jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Suchona Akter (19855604) | Mohammad R. Momeni (2165602)
Source: Figshare (article, 2025-05-13)
Field: Materials Science — Polymers and Plastics

Mechanism of ChargeTransport in Mixed-Valence 2DLayered Hybrid Organic–Inorganic Bronze Materials Two-dimensional layered hybrid organic–inorganic bronze (HOIB) materials are a new class of mixed-valence hybrid metal-oxides that demonstrate great potential as advanced functional materials for next-generation electronics. Recently, new hybrid vanadium bronze materials, (EV)V<sub>8</sub>O<sub>20</sub> and (MV)V<sub>8</sub>O<sub>20</sub>, EV = ethyl viologen and MV = methyl viologen, have been introduced, with EV having ≈3 orders of magnitude higher electrical conductivity than the MV system. Given their stoichiometrically similar inorganic V–O layers and close reduction potentials, the observed significant difference in electrical conductivities is puzzling. Here, through accurate first-principles electronic structure calculations coupled with MACE machine learning molecular dynamics (MD) simulations validated by accurate ab initio MD data, we provide mechanistic molecular-level insights into dominant charge transport and electrical conductivity pathways in these materials. Our detailed structural and electronic properties data identify factors contributing to this significant difference in the electrical conductivity of these materials. Our findings in this work offer clues and provide valuable insights into improving the electrical conductivity of hybrid bronze and similar materials, suggesting new ways to guide the design of next-generation materials with enhanced properties for electronic and energy conversion applications. Electrical resistivity and conductivity ; Bronze ; Electronic structure ; Ab initio ; Vanadium ; Conductivity ; Molecular dynamics ; Ab initio quantum chemistry methods Transition Metal Oxide Nanomaterials ; Conducting polymers and applications ; Organic and Molecular Conductors Research
Authors: Shaowen Xu | Fanhao Jia | Ning Dai
Source: Research Square (preprint, 2025-04-11)
Field: Materials Science — Materials Chemistry

High Throughput Discovery of 2D Ferromagnetic and Multiferroic Transition Metal Oxyhalides and Nitrogen Halides Multiferroics ; Ferromagnetism ; Halide ; Throughput ; Nitrogen ; Materials science ; Transition metal ; Condensed matter physics ; Chemistry ; Inorganic chemistry ; Physics ; Optoelectronics ; Ferroelectricity ; Computer science ; Catalysis ; Organic chemistry 2D Materials and Applications ; Perovskite Materials and Applications ; Multiferroics and related materials
Authors: Marco Herbsommer | Mahavir Singh | Sushrut Karmarkar | Meghana Sudarshan | Christopher J. Welch | Vikas Tomar
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Real-Time Classification of Mixed Plastics Using Machine Learning Based on Terahertz and Raman Spectroscopy Data Principal component analysis ; Dimensionality reduction ; Linear discriminant analysis ; Robustness (evolution) ; Pattern recognition (psychology) ; Raman spectroscopy ; Curse of dimensionality ; Support vector machine ; Chemometrics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Tianzhixi Yin | Ruozhu Feng | Peiyuan Gao | Heather Job | Jie Bao | Yangang Liang | Wei Wang
Source:  (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

pnnl/HypoGen An innovative workflow for automatic knowledge discovery in material science, combining experimental automation with advanced machine learning and artificial intelligence techniques Geography Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Advanced Materials Characterization Techniques
Authors: Emad Al Ibrahim | Nathan Morgan | Simon Müller | Saikiran Motati | William H. Green
Source: ChemRxiv (preprint, 2025-08-06)
Field: Materials Science — Materials Chemistry

Accurately predicting solubility curves via a thermodynamic cycle, machine learning, and solvent ensembles Determining solubilities of organic molecules is critical in various fields such as pharmaceuticals, agrochemicals, and environmental science. Knowing how a solute will dissolve in different solvents and at different temperatures is essential for drug formulation, synthesis, purification, and crystallization. Hard-to-estimate solubility limits currently hinder the design of new processes, making innovation more expensive. We propose a fast and general method for predicting the solubilities of neutral organic molecules in a wide range of solvents and temperatures. Our method uses a thermodynamic fusion cycle to combine machine learning predictions of the activity coefficient, fusion enthalpy, and melting point temperature. This method was tested on a combined dataset with more than 100,000 experimental solubility values, showing better or comparable performance to competing methods on many solubility benchmarks even at elevated temperatures. We also introduce reference ensembling to leverage all available experimental solubilities for a given solute in estimating its solubility in a different solvent. Reference ensembling is also shown to enhance the robustness of models trained directly on solubility data. Solubility ; Solvent ; Thermodynamics ; Phase equilibrium ; Machine learning ; Chemistry ; Computer science ; Artificial intelligence ; Phase (matter) ; Physics ; Physical chemistry ; Organic chemistry Crystallization and Solubility Studies ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Sahith Battula | Nived Alla | Mysore S. Pavan | L. Srinivasa Rao | B Chennakesava Rao
Source: Indian Journal of Science and Technology (article, 2025-05-30)
Field: Materials Science — Surfaces, Coatings and Films

Morphological Insights of Lithium-Phosphate Nano Compounds by Data Driven Pore Analysis of SEM Images with ML Algorithms Objectives: To study how compositional changes (specifically, varying ZnO/Sb2O3 ratios) affect the pore properties of lithium-phosphate-copper nanocompounds, interpreting their morphographs by Machine Learning (ML) algorithms. Methods: Inorganic lithium-phosphate nanocompounds doped with copper (Cu) nanoparticles were synthesized by solid state reaction. The composition of the nanocompounds was modified by varying concentrations of modifiers namely, antimony oxide (Sb2O3) and zinc oxide (ZnO). The morphographs of the samples were recorded by Scanning Electron Microscope (SEM). In this paper, we have described a novel approach to pore detection in SEM images that employs ML algorithms. Findings: The proposed methodology was used to detect pores, calculate their length and area, and count total number of pores in the samples automatically. Furthermore, data visualization plots were drawn to show the distribution of pore lengths and the frequency of pores. According to the study, as ZnO content rises, average pore size increases from 0.939 µm² to 5.003 µm², while pore count significantly decreases from 471 to 153. Novelty: The data visualization allowed us to draw conclusions about the size distribution, density, and spatial arrangement of pores. The number of pores, total area of the pores, and fraction of area occupied by the pores found to be changed as a function of composition of the samples. Overall, this paper has provided a strong framework for automated pore analysis in SEM images of the prepared samples by ML algorithms. Keywords: Nano Compounds, SEM Morpho Graphs, Pore Characteristics, Data Visualization, Machine Learning Lithium (medication) ; Nano- ; Computer science ; Phosphate ; Materials science ; Algorithm ; Chemical engineering ; Chemistry ; Composite material ; Organic chemistry ; Psychology Electron and X-Ray Spectroscopy Techniques ; Electrostatics and Colloid Interactions ; Ion-surface interactions and analysis
Authors: Chi H. Lee | Silabrata Pahari | David Kumar Yesudoss | Abdoulaye Djire | Joseph Sang‐Il Kwon
Source:  (article, 2025-07-08)
Field: Materials Science — Materials Chemistry

Enhancing Predictive Accuracy in Catalysis: A Hybrid Modeling Approach for Dynamic Surface Configuration Analysis One inherent limitation of density functional theory (DFT) is its inability to precisely capture changes in surface configurations and activation energies, both of which are essential for interpreting reaction kinetics effectively. These shortcomings are further exacerbated when combined with the challenges traditional kinetic Monte Carlo (kMC) simulations face in capturing latent reaction mechanisms on electrocatalysts due to complex many-body interactions among adsorbates, reactants, and intermediates. To tackle these issues, we have developed a hybrid model that integrates machine learning (ML) with first-principles model. This model clearly tracks the changes in surface configurations over time, integrating complex physical phenomena often overlooked by conventional DFT models but observable in experiments. Our approach not only boosts the predictive accuracy of these models but also broadens their applicability in testing catalyst performance under diverse conditions. Intriguingly, the ML component of our model does more than fitting empirical data; it identifies critical parameters that refine subsequent DFT calculations to better match real-world surface conditions and coverages. Computer science Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: Shejia Luo (17798418) | Xu Jia (187142) | Xiao Xu (112147)
Source: Figshare (article, 2025-07-08)
Field: Materials Science — Surfaces, Coatings and Films

Sequence-BasedComputational Design of High-AffinityAmphiphilic Copolymers for Protein Targeting: A Machine Learning andCoarse-Grained Simulation Approach Synthetic polymers have been developed as abiotic affinity reagents for biomarker proteins due to their capacity for high-affinity and specific binding to target proteins. However, current structure-based methods for designing polymers that target specific proteins are often inefficient and unsuitable, primarily because of the structural flexibility of most polymers and the lack of experimental data. In this work, we introduce a novel sequence-based computational paradigm for the rational design of high-affinity amphiphilic copolymers for proteins. The approach involves efficient on-lattice coarse-grained simulations to explore the sequence–affinity relationship for short copolymers, followed by a machine learning (ML) model that predicts properties for longer and more complex copolymers based on the previous simulation data. As a demonstration of this method, we constructed a binding data set comprising over 34,000 random copolymers, specifically assessing their binding affinity toward the epithelial cell adhesion molecule (EpCAM) protein. Direct simulations estimated the binding affinities for approximately 3500 copolymers, while the remaining copolymers were accurately predicted by the ML model, achieving an overall mean absolute error of less than 2 <i>k</i><sub>B</sub><i>T</i>. Notably, our method identified a distinctive pattern in copolymer sequences, emphasizing hydrophobic segments on margins favored by the protein, which was corroborated by all-atom molecular dynamics simulations, thus validating our approach. Collectively, we believe that this numerical framework provides a valuable toolkit for the design of polymers as affinity reagents. Copolymer ; Polymer ; Rational design ; Flexibility (engineering) ; Amphiphile ; Set (abstract data type) ; Computational model ; Molecule Polymer Surface Interaction Studies ; Protein purification and stability ; Monoclonal and Polyclonal Antibodies Research
Authors: Syed Mansha Rafiq | Priyanshu Priyam | Richa Mishra | Sanskar Varshney | Shrinath Satyarthi | Vedant Goyal | Komal Chauhan
Source: Research Square (preprint, 2025-07-29)
Field: Chemistry — Analytical Chemistry

Nondestructive Detection of Internal Defects in Potato by Visible/Near Infrared Spectroscopy Spectroscopy ; Infrared ; Infrared spectroscopy ; Nondestructive testing ; Near-infrared spectroscopy ; Materials science ; Optics ; Analytical Chemistry (journal) ; Chemistry ; Physics ; Medicine ; Astronomy ; Environmental chemistry ; Radiology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Yongjie Hou | Zhenzhong Wang | Yunpeng Gong | Shu Jiang | Kaijun Che | Min Jiang
Source:  (article, 2025-10-31)
Field: Materials Science — Materials Chemistry

Hamiltonian Prediction through Hierarchical Multimodal Alignment Machine learning (ML) promises to accelerate quantum Hamiltonian prediction. Although methods using only 3D molecular geometries show considerable success, they often neglect complementary information from other modalities. In this work, we introduce EMoham, an efficient multimodal model that enhances Hamiltonian prediction by aligning 1D molecular strings, e.g., simplified molecular input line entry system (SMILES), with 3D geometries. At its core, a novel hierarchical multi-modal alignment strategy fuses complementary spatial and topological information. This strategy operates on two levels: global representation. Alignment synchronizes the overall molecular representations, while locality-preserving alignment establishes fine-grained correspondences between chemical fragments and local geometries. This process yields a comprehensive molecular representation that enables highly accurate predictions via a subsequent multimodal combination stage. Experiments on the MD17 and QH9 datasets show that EMoham outperforms state-of-the-art methods with up to a 64.4 % MAE reduction, demonstrating the significant benefit of our hierarchical multimodal approach. Hamiltonian (control theory) ; Representation (politics) ; Quantum ; Process (computing) ; Hierarchical database model ; Topology (electrical circuits) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Source: Journal on Applied and Chemical Physics (article, 2025-11-29)
Field: Materials Science — Materials Chemistry

Comparative Conductometric Study of Metal Halides and Nitrates in Binary Solvent Systems: A Machine Learning Approach This study presents a comprehensive quantitative study of the conductance of metal halides and nitrates in binary solvent systems, investigating how solvent composition, electrolyte concentration, and temperature collectively affect the electrical conductivity. Metal halides, including chlorides, bromides, and iodides of alkali and alkaline earth metals, exhibit characteristic nonlinear conductivity variations arising from the differential solvation of cations and anions by binary solvent components. Metal nitrates demonstrate unique behavior due to the displaced charge distribution of the polyatomic nitrate ion and extensive hydrogen bonding capabilities in protic solvents. The experimental dataset contains forty-six observations covering metal halide concentrations from 0.1000 to 0.5900 mol/L, metal nitrate concentrations from 0.0500 to 0.3000 mol/L, solvent ratios from 18.0000 to 118.0000, temperatures from 24°C to 74°C, and resulting conductivity values from 2.7000 to 9.4000 mS/cm. Machine learning approaches, specifically slope-increasing regression and random forest regression, were used to predict solvent ratio effects on conductance measurement behavior. Both models showed exceptional predictive performance, achieving R² values of 1.0000 for the gradient boosting training and 0.9885 for the test data, while random forests achieved R² values of 0.9992 and 0.9934, respectively, confirming strong generalization capabilities and minimal overfitting. Halide ; Conductometry ; Conductance ; Solvent ; Electrolyte ; Metal ; Conductivity ; Solvation Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Xinyu Xu | Kehan Cai | Pinchen Xie | Zhong, Peichen
Source: ArXiv.org (preprint, 2025-11-14)
Field: Materials Science — Materials Chemistry

Intrinsic structure of relaxor ferroelectrics from first principles We develop FIRE-Swap, a first-principles framework for sampling intrinsic compositional structures in complex perovskites with machine-learning interatomic potentials (MLIPs). Using both dedicated and universal MLIPs, we study the relaxor lead magnesium niobate (PMN) and the solid solutions lead zirconate titanate (PZT) and lead strontium titanate (PST). Across MLIP models and exchange-correlation approximations, FIRE-Swap robustly predicts a rock-salt-like chemical order in PMN, which is absent in PZT and PST with the same mixing ratio, consistent with experiments. We further identify in PMN a distinct Nb-cluster morphology. Interconnected, non-coarsened polar nanoregions are found within Nb clusters, providing a mesoscale basis for understanding relaxor ferroelectricity. Reverse Monte Carlo ; Cluster (spacecraft) ; Dielectric ; Monte Carlo method ; Neutron scattering ; Scattering ; Relaxation (psychology) ; Ion ; Dipole Ferroelectric and Piezoelectric Materials ; Solid-state spectroscopy and crystallography ; Multiferroics and related materials
Authors: Muhammad Fauzan | Jaka Adi Baskara | Wahyuningdiah Trisari Harsanti Putri | Quintin Kurnia Dikara
Source: Techno Com (article, 2025-08-18)
Field: Chemistry — Analytical Chemistry

Classification of Oil Loss Levels in Palm Oil Processing Using Near-Infrared Spectroscopy with Machine Learning Oil losses in palm oil processing materials, such as Final Effluent, Empty Fruit Bunches, Kernels, Pressed Fiber, and Decanter Solids, pose significant challenges in ensuring production efficiency. Free and Open Source Software Near Infrared Spectroscopy (FOSS-NIRS) technology has been proven capable of quickly and efficiently detecting oil content, but its detection accuracy requires further analytical support. This study aims to develop a machine learning model that can accurately classify FOSS-NIRS data to detect oil losses that are either above the standard (red category) or below the standard (green category). By utilizing FOSS-NIRS data across five material categories, the proposed model is expected to provide precise predictions and support decision-making in palm oil production processes. The results of the study indicate that applying machine learning methods to FOSS-NIRS data can enhance the accuracy of oil loss classification, making it a potential solution for broader implementation in the palm oil processing industry to optimize production efficiency. Keywords - Oil, Palm Oil, Losses, FOSS-NIRS. Palm oil ; Near-infrared spectroscopy ; Spectroscopy ; Infrared spectroscopy ; Environmental science ; Materials science ; Artificial intelligence ; Petroleum engineering ; Chemistry ; Machine learning ; Computer science ; Food science ; Engineering ; Optics ; Organic chemistry ; Physics Spectroscopy and Chemometric Analyses ; Oil Palm Production and Sustainability ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Imran Chaudhry (3475595) | Mark J. Bronson (11839478) | Lasse Jensen (257519)
Source: Figshare (article, 2025-07-09)
Field: Materials Science — Materials Chemistry

Δ‑MachineLearning of PolarizabilityTensors Using a Dipole Interaction Model As a fundamental response property, the molecular polarizability is responsible for a wide variety of physical phenomena relevant for understanding light-matter and intermolecular interactions. Therefore, it is important to have accurate and efficient methods to calculate the polarizability tensor. In this work, we introduce a model that combines a polarizable dipole interaction model (PIM) with Δ-machine learning to predict polarizability tensors, which we refer to as Δ<sub>PIM</sub><sup>CCSD</sup>. To describe the rotational symmetry, we adapt a unique reference geometry obtained by diagonalizing the PIM polarizability tensor. A major benefit is that only the diagonal elements of the polarizability tensor needs to be learned. The model was parameterized to the coupled cluster singles and doubles (CCSD) polarizabilities from the QM7b data set and was used to predict the polarizabilities for various systems, such as small molecules and molecules from the QM9 data set. We show that the Δ<sub>PIM</sub><sup>CCSD</sup> is comparable in accuracy to density-functional theory with the B3LYP exchange correlation functional (DFT/B3LYP) at a lower cost for molecules with similar chemical composition as the QM7b data set. For the QM9 data set, this was also found, although only after correcting for the smaller basis set used for calculating the polarizabilities in this data set. For molecules smaller and chemically more diverse than the training set, we find that the model performs worse than DFT/B3LYP. Ultimately, our work suggests that larger and more chemically diverse data sets with polarizabilities obtained at a high level of theory are needed. Finally, our results suggest that the model can be improved by incorporating atom-specific polarizabilities into PIM to better account for local environments. In summary, the combination of PIM with Δ-machine learning provides a simple and promising approach for predicting polarizability tensors. Polarizability ; Dipole ; Tensor (intrinsic definition) ; Parameterized complexity ; Intermolecular force ; Molecule ; Diagonal ; Basis (linear algebra) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Molecular spectroscopy and chirality
Authors: Paul Katzberger (12118440) | Felix Pultar (3205788) | Sereina Riniker (1450804)
Source: Figshare (article, 2025-07-28)
Field: Materials Science — Materials Chemistry

Transferring Knowledgefrom MM to QM: A Graph NeuralNetwork-Based Implicit Solvent Model for Small Organic Molecules The conformational ensemble of a molecule is strongly influenced by the surrounding environment. Correctly modeling the effect of any given environment is, hence, of pivotal importance in computational studies. Machine learning (ML) has been shown to be able to model these interactions probabilistically, with successful applications demonstrated for classical molecular dynamics. While first instances of ML implicit solvents for quantum-mechanical (QM) calculations exist, the high computational cost of QM reference calculations hinders the development of a generally applicable ML implicit solvent model for QM calculations. Here, we present a novel way of developing such a general machine-learned QM implicit solvent model by transferring knowledge obtained from classical interactions to QM, emulating a QM/MM setup with electrostatic embedding and a nonpolarizable MM solvent. This has the profound advantages that neither QM/MM reference calculations nor experimental data are required for training and that the obtained graph neural network (GNN)-based implicit solvent model (termed QM-GNNIS) is compatible with any functional and basis set. QM-GNNIS is currently applicable to small organic molecules and describes 39 different organic solvents. The performance of QM-GNNIS is validated on NMR and IR experiments, demonstrating that the approach can reproduce experimentally observed trends unattainable by state-of-the-art implicit-solvent models paired with static QM calculations. Embedding ; Solvent models ; Artificial neural network ; Solvent effects ; Graph ; Molecule ; Molecular dynamics ; Organic molecules ; Solvent Machine Learning in Materials Science ; Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies