jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Bertran, Michael Alejandro Hernandez | Davide Tisi | Federico Grasselli | Michele Ceriotti | Elisa Molinari | Deborah Prezzi
Source: arXiv (Cornell University) (article, 2026-02-26)
Field: Materials Science — Materials Chemistry

Tracking the Lithiation State of Li$_x$Si from Machine-Learned XPS Binding Energies X-ray Photoelectron Spectroscopy (XPS) is a powerful technique to probe chemical states and interfacial processes in battery materials, but a quantitative interpretation is often hindered by the complex, heterogeneous microstructures that form during operation and dominate electrochemical cycling. Silicon based anodes represent a paradigmatic example in Li batteries, as (de)lithiation proceeds through the formation of strongly disordered Li$_x$Si phases and crystal-amorphous transformations that are hard to characterize. Here, we introduce a computational framework that combines machine-learning (ML) prediction of core-level binding energies to large-scale atomistic simulations -- Grand Canonical Monte Carlo (GCMC) complemented with molecular dynamics (MD), driven by a ML potential -- for a systematic sampling of lithiation states and local atomic environments. This approach yields stoichiometry maps that match the characteristic experimental trends observed in operando and ex situ XPS measurements, including the distinctive Si $2p$ spectroscopic signatures associated with the crystal-to-amorphous disordering driving early delithiation. X-ray photoelectron spectroscopy ; Monte Carlo method ; Binding energy ; Stoichiometry ; Anode ; Tracking (education) ; Silicon ; Molecular dynamics ; Spectroscopy Machine Learning in Materials Science ; Advancements in Battery Materials ; Advanced Battery Materials and Technologies
Authors: Xuyang Guo
Source: Open Collections (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Integrating machine learning with scattering calculations to improve molecular collision observable predictions This thesis demonstrates that machine learning can be integrated with rigorous quantum scattering calculations to improve predictions of molecular collision observables, a problem important to fields ranging from cold-atom physics to vacuum metrology. Rigorous quantum scattering calculations are limited by the fitting of high-dimensional potential energy surfaces (PES), the sensitivity of collision observables to the uncertainty in PES fitting, and the computational demands of achieving full convergence with large basis sets. The thesis begins by fitting polyatomic PES using Gaussian process (GP) regression built on quantum kernels. To provide an unbiased comparison between classical and quantum kernels, I develop an algorithm that uses an analog of the Bayesian information criterion to optimize the sequence of quantum gates, increasing the complexity of the quantum circuits incrementally. The algorithm achieves much higher PES fitting accuracy with fewer quantum gates than a fixed quantum circuit ansatz, matching state-of-the-art classical GP regression models. To examine the response of collision observables to the uncertainty in PES fitting, I employ rigorous quantum scattering calculations to perform a comprehensive analysis of the universality in thermal atom-atom collision rate coefficients. I demonstrate that the rate coefficients for heavy, highly polarizable atoms are insensitive to the variations in the interatomic interactions at short range. I provide phase diagrams separating universal from non-universal collisions by treating collision observables as probabilistic predictions determined by a distribution of interaction potentials. I then extend the analysis of the universality to atom-molecule collisions. I demonstrate that the rate coefficients of total (elastic + inelastic) atom-molecule scattering are insensitive to the interaction anisotropy of the underlying PES. Specifically, I show that the rate coefficients for Rb-H2 and Rb-N2 scattering at room temperature can be computed to 1% accuracy with the anisotropy set to zero, reducing the basis set size in coupled-channel quantum scattering calculations to the single-channel limit. To improve collision observables calculated with reduced basis sets, I present a basis-set extrapolation method for quantum scattering calculations based on GP regression. I demonstrate that the method can extrapolate fully converged collision observables using those calculated with reduced basis sets, with accuracy matching experimental requirements and theoretical approximations. Observable ; Quantum ; Scattering ; Quantum algorithm ; Quantum dynamics ; Collision ; Gaussian process ; Probabilistic logic Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Thermal properties of materials
Authors: Doo Bong Lee | Jinwoo Park | Eunji Kim | Woong Kim
Source: Energy storage materials (article, 2026-02-07)
Field: Materials Science — Materials Chemistry

Interpretable enhanced-ECFP-guided deep learning for rational electrolyte design and Coulombic efficiency prediction in lithium metal batteries Faraday efficiency ; Lithium (medication) ; Electrolyte ; Lithium metal ; Deep learning Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Patrizia Mazzeo | Lorenzo Cupellini | Benedetta Mennucci
Source: Journal of Chemical Theory and Computation (article, 2026-02-11)
Field: Materials Science — Materials Chemistry

Multiscale Machine Learning Prediction of Infrared Spectra of Solvated Molecules We introduce a multiscale machine-learning molecular dynamics (MD) strategy for simulating infrared spectra of solvated molecules. Our approach integrates an efficient sampling of environmental configurations with a hierarchical model that predicts forces and dipole moments as analytical derivatives of the energy, allowing IR spectra simulations from MD trajectories. Solvent effects are incorporated through a molecular mechanics (MM) representation of the environment embedded within the ML description of the solute. Applied to representative biorelated systems, the resulting ML/MM framework reproduces experimental spectra with high fidelity and accurately captures solvent-driven vibrational shifts. This approach provides a computationally efficient and robust route for describing solvent effects in vibrational spectroscopy. Molecular dynamics ; Infrared ; Dipole ; Representation (politics) ; Spectral line ; Molecule ; Fidelity ; Infrared spectroscopy Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Molecular spectroscopy and chirality
Authors: Jie Cui | Yao Zhang | Yang Zhang | Yi Luo | Zhen-Chao Dong
Source: arXiv (Cornell University) (preprint, 2026-03-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

TERS-ABNet: A Deep Learning Approach for Automated Single-Molecule Structure Reconstruction with Atomic Precision from TERS Mapping Determining the chemical structure for a single molecule on surface from spectroscopic data represents a challenging high-dimensional inverse problem. Tip-enhanced Raman spectroscopy (TERS) enables chemically specific imaging of single molecules with sub-nanometer spatial resolution, yet reconstructing complete molecular structures from TERS maps remains difficult owing to the ambiguous vibrational signatures and reliance on expert interpretation. Here, we introduce TERS-ABNet, a deep-learning framework that formulates single-molecule structure determination from spectroscopic images as an image-to-graph inference task. Using a "two-track" architecture, the model jointly predicts probabilistic atom and bond maps, enabling direct construction of explicit atom-bond graphs without relying on predefined chemical rules. Trained on simulated datasets, TERS-ABNet achieves about 94% atom-type classification accuracy (with a mean coordinate error of about 0.23 Å), enabling to reliably recovering molecular connectivity and fully reconstruct single-molecule structure from its TERS maps. The framework generalizes across varying spatial resolutions and structural complexity through transfer learning, and successfully reconstructs the atomic structure of a single porphyrin molecule from experimental TERS data. This work establishes a general deep-learning strategy for inferring explicit atom-bond graph representations from high-dimensional spectroscopic imaging data, providing a new pathway towards automated molecular structure determination in nanoscale characterization. Inverse problem ; Probabilistic logic ; Inference ; Deep learning ; Iterative reconstruction ; Graph ; Inverse ; Computation Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Luminescence and Fluorescent Materials
Authors: Anik Sahoo | Sumanta Kumar Sahoo | Rajesh Kumar
Source: Journal of Materials Chemistry A (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Recent advances in carbon-based materials for supercapacitors: synthesis, electrochemical properties, and impact of machine learning Supercapacitor (SC) technology is one of the pioneering energy storage technologies of the current century. Current (fluid) ; Energy (signal processing) ; Applications of artificial intelligence ; Energy storage Supercapacitor Materials and Fabrication ; Electrocatalysts for Energy Conversion ; Catalysis for Biomass Conversion
Authors: Zihao Gui | Fanghui Chen | ShuaiGe Yang | Jiarui Chen | Shijie Hu | H. J. Yang | Liguo Su | Lin Li | Haibo Jiang | Guichun Wu | LEIMING WU
Source: Industrial Crops and Products (article, 2026-03-15)
Field: Chemistry — Analytical Chemistry

A novel strategy combining full Fourier transform infrared spectroscopy with machine learning enhanced the accuracy for analyzing cell wall polymers in maize stover Cellulose, hemicellulose and lignin are three major cell wall polymers extracted from crop feedstocks to produce valuable biofuels and bio-products. Due to the labor-intensive and environmentally hazardous characteristics of chemical methods for plant cell wall fractionation and polymer measurement, there exists an urgent necessity to develop high-throughput and environmentally sustainable techniques. In this study, we combined full Fourier Transform Infrared (FTIR) spectroscopy with advanced machine learning algorithms in 200 maize samples to create excellent models for predicting different cell wall polymers. The full FTIR spectral features were utilized as input dataset in comparison to 14 special peak features. All four algorithms demonstrated exceptional performance, achieving a high correlation coefficient above 0.92 in the training set and exceeding 0.85 in the test set. Notably, Random Forest model emerged as the most effective in predicting cell wall content, achieving an accuracy ranging from from 0.89–0.95 in the test set and demonstrating strong applicability for external validation with considerable robustness. Thus, this novel strategy offers a much simpler, accurate and high-throughput approach for predicting cell wall composition in large-scale maize samples than reported. The advancement of these models would enhance the selection of elite maize germplasms and improve the comprehensive utilization of maize feedstocks for bioenergy and biomass products. • There is a large variation of cell wall components among 200 maize sample stalks. • The full FTIR spectral features were utilized as input dataset for model building. • All four algorithms demonstrated enhanced accuracy based on full FTIR features. • Random Forest model performed strong applicability by external validation. Lignin ; Fourier transform infrared spectroscopy ; Random forest ; Biomass (ecology) ; Bioenergy ; Fourier transform ; Polymer ; Feature selection Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Food Drying and Modeling
Authors: Zhengni Yang | Rui Yang | Weijian Han | Wenyuan Kang | Xiaohan Chen | Ruihan Dong | Jingyi Zhang | Chao Tong | Jingyu Kong
Source: IEEE Transactions on Instrumentation and Measurement (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Enhancing Material Characterization: Multifidelity Deep Neural Network for Stress–Strain Curve Prediction From Small Punch Test Data The accurate determination of a material’s true stress-strain relationship is crucial for predicting its performance in engineering applications. However, traditional methods for determining material behavior often depend on extensive experiments. Although these methods are essential, they are inherently complex, time-consuming, and often suffer from limited accuracy and reliability when small sample sizes are involved. To address these challenges, this paper introduces a multi-fidelity deep neural network architecture that utilizes both low-fidelity and high-fidelity datasets to predict true stress-strain curves from small punch test data. The low-fidelity data are calculated using an empirical function based on the Swift model, providing an initial understanding of material behavior. In contrast, the finite element method provides high-fidelity data, enhancing the model’s ability to capture complex material characteristics. This framework merges low-fidelity and high-fidelity networks to estimate mechanical properties with low-fidelity and high-fidelity data. The effectiveness of the proposed approach has been demonstrated through experimental validation, highlighting its exceptional ability to generalize across material thicknesses and properties beyond the training set. The proposed approach outperformed traditional empirical methods and maintained strong performance even when applied to real-world materials. Artificial neural network ; Curve fitting ; Test data ; Pattern recognition (psychology) ; Backpropagation ; Test (biology) ; Data modeling Machine Learning in Materials Science ; Metal Forming Simulation Techniques ; Model Reduction and Neural Networks
Authors: R M Asha | Ranganathan Balaraman
Source: Polymers for Advanced Technologies (article, 2026-04-01)
Field: Materials Science — Polymers and Plastics

Prediction and Optimization of Mechanical Properties of Water Hyacinth Fiber‐Reinforced Polymer Composites Using Spiking Neural Networks ABSTRACT The research examines a spike neural networks (SNNs) model that uses trust region policy optimization (TRPO) to predict the mechanical properties of water hyacinth fiber‐reinforced composites, focusing specifically on their tensile and compressive strength values. The lightweight and widespread availability of biodegradable water hyacinth makes it an attractive sustainable material for use as a reinforcing component. We collected two datasets from the existing experimental literature. Database 1 contains 420 compressive strength samples (SNN1) and Database 2 contains 185 tensile strength samples (SNN2). The input properties we considered were fiber volume fraction, matrix composition, and curing conditions. Our methodology included normalizing the data and using a diffusion matrix to analyze feature relationships and we used TRPO for training purposes to achieve better stability in convergence. Our regression analysis revealed an impressive fit, with R 2 values soaring above 0.97. We observed slopes ranging from 0.96 to 0.97 for SNN1, and between 0.85 and 0.88 for SNN2, along with prediction errors staying below 0.5 MPa and 0.01 MPa, respectively. The SNN‐TRPO system has proven to be a powerful tool for predictive modeling and optimizing parameters, paving the way for the production of eco‐friendly and good‐performance composite material. Hyacinth ; Ultimate tensile strength ; Compressive strength ; Artificial neural network ; Matrix (chemical analysis) ; Composite number ; Curing (chemistry) ; Linear regression Natural Fiber Reinforced Composites ; Mechanical Behavior of Composites ; Wood Treatment and Properties
Authors: Omid Karimi Sadaghiani | Sahba Tabarraei | Ilgnur Tunc | Wei Peng | Raha Shabani
Source: Canadian Metallurgical Quarterly (article, 2026-02-27)
Field: Materials Science — Materials Chemistry

Machine learning in conductivity development of specific alloys Development (topology) ; Conductivity ; Electrical resistivity and conductivity ; Process (computing) Machine Learning in Materials Science ; Material Properties and Failure Mechanisms ; Ultrasonics and Acoustic Wave Propagation
Authors: Zishen Dong | Yihang Liu | Wei Xia | Yanli Zhang | Di Wang | Zhenhua Jiang | Xinrui Wang | Mingyuan Dong | Kun Chen | Dong Liu
Source: Journal of the Taiwan Institute of Chemical Engineers (article, 2026-03-17)
Field: Chemistry — Inorganic Chemistry

Boosting ethanol adsorption in zeolite catalysts via machine learning-guided framework engineering: synergistic insights from Monte Carlo method and deep learning Boosting (machine learning) ; Zeolite ; Monte Carlo method ; Adsorption ; Catalysis ; Deep learning Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: M. Lee | M. Klein | Mark DelloStritto
Source: The Journal of Chemical Physics (article, 2026-04-08)
Field: Materials Science — Polymers and Plastics

Understanding twist-disorder of polytetrafluoroethylene (PTFE) chains using neural network potential molecular dynamics Molecular dynamics (MD) calculations are used to characterize the temperature-dependent evolution of helicity and twist disorder in polytetrafluoroethylene (PTFE) chains. The MD calculations employ machine-learned neural network potentials (NNPs) trained on density functional theory data. In particular, NNPs were derived using three functionals: PBE-D3, r2SCAN, and r2SCAN-rVV10. As temperature increases, for PBE-D3 and r2SCAN-rVV10, we observe gradual unwinding from the equilibrium helix to less twisted, increasingly disordered conformations, with distinct discontinuous transitions near 250 K (PBE-D3) and 410 K (r2SCAN-rVV10). Notably, the PBE-D3 NNP captures these transitions and associated structural changes most accurately, whereas the r2SCAN-rVV10 NNP shows similar phase behavior with signs of overbinding. In contrast, the NNP trained on r2SCAN suppresses the unwinding transition and produces tighter helical structures with increasing temperature, underscoring the strong dependence on the choice of functional. The onset of chain rotational motion is accompanied by diffusion along the chain axis, supporting the Klug-Franklin hypothesis of screw-like disorder. Observed helical unwinding occurs after the emergence of conformational disorder and correlates with the onset of rotational and translational mobility, revealing a progression from structural to dynamical disorder upon heating. Computed PTFE melt densities highlight long-range van der Waals (vdW) interactions in the reference data used to train the NNP, motivating the incorporation of long-range vdW energies into the training scheme via an analytical Ewald summation for damped dispersion energies. Our MD results provide molecular-scale insights into the structural and dynamical behavior of PTFE, especially the thermally induced twist disorder, and demonstrate the utility of our approach for exploring other fluorocarbon macromolecules. Molecular dynamics ; van der Waals force ; Twist ; Dispersion (optics) ; Chain (unit) ; Phase transition ; Rotation around a fixed axis ; Dynamics (music) ; Polytetrafluoroethylene Polymer crystallization and properties ; Material Dynamics and Properties ; Tribology and Wear Analysis
Authors: Li Yan Anthony Choong | Zhong Chen | Man‐Fai Ng
Source: ACS Applied Energy Materials (article, 2026-03-19)
Field: Materials Science — Materials Chemistry

Correction to “Discovery of Effective Halide Solid Electrolytes for Solid-State Rechargeable Batteries via Machine Learning and DFT Calculations” Halide ; Solid solution ; Electrolyte ; Fast ion conductor Machine Learning in Materials Science ; Thermal Expansion and Ionic Conductivity ; Advanced Battery Materials and Technologies
Authors: Fanmeng Wang
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2026-03-14)
Field: Materials Science — Materials Chemistry

Improve Molecular Conformation Modeling with Geometric Deep Learning Molecular conformations, the stable three-dimensional structures corresponding to local minima on the potential energy surface, govern key molecular properties and consequently underpin a wide range of downstream tasks. However, contemporary learning-based methods often lack scalability, interpretability, and robustness, thereby significantly constraining their practical effectiveness and reliability. In this context, I will introduce my ongoing explorations and the proposed research plan to address these challenges, with the ultimate objective of developing conformation‑centric universal foundation models to accelerate scientific discovery. Maxima and minima ; Deep learning ; Key (lock) ; Foundation (evidence) ; Range (aeronautics) ; Plan (archaeology) ; Potential energy Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Zongyong Hu | Jiebin Su | Jiaxing Liu | Chunjin Wei | Cheng Wang | Da Zhou
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

CHIMERA: A Multi-Level Representation Framework for Predicting Asymmetric Catalytic Reactions Interpretability ; Transferability ; Domain adaptation ; Scarcity ; Representation (politics) Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Advanced Graph Neural Networks
Authors: Hao You | Lewei He | Shoujing Zheng | Hua Li
Source: Engineering Fracture Mechanics (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Facilitating small-data deep learning for predicting polymer mechanical properties using complex discrete networks via community detection algorithms Convolutional neural network ; Scalability ; Representation (politics) ; Deep learning ; Gaussian ; Transfer of learning ; Artificial neural network ; Pixel ; Sensitivity (control systems) Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Gaussian Processes and Bayesian Inference
Authors: Rathachai Chawuthai | Siripan Murathathunyaluk | S. Saengsuradech | A. Nukaew | L. Simasatitkul | Suksun Amornraksa | Suttichai Assabumrungrat | Amata Anantpinijwatna
Source: Scientific Reports (article, 2026-01-21)
Field: Materials Science — Materials Chemistry

A machine learning approach for predicting osmotic coefficients and deriving activity coefficients in alkyl ammonium salts Quaternary Ammonium Salts (Quats) have diverse applications across various domains. They are extensively used as phase-transfer catalysts (PTCs) in chemical reactions, facilitating the transfer of reactants between aqueous and organic phases. Their unique structure enables the formation of ion pairs, enhancing reaction rates at phase boundaries. This research develops a novel method for predicting Quats' osmotic coefficients using Simplified Molecular Input Line Entry System (SMILES) notation and supervised machine learning. A comprehensive dataset of 1,654 data points from 52 distinct Quats was compiled. The structural characteristics were encoded using SMILES notation. The data was evaluated using random splitting and Leave-One-Group-Out (LOGO) validation to train seven machine learning algorithms. Gaussian Process (GP) emerged as the optimal algorithm. The GP model achieved a mean absolute percentage error (MAPE) of 5.29% and root mean square error (RMSE) of 0.034. Comparisons with Electrolyte-NRTL and Extended UNIQUAC models demonstrate that this data-driven approach offers competitive accuracy while enabling generalization to structurally similar compounds. This work marks a significant starting point for the machine learning-enhanced prediction of activity coefficients, with considerable potential for future refinement and application. UNIQUAC ; Generalization ; Mean squared error ; Ammonium ; Support vector machine ; Applicability domain ; Data point ; Gaussian process ; Root mean square ; Heteroscedasticity Machine Learning in Materials Science ; Microwave-Assisted Synthesis and Applications ; Chemistry and Chemical Engineering