jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yongfeng Jin | Junjie Wang | Fanjie Xu | Xiaohong Ji | Zhifeng Gao | Linfeng Zhang | Guolin Ke | Rong Zhu | E Weinan
Source: ArXiv.org (preprint, 2025-08-30)
Field: Materials Science — Materials Chemistry

NMR-Solver: Automated Structure Elucidation via Large-Scale Spectral Matching and Physics-Guided Fragment Optimization Nuclear Magnetic Resonance (NMR) spectroscopy is one of the most powerful and widely used tools for molecular structure elucidation in organic chemistry. However, the interpretation of NMR spectra to determine unknown molecular structures remains a labor-intensive and expertise-dependent process, particularly for complex or novel compounds. Although recent methods have been proposed for molecular structure elucidation, they often underperform in real-world applications due to inherent algorithmic limitations and limited high-quality data. Here, we present NMR-Solver, a practical and interpretable framework for the automated determination of small organic molecule structures from $^1$H and $^{13}$C NMR spectra. Our method introduces an automated framework for molecular structure elucidation, integrating large-scale spectral matching with physics-guided fragment-based optimization that exploits atomic-level structure-spectrum relationships in NMR. We evaluate NMR-Solver on simulated benchmarks, curated experimental data from the literature, and real-world experiments, demonstrating its strong generalization, robustness, and practical utility in challenging, real-life scenarios. NMR-Solver unifies computational NMR analysis, deep learning, and interpretable chemical reasoning into a coherent system. By incorporating the physical principles of NMR into molecular optimization, it enables scalable, automated, and chemically meaningful molecular identification, establishing a generalizable paradigm for solving inverse problems in molecular science. Matching (statistics) ; Fragment (logic) ; Nuclear magnetic resonance spectroscopy ; Interpretation (philosophy) ; Molecular dynamics ; Inverse ; Molecular model ; Inverse problem Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Molecular spectroscopy and chirality
Authors: Krisztina Zsigmond | Akash Surendran | Lexin Chen | Ramón Alain Miranda‐Quintana
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-09-25)
Field: Chemistry — Physical and Theoretical Chemistry

The Shape of Chemical Space Abstract The concept of chemical space is critical in cheminformatics, medicinal chemistry, and machine learning applications. Despite this, the high dimensionality of molecular representations greatly complicates its sampling, analysis, and visualization. A popular approach to overcome problem is to project these representations to a “human-manageable” subspace, usually containing only two dimensions. Non-linear dimensionality reduction techniques are by far the preferred strategy, following the reasoning that their flexibility can accommodate any arbitrary distribution originally present in the high-dimensional space. However, this ignores the elevated computational cost of these methods and the difficulty in tuning their hyper-parameters. Here, we show that basic properties of the metrics used in the original space can be used to infer the shape of the chemical space, which in turns suggests an optimal strategy to project chemical information to lower dimensions. The key insight is to realize that, no matter the set of molecules, their fingerprint representation can be considered to lie on a hyper-spherical surface. The smooth nature of this manifold means that we can use clustering to identify locally-dense sectors of chemical space, and selectively project them simply using linear (hyper-parameter free) methods, like principal component analysis. This approach surpasses non-linear techniques in several neighborhood preservation metrics, while only requiring a fraction of the computational cost. This pipeline is implemented in our N-Ary Mapping Interface (NAMI: https://github.com/mqcomplab/NAMI ), which we tested in the visualization of 10 million molecules. Dimensionality reduction ; Representation (politics) ; Chemical space ; Curse of dimensionality ; Visualization ; Key (lock) ; Pipeline (software) ; Set (abstract data type) ; Cluster analysis ; Flexibility (engineering) History and advancements in chemistry
Authors: A. Sonya | P. Kalarani | Pon Maheskumar | P.M. Sithar Selvam | M. Sathish | P. Vimalanathan | S. Mayakannan
Source: International Journal of Vehicle Structures and Systems (article, 2025-11-25)
Field: Materials Science — Polymers and Plastics

Optimization of Tensile Strength in Nano Silica and Calotropis Gigantea Reinforced Hybrid Polymer Nanocomposites using ANN-BBD Modelling Machine learning, artificial neural networks (ANNs) and artificial intelligence (AI) have all found applications in the engineering field. In this study, a reinforced hybrid polymer nanocomposite (NC) was created utilizing nano silica (nSiO2) and natural fibers (NF) derived from Calotropis gigantea fiber (CGF). The nanocomposite is designed for advanced composite applications. To promote interaction and adhesion between the CGF/polymer matrix, the fibers were modified by applying a chemical solution comprising KMnO4 in C3H6O. This altered the surface of the fibers. A convolutional neural network (CNN) model was used to optimize and predict the tensile strength (TS) of CGF/nSiO2 hybrid nanocomposite (CGF/nSiO2nc). With a hidden layer of five neurons and a one-layer perceptron structure of 3-5-1, this model, which was built using the Box-Behnken Design (BBD), showcased very different optimization. Evidence of the effect of the KMnO4 alteration on the hybridized nanocomposite's TS may be seen in scanning electron micrographs. As demonstrated by ANOVA with R2 = 0.9985, the mechanical test output demonstrate that these variables had an effect on the CGF/nSiO2 TS. The experimental results were quite close to matching the predicted outcomes. The ideal TS, according to the model, is 53.2025 MPa. The validity of the empirical experimental study was confirmed by doing TS analysis at the optimal parameters that were predicted. The average strength was found to be 40.28 MPa in the TS data. The model captures around 98.68% of the predicted TS. Finding the right values for mechanical properties fast, while cutting production costs and conserving resources, is possible with the help of the ANN-BBD modelling approach. Nanocomposite ; Ultimate tensile strength ; Multilayer perceptron ; Composite number ; Polymer ; Fiber ; Fibre-reinforced plastic ; Artificial neural network ; Natural fiber Natural Fiber Reinforced Composites ; Polymer Nanocomposites and Properties ; Material Selection and Properties
Authors: Y. A. Tang
Source: Fusion Engineering and Design (article, 2025-12-09)
Field: Materials Science — Materials Chemistry

A robust predictive tool for the yield strength of irradiated RAFM steels for fusion engineering applications Benchmark (surveying) ; Irradiation ; Fusion power ; Tungsten ; Yield (engineering) ; Fusion ; Blanket ; Helium Fusion materials and technologies ; Nuclear reactor physics and engineering ; Nuclear Materials and Properties
Authors: Youngchun Kwon | Yongsik Jung | Youn-Suk Choi | Seokho Kang
Source: Machine Learning Science and Technology (article, 2025-06-02)
Field: Materials Science — Materials Chemistry

Interpretation of chemical reaction yields with graph neural additive network Abstract Prediction of chemical yields is crucial for exploring untapped chemical reactions and optimizing synthetic pathways for targeted compounds. Recently, graph neural networks have proven successful in achieving high predictive accuracy. However, they remain intrinsically black-box models, offering limited interpretability. Understanding how each reaction component contributes to the yield of a chemical reaction can help identify critical factors driving the success or failure of reactions, thereby potentially revealing opportunities for yield optimization. In this study, we present a novel method for interpretable chemical reaction yield prediction, which represents the yield of a chemical reaction as a simple summation of component-wise contributions from individual reaction components. To build an interpretable prediction model, we introduce a graph neural additive network architecture, wherein shared neural networks process individual reaction components in an input reaction while leveraging a reaction-level embedding to derive their respective contributions. The predicted yield is obtained by summing these component-wise contributions. The model is trained using a learning objective designed to effectively quantify the contributions of individual components by amplifying the influence of significant components and suppressing that of less influential components. The experimental results on benchmark datasets demonstrated that the proposed method achieved both high predictive accuracy and interpretability, making it suitable for practical use in synthetic pathway design for real-world applications. Interpretability ; Artificial neural network ; Computer science ; Benchmark (surveying) ; Component (thermodynamics) ; Yield (engineering) ; Graph ; Artificial intelligence ; Machine learning ; Embedding ; Process (computing) ; Biological system ; Biochemical engineering ; Theoretical computer science ; Materials science ; Engineering Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Bioinformatics and Genomic Networks
Authors: Ayman Sabry | Alaa Adel Zaki | Mazen Ebrahim | Tamer M. Nassef
Source:  (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

AI-Driven 3D Molecular Reconstruction and Generative Modeling for Property-Aware Molecule Design The 3D molecular structure is one of the most important pillars in understanding chemical behavior, drug interactions, and material properties. However, obtaining 3D geometries using quantum simulations is computationally very costly, and many machine learning approaches still rely on reconstructing molecular structures from 2D representations such as SMILES and molecular graphs. This research presents an AI-driven framework that performs accurate 3D molecular reconstruction from 2D input and expands its capabilities in generation. It can generate candidates with known properties. The system integrates traditional methods such as GNNs to achieve spatial coordinate prediction with a generative latent model, allowing users to obtain controlled examples of valid chemical structures with specified properties. Experiments were conducted on the QM7b data set and demonstrated competitive reconstruction, achieving RMSD errors below 1.2 Å for stable molecules and bond-length deviations below 0.08 Å, while maintaining valid chemical connectivity. The predictive model accurately estimated quantum-derived molecular properties, attaining a test RMSE of 0.6433 for the HOMO–LUMO gap and showcasing stable learning behavior across epochs. The module achieved 82% chemical validity, 91% uniqueness, and 69% novelty in producing new molecular candidates. In addition, optimization of the latent-space allowed orientation of target properties, reducing the predicted molecular energy from 1592.80 kcal/mol to 1614.46 kcal/mol through gradient-based refinement. These results highlight the potential of unifying 3D reconstruction and generative modeling into a single, scalable framework for rapid molecular design. Generative grammar ; Pattern recognition (psychology) ; Set (abstract data type) ; Generative model ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gene Regulatory Network Analysis
Authors: Subhash V. S. Ganti | Lukas Wölfel | Christopher Künneth
Source: EPub Bayreuth (University of Bayreuth) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

AI-Driven Discovery of High Performance Polymer Electrodes for Next-Generation Batteries The use of transition group metals in electric batteries requires extensive usage of critical elements like lithium, cobalt, and nickel, which pose significant environmental challenges. Replacing these metals with redox-active organic materials offers a promising alternative, thereby reducing the carbon footprint of batteries by one order of magnitude. However, this approach faces critical obstacles, including the limited availability of suitable redox-active organic materials and issues such as lower electronic conductivity, voltage, specific capacity, and long-term stability. To overcome the limitations for lower voltage and specific capacity, a machine learning (ML) driven battery informatics framework is developed and implemented. This framework utilizes an extensive battery dataset and advanced ML techniques to accelerate and enhance the identification, optimization, and design of redox-active organic materials. In this contribution, a data-fusion ML coupled meta learning model capable of predicting the battery properties, voltage, and specific capacity for various organic negative electrodes and charge carriers (positive electrode materials) combinations is presented. The ML models accelerate experimentation, facilitate the inverse design of battery materials, and identify suitable candidates from three extensive material libraries to advance sustainable energy-storage technologies. Battery (electricity) ; Organic radical battery ; Carbon footprint ; Voltage ; Electrode Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advanced Battery Technologies Research
Authors: Katikala Geetha Pravallika, Saripalli Sri Lakshmi*, Yerruboina Supriya
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-12-01)
Field: Chemistry — Spectroscopy

AI-ENHANCED HPLC: BRIDGING ANALYTICAL EFFICIENCY AND INTELLIGENT DECISION-MAKING IN CHROMATOGRAPHY ABSTRACT One of the most crucial analytical instruments in pharmaceutical research is High-Performance Liquid Chromatography (HPLC), which guarantees precise complex chemical separation, identification, and quantification. However, because several variables, including temperature, flow rate, mobile phase composition, and column selection, must be optimized, traditional method development continues to be labour-intensive and time-consuming. Artificial Intelligence (AI) integration with HPLC has become a game-changing solution. By analysing massive chromatographic datasets, machine learning (ML), deep learning (DL), and artificial neural networks (ANNs) facilitate intelligent decision-making, automatic optimization, and predictive modeling. AI reduces experimental trials and predicts retention times accurately, improving efficiency, repeatability, and data quality. Real-world case studies demonstrate its influence: Merck effectively used AI to improve chromatographic parameters for biologics, increasing robustness and reducing experimental workload, while Pfizer's AI-driven platform dramatically shortened method development time by forecasting ideal separation circumstances. AI-powered solutions also ensure regulatory compliance by supporting quality control, anomaly detection, and real-time monitoring. AI-enhanced HPLC represents a significant advancement in intelligent, data-driven analytical research with wide-ranging applications in medicines, food safety, clinical diagnostics, and environmental monitoring. Robustness (evolution) ; Bridging (networking) ; Artificial neural network ; High-performance liquid chromatography ; Experimental data ; Anomaly detection ; Deep learning Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Billy G. Ram | Yu Zhang | Maria Villamil Mahecha | Jack C. McCarty | Johnie N. Jenkins | Xin Sun
Source: Journal of Agriculture and Food Research (article, 2025-10-17)
Field: Chemistry — Analytical Chemistry

Deep learning spectral classification of spatial twins in three cotton breeding lines The increasing popularity of cotton as a personal clothing material has made fire resistance testing of cotton fibers crucial. Current methods for testing fire resistance in cotton textiles involve destructive open flame and smoldering tests. Due to advancements in spectroscopic sensor technologies and deep learning (DL) frameworks, non-destructive flame resistance testing has been made possible. This study investigates the effectiveness of hyperspectral sensors for spectral classification of lint from three breeding lines of cotton fibers: SG 747 (a commercial cultivar), RIL 385 (a fire-resistant breeding line), and RIL 225 (a fire-susceptible breeding line). Spectral data was collected in the visible near-infrared range (300–1000 nm) and the shortwave infrared region (900–1700 nm). A one-dimensional convolutional neural network (1D-CNN) was developed as the foundational model in this study. Machine learning frameworks, including neural architectural search, hyperparameter tuning, and distributed learning, were applied to develop an optimized DL model architecture with hyper-tuned parameters. This systematic approach enabled fine-tuning of the model to achieve improved performance in cotton fire resistance classification tasks. The performance of the hyper-tuned model in the 900–1700 nm range was found to be higher than that of the base model, achieving a test accuracy of 86% compared to the base model's 84% accuracy. This study demonstrates the potential for non-destructive fire resistance testing in cotton fibers with the successful implementation of DL modeling for spectral classification of spatial twins. • Imaging-based hyperspectral classification of spatially twin cotton cultivar samples. • Implementation of neural architecture search (NAS) and hyperparameter tuning (HPT) to enhance deep learning model performance. • Development of a 1D-CNN framework for classifying cotton fibers by fire resistance, susceptibility, and cultivar lines. Deep learning ; Convolutional neural network ; Hyperparameter ; Hyperspectral imaging ; Artificial neural network ; Pattern recognition (psychology) ; Test data Spectroscopy and Chemometric Analyses ; Research in Cotton Cultivation ; Remote Sensing in Agriculture
Authors: S.K. Xia | Jian‐Xin Zhu | Wenguan Liu | Yang Guo | Wenjie Li | Dan Sun | Xue-Kun Chen | Xi-Jun Wu
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Molecular Dynamics Study on the Evolution of Primary Irradiation Defects in Uranium-Molybdenum-Niobium Ternary Alloys Niobium ; Ternary operation ; Molybdenum ; Uranium ; Materials science ; Molecular dynamics ; Irradiation ; Primary (astronomy) ; Radiochemistry ; Metallurgy ; Chemistry ; Nuclear physics ; Computational chemistry ; Physics ; Computer science Nuclear Materials and Properties ; Fusion materials and technologies ; Nuclear materials and radiation effects
Authors: Giulia Sormani (8157789) | Alex Rodriguez (676778) | Ali Hassanali (2343622)
Source: Figshare (article, 2025-08-05)
Field: Materials Science — Materials Chemistry

Opportunities andChallenges in Unsupervised Learning:The Case of Aqueous Electrolyte Solutions Machine learning has emerged as a powerful tool in atomistic simulations, enabling the identification of complex patterns in molecular systems, limiting human intervention and bias. However, the practical implementation of these methods presents significant technical challenges, particularly in the selection of hyperparameters and in the physical interpretability of machine-learned descriptors. In this work, we systematically investigate these challenges by applying an unsupervised learning protocol to a fundamental problem in physical chemistry, namely, how ions perturb the local structure of water. Using the Smooth Overlap of Atomic Positions (SOAP) descriptors, we demonstrate how the intrinsic dimension (ID) serves as a guide for selecting hyperparameters and interpreting the structural complexity. Furthermore, we construct a high-dimensional free-energy landscape encompassing all water environments surrounding different ions. This analysis reveals how the physical properties of ions are intricately reflected in their hydration shells, shaping the landscape through specific connections between different minima. Our findings highlight the difficulty in balancing algorithmic automation with the need of employing both physical and chemical intuition, particularly for the construction of meaningful descriptors and for the interpretation of final results. By critically assessing the methodological hurdles associated with unsupervised learning, we provide a roadmap for researchers looking to harness these techniques for studying electrolyte and aqueous solutions in general. Interpretability ; Construct (python library) ; Identification (biology) ; Dimension (graph theory) ; Limiting ; Automation ; Selection (genetic algorithm) ; Hyperparameter Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Yu Wang | Heng Chen | Rui Su | Bin Xu | Rulong Zhou | Dongdong Li | Yu-Wei You | Pengfei Guan | Changsong Liu
Source: Journal of Nuclear Materials (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Development of a neural network potential for osmium enables irradiation damage simulations Irradiation ; Osmium ; Fusion ; Radiation damage ; Artificial neural network ; Molecular dynamics ; Lattice constant ; Cluster analysis ; Ab initio Fusion materials and technologies ; Nuclear Materials and Properties ; Nuclear materials and radiation effects
Authors: Fang Sun (336111) | Qing Tang (167623)
Source: Figshare (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

Capturing Dynamic CoreReconstruction and Ligand Desorptionof Atomically Precise Ag Nanoclusters with Machine Learning ForceField Atomically precise silver nanoclusters (NCs) protected by alkynyl ligands represent an emerging class of electrocatalysts demonstrating high activity and selectivity in reactions, such as CO<sub>2</sub> electroreduction. However, their dynamic structural evolution mechanisms under electrochemical operating conditions remain elusive. Conventional experimental characterization faces a grand challenge to resolve atomic-scale dynamic processes, while <i>ab initio</i> molecular dynamics (AIMD) simulations are solely confined to picosecond time scales, insufficient for capturing the dynamics of evolution over longer times. Combining multiscale constant potential simulations and a deep potential molecular dynamics (DPMD) scheme, here we developed a high-accuracy machine learning force field within the deep-learning framework to elucidate the electrochemical structural evolution in all-alkynyl-protected Ag<sub>15</sub> NC and its doped systems (Ag<sub>8</sub>Au<sub>7</sub>, Ag<sub>9</sub>Cu<sub>6</sub>, and Ag<sub>14</sub>Cl NCs). We found that the metal cores of all NCs undergo a transition from octahedral to disordered, accompanied by partial or complete cleavage of surface alkynyl ligands. The dopants critically modulate the stability by regulating desorption pathways, with Ag<sub>9</sub>Cu<sub>6</sub> NC exhibiting exceptional resistance to dissociation due to robust Cu–C bonding. Our nanosecond-level DPMD simulations based on trained machine learning force fields further confirmed that doping dramatically affects the number of desorbed alkyne ligands (4 for Ag<sub>15</sub>, 6 for Ag<sub>8</sub>Au<sub>7</sub>, and 8 for Ag<sub>14</sub>Cl) and the degree of core ordering, and a long-term simulation of >2000 ps was crucial for capturing the dynamic electrochemical interface. This study established the first quantitative correlation between electrochemical interface dynamics and doping effects, providing a theoretical paradigm for designing highly stable atomically precise catalysts. Nanoclusters ; Molecular dynamics ; Dopant ; Doping ; Force field (fiction) ; Dissociation (chemistry) ; Characterization (materials science) ; Electrochemistry ; Desorption Nanocluster Synthesis and Applications ; CO2 Reduction Techniques and Catalysts ; Machine Learning in Materials Science