jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yongqiang Cheng | Dr Anibal Ramirez-Cuesta | Murillo L. Martins | Chang Liu
Source: Structural Dynamics (article, 2026-03-01)
Field: Chemistry — Physical and Theoretical Chemistry

Integrating neutron vibrational spectroscopy and computer simulation to elucidate structure and dynamics of hydrogen Understanding the structure and dynamics of hydrogen is critically important, yet direct experimental measurements are often challenging. Hydrogen interacts only weakly with common probing particles such as photons and electrons, and strong nuclear quantum effects can produce large nonthermal and anisotropic atomic displacements. Neutron scattering, however, provides a uniquely powerful approach due to the strong and distinct interactions of neutrons with atomic hydrogen, molecular hydrogen, and deuterium. Beyond neutron diffraction, which enables direct determination of hydrogen and deuterium positions, neutron vibrational spectroscopy-particularly when combined with computer simulations and modeling-offers unparalleled insights into hydrogen structure and dynamics that are inaccessible by other techniques. In this paper, after briefly summarizing the theoretical foundations, we review recent advances in applying neutron vibrational spectroscopy and computational methods to hydrogen-containing materials, ranging from molecular hydrogen adsorption to organic, inorganic, and hybrid compounds with diverse hydrogen local structure. Finally, we discuss opportunities offered by the recent progress in machine learning to further enhance the capabilities of this method. Neutron spectroscopy ; Hydrogen ; Neutron ; Deuterium ; Neutron scattering ; Spectroscopy ; Neutron diffraction ; Molecular dynamics Crystallography and molecular interactions ; Quantum, superfluid, helium dynamics ; Inorganic Fluorides and Related Compounds
Authors: Max Geier | Liang Fu
Source: arXiv (Cornell University) (article, 2026-02-09)
Field: Materials Science — Materials Chemistry

Predicting magnetism with first-principles AI Computational discovery of magnetic materials remains challenging because magnetism arises from the competition between kinetic energy and Coulomb interaction that is often beyond the reach of standard electronic-structure methods. Here we tackle this challenge by directly solving the many-electron Schrödinger equation with neural-network variational Monte Carlo, which provides a highly expressive variational wavefunction for strongly correlated systems. Applying this technique to transition metal dichalcogenide moiré semicondutors, we predict itinerant ferromagnetism in WSe$_2$/WS$_2$ and an antiferromagnetic insulator in twisted $Γ$-valley homobilayer, using the same neural network without any physics input beyond the microscopic Hamiltonian. Crucially, both types of magnetic states are obtained from a single calculation within the $S_z=0$ sector, removing the need to compute and compare multiple $S_z$ sectors. This significantly reduces computational cost and paves the way for faster and more reliable magnetic material design. Magnetism ; Ferromagnetism ; Antiferromagnetism ; Artificial neural network ; Coulomb ; Wave function ; Monte Carlo method ; Electromagnetism Machine Learning in Materials Science ; 2D Materials and Applications ; Topological Materials and Phenomena
Authors: Kong Meng | Haoran Lu | Xuhui Xu | Oleg V. Prezhdo | Run Long
Source: Journal of Chemical Theory and Computation (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

Rapid Prediction of Hot-Carrier Relaxation by Learning of Nonadiabatic Hamiltonians with Graph Neural Networks An electron-vibrational Hamiltonian fully encodes corresponding quantum dynamics; however, extracting the dynamics still relies on time and memory-consuming trajectory-based nonadiabatic molecular dynamics (NAMD) simulations, typically stochastic surface hopping. Here, we develop a general graph neural network, artificial intelligence ab initio NAMD (AI<sup>2</sup>NAMD) that establishes an end-to-end mapping from Hamiltonian to hot carrier relaxation dynamics. We validated the generality of AI<sup>2</sup>NAMD across multiple materials, including a zero-dimensional Si quantum dot (QD), a one-dimensional carbon nanotube (CNT), a two-dimensional twisted MoS<sub>2</sub>/WS<sub>2</sub> bilayer, and a three-dimensional soft-lattice MAPbI<sub>3</sub> perovskite. With only 10% training data, AI<sup>2</sup>NAMD can rapidly and accurately generate picosecond energy decay curves for hot electron and hot hole relaxation for the remaining 90% Hamiltonians, while delivering a computational speed-up of more than 6 orders of magnitude compared to standard CPU-based NAMD simulations. Moreover, AI<sup>2</sup>NAMD can also map directly the Hamiltonian to the carrier relaxation time, bypassing generation of the energy decay curves and demonstrating the ability to handle complex NAMD tasks. Further, by projecting high-dimensional Hamiltonian encoding features into a two-dimensional space with unsupervised learning, we demonstrate that AI<sup>2</sup>NAMD can effectively distinguish Hamiltonian types, verifying its ability to identify a particular system (QD, CNT, MoS<sub>2</sub>/WS<sub>2</sub> and MAPbI<sub>3</sub>) and a charge carrier (electron or hole). Overall, the developed AI<sup>2</sup>NAMD approach provides a novel computational methodology and a conceptual framework for accelerating NAMD simulations with machine learning by many orders of magnitude. Hamiltonian (control theory) ; Ab initio ; Quantum ; Artificial neural network ; Quantum dynamics ; Relaxation (psychology) ; Graph ; QM/MM Machine Learning in Materials Science ; Quantum many-body systems ; Topological Materials and Phenomena
Authors: Carlos Bornes | Chiheb Ben Mahmoud | Volker L. Deringer | Christopher J. Heard | Lukáš Grajciar
Source: arXiv (Cornell University) (preprint, 2026-03-23)
Field: Chemistry — Spectroscopy

An Accurate Tensorial Model for Prediction of Full Zeolite NMR Spectra Solid state nuclear magnetic resonance (ss-NMR) is one of the most sensitive and popular techniques for structure elucidation in geometrically complex crystalline materials, such as zeolites. Synergistic support from computational modelling is vital to interpret experimental spectra, and relate ss-NMR to atomistic models. Nevertheless, computational predictions are hindered by the high expense of calculating magnetic shielding (MS) and electric field gradient (EFG) tensors from first principles. In this work, we leverage a novel tensorial machine learning approach to train a general model for predicting complete NMR tensors. We demonstrate the utility of the approach for a diverse dataset of zeolitic materials and NMR-active nuclei ($^{27}$Al, $^{29}$Si, $^{17}$O, $^{23}$Na and $^{1}$H), predicting all NMR observables to a high degree of precision. These observables are then translated into predictions of the full $^{27}$Al and $^{29}$Si ss-nMR spectra for the exemplary zeolite RTH. Thus, this work opens a pathway to accurate, high-throughput NMR simulation for large-scale and realistic models of chemically complex zeolites. Observable ; Leverage (statistics) ; Zeolite ; Spectral line ; NMR spectra database ; Work (physics) ; Solid-state nuclear magnetic resonance ; Nuclear magnetic resonance spectroscopy ; Electromagnetic shielding Advanced NMR Techniques and Applications ; Zeolite Catalysis and Synthesis ; NMR spectroscopy and applications
Authors: Junfeng Cui | Yi Rao | Jianbao Gao | Hao Zhang | Cheng Lin | Jiale Zhao | Jiawen Zeng | Chun Fang | Zhiqiang Wang | Jinyu Wen | Bo Song | Yunhui Huang | Haiping Yang | Jia Xie | Yonggang Yao
Source: Nature Communications (article, 2026-03-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-driven intelligent carbonization unifies diverse biomass into high-performance hard carbon negative electrodes Sustainable batteries necessitate high-performance hard carbon negative electrodes derived from abundant biomass. However, realizing their full potential is significantly limited by the inherent diversity of biomass feedstocks, the intricate control over carbonization and resulting microstructures, and the complex interplay between processing, structure, and electrochemical performance. Here, we introduce "intelligent carbonization", a strategy integrating programmable Joule heating (1000-2000 °C, 10-60 s) with machine learning to substantially accelerate the discovery and optimization of biomass-derived hard carbons. By mapping over 1000 synthetic pathways and decoding the multidimensional feature space, we reveal a performance-correlated factor that serves as a crucial predictor of capacity, complementing conventional graphitic descriptors (in-plane crystallite size/ interlayer spacing). By a minimal energy input (0.1 kWh g<sup>-1</sup>), our strategy converts biochar into advanced hard carbon delivering 369 mAh g<sup>-1</sup> reversible capacity, high rate capability, and improved cycling stability (>5000 cycles at a specific current of 3 A g<sup>-1</sup>). This data-centric approach allows low-cost and intelligent manufacturing of diverse biomass resources into performance-unified hard carbon negative electrodes, thereby paving the way for practical and large-scale biomass valorization towards sustainable energy storage solutions. Biomass (ecology) ; Carbonization ; Carbon fibers ; Electrode ; Bioenergy Supercapacitor Materials and Fabrication ; Ammonia Synthesis and Nitrogen Reduction ; CO2 Reduction Techniques and Catalysts
Authors: Sandeep Jain | Rahul Singh Mourya | Reliance Jain | Sheetal Kumar Dewangan | Saurabh Tiwari
Source: Processes (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

Interpretable Machine-Learning Prediction of Atmospheric Zinc Corrosion Depth Under Diverse Environmental Conditions Understanding the depth and severity of corrosion is vital for evaluating the long-term durability and economic performance of Zn-based structures. In this study, a machine learning (ML) framework was applied to forecast the corrosion depth of zinc under varying environmental circumstances. A dataset consisting of 300 samples compiled from previously published atmospheric corrosion studies under various environmental conditions was used to develop and evaluate the machine learning models. Seven ML algorithms were developed by integrating different environmental constraints such as temperature, time of wetness (TOW), SO2 concentration, Cl− concentration, and exposure time as input parameters. The models were trained using cross-validation and hyperparameter optimization to ensure robust predictive performance and minimize overfitting. The Random Forest (RF) model confirmed superior predictive performance with an R2 of 96.4% and RMSE of 0.642 µm among all used models. The predictive ability of the optimized RF model was further confirmed using five new environmental systems, attaining excellent agreement with predicted values (R2 = 97.9%, RMSE = 0.87 µm). Model interpretability analysis using SHAP (SHapley Additive exPlanations) discovered that exposure time and SO2 concentration are the most significant parameters leading zinc corrosion behaviour. The developed ML framework provides interpretable insights into the influence of environmental parameters on atmospheric zinc corrosion behaviour and provides a reliable tool for forecasting corrosion depth. These findings highlight the potential of ML approaches to support corrosion mitigation strategies and accelerate materials design by reducing reliance on conventional trial-and-error experimentation. Corrosion ; Interpretability ; Random forest ; Hyperparameter ; Zinc ; Predictive modelling ; Mean squared error Corrosion Behavior and Inhibition ; Structural Integrity and Reliability Analysis ; Concrete Corrosion and Durability
Authors: Abigail Park | Shriram Chennakesavalu | Grant M. Rotskoff
Source: arXiv (Cornell University) (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Scaling Transferable Coarse-graining with Mean Force Matching Coarse-grained molecular dynamics often sacrifices accuracy and transferability for computational efficiency, but the use of machine learned potentials is helping coarse-grained models attain performance on par with atomistic molecular dynamics. Nevertheless, developing representations of the coarse-grained potential energy surface faces severe scaling challenges due to the extreme data demands of widely used "bottom-up" coarse-graining objectives. In this work, we show that mean force matching, a strategy for training thermodynamically consistent coarse-grained models, requires 50x fewer training samples and 87% less total atomistic simulation time, while obtaining better accuracy on the potential of mean force for unseen proteins compared to other commonly used objectives. By systematically removing noise from the objective function, we demonstrate that it is possible to scale machine learning architectures for coarse-graining, enabling highly accurate and transferable models. We show the advantages of mean force matching both theoretically and through exhaustive benchmarking using thermodynamic consistency as the primary metric of accuracy. Scaling ; Metric (unit) ; Transferability ; Benchmarking ; Potential of mean force ; Matching (statistics) ; Consistency (knowledge bases) ; Noise (video) Block Copolymer Self-Assembly ; Machine Learning in Materials Science ; Nanopore and Nanochannel Transport Studies
Authors: Zehra Evrim Kanat
Source: TEKSTİL VE KONFEKSİYON (article, 2026-03-30)
Field: Materials Science — Polymers and Plastics

Prediction of Water Vapor Permeability of Single Jersey Fabrics by Linear Regression and Artificial Neural Network Methods Garments should maintain thermal balance of the body under various environmental conditions. In order to provide comfort for wearer, it is necessary for clothing to transmit water vapor from the body to the environment as fast as possible. In this study, water vapor permeability of single jersey knitted fabrics produced from different materials and different yarn and fabric properties were predicted with both linear regression and artificial neural network (ANN). Results showed that material, yarn count and fabric density, had an important effect on the water vapor permeability of the fabrics. Both linear regression and ANN were appropriate for predicting water vapor permeability of single jersey fabrics, but predicting performance of ANN was better. While R2 values of linear regression models were in the range of 0.70 Linear regression ; Artificial neural network ; Water vapor ; Permeability (electromagnetism) ; Regression analysis ; Yarn ; Linear relationship Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials ; Dyeing and Modifying Textile Fibers
Authors: Tobias Pointner | Claudia Gonzalez Viejo | Fuentes Sigfredo | Marc Pignitter
Source: Future Foods (article, 2026-02-25)
Field: Chemistry — Analytical Chemistry

Machine learning-based prediction of cold-pressed oil storage time using NIR and electronic-nose data • NIR spectroscopy and e-nose data were combined with machine learning models • Fatty acids, polyphenols and volatiles of cold-pressed oils were predicted • Oil type and oxidative progression were accurately classified over six months • Multi-marker models enabled prediction of cold-pressed oil storage time Cold-pressed vegetable oils are rich in polyunsaturated fatty acids (PUFAs) and bioactive minor compounds, making them nutritionally valuable but highly susceptible to oxidative degradation. Current shelf-life estimation relies on destructive laboratory analyses and single-parameter indices, which insufficiently reflect the multi-factorial nature of lipid oxidation. This study presents a non-destructive, machine-learning (ML)-based framework to predict chemical deterioration and storage time of six cold-pressed oils (black cumin, sunflower, high-oleic sunflower, canola, linseed, and hempseed) stored for 168 days under household-relevant conditions. Target datasets comprised five fatty acids (GC-FID), 48 polyphenols (LC-MS/MS), and 18 secondary lipid oxidation products (SPME-GC-MS). Near-infrared (NIR) spectra and electronic-nose (e-nose) signals served as inputs for artificial neural network (ANN) classification and regression models. Using Bayesian regularization and Levenberg–Marquardt algorithms, fatty acids (R = 0.95), polyphenols (R = 0.97), and volatile oxidation markers (R = 0.82) were accurately predicted. Predicted multi-marker fingerprints were subsequently integrated into storage-time models using Partial Least Squares (PLS), Principal Component Regression (PCR), and Random Forest (RF). PLS captured linear deterioration trends (R = 0.87), while RF achieved the lowest prediction error (MAPE = 23%). Unlike previous NIR- or e-nose-based approaches focusing on single quality parameters, this study introduces a multi-marker framework enabling non-destructive estimation of oil storage time, supporting real-time quality monitoring and reduced food waste. Partial least squares regression ; Principal component analysis ; Polyphenol ; Artificial neural network ; Predictive modelling ; Polyunsaturated fatty acid ; Random forest ; Mean squared prediction error Petroleum Processing and Analysis ; Power Transformer Diagnostics and Insulation ; Enhanced Oil Recovery Techniques
Authors: Hongdoo Kim | Jemin Lee | Sung Won Kim | Hyung Gyu Park
Source: Scientific Reports (article, 2026-02-05)
Field: Chemistry — Analytical Chemistry

Plasmonic artificial inspector for herbal medicines via surface-enhanced Raman spectroscopy and deep learning For the safety monitoring of herbal medicines (HMs), HM inspectors carry out an organoleptic examination before acceptance for market supply. The organoleptic test processes are often regarded as labor-intensive, thus calling for efficient and reliable aids. Here, we propose a plasmonic artificial HM inspector based on a collaboration between surface-enhanced Raman spectroscopy (SERS) and deep learning (DL). Inherently, a SERS spectrum of an HM specimen contains several peaks that match bioactive compounds in the sample, yielding so-called fingerprint information of HM. Besides, its rapid, few-second data-acquisition speed lends the SERS-DL analysis adaptability to a complementary inspection method for organoleptic examination. Regarding the accuracy and reliability of this new method, the synergistic integration of SERS with DL attains ~95% accuracy in labor-saving differentiation of 35 HM species with similar appearances or of the same genus. Our SERS-DL-based analysis can potentially aid the organoleptic HM inspection and help upgrade the HM database, along with images and other analytical chemistry data. Organoleptic ; Raman spectroscopy ; Reliability (semiconductor) ; Deep learning ; Plasmon ; Adaptability Spectroscopy and Chemometric Analyses ; Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Max Geier | Liang Fu
Source: Open MIND (preprint, 2026-02-09)
Field: Materials Science — Materials Chemistry

Predicting magnetism with first-principles AI Computational discovery of magnetic materials remains challenging because magnetism arises from the competition between kinetic energy and Coulomb interaction that is often beyond the reach of standard electronic-structure methods. Here we tackle this challenge by directly solving the many-electron Schrödinger equation with neural-network variational Monte Carlo, which provides a highly expressive variational wavefunction for strongly correlated systems. Applying this technique to transition metal dichalcogenide moiré semicondutors, we predict itinerant ferromagnetism in WSe$_2$/WS$_2$ and an antiferromagnetic insulator in twisted $Γ$-valley homobilayer, using the same neural network without any physics input beyond the microscopic Hamiltonian. Crucially, both types of magnetic states are obtained from a single calculation within the $S_z=0$ sector, removing the need to compute and compare multiple $S_z$ sectors. This significantly reduces computational cost and paves the way for faster and more reliable magnetic material design. Magnetism ; Ferromagnetism ; Antiferromagnetism ; Artificial neural network ; Coulomb ; Wave function ; Monte Carlo method ; Electromagnetism Machine Learning in Materials Science ; 2D Materials and Applications ; Topological Materials and Phenomena
Authors: Tomasz Urbańczyk | Jakub Bożek | J. Koperski | Marek Krośnicki
Source: International Journal of Molecular Sciences (article, 2026-02-20)
Field: Materials Science — Materials Chemistry

The SHAP Explainer Model for Binary Classifiers Detecting Functional Groups in Molecules Based on FTIR Spectra One of the disadvantages of deep learning models is the difficulty in understanding the premises on the basis of which the models make decisions. This might hinder the applicability of the model due to legal issues. In this paper, we investigate the decision process of CNN-KAN model trained to recognize chemical groups based on recorded FTIR spectra. The CNN-KAN model was trained as a binary classifier and using SHAP values, we could trace back the decision-making process and point out which part of the FTIR spectra are responsible for each positive or negative decision. It appears that the decision-making process of the deep learned CNN-KAN model is based on spectral regions that according to the literature have a large impact on the detection of particular functional groups. Binary number ; Classifier (UML) ; Pattern recognition (psychology) ; Binary classification ; TRACE (psycholinguistics) ; Fourier transform infrared spectroscopy ; Process (computing) ; Fourier transform Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics
Authors: Srinivas Beduru | Andrei G. Kutateladze
Source: Natural Product Reports (article, 2026-01-01)
Field: Chemistry — Spectroscopy

Brief overview of recently reported misassigned natural products and their <i>in silico</i> revisions enabled by DU8ML, a machine learning-augmented DFT computational NMR method Covering: 2018 to 2025This <i>Highlight</i> article describes a personal selection of recent misassigned structures of natural products and their revision with the aid of DU8ML, a machine learning-augmented DFT computational method for fast and accurate calculations of solution NMR chemical shifts and spin-spin coupling constants. Density functional theory ; Natural (archaeology) ; Coupling (piping) ; Selection (genetic algorithm) ; Computational model Molecular spectroscopy and chirality ; Advanced NMR Techniques and Applications ; Machine Learning in Materials Science