jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiaxian Zhang | Hongxia Guo
Source: Macromolecular Rapid Communications (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

Machine Learning‐Assisted Multi‐Target Coarse‐Graining Strategy for Polystyrene Coarse-grained (CG) molecular dynamics offers a powerful means to bridge atomistic simulations and macroscopic experiments, but constructing CG models that simultaneously preserve structural, thermodynamic, and dynamical consistency remains challenging. Here, we present a machine learning-assisted, multi-objective parameterization strategy for atactic polystyrene (PS) based on a 2:1 mapping scheme. By integrating Support Vector Regression (SVR) and Particle Swarm Optimization (PSO), we systematically optimize Lennard-Jones parameters to reproduce atomistic-level radial distribution functions, density, cohesive energy density, and self-diffusion coefficients at 600 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:semantics><mml:mi>K</mml:mi> <mml:annotation>$K$</mml:annotation></mml:semantics> </mml:math> and 1 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:semantics><mml:mrow><mml:mi>a</mml:mi> <mml:mi>t</mml:mi> <mml:mi>m</mml:mi></mml:mrow> <mml:annotation>$atm$</mml:annotation></mml:semantics> </mml:math> . Notably, the inclusion of the diffusion coefficient as an optimization target enables the construction of a dynamically consistent CG model. The resulting CG force field achieves remarkable agreement with all-atom (AA) simulations across multiple observables, establishing a robust framework for predictive polymer modeling. This methodology provides a framework that could be extended to materials discovery and rational polymer design in future studies. Polystyrene ; Particle swarm optimization ; Molecular dynamics ; Consistency (knowledge bases) ; Polymer ; Force field (fiction) ; Field (mathematics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Polymer crystallization and properties
Authors: Pooja Arora | Shubham Kharbanda
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2025-05-30)
Field: Chemistry — Physical and Theoretical Chemistry

Deep Chem: An AI-Powered Tool for Automated Recognition of Chemical Structures and Predicting their Impact on Health Abstract: It has become nearly impossible to extract and retain chemical structure data from earlier papers that are only available in printed or scanned form as a result of the expanding availability of chemical structure data. Although systems for rule-based optical chemical structure recognition (OCSR) have been created to automate this procedure, they have drawbacks including being sluggish and prone to mistakes. The Deep Chem project has developed an application for recognizing chemical structures in order to address this problem and comparing the impact of these chemical structures in different foods on human health based on their molar mass. The app uses deep learning approaches to automate the recognition of chemical structures from printed or scanned articles. Traditional rule-based optical chemical structure recognition (OCSR) tools can be slow and prone to errors, but the Deep Chem app aims to provide a faster and more reliable solution. A collection of 50–100 million molecules were used to predict SMILES encodings of chemical structure renderings with over 96% accuracy for structures without stereochemical information and over 89% accuracy for structures with stereochemistry information. Additionally, the app provides a feature to compare the impact of different foods on health based on their molar mass. This work is entirely based on open-source software and open data and is available to the general public for any purpose. Artificial intelligence ; Computer science Various Chemistry Research Topics ; Computational Drug Discovery Methods ; History and advancements in chemistry
Authors: Lu-Yao Zhu (20349448) | Yu Han (251741) | Yu-Fan Zhang (6669002) | Hang Li (123039) | Li Dong (37011) | Chao Zhang (51048) | Yang Zhang (30734) | Yao Zhang (134381) | Zhen-Chao Dong (1680838)
Source: Figshare (article, 2025-09-25)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Algorithm-AssistedStructure Identification of IndividualNucleobases in Single DNA Chains through Tip-Enhanced Raman Spectromicroscopy The functions of biomolecules such as DNA are not only determined by their sequences but also strongly dependent on their stereostructures and geometric configurations of constituent units. However, most DNA sequencing techniques developed to date heavily rely on fluorescent labeling and extensive replication, and the DNA chains might thus be unwound and even modified during these treatments, resulting in the loss of structural information. Recent development of single-molecule tip-enhanced Raman spectroscopy (TERS) offers a label-free approach to identifying individual nucleobases with both high spatial resolution and chemical sensitivity but amounts to mainly proof-of-principle demonstrations of very short single-stranded DNA molecules. Here, we propose an algorithm-assisted strategy for determining the structures of long-chain DNA molecules based on TERS imaging in real space. We first develop a matching algorithm to rapidly simulate the TERS spectra and mapping images for long-chain DNA containing tens of thousands of atoms, circumventing the huge computational cost of the quantum chemistry simulation of large molecules. After validating the accuracy and efficiency of this algorithm in various DNA molecular systems, we further combine it with the Bayesian algorithm to determine the experimental molecular structures with single-nucleobase precision from the TERS measurements on two long-chain ssDNA model systems on surfaces, showing a general agreement between theories and experiments. Our approach will promote the machine-learning assisted TERS technique as a generic tool to identify the sequence and configuration of complicated biomolecular systems, including long DNA/RNA chains, proteins, and even glyco-peptide complexes. Nucleobase ; Biomolecule ; DNA ; Molecular biophysics ; Identification (biology) ; DNA sequencing ; Raman spectroscopy Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; DNA and Nucleic Acid Chemistry
Authors: Adam Hložný | Ján Brndiar | Michele Casula | I. Štich
Source: The Journal of Chemical Physics (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

Structure and dynamics of sulfur vacancies in monolayer MoS2 studied by DFT-based machine learning potentials We have developed a multi-step strategy for training stable and precise machine learning potentials (MLPs) for activated processes that are accurate for both in-domain interpolation and out-of-domain (OOD) extrapolation regimes and applied it in the realm of vacancies in 2D materials. An essential part of obtaining well-performing MLPs is balanced and properly sampled datasets. To achieve this, we have designed a sampling technique based on the nudged elastic band and constrained molecular dynamics. Our analysis goes well beyond the calculation of conventional metrics, such as the root mean square error on the validation dataset. We use tailor-made metrics that focus on the atoms that critically determine the defect migration process. In the context of chalcogen vacancy dynamics in monolayer MoS2, we extensively benchmarked the MACE MLP model and checked its behavior for atoms close to the vacancy or in near-barrier configurations, which are the most difficult to describe since they require very robust OOD generalization performance. Generally, we found that a properly trained MACE model is able to reliably reproduce energies and forces even in these extreme cases. To demonstrate the utility of our approach, we calculated relaxations and minimum energy paths for single- and multi-vacancy transitions in monolayer MoS2, as well as free energy barriers utilizing thermodynamic integration. We believe our conclusions are also valid for other equivariant message passing neural network potentials due to their general similarity. Finally, we discuss the possibility of tuning the density functional theory-based MLP toward quantum Monte Carlo accuracy. Extrapolation ; Vacancy defect ; Overfitting ; Artificial neural network ; Context (archaeology) ; Interpolation (computer graphics) ; Density functional theory ; Energy (signal processing) 2D Materials and Applications ; Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Dorothee Silbernagl | Anja Kömmling | Maha Zaghdoudi | Media Ghasem Zadeh Khorasani | Matthias Jaunich
Source: Polymer Degradation and Stability (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Data-driven nanomechanical study of filled fluoroelastomer aged in air and hydrogen atmosphere • AFM force spectroscopy gives insights into aging mechanisms of filled fluoroelastomer under high temperature, high pressure conditions (HPHT). • Filler particles, interphase, and matrix are resolved, and effects of aging investigated separately. • Physical aging of the interphase and chemical aging of the matrix are distinguished. • The use of machine learning tools on a large set of AFM force spectroscopy data successfully confirms and quantifies findings. • The dominant aging mechanism of fluoroelastomer in HPHT hydrogen atmosphere is established as gas induced physical aging. Fluoroelastomer (FKM) composites are typically used as sealing materials in challenging non-ambient environments. Depending on the environment, two main aging mechanisms, chemical aging, and physical aging, can be identified. Chemical aging, the degradation of the elastomer, is present for example in thermal-oxidative conditions and can be directly observed as it affects the bulk. Physical aging, relaxation and rearrangement of the elastomers segmental conformation is commonly observed at elevated temperatures and effects predominantly the elastomer interphase. As a highly localized nanoscopic effect it is usually observed indirectly by phenomological approaches and not systematically understood. In this study, as a typical example for chemical aging, filled FKM was aged in air (150°C, 100 days). Physical aging of FKM was realized by exposure to chemically inert H 2 (150°C, 50 bar, 100 days), since temperature and gas-induced swelling is known to promote physical aging. The effects of both conditions are directly compared with the initial unaged material. We use atomic force microscopy (AFM) force spectroscopy as a method to resolve nanoscopic heterogeneous FKM. With this method the effect of aging on the spatially distinguishable material phases was directly observed. In thermal oxidative aged FKM the matrix shows a decrease in van der Waals interactions and stiffness, indicating dehydrofluorination and chain scission. In H 2 aged FKM, the development of an immobilized amorphous interphase (IAP) was observed, indicating physical aging. By additionally evaluating a larger data set with supervised machine learning, these observations were validated for a larger, statistically representative sample area, allowing conclusions to be drawn about the macroscopic behaviour of the material. Elastomer ; Nanoscopic scale ; Interphase ; Hydrogen ; Force spectroscopy ; Matrix (chemical analysis) ; Spectroscopy ; Relaxation (psychology) Silicone and Siloxane Chemistry ; Polymer Nanocomposites and Properties ; Tribology and Wear Analysis
Authors: Gyoung S. Na (8367210) | Yecheol Rho (1782613)
Source: Figshare (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

ExplainableMachine Learning for Characterizing UnknownMolecular Structures in Infrared Spectra Various machine learning methods for detecting functional groups in infrared (IR) spectra have been developed to accelerate time-consuming and labor-intensive IR spectrum analysis. Nevertheless, existing machine learning methods have exhibited two fundamental limitations: (1) Existing methods do not incorporate prior knowledge of IR spectrum analysis during the training and inference processes. (2) Machine learning approaches are inherently black-box models, and therefore, the prediction results of existing methods lack explainability. To overcome these limitations, we propose the substructure-directed spectrum interpreter network (SSIN), a deep learning method for efficient and explainable functional group detection in IR spectra of unknown molecules. SSIN achieved a detection accuracy greater than 0.920 on the NIST database, which contains experimentally measured 8845 gas-phase IR spectra. Furthermore, SSIN accurately identified the absorption peaks corresponding to the existence of target functional groups. Quantitatively, human-readable IR spectrum analysis reports generated by SSIN with large language models showed 81-99% accuracy when compared with the ground-truth IR spectroscopy absorption table. Source codes and optimized models of SSIN are publicly available at https://github.com/ngs00/SSIN. NIST ; Pattern recognition (psychology) ; Infrared spectroscopy ; Inference ; Infrared ; Spectroscopy ; Spectrum (functional analysis) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Ju Huang | Shu Zhao | Junfeng Gao | Rong Xiang | Wenbin Li
Source: ACS Nano (article, 2025-12-25)
Field: Materials Science — Materials Chemistry

An Atlas of Chirality-Dependent Electronic Structures of MoS <sub>2</sub> Nanotubes from Deep Learning Transition-metal dichalcogenide (TMD) nanotubes represent an emerging class of one-dimensional (1D) materials. However, our understanding of their chirality-dependent electronic properties has been limited. Here, we develop an integrated machine learning (ML) framework, combining ML interatomic potential with deep-learning density functional theory, to enable accurate and efficient prediction of the electronic structures of MoS<sub>2</sub> nanotubes across the entire chirality space. We construct a comprehensive atlas of their bandgaps, carrier effective masses, and direct-versus-indirect bandgap classification. We find that the bandgaps are primarily determined by tube diameter, whereas the carrier effective masses show a significant, nontrivial dependence on both tube curvature and chirality. In particular, a sharp increase in hole effective mass occurs for tube diameters below 62 Å, attributed to a universal strain-induced transition in the valence band maximum. These results provide significant insights into the properties of 1D TMD systems. Atlas (anatomy) ; Valence band ; Valence (chemistry) ; Band gap ; Curvature ; Electronic structure ; Deep learning ; Effective mass (spring–mass system) 2D Materials and Applications ; Nanowire Synthesis and Applications ; Graphene research and applications
Authors: Robert
J. Appleton (17880817) | Brian C. Barnes (2042218) | Alejandro Strachan (1435693)
Source: Figshare (article, 2025-10-27)
Field: Materials Science — Materials Chemistry

Data Fusion ofDeep Learned Molecular Embeddings forProperty Prediction Data-driven approaches such as deep learning can result in predictive models for material properties with exceptional accuracy and efficiency. However, in many applications, data is sparse, severely limiting their accuracy and applicability. To improve predictions, techniques such as transfer learning and multitask learning have been used. The performance of multitask learning models depends on the strength of the underlying correlations between tasks and the completeness of the data set. Standard multitask models tend to underperform when trained on sparse data sets with weakly correlated properties. To address this gap, we fuse deep-learned embeddings generated by independent pretrained single-task models, resulting in a multitask model that inherits rich, property-specific representations. By reusing (rather than retraining) these embeddings, the resulting fused model outperforms standard multitask models and can be extended with fewer trainable parameters. We demonstrate this technique on a widely used benchmark data set of quantum chemistry data for small molecules as well as a newly compiled sparse data set of experimental data collected from literature and our own quantum chemistry and thermochemical calculations. Multi-task learning ; Benchmark (surveying) ; Property (philosophy) ; Data set ; Set (abstract data type) ; Transfer of learning ; Deep learning ; Fuse (electrical) ; Sensor fusion ; Training set Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Sami Holopainen | Thierry Barrière | Xavier Gabrion | N. Niang | Vincent Placet
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Predicting the Deformability of Natural Short-Fiber Reinforced Polymer Composites Through Combined Constitutive Mathematical and Ai-Based Modeling Approaches Composite material ; Materials science ; Constitutive equation ; Natural fiber ; Fiber ; Polymer ; Synthetic fiber ; Polymer science ; Structural engineering ; Engineering ; Finite element method Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Tribology and Wear Analysis
Authors: S. Osaki | Kazuki Hoshitani | Makoto Nakamura | Kôichi Kimura | Tomoyuki Yamamoto
Source:  (article, 2025-08-30)
Field: Materials Science — Materials Chemistry

Advantages of Quantum Circuit Learning for the Development of High Entropy Materials In this study, we have explored the validity of the quantum circuit learning method by comparing the prediction results of Vickers hardness of the high entropy alloys with those predicted by the conventional linear and nonlinear types of machine learning methods. Nonlinear system ; Entropy (arrow of time) ; Quantum ; Development (topology) ; Measure (data warehouse) ; High dimensional Machine Learning in Materials Science ; High Entropy Alloys Studies ; Advanced Thermodynamics and Statistical Mechanics
Authors: Yihao Zheng (12872164) | Xiangcui Qiu (17118535) | Haibo Li (629817) | Konggang Qu (2040433) | Hui Yan (117134) | Rui Li (4631)
Source: Figshare (article, 2025-05-13)
Field: Materials Science — Materials Chemistry

Predictingand Understanding Work Functions of DoubleTransition Metal MXenes via Interpretable Machine Learning Methods In this study, we employed interpretable machine learning models to predict and understand the work functions of double transition metal MXenes with the formula (M1)<sub>2</sub>(M2)X<sub>2</sub>O<sub>2</sub>. We created a comprehensive data set of 242 structures covering 11 transition metals and two X elements (C, N) using first-principles calculations. Various machine learning methods, including linear regression, support vector regression, random forest regression, and artificial neural networks, were thoroughly evaluated. The random forest model achieved the best performance on the test set, with a mean absolute error of 0.17 ± 0.02 eV, root-mean-square error of 0.24 ± 0.03 eV, and <i>R</i><sup>2</sup> of 0.86 ± 0.03. The feature importance analysis revealed a hierarchical influence mechanism: the properties of the outer transition metal (M1) dominate the work function, followed by the X element, while the inner transition metal (M2) has minimal impact. Furthermore, we employed the Sure Independence Screening and Sparsifying Operator method to derive analytical expressions that relate element features to work functions, achieving comparable accuracy (<i>R</i><sup>2</sup> = 0.82 ± 0.04) while providing physical insights. Our findings not only enable rapid prediction of MXene work functions but also provide valuable guidance for the rational design of MXene-based materials. MXenes ; Random forest ; Support vector machine ; Work (physics) ; Set (abstract data type) ; Artificial neural network ; Feature (linguistics) ; Operator (biology) ; Test set MXene and MAX Phase Materials ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Jun Tae Song | Ying Zhao
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

<i>(Invited)</i> Defects-Enabled Basal Plane Activation in Tmdcs for Efficient Electrocatalytic Conversion Two-dimensional transition metal dichalcogenides (TMDCs) are promising and cost-effective electrocatalysts for energy conversion reactions, such as the hydrogen evolution reaction (HER) and CO₂ reduction reaction (CO₂RR). Their tunable structure, adjustable electronic properties, high surface area, and low cost make them attractive candidates for electrocatalysis. However, the inherent inertness of their basal planes significantly limits their catalytic activity, posing a major challenge for practical applications in electrochemical energy conversion. In our studies, employing systematic first-principles calculations, we showed that the afore-mentioned challenge may be addressed through structural modification utilizing defects including vacancies, interstitials, grain boundaries, phase boundaries, and alloying. These defect engineering strategies effectively expose active sites, modulate the electronic structure, and enhance charge transfer properties, to synergistically lead to improved catalytic activity. Such enhancement is demonstrated by defects-induced new reaction pathways and reduction in the Gibbs free energy of key reaction steps which lowers the thermodynamic energy barriers for HER and CO₂RR, thereby improving overall catalytic performance. Furthermore, the first-principles calculations are also integrated with machine learning-driven high-throughput screening to accelerate the exploration and identification of TMDC candidates with optimal electrocatalytic properties. This data-driven approach enables the rapid discovery of materials with superior catalytic activity, and meanwhile provide deeper insights into the structure–property relationships that govern electrocatalysis. Beyond TMDCs, our findings also highlight the broader applicability of defect engineering in low-dimensional materials for electrocatalytic reactions. For instance, similar defects-centric strategies have been applied with success to III-nitride nanowires. The defect structures introduced via doping and nanoparticle decoration have been shown to provide great flexibility in tailoring catalytic properties, enabling high efficiency and selectivity in energy conversion paths and end products. These results underscore the transformative potential of defect engineering in bottom-up design of next generation electrocatalysts towards more efficient and sustainable energy conversion technologies for addressing global energy challenges. Catalysis ; Electrocatalyst ; Flexibility (engineering) ; Energy transformation ; Electrochemical energy conversion ; Basal plane ; Electrochemistry ; Transition metal ; Gibbs free energy Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts