jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaojie He | Jiachen Wan | Songyang Zhang | Chenggang Zhang | Peng Xiao | Feng Zheng | Qinghua Lu
Source: Polymers (article, 2025-06-11)
Field: Materials Science — Polymers and Plastics

Interpretable Machine Learning Prediction of Polyimide Dielectric Constants: A Feature-Engineered Approach with Experimental Validation Low-dielectric polyimides (PIs) have emerged as essential materials for next-generation microelectronics and communication technologies, yet traditional experimental and theoretical calculation methods for acquiring dielectric constant data face challenges in cost, accuracy, and scalability. This study presents a machine learning (ML) framework that combines polymer domain knowledge with advanced data-driven modeling techniques for accurate prediction of PI dielectric constants at 1 kHz. A dataset of 439 PIs was constructed, and 208 molecular descriptors were derived from SMILES-encoded structures. Through rigorous feature engineering-variance filtering, correlation analysis, and recursive feature elimination-10 key descriptors were identified, capturing electronic and polar interaction, surface area, and structural complexity. Five ML algorithms were evaluated, with Gaussian Process Regression (GPR) achieving superior predictive accuracy (test set: R<sup>2</sup> = 0.90, RMSE = 0.10). Shapley additive explanations (SHAP) analysis quantifies the contribution of molecular descriptors to PI dielectric constants. By means of SHAP values, it discloses the positive or negative impacts of descriptors on the predictions. Three novel PIs were synthesized for experimental validation, showing strong agreement between predicted and measured dielectric constants (mean percentage deviation: 2.24%). The model demonstrates robust predictions for other structurally similar polymers but reveals a 40% accuracy reduction (R<sup>2</sup> = 0.60) in 10 GHz cross-frequency predictions, emphasizing the requirement for multi-frequency training datasets to enhance model generalizability. This work advances the research paradigm of polymer dielectric materials and provides a pathway for the rational design of materials guided by machine learning. Dielectric ; Polyimide ; Materials science ; Feature (linguistics) ; Artificial intelligence ; Machine learning ; Computer science ; Composite material ; Optoelectronics Synthesis and properties of polymers ; Epoxy Resin Curing Processes
Authors: Michele Cascella | Sigbjørn Løland Bore | Odile Eisenstein
Source: Chemical Science (review, 2025-01-01)
Field: Chemistry — Physical and Theoretical Chemistry

The fellowship of the Grignard: 21st century computational tools for hundred-year-old chemistry This perspective begins with the discovery of the Grignard reaction by a graduate student in the last years of the 19th century, followed by describing why it has remained largely unexplained for more than a century. From the summary of what has been achieved, focusing on the computational aspects, it is now clear that further studies of the chemistry of any chemical species that is highly sensitive to solvents, such as Group I and II elements, require a holistic approach that includes the solute and the solvent together. <i>Ab initio</i> molecular dynamics, which meets these requirements, has produced some results but has hit hard limits due to its relatively high computational costs. In these days, it is becoming clear that data-driven methods, including machine learning potentials and simulations driven by quantitative on-the-fly calculation of relevant observables, have the potential to better and more completely explore the very large chemical space associated with the presence of a large number of species in solution. These methodologies have the chance to give the keys to enter the challenging and still poorly explored world of chemical species whose behaviour and reactivity are strongly influenced by the solvent and the experimental conditions. Chemistry History and advancements in chemistry ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Shunya Minami | Yoshihiro Hayashi | Stephen Wu | Kenji Fukumizu | Hiroki Sugisawa | Masashi Ishii | Isao Kuwajima | Kazuya Shiratori | Ryo Yoshida
Source: npj Computational Materials (article, 2025-05-24)
Field: Materials Science — Materials Chemistry

Scaling law of Sim2Real transfer learning in expanding computational materials databases for real-world predictions Abstract To address the challenge of limited experimental materials data, extensive physical property databases are being developed based on high-throughput computational experiments, such as molecular dynamics simulations. Previous studies have shown that fine-tuning a predictor pretrained on a computational database to a real system can result in models with outstanding generalization capabilities compared to learning from scratch. This study demonstrates the scaling law of simulation-to-real (Sim2Real) transfer learning for several machine learning tasks in materials science. Case studies of three prediction tasks for polymers and inorganic materials reveal that the prediction error on real systems decreases according to a power-law as the size of the computational data increases. Observing the scaling behavior offers various insights for database development, such as determining the sample size necessary to achieve a desired performance, identifying equivalent sample sizes for physical and computational experiments, and guiding the design of data production protocols for downstream real-world tasks. Scaling ; Scaling law ; Transfer of learning ; Computer science ; Database ; Data science ; Artificial intelligence ; Mathematics ; Geometry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Congcong Cui | Yuanyuan Cao | Lu Han
Source: ACS Nano (article, 2025-03-12)
Field: Materials Science — Materials Chemistry

Deep-Learning-Assisted Understanding of the Self-Assembly of Miktoarm Star Block Copolymers The self-assemblies of topological complex block copolymers, especially the AB<sub><i>n</i></sub> type miktoarm star ones, are fascinating topics in the soft matter field, which represent typical self-assembly behaviors analogous to those of biological membranes. However, their diverse topological asymmetries and versatile spontaneous curvatures result in rather complex phase separations that deviate significantly from the common mechanisms. Thus, numerous trial-and-error experiments with tremendous parameter space and intricate relationships are needed to study their assemblies. Herein, we applied deep learning technology to decipher the phase behaviors of the miktoarm star block copolymer PEO-<i>s</i>-PS<sub>2</sub> in an evaporation-induced self-assembly system. A neural network model was trained from practical experimental data encompassing two polymer properties and three synthesis condition parameters as input variables, which successfully predicted a three-dimensional (3D) synthesis-field diagram and mined the relationship between input parameters and obtained structures. This model demonstrated the highly flexible structure modulation directions of the miktoarm star block copolymer, revealing the correlation between the polymer parameters, synthesis conditions, and the output structures due to the significant influence of the variables on spontaneous curvatures. This work demonstrated the efficiency of a deep learning technique in uncovering the underlying rules of complex self-assembly systems, providing valuable insights into the exploration of soft matter science. Copolymer ; Star (game theory) ; Materials science ; Self-assembly ; Nanotechnology ; Block (permutation group theory) ; Polymer science ; Polymer ; Physics ; Composite material ; Astrophysics Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science
Authors: Viktor C. Birschitzky | Luca Leoni | Michele Reticcioli | Cesare Franchini
Source: Physical Review Letters (article, 2025-05-27)
Field: Materials Science — Materials Chemistry

Machine Learning Small Polaron Dynamics Polarons are crucial for charge transport in semiconductors, significantly impacting material properties and device performance. The dynamics of small polarons can be investigated using first-principles molecular dynamics. However, the limited timescale of these simulations presents a challenge for adequately sampling infrequent polaron hopping events. Here, we introduce a message-passing neural network combined with first-principles molecular dynamics within the Born-Oppenheimer approximation that learns the polaronic potential energy surface by encoding the polaronic state, allowing for simulations of polaron hopping dynamics at the nanosecond scale. By leveraging the statistical significance of the long timescale, our framework can accurately estimate polaron (anisotropic) mobilities and activation barriers in prototypical polaronic oxides across different scenarios (hole polarons in rocksalt MgO and electron polarons in pristine and F-doped rutile TiO_{2}) within experimentally measured ranges. Polaron ; Nanosecond ; Condensed matter physics ; Physics ; Molecular dynamics ; Electron ; Charge (physics) ; Anisotropy ; Chemical physics ; Statistical physics ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Magnetic and transport properties of perovskites and related materials
Authors: Xiaochen Du | M. Liu | Jiayu Peng | Hoje Chun | Alexander J. Hoffman | Bilge Yildiz | Lin Li | Martin Z. Bazant | Rafael Gómez‐Bombarelli
Source: ACS Central Science (article, 2025-07-21)
Field: Materials Science — Materials Chemistry

Accelerating and Enhancing Thermodynamic Simulations of Electrochemical Interfaces Electrochemical interfaces are crucial in catalysis, energy storage, and corrosion, where their stability and reactivity depend on complex interactions between the electrode, adsorbates, and electrolyte. Predicting stable surface structures remains challenging, as traditional surface Pourbaix diagrams tend to either rely on expert knowledge or costly <i>ab initio</i> sampling and neglect thermodynamic equilibration with the environment. Machine learning (ML) potentials can accelerate static modeling but often overlook dynamic surface transformations. Here, we extend the Virtual Surface Site Relaxation-Monte Carlo (VSSR-MC) method to autonomously sample surface reconstructions modeled under aqueous electrochemical conditions. Through fine-tuning foundational ML force fields, we accurately and efficiently predict surface energetics, recovering known Pt(111) phases and revealing new LaMnO<sub>3</sub>(001) surface reconstructions. By explicitly accounting for bulk-electrolyte equilibria, our framework enhances electrochemical stability predictions, offering a scalable approach to understanding and designing materials for electrochemical applications. Electrochemistry ; Computer science ; Chemistry ; Electrode ; Physical chemistry Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Advanced Thermoelectric Materials and Devices
Authors: Gunhee Lee | Hyung Sup Shim | Juhyun Cho | Sang‐Il Choi
Source: ACS Omega (article, 2025-03-19)
Field: Chemistry — Analytical Chemistry

Machine-Learning Approach to Identify Organic Functional Groups from FT-IR and NMR Spectral Data Interpreting spectral data to analyze the structure and properties of unknown chemicals requires a lot of time and effort. Herein, we developed a machine-learning model that simultaneously trains on multiple spectroscopic data to identify functional groups of compounds more accurately and quickly. An artificial neural network model trained on Fourier-transform infrared, proton nuclear magnetic resonance, and <sup>13</sup>C nuclear magnetic resonance together identified 17 functional groups with a macro-average F1 score of 0.93, outperforming the model using a single type of spectroscopy. The results indicated that training a machine-learning model with multiple spectral data can provide more accurate structural analysis when analyzing the structure of unknown chemicals, as can using multiple spectroscopy methods simultaneously. Carbon-13 NMR ; Artificial intelligence ; Chemistry ; Computer science ; Pattern recognition (psychology) ; Computational chemistry ; Organic chemistry Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Timothy J. Giese | Jinzhe Zeng | Darrin M. York
Source: The Journal of Physical Chemistry B (article, 2025-05-26)
Field: Materials Science — Materials Chemistry

Transferability of MACE Graph Neural Network for Range Corrected Δ-Machine Learning Potential QM/MM Applications We previously introduced a "range corrected" Δ-machine learning potential (ΔMLP) that used deep neural networks to improve the accuracy of combined quantum mechanical/molecular mechanical (QM/MM) simulations by correcting both the internal QM and QM/MM interaction energies and forces [J. Chem. Theory Comput. 2021, 17, 6993-7009]. The present work extends this approach to include graph neural networks. Specifically, the approach is applied to the MACE message passing neural network architecture, and a series of AM1/d + MACE models are trained to reproduce PBE0/6-31G* QM/MM energies and forces of model phosphoryl transesterification reactions. Several models are designed to test the transferability of AM1/d + MACE by varying the amount of training data and calculating free energy surfaces of reactions that were not included in the parameter refinement. The transferability is compared to AM1/d + DP models that use the DeepPot-SE (DP) deep neural network architecture. The AM1/d + MACE models are found to reproduce the target free energy surfaces even in instances where the AM1/d + DP models exhibit inaccuracies. We train "end-state" models that include data only from the reactant and product states of the 6 reactions. Unlike the uncorrected AM1/d profiles, the AM1/d + MACE method correctly reproduces a stable pentacoordinated phosphorus intermediate even though the training did not include structures with a similar bonding pattern. Furthermore, the message passing mechanism hyperparameters defining the MACE network are varied to explore their effect on the model's accuracy and performance. The AM1/d + MACE simulations are 28% slower than AM1/d QM/MM when the ΔMLP correction is performed on a graphics processing unit. Our results suggest that the MACE architecture may lead to ΔMLP models with improved transferability. Transferability ; Mace ; Computer science ; Artificial neural network ; Range (aeronautics) ; Graph ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Psychology ; Engineering ; Aerospace engineering Machine Learning in Materials Science ; Machine Learning and ELM ; Computational Drug Discovery Methods
Authors: Aditya Thakur | Ruchi Singh | Vikas Yadav | Soumik Siddhanta | Kolleboyina Jayaramulu
Source: Small Methods (review, 2025-02-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Advancing SERS Applications of 2D Materials through the Interplay of Rational Design and Structure‐Property Relationships Surface-enhanced Raman spectroscopy (SERS) is a highly sensitive analytical tool for molecular investigations, particularly in biological systems. While metal nanoparticles (NPs) have been widely explored for SERS, their performance depends on size, shape, and crystal structure. However, their Raman scattering efficiency is low, limiting applications. To overcome these challenges, 2D materials have emerged as promising SERS substrates due to their high surface area, charge transfer capabilities, stability, and tunable optical properties. Their biocompatibility makes them ideal for chemical and biomedical applications, including microfluidic systems, drug delivery, and in vivo diagnostics. This review comprehensively examines the development, structural characteristics, and plasmonic integration of 2D materials in SERS. It highlights design considerations, structural optimization using machine learning (ML), and material performance. ML-driven approaches enable precise tuning of 2D materials' optical, electrical, and chemical properties, enhancing biosensing capabilities. Computational algorithms facilitate the detection of ultra-low concentrations of biomolecules such as deoxyribonucleic acid (DNA), proteins, and metabolites. ML also offers powerful tools for data analysis, material optimization, and automated sensing, significantly advancing SERS applications. The synergy between ML and 2D materials opens new avenues for high-performance biosensing and analytical technologies. Nanotechnology ; Rational design ; Biomolecule ; Biosensor ; Materials science ; Raman scattering ; Microfluidics ; Biocompatibility ; Plasmon ; Raman spectroscopy ; Computer science ; Optoelectronics Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Nanocluster Synthesis and Applications
Authors: Peixin Liu | Hao Lü | Guojing Xu | Feng Cheng | C.S. Han | Xiaoyan Song
Source: Acta Materialia (article, 2025-03-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Breaking through the trade-off between saturation magnetization and coercivity: A data-driven strategy Coercivity ; Materials science ; Condensed matter physics ; Magnetization ; Saturation (graph theory) ; Engineering physics ; Magnetic field Magnetic Properties and Applications ; Magnetic properties of thin films ; Machine Learning in Materials Science
Authors: Ehsan Alibagheri | Mohammad Khazaei | Mehdi Estili | Alireza Seyfi | Hiroshi Mizoguchi | Kaoru Ohno | Hideo Hosono | S. Mehdi Vaez Allaei
Source: InfoMat (article, 2025-02-17)
Field: Materials Science — Materials Chemistry

<scp>LAX</scp> phases: A family of novel stable layered materials, informatics‐based discovery Abstract Ternary MAX phases, characterized by the chemical formula M₂AX, represent a group of layered materials with hexagonal lattices. These MAX phases have been the subject of extensive experimental and theoretical studies. Formation energy and thermodynamic calculations indicate that MAX phases containing late transition metals, such as Rh, Ru, Pt, Pd, Co, and Ni, are unlikely to form. Here, we introduce an alternative family of orthorhombic and monoclinic materials, the LAX phases, which exhibit similarities to MAX phases in terms of their layered structure and A and X elements. However, LAX materials incorporate late transition metals in place of the early transition metals. Advanced techniques for predicting the crystal structure of materials, coupled with data‐driven materials research and machine learning algorithms, were employed to investigate the stable structures containing transition metals from the last groups of the d‐block elements. The analyses revealed 207 ternary LAX systems that demonstrate robust stability against decomposition, with 100 of these systems showing dynamic stability. An in‐depth examination of the top 10 structures revealed five LAX systems that are phase stable and exhibit superior mechanical properties, outperforming MAX phase counterparts in Young's modulus, stiffness, and hardness. These findings indicate that many LAX phase structures are viable candidates for future synthesis, highlighting the potential of heuristic‐based structure searches in material discovery. image Orthorhombic crystal system ; Ternary operation ; Materials science ; Monoclinic crystal system ; Phase transition ; Transition metal ; Bulk modulus ; Crystal structure ; Crystallography ; Thermodynamics ; Chemical physics ; Chemistry ; Computer science ; Physics ; Composite material MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices ; 2D Materials and Applications
Authors: Heshani Balasooriya | Chunqing Li | Feng Wang
Source: Processes (article, 2025-06-22)
Field: Materials Science — Materials Chemistry

Understanding Steel Corrosion: Surface Chemistry and Defects Explored Through DFT Modelling—A Review Corrosion poses a critical challenge to the durability and performance of metals and alloys, particularly steel, with significant economic, environmental, and safety implications. The corrosion susceptibility of steel is influenced by aggressive chemical species, intrinsic material defects, and environmental factors. Understanding the atomic-scale mechanisms governing corrosion is essential for developing advanced corrosion-resistant materials. Density functional theory (DFT) has become a powerful computational tool for investigating these mechanisms, providing insight into the adsorption, diffusion, and reaction of corrosive species on iron surfaces, the formation and stability of metal oxides, and the influence of defects such as vacancies and grain boundaries in localised corrosion. This review presents a comprehensive analysis of recent DFT-based studies on iron and steel surfaces, emphasising the role of solvation effects and van der Waals corrections in improving model accuracy. It also explores defect-driven corrosion mechanisms and the formation of protective and reactive oxide layers under varying oxygen coverages. By establishing accurate DFT modelling approaches, this review provides up-to-date literature insights that support future integration with machine learning and multiscale modelling techniques, enabling reliable atomic-scale predictions. Corrosion ; Metallurgy ; Materials science ; Chemistry Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Liangliang Xu | Xingkun Wang | Xiaojuan Hu | Yue Wang | Canhui Zhang | Wenwu Xu | Wenhui Zhao | Ning Xu | Dongyoon Woo | Hanxu Yao | Xiaofan Li | Heqing Jiang | Minghua Huang | Jinwoo Lee | Xiao Cheng Zeng | Zhongkang Han
Source: The Innovation (article, 2025-04-17)
Field: Materials Science — Materials Chemistry

Artificial-intelligence-assisted design principle for developing high-performance single-atom catalysts Artificial intelligence (AI)-assisted approaches are powerful means for advancing catalyst design, as they can significantly accelerate the development of novel catalysts. However, the underlying mechanisms of these approaches often remain elusive, which may lead to unreliable results due to a lack of clear understanding of the involved processes. Herein, we present an AI strategy that combines machine learning (ML) and data mining (DM) to identify high-performance catalysts while elucidating the key factors that govern catalytic performance in complex reactions. Applying this AI strategy to evaluate the electrocatalytic oxygen reduction performance of 10,179 single-atom catalysts (SACs), we identified several high-performance SACs and determined the critical influencers of their activity. Experimental validations further confirm the effectiveness of the AI strategy, with the optimal target Co-S<sub>2</sub>N<sub>2</sub>/g-SAC achieving a high half-wave potential of 0.92 V. This AI-assisted approach significantly enhances the transparency and reliability of data-driven discoveries, providing new insights that benefit the rational design of materials. Atom (system on chip) ; Catalysis ; Computer science ; Materials science ; Psychology ; Nanotechnology ; Chemistry ; Embedded system Catalytic Processes in Materials Science ; Machine Learning in Materials Science ; Catalysis and Oxidation Reactions