jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Venkata Surya Kumar Choutipalli | Michael L. Klein | Mark DelloStritto
Source: The Journal of Chemical Physics (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

On the origin of hydrophilic interactions at alumina surfaces Despite advances in understanding macroscopic wetting behavior, the atomistic mechanisms underlying hydrophilicity at solid-liquid interfaces remain only partially understood, particularly for chemically and structurally complex surfaces. Alumina, a widely used oxide in catalysis and surface coatings, exhibits such complexity due to the variability in surface termination and hydroxylation. In this study, we investigate the hydrophilicity of three crystallographic terminations of hydroxylated alumina-(0001), (1120), and (0112)-using molecular dynamics simulations based on neural network potential trained on density functional theory data. We quantify hydrophilicity through mean square displacement, density fluctuations, hydrogen bonding characteristics, vibrational density of states, the tetrahedral order parameter, and the contact angle. Our results reveal a clear hydrophilicity trend of (1120) < (0001) < (0112), with the relatively flat surface (1120) showing the least structured interfacial water and the corrugated surface (0112) showing the most pronounced confinement and hydrogen bonding. This trend is reflected in reduced water mobility, suppressed density fluctuations, stronger surface-to-water hydrogen bonds, and greater disruption of tetrahedral order near the (0112) interface. These findings demonstrate that atomic-scale surface roughness and the degree to which it can disrupt the structure of water are key to forming strong surface-to-water hydrogen bonds, thereby offering a microscopic rationale for hydrophilic behavior at oxide surfaces. Wetting ; Hydrogen bond ; Oxide ; Tetrahedron ; Hydrogen ; Surface roughness ; Molecular dynamics ; Density functional theory Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Princy Jarngal | Benjamin A. Jackson | Simuck F. Yuk | Difan Zhang | Mal-Soon Lee | Maria Cristina Menziani | Vassiliki-Alexandra Glezakou | Roger Rousseau | GiovanniMaria Piccini
Source: Journal of Catalysis (article, 2025-12-24)
Field: Chemistry — Inorganic Chemistry

Protonation dynamics of confined ethanol–water mixtures in H-ZSM-5 from machine learning-driven metadynamics Thermodynamic analysis of protonation equilibria in ethanol–water mixtures confined in zeolites shows how hydration controls excess-proton preference and modulates the nature of transient protonated intermediates. • MLP-based metadynamics reveals hydration-dependent protonation dynamics at BAS. • Proton shifts from ethanol to water with increasing water loading. • Minimum three water molecules required to stabilize hydronium ion inside pores. • Entropy and H-bond dynamics govern proton exchange between BAS, ethanol, and water. Zeolites are indispensable heterogeneous catalysts in industrial chemical processes, valued for their strong Brønsted acidity, well-defined microporous frameworks, and tunable pore structures. Their catalytic activity arises primarily from Brønsted acid sites (BAS), typically present as bridging hydroxyl groups (Si–OH–Al). Under aqueous reaction conditions, these protons interact dynamically with water and alcohol molecules, leading to complex solvation and protonation behavior within confined pores. In this study, we investigate the protonation equilibrium occurring between ethanol and water at the BAS of acidic zeolites under varying hydration levels, i.e., C 2 H 5 OH–(H 2 O) n , n = 1–4. Local structure was analyzed through an adaptive-learning global optimization algorithm, while enhanced sampling molecular dynamics simulations with Well-Tempered Metadynamics (WTMetaD) and machine learning interatomic potentials (MLPs) provide free-energy surfaces (FES) at variable hydration levels. The results reveal a strong dependence of proton localization on the degree of hydration. In presence of just 1 water molecule, the proton resides predominantly on ethanol; with 2 water molecules, it shifts toward water, and starting at 3, it becomes delocalized over the water cluster. These findings underscore the critical role of solvation in modulating acid site behavior and suggest that a minimum of three water molecules is necessary to fully stabilize the proton on water within the zeolite framework. This solvation threshold has significant implications for catalytic processes, particularly in biomass conversion reactions where alcohol protonation is a key step in dehydration mechanisms. Metadynamics ; Protonation ; Solvation ; Proton ; Molecular dynamics ; Hydronium ; Delocalized electron ; Aqueous solution Zeolite Catalysis and Synthesis ; Machine Learning in Materials Science ; Catalysis for Biomass Conversion
Authors: Chen, Maoxin | He, Yuanyuan | Li, Huan | Wu, Zhitan | Chen, Jinxing | Du, Ao | Wang, Li | Lu, Jiong | Wang, Weichao | Yang, Chunpeng | Yang, Quan-Hong
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-11-17)
Field: Materials Science — Materials Chemistry

4f-5d orbital tag-team catalysis empowers high-loading zinc–iodine batteries The geometric configuration of the M-N-C single-atom catalyst of 3d 4d 5d metal and the adsorption configuration of its intermediate in the iodine reduction process. The original data and code related to machine learning in this article. Catalysis ; Adsorption ; Reduction (mathematics) ; Metal Covalent Organic Framework Applications ; CO2 Reduction Techniques and Catalysts ; Oxidative Organic Chemistry Reactions
Authors: Baiyu Wang | W. Xu | Xiaoying Sun | Ji Qi | LingLing Shang | Zhen Zhao | Bo Li
Source: iScience (article, 2025-11-21)
Field: Materials Science — Materials Chemistry

First-principles calculations steered multi-task transformer model to screen dual-atom catalysts for C-H activation Activating C-H bonds in light alkanes (methane, ethane, and propane) remains a major challenge in heterogeneous catalysis due to their high stability, while dual-atom catalysts (DACs) offer a promising balance of activity and durability. Herein, we developed an optimized multi-task transformer model to accelerate active DACs design by integrating first-principles calculations with machine learning. Over 200 DACs composed of fourth/fifth period transition metals anchored on N-doped graphene were studied. The transformer, modified with additional decoder layers and attention head, outperformed conventional methods (K-nearest neighbors, resource description framework, and gradient boosting regression) for small datasets, achieving R<sup>2</sup> > 0.85 in predicting both adsorption energies and C-H activation barriers. Gradient boosting regression tree analysis identified the metal-N coordination distance as the key performance-determining factor. This work provides an accurate predictive model for efficient DACs screening in light alkane conversion and advances DACs design principles. Boosting (machine learning) ; Catalysis ; Transformer ; Gradient boosting ; Graphene ; Converters ; Decision tree Machine Learning in Materials Science ; Catalytic C–H Functionalization Methods ; Catalysis and Oxidation Reactions
Authors: Yue‐Fei Zhang | Ze Sheng Lu | Jianli Zhou | Jiahao Li | Ao Liu | Ze Zhang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

DT-MIM: A network suitable for alloy microstructure evolution prediction and characterization Microstructure ; Characterization (materials science) ; Shape-memory alloy ; Nucleation ; Artificial neural network ; Nonlinear system ; Deformation (meteorology) Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Metallurgy and Material Forming
Authors: Rui Zhao (43391) | Junjie Ning (12424267) | Shumin Chen (188991) | Chengfeng Liang (22130467) | Kun Shen (775056) | Yuxuan Chen (1511149) | Longqiang Xiao (684375) | Jingyu Cai (1681207) | Linxi Hou (3236508)
Source: Figshare (article, 2025-08-27)
Field: Chemistry — Inorganic Chemistry

High-Throughputand Machine Learning for Predictingthe Separation Performance of Metal–Organic Frameworks forSF<sub>6</sub>/N<sub>2</sub> In view of the greenhouse effect of SF<sub>6</sub> and its recycling benefits, the development of highly efficient SF<sub>6</sub>/N<sub>2</sub> separation is extremely urgent. In this study, high-throughput screening and machine learning methods were used to analyze the relationship between features and performance, and a machine learning model capable of predicting materials with high SF<sub>6</sub>/N<sub>2</sub> adsorption and separation performance was trained. By implementing a database cleaning strategy, unreasonable structural data were systematically removed, improving the prediction accuracy of the model. Feature construction was carried out to obtain characteristic information covering aspects of structure, chemistry, force field, and energy. The adsorption data of the SF<sub>6</sub>/N<sub>2</sub> mixed gas were calculated based on the RASPA2 software. The importance analysis shows that the Henry’s coefficient of SF<sub>6</sub> plays a decisive role in the adsorption performance score. And cross-database prediction was conducted on the Tobacco database. The coefficient of determination of the logarithmic selectivity reaches 0.969. Separation (statistics) ; Feature (linguistics) ; Adsorption ; Logarithm ; Support vector machine Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Adsorption and biosorption for pollutant removal
Authors: Yang Pan | Mingyu Ye | Rui Xu | Dong Zhang | Chang Su | Xuan Luo | Yuxin Huang | Yong Tang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Non-Destructive Origin Discrimination of Angelica Dahurica Slices and Interpretation of Characteristic Wavelengths Based on Near-Infrared Spectroscopy and Explainable Machine Learning Interpretation (philosophy) ; Infrared ; Spectroscopy ; Wavelength ; Infrared spectroscopy ; Artificial intelligence ; Pattern recognition (psychology) ; Materials science ; Analytical Chemistry (journal) ; Physics ; Computer science ; Chemistry ; Optics ; Astronomy ; Chromatography ; Quantum mechanics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: Feichen Ye
Source:  (article, 2025-12-26)
Field: Chemistry — Analytical Chemistry

Construction of Deep Learning Model for HPLC Retention Time Prediction and Research on GC-MS Spectrum Recognition This paper proposes a fusion of Graph Neural Network (GNN), Spectral Autoencoder (SAE), and Bayesian Optimization. The deep learning model of BO - Bayesian Spectral Neural Encoder (BSNE), is used for HPLC retention time prediction and GC-MS spectrum recognition. BSNE captures the complex relationships in molecular diagrams or spectra through the powerful graph structure data processing capabilities of GNN, uses SAE to extract spectral features for efficient encoding and decoding, extracts key feature information, and then combines BO to automatically optimize model hyperparameters to enhance the accuracy of prediction and recognition. The experimental results show that BSNE performs well in the tasks of HPLC retention time prediction and GC-MS spectrum recognition. Compared with traditional methods, the BSNE model has significant improvements in both prediction accuracy and recognition accuracy. This method effectively enhances the accuracy in retention time prediction and spectrum identification, providing a new technical means for chromatographic analysis and metabolomics research. Deep learning ; Pattern recognition (psychology) ; Artificial neural network ; Retention time ; Automatic target recognition ; Principal component analysis Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Machine Learning in Bioinformatics
Authors: S. Megha | S. P. Maniraj | T. Kavitha | Sindhusaranya Balraj | C.S. Preetham Reddy | Amit Prabhakar
Source: Microchemical Journal (article, 2025-09-22)
Field: Chemistry — Electrochemistry

Fast iterative neural network-combined carbon composite electrodes for selective detection of emerging waterborne pollutants Pollutant ; Graphene ; Detection limit ; Composite number ; Adsorption ; Electrode ; Electrochemistry ; Artificial neural network Electrochemical Analysis and Applications ; Electrochemical sensors and biosensors ; Analytical Chemistry and Sensors
Authors: Anmol (15201078) | Anuj Kumar Sirohi (21521683) | Neha (9893921) | Jayadeva (14384580) | Sandeep Kumar (145759) | Tarak Karmakar (810867)
Source: Figshare (article, 2025-06-11)
Field: Materials Science — Materials Chemistry

Locality-Sensitive Hashing-Based Data Set Reductionfor Deep Potential Training Machine learning methods provide a great scope for developing ab initio quality potentials for diverse systems, ranging from simple fluids to complex solids. However, these methods typically require extensive data sets for effective model training, and the accuracy of the ML potential is highly dependent on data quality, necessitating expensive ab initio calculations. To address this challenge, we present a novel method based on locality-sensitive hashing, designed to minimize the data set size, thereby reducing the number of expensive quantum chemical calculations while preserving the data set’s diversity and accuracy. Our approach achieves data set reductions of nearly an order of magnitude. To demonstrate the method’s effectiveness, we applied it to develop ML potentials to study a prototypical chemical reaction in an explicit solvent and a first-order phase transition. Finally, well-tempered metadynamics simulations utilizing these ML potentials enabled us to calculate the converged free energy surfaces for both the chemical reaction and the phase transition. Metadynamics ; Ab initio ; Set (abstract data type) ; Data set ; Experimental data ; Reduction (mathematics) ; Data reduction ; Training set ; Potential energy Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Graph Neural Networks
Authors: Xinyuan Liang | Renxi Liu | Mohan Chen
Source: ArXiv.org (preprint, 2025-03-31)
Field: Materials Science — Materials Chemistry

A Deep Learning Framework for the Electronic Structure of Water: Towards a Universal Model Accurately modeling the electronic structure of water across scales, from individual molecules to bulk liquid, remains a grand challenge. Traditional computational methods face a critical trade-off between computational cost and efficiency. We present an enhanced machine-learning Deep Kohn-Sham (DeePKS) method for improved electronic structure, DeePKS-ES, that overcomes this dilemma. By incorporating the Hamiltonian matrix and their eigenvalues and eigenvectors into the loss function, we establish a universal model for water systems, which can reproduce high-level hybrid functional (HSE06) electronic properties from inexpensive generalized gradient approximation (PBE) calculations. Validated across molecular clusters and liquid-phase simulations, our approach reliably predicts key electronic structure properties such as band gaps and density of states, as well as total energy and atomic forces. This work bridges quantum-mechanical precision with scalable computation, offering transformative opportunities for modeling aqueous systems in catalysis, climate science, and energy storage. Electronic structure ; Eigenvalues and eigenvectors ; Deep learning ; Key (lock) ; Scalability ; Hamiltonian (control theory) ; Electronics ; Density functional theory Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Haisong Xu | Tingting Du | Jiali Lin | Feiying You | Yingbo Shao | Qi Yang | Xin Li
Source: Angewandte Chemie (article, 2025-12-10)
Field: Chemistry — Organic Chemistry

Physicochemically Informed Axial Chirality Descriptors Enable Accurate Prediction of Atropisomeric Stability Abstract Atropisomers play a vital role in asymmetric synthesis, drug discovery, and the development of functional materials. However, the rational design of atropisomers is challenging due to the difficulty in predicting their configurational stability, which depends on the rotational barrier (Δ G ‡ ). Here, we introduce ACSD‐GAT, a deep learning framework that addresses this issue. Our approach comprises a newly curated benchmark dataset of 1015 experimentally measured rotational barrier, along with a physicochemically informed axial chirality structure descriptor (ACSD) that explicitly quantifies both static and dynamic steric repulsion during rotation. By integrating the ACSD with a graph attention network (GAT), our model accurately predicts the rotational barrier, achieving an R 2 of 0.91 and a RMSE of 2.02 kcal mol −1 on test datasets. The robustness and real‐world applicability of the model are also demonstrated through rigorous validation with complex pharmaceuticals, molecular switches, and newly synthesized atropisomers. Atropisomer ; Chirality (physics) ; Robustness (evolution) ; Steric effects ; Benchmark (surveying) ; Graph Axial and Atropisomeric Chirality Synthesis ; Supramolecular Chemistry and Complexes ; Crystallography and molecular interactions
Authors: Xinyuan Liang (7256672) | Renxi Liu (14205418) | Mohan Chen (3744244)
Source: Figshare (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

A Deep LearningFramework for the Electronic Structureof Water: Toward a Universal Model Accurately modeling the electronic structure of water across scales, from individual molecules to bulk liquid, remains a grand challenge. Traditional computational methods face a critical trade-off between computational cost and efficiency. We present an enhanced machine-learning Deep Kohn–Sham (DeePKS) method for improved electronic structure, DeePKS-ES, that overcomes this dilemma. By incorporating the Hamiltonian matrix and its eigenvalues and eigenvectors into the loss function, we establish a universal model for water systems, which can reproduce high-level hybrid functional (HSE06) electronic properties from inexpensive generalized gradient approximation (PBE) calculations. Validated across molecular clusters and liquid-phase simulations, our approach reliably predicts key electronic structure properties such as band gaps and density of states, as well as total energy and atomic forces. This work bridges quantum-mechanical precision with scalable computation, offering transformative opportunities for modeling aqueous systems in catalysis, climate science, and energy storage. Electronic structure ; Eigenvalues and eigenvectors ; Deep learning ; Key (lock) ; Scalability ; Hamiltonian (control theory) ; Electronics ; Matrix (chemical analysis) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Xian-Yang Zhang (21012677) | Xue-Qing Gong (1303053)
Source: Figshare (article, 2025-04-07)
Field: Chemistry — Spectroscopy

Comprehensive and Explainable Fragmentation: A MachineLearning Approach for Fast and Accurate Mass Spectrum Prediction Mass spectrometry (MS) is a fundamental tool for chemical identification. The current in-silico prediction tools can handle broad instrument conditions, large molecular libraries or fragment structures only on a very limited level. In this work, we propose a dual-model machine learning strategy that can solve this problem by jointly a classification model for fragment identification and noise filtering, and a regression model for spectral prediction. With the help of attention mechanism, our method outperforms other algorithms in accuracy and efficiency, providing a deeper understanding of the molecular fragmentation behavior in mass spectra. Our method can facilitate the large-scale in-silico spectra calculations and the analysis of unknown molecular structures, which may promote wider applications for MS. Fragment (logic) ; Noise (video) ; Identification (biology) ; Fragmentation (computing) ; Noisy data ; Pattern recognition (psychology) ; Mass spectrum Mass Spectrometry Techniques and Applications ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Pietro Chiarantoni | Oscar Serra | Mohammad Erfan Mowlaei | Venkata Surya Kumar Choutipalli | Mark DelloStritto | Xinghua Shi | M. Klein | Vincenzo Carnevale
Source: ArXiv.org (preprint, 2025-12-10)
Field: Materials Science — Materials Chemistry

Generative Modeling of Entangled Polymers with a Distance-Based Variational Autoencoder We present a variational autoencoder framework for learning and generating configurations of structured polymer globules from distance matrices. We used coarse-grained molecular dynamics to sample polyethylene structures, which we used as the training set for our deep learning model. By combining convolution and attention layers, the model encodes the structural patterns of distance matrices into an organized and roto-translationally invariant latent space of lower dimensionality. The generative capability of the variational autoencoder, coupled with a post-processing pipeline based on multidimensional scaling and short molecular dynamics, enables the recovery of physically meaningful configurations. The reconstructed and generated samples reproduce key observables, including energy, size, and entanglement, despite minor discrepancies in the raw decoder output. Autoencoder ; Convolution (computer science) ; Generative model ; Scaling ; Generative grammar ; Pattern recognition (psychology) ; Set (abstract data type) ; Invariant (physics) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Ignacio Pickering (17409430) | Jonathan A. Semelak (16540830) | Jinze Xue (5400851) | Adrian E. Roitberg (122734)
Source: Figshare (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

TorchANI-Amber:Bridging Neural Network Potentialsand Classical Biomolecular Simulations In this work we introduce TorchANI-Amber, an interface for routine molecular dynamics simulations of biomolecular systems using ANI-style machine learning potentials. TochANI-Amber incorporates the ANI neural network potentials into the Amber software suite, supporting Amber’s two engines: <i>sander</i> and <i>pmemd</i>. In addition to implementing all published ANI models, the interface is extensible to other energy predicting potentials through a simple mechanism requiring no knowledge of Amber’s codebase. To illustrate this versatility, we implement extensions to the AIMNet2 and Nutmeg potentials. The interface is integrated with Amber’s neighborlists, and it also supports an optimized CUDA implementation for computing the ANI models’ feature vectors, enabling simulations of systems with hundreds of thousands of atoms at the neural network’s level of theory (approaching DFT accuracy). The interface is designed so that all amber capabilities can be used with ANI potentials instead of force fields. To evaluate the energy conservation, stability, and performance of the ANI potentials as used through the interface, we run MD simulations on different biomolecular systems, including ubiquitin and Trp-cage proteins in explicit solvent. Additionally, we demonstrate the use of these potentials in the context of enhanced sampling techniques, such as temperature replica-exchange molecular dynamics. Interface (matter) ; Artificial neural network ; Software ; Molecular dynamics ; Context (archaeology) ; Bridging (networking) ; Energy (signal processing) ; Potential energy Machine Learning in Materials Science ; Protein Structure and Dynamics ; Machine Learning in Bioinformatics