jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: William Ohlinger | Philip E. Klunzinger | Thomas Hehre | Andy Driessen | Bernard Deppmeier | Jeffrey Johnson | Connor Ohlinger | Warren J. Hehre | Jürgen Schnitker | Noritaka Uchida | Yuusuke Watanabe | Ryan D. Cohen | Ian Bruno | Masaru Hashimoto | Ryuhi Kanehara | Seth B. Herzon | Hannah J. Riseman | José Luis López-Pérez
Source: ChemRxiv (article, 2026-03-30)
Field: Chemistry — Spectroscopy

Practical Machine Learning Strategies. 5. Rapid, Accurate Prediction of 1H and 13C NMR Shifts in Conformationally Flexible Organic Molecules Accurate prediction of NMR chemical shifts for conformationally flexible natural products remains a central challenge in structure elucidation. Traditional quantum‑chemical (QM) approaches typically achieve ≈2.0–3.0 ppm 13C accuracy for flexible natural products but often require hours of computation per molecule. MLHF‑NMR reproduces high‑level QM 13C accuracy for flexible molecules while reducing computation times by factors of hundreds to more than a thousand, enabling routine, high‑throughput NMR prediction for conformationally flexible natural products. Against the high‑accuracy DELTA50 experimental 1H and 13C shift benchmark, MLHF‑NMR achieves RMS errors of 0.15 ppm (1H) and 1.73 ppm (13C). Across more than 8.2k 13C chemical shifts from 420 predominantly flexible natural products, the protocol yields an average RMS error of 2.23 ppm. Practical applications include structure assignment for newly isolated natural products, structure confirmation in independently synthesized molecules, rapid detection and correction of misassignments, and strategies for improving predicted 13C shift accuracy. Validation sets and sample MLHF‑NMR 13C shift predictions for more than 1900 synthetic drugs and drug‑like molecules are available at https://mlnmr.wavefun.com. For flexible molecules, MLHF‑NMR routinely delivers 13C shift accuracy comparable to the best QM methods while reducing computation time by 2–3 orders of magnitude. Computation ; Carbon-13 NMR ; Chemical shift ; Molecule ; Organic molecules Molecular spectroscopy and chirality ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Rong Wang | Di Jin | Junkai Hu | Wenbo Liu | Yuning Zhang | Irfan Haider Abidi | Sumeet Walia | Baohua Jia | Duan Huang | Jiayang Wu | David J. Moss
Source: arXiv (Cornell University) (preprint, 2026-03-08)
Field: Materials Science — Materials Chemistry

AI based design of 2D material integrated optical polarizers On-chip integration of highly anisotropic two-dimensional (2D) materials offers new opportunities for realizing high performance polarization selective devices. Obtaining optimized designs for such devices requires extensively sweeping large parameter spaces, which in conventional approaches relies on massive mode simulations that demand considerable computational resources. Here, we address this limitation by developing a machine learning (ML) model based on fully connected neural networks (FCNNs). Trained by using mode simulation results for low resolution structural parameters, the FCNN model can accurately predict polarizer figures of merits (FOMs) for high resolution parameters and rapidly map the global variation trend across the entire parameter space. We test the performance of the FCNN model using two types of polarizers with 2D graphene oxide (GO) and molybdenum disulfide (MoS2). Results show that, compared to conventional mode simulation approach, our approach can not only reduce the overall computing time by about 4 orders of magnitude, but also achieve highly accurate FOM predictions with an average deviation of less than 0.04. In addition, the measured FOM values for the fabricated devices show good agreement with the predicted ones, with discrepancies remaining below 0.2. These results validate artificial intelligence (AI) as an effective approach for designing and optimizing 2D-material based optical polarizers with high efficiency. Polarizer ; Artificial neural network ; Polarization (electrochemistry) ; Anisotropy ; Mode (computer interface) ; Bridging (networking) ; Optical proximity correction 2D Materials and Applications ; Photorefractive and Nonlinear Optics ; Photonic and Optical Devices
Authors: Chuang Cai | Fashuai Xiong | Zilin Chen | Hui Chen | Ping Tang | Xuanyu Jin | Zejun Xian | Hua Tang
Source: Engineering Applications of Artificial Intelligence (article, 2026-02-23)
Field: Materials Science — Materials Chemistry

Tensile property prediction of titanium and aluminum alloys dissimilar joint by plasma plume characteristics based on a multi-stage cascade model Cascade ; Ultimate tensile strength ; Plume ; Welding ; Joint (building) ; Titanium ; Backpropagation ; Plasma Fusion materials and technologies ; Welding Techniques and Residual Stresses ; High-Velocity Impact and Material Behavior
Authors: Anand Babu | Rogério Almeida Gouvêa | Pierre Vandergheynst | Gian-Marco Rignanese
Source: Open MIND (preprint, 2026-01-29)
Field: Materials Science — Materials Chemistry

MEIDNet: Multimodal generative AI framework for inverse materials design In this work, we present Multimodal Equivariant Inverse Design Network (MEIDNet), a framework that jointly learns structural information and materials properties through contrastive learning, while encoding structures via an equivariant graph neural network (EGNN). By combining generative inverse design with multimodal learning, our approach accelerates the exploration of chemical-structural space and facilitates the discovery of materials that satisfy predefined property targets. MEIDNet exhibits strong latent-space alignment with cosine similarity 0.96 by fusion of three modalities through cross-modal learning. Through implementation of curriculum learning strategies, MEIDNet achieves ~60 times higher learning efficiency than conventional training techniques. The potential of our multimodal approach is demonstrated by generating low-bandgap perovskite structures at a stable, unique, and novel (SUN) rate of 13.6 %, which are further validated by ab initio methods. Our inverse design framework demonstrates both scalability and adaptability, paving the way for the universal learning of chemical space across diverse modalities. Generative grammar ; Inverse ; Artificial neural network ; Scalability ; Similarity (geometry) ; Property (philosophy) ; Graph ; Space (punctuation) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Block Copolymer Self-Assembly
Authors: Handong Zhao | Mengqi Li | Taiyang Zhang | Shi-Zhao Kang | Lixia Qin | Xiangqing Li
Source: Sensors and Actuators B Chemical (article, 2026-02-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Intelligent identification of Raman-inactive molecules on a plasmonic ZIF-derived SERS platform Substrate (aquarium) ; Plasmon ; Zeolitic imidazolate framework ; Molecule ; Raman scattering ; Raman spectroscopy Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced oxidation water treatment
Authors: Hao Liu | M. C. Ekosso | Avery Glagovich | Sarah Maurer | Joshua Schrier
Source: ChemRxiv (article, 2026-01-28)
Field: Chemistry — Organic Chemistry

Molecular foundation models for predicting self-assembly in aqueous mixtures Pretrained foundation models offer new representations for molecular structures, but have predominantly been applied to single-component property prediction. Here, we develop a concentration-weighted average of the latent representations of graph neural network and transformer-based molecular foundation models to predict the critical micelle concentrations of single surfactant, biphasic separation of binary mixtures, and liposome formation in 7-component amphiphile mixtures. Simple random forest and feed-forward neural network models trained with these representations are comparable to or exceed prior work using bespoke graph neural network methods, physicochemical features, and binary fingerprinting. High-throughput automated experiments validate the ability of our models to extrapolate to novel surfactant mixtures and to guide iterative exploration of liposome design. Artificial neural network ; Binary number ; Foundation (evidence) ; Bespoke ; Graph ; Aqueous solution ; Molecular graph ; Pulmonary surfactant Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly ; Surfactants and Colloidal Systems
Authors: Natan Cristian Pedroso Pereira | Marcelle Beltrao Bedouch | ENDLER MARCEL BORGES
Source: Digital (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

