated by precursory metastable phases or states, also called non-classical nucleation. For face-centered cubic (FCC) materials, latest experimental and computational studies suggest that metastable hexagonal-closed packed (HCP) structures facilitate equilibrium FCC formation. However, the underlying nucleation mechanism remains unclear. Here, we examine structural changes and energetic barriers associated with such a non-classical mechanism, by performing molecular dynamics (MD) simulations using pure Al, Al-0.5 at. %Cu, and Al-0.5 at. %Ni (all FCC-formers) and phenomenologically coupling MD results with phase-field (PF) modeling. Such a coupling involved initializing PF simulation domains and constructing Landau polynomials-consistent with MD observations. Unsupervised machine learning was utilized to capture nuclei structures from MD simulations, while neural networks helped in extracting equilibrium interfacial energies from PF modeling. Atomistic simulations showed that precursory nuclei are comprised of collection of metastable-HCP states with medium ranged ordering. The pockets of HCP states later transform to critical nuclei-containing an FCC core and an outer layer of HCP. PF modeling qualitatively replicated the precursory-to-critical nuclei transformation and showed that the energetic barriers between the precursory and critical nuclei are substantially smaller than predictions obtained from classical nucleation theory. Together, these observations permitted us to propose a holistic non-classical mechanism that links triangular motifs within Al-based supercooled liquids to the critical nuclei via in-liquid structural transformations. Supercooling ; Metastability ; Nucleation ; Chemical physics ; Crystallization ; Materials science ; Molecular dynamics ; Phase (matter) ; Classical nucleation theory ; Thermodynamics ; Crystallography ; Condensed matter physics ; Physics ; Chemistry ; Computational chemistry Material Dynamics and Properties ; nanoparticles nucleation surface interactions ; Solidification and crystal growth phenomena
Authors: Jasper De Landsheere | Anton Zamyatin | Johannes Karwounopoulos | Esther Heid
Source: ChemRxiv (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

ChemTorch: A Deep Learning Framework for Benchmarking and Developing Chemical Reaction Property Prediction Models Benchmarking ; Deep learning ; Modular design ; Property (philosophy) ; Benchmark (surveying) ; Software ; Hyperparameter Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Benkun Hong | Guoao Li | P Liu | Wei Li | Manyi Yang | Shuhua Li
Source: The Journal of Chemical Physics (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Phase diagram of CO2-I/III from molecular dynamics simulation using a PBE0-accuracy machine learning potential We employ the PBC-GEBF-AL workflow, integrating active learning (AL), the linear-scaling generalized energy-based fragmentation approach under periodic boundary conditions (PBC-GEBF), and multiTPU-OPES enhanced sampling, to create a PBE0-D3(BJ)/aug-cc-pVDZ-accuracy machine learning potential for studying the carbon dioxide (CO2) molecular crystal phase diagram. Based on up to 170 ns MLP-based multiTPU-OPES simulations, we obtain the phase diagram across a wide temperature range (250-700 K) and pressure range (10.5-14.0 GPa). It suggests a CO2-I/III coexistence line peaks at ∼12.3 GPa, 525 K, with a negative slope at higher temperatures, closely matching experimental I/III and I/VII transition pressures. Trajectory analysis revealed a concerted CO2-I/III transition mechanism driven by molecular rotation, lattice deformation, and non-monotonic volume changes (expansion followed by contraction). We propose a new Cmca space group structure for the true CO2-III phase, characterized by molecules tilted relative to the ac plane and lattice parameters a > b. It differs from the reported CO2-III structure determined by powder x-ray diffraction but closely resembles the CO2-VII crystal, where CO2 molecules are aligned parallel to the ac plane. This proposed tilted Cmca structure coexists with CO2-VII at high temperatures. We conject that CO2-III and CO2-VII belong to the same phase, with the discrepancies in their experimental Raman spectra primarily caused by slight structural changes due to the thermal effect. This work provides deeper insights into CO2 phase transitions and establishes a generalizable strategy for high-precision MLPs in complex rare event systems. Phase diagram ; Crystal structure prediction ; Phase transition ; Lattice (music) ; Molecular dynamics ; Diffraction ; Crystal structure ; Raman spectroscopy ; Molecule Machine Learning in Materials Science ; High-pressure geophysics and materials ; Advanced Chemical Physics Studies
Authors: Yuanxi Huang | Lingjun Bu | Jian Ao | Ruiyang Xiao | Shiqing Zhou | Shumin Zhu
Source: Journal of Hazardous Materials (article, 2025-03-11)
Field: Chemistry — Electrochemistry

Beyond the reaction kinetics: Interpretable machine learning reveals unique pathways of sulfate and carbonate radicals Radical ; Kinetics ; Carbonate ; Sulfate ; Chemistry ; Reaction mechanism ; Organic chemistry ; Catalysis ; Physics Electrochemical Analysis and Applications ; Free Radicals and Antioxidants ; Advanced oxidation water treatment