jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Elia Stocco | Christian Carbogno | Mariana Rossi
Source: npj Computational Materials (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

Electric-field driven nuclear dynamics of liquids and solids from a multi-valued machine-learned dipolar model The driving of vibrational motion by external electric fields is a topic of continued interest, due to the possibility of assessing new or metastable material phases with desirable properties. Here, we combine ab initio molecular dynamics within the electric-dipole approximation with machine-learning neural networks (NNs) to develop a general, efficient and accurate method to perform electric-field-driven nuclear dynamics for molecules, solids, and liquids. We train equivariant and autodifferentiable NNs for the interatomic potential and the dipole, modifying the model infrastructure to account for the multi-valued nature of the latter in periodic systems. We showcase the method by addressing property modifications induced by electric field interactions in a polar liquid and a polar solid from nanosecond-long molecular dynamics simulations with quantum-mechanical accuracy. For liquid water, we present a calculation of the dielectric function in the GHz to THz range and the electrofreezing transition, showing that nuclear quantum effects enhance this phenomenon. For the ferroelectric perovskite LiNbO<sub>3</sub>, we simulate the ferroelectric to paraelectric phase transition and the non-equilibrium dynamics of driven phonon modes related to the polarization switching mechanisms, showing that a full polarization switch is not achieved in the simulations. Ferroelectricity ; Polarization (electrochemistry) ; Dipole ; Dielectric ; Metastability ; Molecular dynamics ; Phonon ; Electric field ; Polar Solid-state spectroscopy and crystallography ; Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies
Authors: Zean Li | Tao Yang | Liangshun Zhang
Source: Polymer International (article, 2025-06-07)
Field: Materials Science — Materials Chemistry

Data‐augmented machine learning for inverse design of homopolymers with targeted glass transition temperature Abstract Establishing a reliable workflow of inverse design by data‐driven machine learning (ML) models offers significant potential to accelerate molecular design of polymeric materials. Nevertheless, there exist scarcity issues of training datasets in current data‐driven models for polymers. In this contribution, we integrate the ML method with a data augmentation strategy to build upon a workflow of inverse design of polymeric materials with targeted glass transition temperature T g . Results show that the data‐augmented ML model significantly enhances the prediction accuracy of T g in spite of a small training dataset. Furthermore, the data augmentation strategy has the capability of generating the monomers of homopolymers with higher novelty and uniqueness, whose T g values are validated by the simulations of all‐atomic molecular dynamics. The ML‐assisted inverse design workflow offers significant advantages in establishing structure–property relationships and also provides an accelerated pathway for the targeted design of polymer systems. © 2025 Society of Chemical Industry. Glass transition ; Inverse ; Inverse temperature ; Materials science ; Computer science ; Composite material ; Mathematics ; Thermodynamics ; Physics ; Polymer ; Geometry Machine Learning in Materials Science ; Epoxy Resin Curing Processes ; Additive Manufacturing and 3D Printing Technologies
Authors: M. K. Teng | Liqin Deng | Pin-Zhen Jia | Wu‐Xing Zhou
Source: Journal of Physics Condensed Matter (article, 2025-03-31)
Field: Materials Science — Materials Chemistry

Machine learning-assisted study on the thermal transport properties of two-dimensional M<sub>3</sub>(C<sub>6</sub>O<sub>6</sub>)<sub>2</sub> (M = Fe, Co, Ni) materials Two-dimensional metal-organic frameworks (MOF) are widely used in electronic devices and energy storage due to their large surface area, abundant active sites, and tunable sizes. A deeper understanding of the thermal transport properties of two-dimensional MOF materials is essential for these applications. In this work, we systematically studied the thermal transport properties of M<sub>3</sub>(C<sub>6</sub>O<sub>6</sub>)<sub>2</sub>(M = Fe, Co, Ni) by using a machine learning interatomic potential method combined with the phonon Boltzmann transport equation. The results show that the lattice thermal conductivities of Fe<sub>3</sub>(C<sub>6</sub>O<sub>6</sub>)<sub>2</sub>, Co<sub>3</sub>(C<sub>6</sub>O<sub>6</sub>)<sub>2</sub>, and Ni<sub>3</sub>(C<sub>6</sub>O<sub>6</sub>)<sub>2</sub>at room temperature are 4.0 W mK<sup>-1</sup>, 5.5 W mK<sup>-1</sup>, and 5.8 W mK<sup>-1</sup>, respectively. The differences in thermal conductivity primarily arise from variations in phonon relaxation times, which can be elucidated by examining the three-phonon scattering phase space. Further analysis of bond strengths reveals that the strong bonding between Fe and O impedes phonon propagation through the oxygen atoms, resulting in lower lattice thermal conductivity. Our work provides a fundamental reference for understanding thermal transport in two-dimensional MOF. Phonon ; Thermal conductivity ; Boltzmann equation ; Thermal ; Scattering ; Condensed matter physics ; Lattice (music) ; Work (physics) ; Phonon scattering ; Relaxation (psychology) ; Chemistry ; Materials science ; Thermodynamics ; Chemical physics ; Physics ; Optics Machine Learning in Materials Science ; Thermal properties of materials
Authors: Xiongwei Tian | Axel Tosello Gardini | Umberto Raucci | Hai Xiao | Yuqun Zhuo | Michele Parrinello
Source: ChemRxiv (preprint, 2025-05-28)
Field: Chemistry — Electrochemistry

Electrochemical Potential-Driven Water Dynamics Control CO2 Electroreduction at the Ag/H2O Interface A molecular-level understanding of the catalyst–electrolyte interface under realistic operating conditions remains a central challenge in electrocatalysis. In particular, the role of the electrochemical potential in modulating interfacial solvation, and its consequences for CO2 electroreduction, has yet to be fully elucidated. Here, using machine learning accelerated molecular dynamics simulations, an explicit solvent model within the grand canonical DFT framework, and enhanced sampling techniques, we systematically investigate the impact of the working potentials on CO2 reduction process at the Ag(111)/H2O interface. Our results reveal that the applied potential significantly reshape the orientation of interfacial water and modulate the strength of hydrogen-bond network. This collective solvent response to the electric potential plays an important role in stabilizing reactive intermediates, regulating reaction kinetics, and facilitating key steps such as proton transfer and hydroxide diffusion. These findings underscore the critical role of solvent dynamics in CO2 reduction, highlighting the importance of simulating electrochemical reactions under realistic operating conditions. Rather than acting as a passive background medium, the solvent emerges as a dynamic, potential-sensitive participant that plays an active role in the catalytic process. Electrochemistry ; Interface (matter) ; Control (management) ; Dynamics (music) ; Materials science ; Chemical physics ; Chemistry ; Chemical engineering ; Nanotechnology ; Computer science ; Aqueous solution ; Electrode ; Physical chemistry ; Physics ; Engineering ; Artificial intelligence Electrochemical Analysis and Applications ; CO2 Reduction Techniques and Catalysts ; Electrocatalysts for Energy Conversion
Authors: Stef A. H. Jansen | Ghislaine Vantomme | E. W. Meijer
Source: Angewandte Chemie International Edition (review, 2025-08-22)
Field: Materials Science — Biomaterials

Evolving Data‐Driven Strategies for the Characterization of Supramolecular Polymers and Systems Inspired by the dynamic assembly of fibrillar proteins in biology, research on supramolecular polymers has progressed rapidly toward the development of synthetic multicomponent systems. In this review, we highlight recent advances in the study of supramolecular polymers in solution, with an emphasis on how combined computational and experimental approaches deepen our understanding of these systems. In particular, these studies have elucidated the mechanisms of protein aggregation and provided insights into the characteristics of synthetic systems. We discuss the classification of these polymers and systems, highlighting how their different interaction modes and microstructures give rise to diverse structural and functional properties. In addition, we outline the emerging role of machine learning as a powerful tool to navigate the inherent complexity of these systems, thereby enhancing strategies for rational design and characterization. This review highlights how computational approaches, from traditional modeling to emerging machine learning techniques, enable the experimental characterization and understanding of supramolecular polymer chemistry. Characterization (materials science) ; Supramolecular chemistry ; Supramolecular polymers ; Polymer ; Nanotechnology ; Computer science ; Materials science ; Chemistry ; Organic chemistry ; Molecule Supramolecular Self-Assembly in Materials ; Polydiacetylene-based materials and applications ; Supramolecular Chemistry and Complexes
Authors: Cheol Hwan Kim | Jae H. Kim | Sung‐Yeob Jeong | Bo Sung Shin
Source: Scientific Reports (article, 2025-08-27)
Field: Materials Science — Materials Chemistry

Atomistic and data-driven modeling of laser-induced graphene formation on sustainable polymer substrates Wood-based substrates-known for their renewability, abundance, and surface functionalization potential-have recently gained attention as polymers for laser-induced graphene (LIG) synthesis because of their environmentally friendly attributes. These environment-friendly properties also make them pollution-free and easy to dispose of after use. However, the formation of LIG on wood substrates lacks robust theoretical support, and molecular dynamics (MD) simulations, which are a potential theoretical framework, are time-consuming and computationally intensive. Herein, we employed temperature-dependent MD simulations to explore LIG formation on wood-based materials, validating our findings through a comparative analysis with atomic-scale characterization results. To address the high computational requirements of MD simulations, machine learning (ML) models, including long short-term memory (LSTM) networks, support vector regression (SVR), and multilayer perceptrons (MLP), were implemented to extrapolate predictions beyond direct simulation conditions. Each model exhibited high data explanatory power (R<sup>2</sup> values ≥ 0.9), and the computational time was significantly reduced compared to the MD simulations. ML-based predictions revealed a substantial correlation between the temperature and LIG formation extent, establishing an efficient framework for optimizing LIG synthesis from wood-based materials under various laser processing conditions. This framework has considerable potential for applications in energy storage devices, high-sensitivity sensors, and advanced catalytic materials. Graphene ; Polymer ; Laser ; Materials science ; Nanotechnology ; Chemical engineering ; Composite material ; Physics Graphene research and applications ; Laser-Ablation Synthesis of Nanoparticles ; Carbon Nanotubes in Composites
Authors: Sadhana Barman | Utpal Sarkar
Source: Advanced Theory and Simulations (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Prediction of Chemical Reactivity Parameters via Data‐Driven Approach Abstract Novel material designing in an efficient way and its property prediction is empowered by data‐driven approach. For system designing or synthesis, stable and compatible chemical counterparts containing functional materials are preferred. In this regard, the knowledge of chemical reactivity is indispensable and is closely associated with how a substance reacts in a particular chemical reaction. In this work, chemical reactivity parameters of some organic molecules through machine learning (ML) algorithms are predicted. Several categories of descriptors are used as input features to predict HOMO‐LUMO energy gap, ionization potential, electron affinity, chemical potential, chemical hardness and electrophilicity index. The accurately achieved reactivity parameters confirm the descent training of the model from the integrated data of organic molecules. This work confirms that chemical properties reproduced through ML approach are closely correlated with density functional theory (DFT) ‐based results, so the proposed ML approach provides reactivity information at a very cheap cost. The prediction of chemical reactivity, as well as perception of the correlations between input features and targeted properties of organic molecules, may lead the experimentalist to know more about the observation. Reactivity (psychology) ; Computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemistry and Chemical Engineering
Authors: Rongchang Xing | Haodong Yao | Zuoxin Xi | Ming‐Hui Sun | Qingmeng Li | Jinglong Tian | Hairui Wang | Dazhuan Xu | Zhaohai Ma | Lina Zhao
Source: npj Computational Materials (article, 2025-08-28)
Field: Materials Science — Materials Chemistry

Interpretable X-ray diffraction spectra analysis using confidence evaluated deep learning enhanced by template element replacement X-ray Diffraction analysis is crucial for understanding material structures but is hindered by complex patterns and the need for expert interpretation. Deep learning offers automation in phase identification but faces challenges such as data scarcity, overconfidence in predictions and lack of interpretability. This study addresses these by employing Template Element Replacement to generate a perovskite chemical space containing physically unstable virtual structures, enhancing model understanding of XRD-crystal structure relationships and improving classification accuracy by ~5%. A Bayesian-VGGNet model was developed, achieving 84% accuracy on simulated spectra and 75% on external experimental data, while simultaneously estimating prediction uncertainty. Evaluation using Bayesian methods revealed low entropy values, indicating high model confidence. Quantifying the importance of input features to crystal symmetry, aligning significant features of seven crystal systems with physical principles. These approaches enhance the model’s robustness and reliability, making it suitable for practical applications. Diffraction ; Materials science ; X-ray ; Spectral line ; X-ray crystallography ; Crystallography ; Optics ; Chemistry ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Zijuan You | Hanlin Chen | Tongyu Liu | Hanying Wang | Bangfen Wang | Meiqin Chen | Suhua Wang
Source: Process Safety and Environmental Protection (article, 2025-10-11)
Field: Materials Science — Materials Chemistry

Recent advances of multicomponent VOCs catalytic oxidation: Insights into mechanism, mixing effect, and catalyst design Catalysis ; Catalytic oxidation ; Mixing (physics) ; Reactor design ; Clean energy ; Chemical reaction engineering Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Jingdan Chen | Nicholas E. Jackson
Source: Chemistry of Materials (article, 2025-09-18)
Field: Materials Science — Materials Chemistry

A Machine Learning Model for Copolymer Radical Reactivity Ratio Predictions with Frontier-Orbital Insights Accurate predictions of reactivity ratios (RRs) are crucial for understanding and controlling copolymerization kinetics and the resulting copolymer microstructure. While various methods have been proposed for RR prediction, prior efforts have been limited by a lack of data accessibility, model interpretability, and out-of-distribution performance on new chemical spaces. We address these challenges by assembling a data set of copolymer RRs extracted from the experimental literature and then developing a machine learning model that demonstrates robustness in predicting RRs for diverse monomers and radical chemistries. The Shapley additive explanations (SHAP) analysis of the machine learning model reveals the significant role of frontier molecular orbital (FMO) interactions, corroborating earlier RR prediction models emphasizing the bipolar reactivity of radicals in copolymerization. Importantly, this machine learning model leads to an intuitive argument based on the relative chemical potential and chemical hardness of comonomers that enables predictions of copolymerization regimes based on simple density functional theory calculations. Copolymer ; Robustness (evolution) ; Reactivity (psychology) ; Monomer ; Argument (complex analysis) ; Radical ; Training set Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Chaoqiang Feng | Bin Jiang
Source: JACS Au (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerated Finite-Field Simulations for Electrochemical Interfaces Electrochemical interfaces are of fundamental importance in electrocatalysis, batteries, and metal corrosion. Finite-field methods are one of the most reliable approaches for modeling electrochemical interfaces in complete cells under realistic constant-potential conditions. However, previous finite-field studies have been limited to either expensive ab initio molecular dynamics or less accurate classical descriptions of electrodes and electrolytes. To overcome these limitations, we present a machine learning-based finite-field approach that combines two neural network models: one predicts atomic forces under applied electric fields, while the other describes the corresponding charge response. Both models are trained entirely on first-principles data without employing any classical approximations. As a proof-of-concept demonstration in a prototypical Au(100)/NaCl-(aq) system, this approach accelerates fully first-principles finite-field simulations by roughly 4 orders of magnitude compared to ab initio molecular dynamics, allowing the extrapolation to cell potentials beyond the training range and accurate prediction of Helmholtz capacitance. Interestingly, we reveal a turnover of both density and orientation distributions of interfacial water molecules at the anode, arising from competing interactions between the positively charged anode and adsorbed Cl<sup>-</sup> ions with water molecules as the applied potential increases. This novel computational scheme shows great promise in efficient first-principles modeling of large-scale electrochemical interfaces under potential control. Extrapolation ; Helmholtz free energy ; Artificial neural network ; Ab initio ; Molecular dynamics ; Electrochemistry ; Orientation (vector space) ; Electrode Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Spectroscopy and Quantum Chemical Studies
Authors: Artur Brotons Rufes | Sergio Posada Pérez | Albert Poater
Source: Digital Chemical Engineering (article, 2025-12-17)
Field: Materials Science — Materials Chemistry

DFT in catalysis: Complex equations for practical computing applications in chemistry Density Functional Theory (DFT) has become the cornerstone of modern computational catalysis, providing a practical balance between accuracy and efficiency in describing molecular structure, bonding, and reactivity. This review presents a comprehensive overview of DFT methodology, from its quantum-mechanical foundations and basis-set construction to the hierarchy of exchange–correlation functionals defined by Jacob’s ladder. We discuss how DFT enables mechanistic elucidation of homogeneous and heterogeneous catalytic processes, highlighting benchmark studies that compare functional performance across representative reactions and transition states. Key interpretative tools, such as bond order analysis (Mayer, Wiberg, AIM/QTAIM), Natural Bond Orbital (NBO) theory, Energy Decomposition Analysis (EDA), and Non-Covalent Interaction (NCI) plots, are introduced as essential descriptors linking electronic structure to reactivity. The review also explores the integration of DFT with machine learning, microkinetic modeling, and automated reaction discovery, outlining recent advances toward predictive catalysis. Collectively, this work provides both conceptual and practical guidance for applying DFT to catalytic problems, emphasizing methodological awareness, descriptor-based interpretation, and emerging data-driven strategies for rational catalyst design. However, the main take-home message is that for DFT calculations, while in-depth methodological expertise is not essential, a clear comprehension of the theory’s practical application is crucial. Density functional theory ; Hierarchy ; Benchmark (surveying) ; Decomposition ; Key (lock) ; Cornerstone ; Work (physics) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Asymmetric Hydrogenation and Catalysis
Authors: Dajana Vukić | Biljana Lončar | Lato Pezo | Vladimir Vukić
Source: Foods (article, 2025-06-20)
Field: Chemistry — Analytical Chemistry

Application of Predictive Modeling and Molecular Simulations to Elucidate the Mechanisms Underlying the Antimicrobial Activity of Sage (Salvia officinalis L.) Components in Fresh Cheese Production Plant-derived materials from <i>Salvia officinalis</i> L. (sage) have demonstrated significant antimicrobial potential when applied during fresh cheese production. In this study, the mechanism of action of sage components against <i>Listeria monocytogenes, Escherichia coli</i>, and <i>Staphylococcus aureus</i> was investigated through the development of predictive models that describe the influence of key parameters on antimicrobial efficacy. Molecular modeling techniques were employed to identify the major constituents responsible for the observed inhibitory activity. Epirosmanol, carvacrol, limonene, and thymol were identified as the primary compounds contributing to the antimicrobial effects during cheese production. The highest weighted predicted binding energy was observed for thymol against the KdpD histidine kinase from <i>Staphylococcus aureus</i>, with a value of -33.93 kcal/mol. To predict the binding affinity per unit mass of these sage-derived compounds against the target pathogens, machine learning models-including Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Boosted Trees Regression (BTR)-were developed and evaluated. Among these, the ANN model demonstrated the highest predictive accuracy and robustness, showing minimal bias and a strong coefficient of determination (R<sup>2</sup> = 0.934). These findings underscore the value of integrating molecular modeling and machine learning approaches for the identification of bioactive compounds in functional food systems. SAGE ; Salvia officinalis ; Antimicrobial ; Salvia ; Traditional medicine ; Biology ; Microbiology ; Botany ; Medicine ; Physics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Essential Oils and Antimicrobial Activity