Leveraging Microsoft Copilot (GPT-5) for Calculations and Interactive Data Visualization Large Language Models (LLMs) have successfully performed calculation-based tasks, generated diverse data visualizations, and executed chemometric analyses. This study systematically evaluated the performance of Microsoft M365 Copilot (GPT-5) across 35 representative questions spanning five domains: (1) chemical equilibrium, pH, titration, and buffer calculations; (2) data visualization, including histograms, box plots, correlation plots, and heatmaps; (3) analysis of periodic table properties using principal component analysis (PCA); (4) image interpretation and generation in classroom contexts; and (5) machine learning applications using Partial Least Squares Discriminant Analysis (PLS-DA). All questions were assessed without the use of additional prompting. Across two independent user accounts, identical question sets were administered twice per month between October and December 2025. Copilot consistently produced accurate, step-by-step solutions for equilibrium and acid–base problems, generated high-quality visualizations directly from uploaded datasets, and correctly constructed PCA score and loading plots with appropriate data standardization. Collectively, these findings demonstrate that Copilot offers substantial value for both research-oriented tasks and chemistry education. Visualization ; Principal component analysis ; Upload ; Data visualization ; Table (database) ; Linear discriminant analysis ; Principal (computer security) ; Microsoft excel Machine Learning in Materials Science ; Various Chemistry Research Topics ; Computational Drug Discovery Methods
Authors: Xiong Li | Liang Zhao | Jianhong Lai | Zengping Chen | Jiakun Su | Shaohe Jiao | Junwei Guo
Source: Journal of Chemometrics (article, 2026-03-22)
Field: Chemistry — Analytical Chemistry

Augmenting Near‐Infrared Spectral Data With Auxiliary Regression Generative Adversarial Network for Enhanced Prediction of Watermelon Soluble Solids Content ABSTRACT This study addresses the critical challenge of small sample sizes in deep learning‐based detection of watermelon soluble solids content (SSC) using near‐infrared spectroscopy (NIRS). We propose an auxiliary regression generative adversarial network (ARGAN) that overcomes the limitations of conventional deep convolutional generative adversarial networks (DCGAN) in regression tasks, particularly its poor controllability over chemical properties. The ARGAN framework introduces three key innovations: (1) a semisupervised architecture incorporating continuous SSC labels as conditional inputs to the generator, (2) a discriminative linear activation layer for direct SSC regression, and (3) a mean squared error‐based loss function to enforce chemical‐property correlations. The spectral similarity and SSC distribution consistency generated by ARGAN was higher compared to DCGAN through box‐and‐line plot analysis, average spectral visualization, structural similarity index (SSIMmax = 0.993), maximum mean difference (MMDmin = 0.002), and mahalanobis distance (MDmin = 1.332) analysis. More importantly, ARGAN‐enhanced data improve the prediction performance of partial least squares regression (PLSR), support vector machine (SVM), and convolutional neural network (CNN) models by 9%, 9%, and 13%, respectively, with a significant reduction of 48% in the root mean square error (RMSE) of CNN. As the first successful application of ARGAN for NIRS regression enhancement, this work establishes a novel paradigm for chemically attribute‐controllable data augmentation in agricultural quality detection, effectively addressing small‐sample learning challenges in food spectroscopy analysis. Discriminative model ; Regression ; Pattern recognition (psychology) ; Mean squared error ; Similarity (geometry) ; Consistency (knowledge bases) ; Mahalanobis distance ; Partial least squares regression ; Feature (linguistics) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Smart Agriculture and AI
Authors: Niket Sharma
Source: ChemRxiv (article, 2026-03-25)
Field: Materials Science — Materials Chemistry

ChemAgents: A Hybrid Agentic Framework Coupling Large Language Models, Molecular Transformers, and Physics-Based Simulations for Accessible Computational Chemistry The rapid advancement of artificial intelligence in chemistry has produced a powerful yet fragmented ecosystem of predictive models, creating a significant accessibility gap for domain experts who lack specialized machine learning expertise. To bridge this divide, we introduce ChemAgents, a practical, domain-specific agentic AI framework designed to make molecular property prediction and simulation more intuitive and reproducible. Expanding upon previous conversational agent architectures such as ChemCrow [1] and ChemToolAgent [2], ChemAgents employs a layered system of 13 specialized agents, a custom toolset integrating PubChem, LAMMPS molecular dynamics, and a novel Simulation-AI Coupler, alongside an aggressive duallayer caching architecture (in-memory LRU + persistent SQLite) that reduces repeated-query latency by up to 200×. The framework offers two complementary interaction modes: (i) a direct conversational agent for rapid task routing and (ii) a hybrid LLM-tool agent that leverages multistep ReAct reasoning for complex inquiries. A novel confidence-triggered hybrid prediction scheme dynamically routes molecules between fast AI screening and computationally expensive physics-based validation. The core specialist models were developed by fine-tuning the ChemBERTa-2 foundation model [3] on the MoleculeNet benchmarks ESOL, Tox21, and ClinTox [4], demonstrating competitive performance against established GCN baselines. By combining generalist LLM reasoning with domain-specialized transformer models and traditional simulation engines, ChemAgents demonstrates a practical path toward democratizing access to advanced computational chemistry workflows. Domain (mathematical analysis) ; Transformer ; Task (project management) ; Reinforcement learning ; Bridge (graph theory) ; Conversation ; Coupling (piping) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Jinyu Liang | Fan Zhao | Guoliang Xie | Xinhua Liu
Source: Materials Science and Engineering A (article, 2026-03-26)
Field: Materials Science — Materials Chemistry

Data-driven optimization of two-stage deformation-aging parameters enhances mechanical and electrical properties of Cu-Ni-Si-Cr alloy Alloy ; Deformation (meteorology) ; Work (physics) ; Nickel alloy ; Weldability Microstructure and mechanical properties ; Intermetallics and Advanced Alloy Properties ; High Temperature Alloys and Creep
Authors: Yuzhi Liu | Duo Zhang | Anyang Peng | Weinan E | Linfeng Zhang | Han Wang
Source: ArXiv.org (article, 2026-03-09)
Field: Materials Science — Materials Chemistry

Scaling Machine Learning Interatomic Potentials with Mixtures of Experts Machine Learning Interatomic Potentials (MLIPs) enable accurate large-scale atomistic simulations, yet improving their expressive capacity efficiently remains challenging. Here we systematically develop Mixture-of-Experts (MoE) and Mixture-of-Linear-Experts (MoLE) architectures for MLIPs and analyze the effects of routing strategies and expert designs. We show that sparse activation combined with shared experts yields substantial performance gains, and that nonlinear MoE formulations outperform MoLE when shared experts are present, underscoring the importance of nonlinear expert specialization. Furthermore, element-wise routing consistently surpasses configuration-level routing, while global MoE routing often leads to numerical instability. The resulting element-wise MoE model achieves state-of-the-art accuracy across the OMol25, OMat24, and OC20M benchmarks. Analysis of routing patterns reveals chemically interpretable expert specialization aligned with periodic-table trends, indicating that the model effectively captures element-specific chemical characteristics for precise interatomic modeling. Scaling ; Routing (electronic design automation) ; Nonlinear system ; Interatomic potential ; Molecular dynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Block Copolymer Self-Assembly
Authors: Son Dinh Le | Nhan Nu Thanh Ton | Panitha Phulkerd | Poulami Mukherjee | Naoto Nagata | Toshiaki Taniike
Source: Catalysis Science & Technology (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Designing less noble yet high-performing multimetallic catalysts for catalytic converters using a high-throughput approach High-throughput screening of 140 multimetallic catalysts identifies cost-effective formulations with 40–60% PGM achieving superior NO reduction ( T 50 = 271–296 °C). Machine learning and statistics provide insights into metal synergies and promoters. Catalysis ; Converters ; Noble metal ; Reduction (mathematics) ; Metal Catalytic Processes in Materials Science ; Ammonia Synthesis and Nitrogen Reduction ; Electrocatalysts for Energy Conversion
Authors: Darrin Bath | Bruno Mochi | Mary Jo Ondrechen
Source: Biophysical Journal (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

BPS2026 – Some applications of AI to biomolecules for both answers and insight Biomolecule ; Folding (DSP implementation) ; Ab initio ; Protein folding ; Protein structure ; Amino acid residue ; Amino acid Machine Learning in Materials Science ; Machine Learning in Bioinformatics ; Protein Structure and Dynamics
Authors: Yuqian Wu | Yihang Li | Bangwen He | Jianqiu Liu | Minsheng Huang | Zhenhuan Li
Source: Engineering Fracture Mechanics (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

A physics-informed GNN-LSTM framework for prediction of microvoid growth in heterogeneous polycrystals Randomness ; Void (composites) ; Artificial neural network ; Granularity ; Microstructure ; Crystallite ; Range (aeronautics) ; Context (archaeology) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Generative Adversarial Networks and Image Synthesis
Authors: Qiang Suo | Yan Liu | Ye Yuan
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Machine learning prediction of combined blast and fragments loading using a hybrid data-driven algorithm Extrapolation ; Artificial neural network ; Support vector machine ; Principal component analysis ; Redundancy (engineering) ; Structured prediction ; Kernel (algebra) ; Leverage (statistics) ; Radial basis function ; Transfer of learning High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Cellular and Composite Structures
Authors: Z. Wang | Shuo Yan | Jingyu Gao | Haoyu Wu | Baili Wang | Xiuying Zhao | Shikai Hu
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2026-01-01)
Field: Materials Science — Polymers and Plastics

Machine Learning-Driven Prediction of the Glass Transition Temperature of Styrene-Butadiene Rubber The glass transition temperature (Tg) of styrene-butadiene rubber (SBR) is a key parameter determining its low-temperature flexibility and processing performance. Accurate prediction of Tg is crucial for material design and application optimisation. Addressing the limitations of traditional experimental measurements and theoretical models in terms of efficiency, cost, and accuracy, this study proposes a machine learning prediction framework that integrates multi-model ensemble and Bayesian optimization by constructing a multi-component feature dataset and algorithm optimization strategy. Based on the constructed high-quality dataset containing 96 SBR samples, nine machine learning models were employed to predict the Tg of SBR and compare their prediction performance. Ultimately, a GPR-XGBoost mixed model was constructed through model ensemble, achieving high-precision prediction with R2 values greater than 0.9 on both the training and test sets. Further feature attribution and local effect analysis were conducted using feature analysis methods such as SHAP and ALE, revealing the nonlinear influence patterns of various components on Tg, providing a theoretical basis for SBR formulation design and Tg regulation. The machine learning prediction framework established in this study combines high-precision prediction with interpretability, significantly enhancing the prediction performance of the Tg of SBR. It offers an efficient tool for SBR molecular design and holds great potential for promotion and application. Flexibility (engineering) ; Natural rubber ; Feature (linguistics) ; Nonlinear system ; Predictive modelling ; Bayesian probability ; Key (lock) ; Ensemble forecasting Polymer Nanocomposites and Properties ; Machine Learning in Materials Science ; Material Dynamics and Properties
Authors: Cheng Qi | Jinpeng Zhao | Weidong Dai | Xin Peng | Yunkang Tang | Shangqing Xu | Mengya Zhang | Yicheng Wang | Xing Xu | Yipeng Zhao | Honglai Li | Wei Zhou | Liang Ma | Xiaolin Wei
Source: Laser & Photonics Review (article, 2026-04-02)
Field: Materials Science — Materials Chemistry

2D NbOCl <sub>2</sub> ‐Based Anisotropic Optoelectronic Synapses for Neuromorphic Vision Systems ABSTRACT Against the backdrop of rapid advances in big data and artificial intelligence, the exponential growth of visual information has driven increasingly demanding requirements for imaging quality and intelligent perception. However, inherent limitations in conventional image sensing—including restricted sensitivity, narrow spectral response, low integration density, and insufficient adaptability—impede their ability to meet these demands. Here, we report an anisotropic optoelectronic synaptic image sensor based on 2D non‐centrosymmetric van der Waals ferroelectric NbOCl 2 . Distinct from conventional centrosymmetric 2D semiconductors, NbOCl 2 possesses intrinsic spontaneous polarization and strong in‐plane anisotropy arising from its polar crystal structure, enabling built‐in polarization‐sensitive responses and anisotropic carrier transport. The device exhibits pronounced anisotropic properties, with electrical transport and photoresponse anisotropy factors reaching 1.68 and 1.76. Furthermore, temporally stretched excitatory postsynaptic current behavior enables angle‐resolved optical storage. By integrating the NbOCl 2 synaptic element with an image‐signal‐recognition system, polarization‐encoded preprocessing of the Modified National Institute of Standards and Technology handwritten digit dataset is achieved, and a three‐layer artificial neural network attains 95.1% recognition accuracy. Our findings establish 2D NbOCl 2 as a highly promising material platform for polarization‐sensitive optoelectronic synapses, providing a compact and energy‐efficient foundation for next‐generation neuromorphic vision and bioinspired perception systems. Neuromorphic engineering ; Anisotropy ; Artificial neural network ; Polarization (electrochemistry) ; Reconfigurability ; Machine vision ; Image quality 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing