jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Guohui Li | Dong Hui Zhang | Ye Liu | Haojiang Yao | Zhenhao Zhou
Source: Chemical Science (article, 2026-01-01)
Field: Chemistry — Organic Chemistry

Solvent–temperature coupled hydride transfer controls product selectivity in the HSiR <sub>3</sub> /KO <sup> <i>t</i> </sup> Bu catalytic system The HSiR<sub>3</sub>/KO <sup><i>t</i></sup> Bu catalytic system affords a notable regioselective C-H silylation reaction using alkaline earth metals, yet its mechanistic origins and the temperature-dependent selectivity inversion remain unresolved. Here, we combine active learning neural network potentials with long-timescale <i>ab initio</i> quality molecular dynamics and enhanced sampling to simulate the HSiEt<sub>3</sub>/KO <sup><i>t</i></sup> Bu catalyzed deprotonation reaction of <i>N</i>-methylindole in a complete <i>operando</i> solution environment. We found that KO <sup><i>t</i></sup> Bu can rapidly aggregate in organic solution to form catalyst clusters, which are able to activate and stabilize a hydride derived from silane, and this <i>operando</i> generated hydride is the catalytically active species. The hydride is able to deprotonate C2 and C3 protons on <i>N</i>-methylindole, and this reaction site selectivity is affected by the stability of the cluster controlled by solvent and temperature coupling, which gives rise to nonlinear activation entropy of the C3 pathway in the neat silane environment. Unlike tetrahydrofuran solutions, the weak polarity of neat silanes makes it difficult to support deprotonation reactions at the less acidic C3 position at high temperature, thus affecting temperature-dependent selectivity. These results provide a microscopic predictive framework for tuning the selectivity in base-promoted hydrosilane chemistry and illustrate how explicit operational solvation and cluster formation can reshape mechanistic conclusions drawn from static models. Selectivity ; Regioselectivity ; Catalysis ; Silylation ; Hydride Catalytic C–H Functionalization Methods ; Organoboron and organosilicon chemistry ; Catalytic Cross-Coupling Reactions
Authors: Charles Parton-Barr | Richard J. Mandle
Source: ArXiv.org (article, 2026-02-19)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-Driven Prediction of Dielectric Anisotropy in Nematic Liquid Crystals We curate a large-scale dataset of low frequency dielectric anisotropy values for low molecular weight liquid crystals. Using this dataset, we demonstrate that supervised machine-learning models can predict dielectric anisotropy with substantially improved accuracy (RMSE 2.6) compared to estimates obtained from the Maier-Meier relations using molecular properties from both the widely used semiempirical AM1 method (RMSE 9.7) and the modern r2scan-3c composite method (RMSE 11.2). Realising the potential of machine learning techniques for liquid crystalline materials requires carefully curated data to be accessible, and on this basis we propose a simple and standard template for reporting data. Anisotropy ; Dielectric ; Liquid crystal ; Simple (philosophy) ; Experimental data ; Basis (linear algebra) Liquid Crystal Research Advancements ; Material Dynamics and Properties ; Dielectric materials and actuators
Authors: P. V. Chirkov | G. S. Eltsov | R. M. Kichigin | A. A. Mirzoev | A. V. Karavaev | V. V. Dremov
Source: Physical review. B./Physical review. B (article, 2026-02-24)
Field: Materials Science — Materials Chemistry

Thermodynamic and transport properties of platinum from machine learning enhanced quantum molecular dynamics Platinum ; Quantum ; Molecular dynamics ; Feature (linguistics) ; Dynamics (music) Machine Learning in Materials Science ; Quantum many-body systems ; Quantum, superfluid, helium dynamics
Authors: Ismail Badran | Abdelrahman Eid | Motasem Far | Nadeen Abbas | Raghad Tayeh | Sahar Salman | Yasmeen Hamdan
Source: International Journal of Quantum Chemistry (article, 2026-04-09)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning for Benchmarking <scp>DFT</scp> Electron Affinity Predictions: A Generalized Additive Model Approach ABSTRACT Electron affinity (EA) is vital for understanding charge transfer, redox reactions, and material design across chemistry and materials science. This study introduces a novel methodology that integrates interpretable machine learning with density functional theory (DFT) to provide a guideline for EA predictions. Using Generalized Additive Models (GAM), we reveal how functionals, basis sets, and molecular species interact in complex and nonlinear ways, offering insights that are not accessible through conventional analyses. A curated dataset of 47 atoms and molecules was used to assess the predictive performance of nine functionals across five basis sets. The results show that basis set selection has a more pronounced effect on EA prediction accuracy than functional choice, with the “ augmented ” ma‐def2‐SVP basis set, particularly in combination with B3LYP, providing the most reliable predictions. In contrast, the “ unaugmented ” def2‐TZVP and def2‐SVP basis sets frequently introduced large errors and high variability. The GAM framework identified species‐specific challenges, especially for Cl 2 , NH 3 , and CN, and demonstrated that although statistically significant differences exist among functionals, their performance in predicting EA values is largely equivalent from a chemical perspective. The GAM‐based ranking showed a spread of 1.6 kJ/mol among the average predicted deviations of the tested functionals, indicating that their practical differences are small from a chemical perspective under the studied conditions. This interpretable machine learning approach therefore provides a transparent and reproducible strategy for guiding the selection of DFT methods for EA calculations. To our knowledge, this is the first study to apply interpretable ML to systematically investigate and challenge generalized assumptions in EA benchmarking, establishing a data‐driven framework for practical decision‐making in computational chemistry. Basis (linear algebra) ; Benchmarking ; Ranking (information retrieval) ; Set (abstract data type) ; Selection (genetic algorithm) ; Electron affinity (data page) ; Quantitative structure–activity relationship ; Density functional theory ; Additive model Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Chemical Physics Studies
Authors: Jalal Abdolahi | Dominic Robe | Ronald G. Larson | Elnaz Hajizadeh
Source: arXiv (Cornell University) (article, 2026-02-14)
Field: Materials Science — Materials Chemistry

Machine Learning-integrated Multiscale Simulation Framework: Bridging Scales in Associative Polymer-Colloid Suspensions Predicting the rheological behavior of associative polymers bridging colloidal particles into transient networks is fundamentally challenging because the coupled spatiotemporal scales prevent efficient molecular-fidelity modeling. We address this through a novel, unified multiscale simulation framework for telechelic polymer-colloid suspensions integrating: explicit-chain Brownian dynamics resolving polymer-particle association kinetics; active learning metamodels compressing kinetics into efficient surrogates; and Population Balance-Brownian Dynamics (Pop-BD) computing network-scale dynamics from metamodel predictions. Validated against explicit-chain Brownian dynamics, our framework accurately reproduces time-and frequency-dependent stress relaxation moduli, enabling simulations of larger systems over longer timescales. Systematic investigations reveal that network connectivity exhibits critical transitions at specific chain-to-particle ratios, with bond density and lifetime correlating to enhanced relaxation times and moduli. Higher particle volume fractions yield more persistent bonds and slower relaxation. This framework connects chain-level dynamics to macroscopic rheology, enabling computationally efficient rational design of associative colloidal materials for waterborne coatings and soft-matter applications. Bridging (networking) ; Brownian dynamics ; Multiscale modeling ; Associative property ; Rheology ; Population ; Molecular dynamics ; Relaxation (psychology) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: Yanchao Zhang | Man Zhang | Wantong Zhang | Shuangshuang Ji | Xufang Hu | Nianrong Sun | Chunhui Deng
Source: Chinese Chemical Letters (article, 2026-04-01)
Field: Chemistry — Organic Chemistry

Smart peptidomic diagnostics: Hydrophilic molecular traps with machine learning-driven pattern recognition Pattern recognition (psychology) ; Feature (linguistics) ; Molecular recognition ; Field (mathematics) Supramolecular Chemistry and Complexes ; Molecular Sensors and Ion Detection ; Supramolecular Self-Assembly in Materials
Authors: A. V. Luebben | J. Luebben
Source: Acta Crystallographica Section A Foundations and Advances (article, 2026-03-30)
Field: Materials Science — Materials Chemistry

Deep learning for smart sample alignment Development of new Deep Learning techniques have impacted a wide range of research areas [1].One area where deep neural networks have proven powerful is the processing and interpretation of images [2].We built on top of these methods to develop a novel technique to streamline the process of aligning crystal samples in the centre of a single crystal X-ray diffractometer. Deep learning ; Artificial neural network ; Sample (material) ; Process (computing) ; Interpretation (philosophy) ; Deep neural networks Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Jalal Abdolahi | Dominic Robe | Ronald G. Larson | Elnaz Hajizadeh
Source: Open MIND (preprint, 2026-02-14)
Field: Materials Science — Materials Chemistry

Machine Learning-integrated Multiscale Simulation Framework: Bridging Scales in Associative Polymer-Colloid Suspensions Predicting the rheological behavior of associative polymers bridging colloidal particles into transient networks is fundamentally challenging because the coupled spatiotemporal scales prevent efficient molecular-fidelity modeling. We address this through a novel, unified multiscale simulation framework for telechelic polymer-colloid suspensions integrating: explicit-chain Brownian dynamics resolving polymer-particle association kinetics; active learning metamodels compressing kinetics into efficient surrogates; and Population Balance-Brownian Dynamics (Pop-BD) computing network-scale dynamics from metamodel predictions. Validated against explicit-chain Brownian dynamics, our framework accurately reproduces time-and frequency-dependent stress relaxation moduli, enabling simulations of larger systems over longer timescales. Systematic investigations reveal that network connectivity exhibits critical transitions at specific chain-to-particle ratios, with bond density and lifetime correlating to enhanced relaxation times and moduli. Higher particle volume fractions yield more persistent bonds and slower relaxation. This framework connects chain-level dynamics to macroscopic rheology, enabling computationally efficient rational design of associative colloidal materials for waterborne coatings and soft-matter applications. Bridging (networking) ; Brownian dynamics ; Multiscale modeling ; Associative property ; Rheology ; Population ; Molecular dynamics ; Relaxation (psychology) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Material Dynamics and Properties
Authors: N.M. Sakan | V.A. Srećković | V. Vujčić
Source: Contributions of the Astronomical Observatory Skalnaté Pleso (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Advancing computational spectroscopy: machine learning approaches for reconstructing incomplete spectroscopic and collisional datasets Spectroscopy remains a cornerstone of modern physics and chemistry, providing critical insights into molecular structures and interstellar phenomena.Accurate interpretation of interstellar line spectra through radiative transfer modeling relies heavily on two essential types of molecular input data: spectroscopic information (including energy levels, transition probabilities, and statistical weights) and collisional data.However, the completeness and precision of these datasets are often limited, constraining the reliability of astrophysical models.This work explores the application of machine learning (ML) and artificial neural networks (ANNs) for predicting and reconstructing missing spectroscopic/collisional data.We analyze their potential to enhance spectral databases, thereby improving stellar spectral analyses and the determination of fundamental stellar parameters.The study also addresses key challenges, methodological limitations, and validation issues inherent to data-driven approaches.Finally, we discuss current progress, share practical experiences, and outline future prospects and research needs in the rapidly evolving field of computational spectroscopy. Feature (linguistics) ; Training set ; Pattern recognition (psychology) ; Support vector machine ; Noisy data Machine Learning in Materials Science ; Spectroscopy and Laser Applications ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Bardia Afsordeh | Hadi Shirali
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Physicochemical Descriptor Framework for Machine Learning Prediction of Glass Transition Temperature in Polymer/Graphene Nanocomposites Graphene ; Nanocomposite ; Glass transition ; Set (abstract data type) ; Polymer ; Polymer nanocomposite ; Hydrogen bond ; Predictive modelling ; Quantitative structure–activity relationship Machine Learning in Materials Science ; Graphene research and applications ; Polymer crystallization and properties
Authors: Xiao Zhu | Srinivasan S. Iyengar
Source: Physical Review X (article, 2026-01-05)
Field: Materials Science — Materials Chemistry

Large Language Model-Type Architecture for High-Dimensional Molecular Potential Energy Surfaces Computing high-dimensional potential energy surfaces for molecular systems and materials is considered to be a great challenge in computational chemistry with potential impact in a range of areas including the fundamental prediction of reaction rates. In this paper, we design and discuss an algorithm that has similarities to large language models in generative AI and natural language processing. Specifically, we represent a molecular system as a graph which contains a set of nodes, edges, faces, etc. Interactions between these sets, which represent molecular subsystems in our case, are used to construct the potential energy surface for a reasonably sized chemical system with 51 nuclear dimensions. For this purpose, a family of neural networks that pertain to the graph-theoretically obtained subsystems get the job done for this 51 nuclear dimensional system. We then ask if this same family of lower-dimensional graph-based neural networks can be transformed to provide accurate predictions for a 186-dimensional potential energy surface. We find that our algorithm does provide accurate results for this larger-dimensional problem with sub-kcal/mol accuracy for the higher-dimensional potential energy surface problem. Indeed, as a result of these developments, here we produce efforts toward a full-dimensional potential energy surface for the protonated 21-water cluster (186 nuclear dimensions) at coupled-cluster single and double level accuracy. Potential energy ; Potential energy surface ; Energy (signal processing) ; Set (abstract data type) ; Construct (python library) ; Range (aeronautics) ; Artificial neural network ; Surface (topology) Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Gaopeng Ren | Yijie Yang | Jiajun Zhou | Kim E. Jelfs
Source: arXiv (Cornell University) (preprint, 2026-03-02)
Field: Materials Science — Materials Chemistry

PolyMon: A Unified Framework for Polymer Property Prediction Accurate prediction of polymer properties is essential for materials design, but remains challenging due to data scarcity, diverse polymer representations, and the lack of systematic evaluation across modelling choices. Here, we present PolyMon, a unified and accessible framework that integrates multiple polymer representations, machine learning methods, and training strategies within a single, accessible platform. PolyMon supports various descriptors and graph construction strategies for polymer representations, and includes a wide range of models, from tabular models to graph neural networks, along with flexible training strategies including multi-fidelity learning, Δ-learning, active learning, and ensemble learning. Using five key polymer properties as benchmarks, we perform systematic evaluations to assess how representations and models affect predictive performance. These case studies further illustrate how different training strategies can be applied within a consistent workflow to leverage limited data and incorporate physical model derived information. Overall, PolyMon provides a comprehensive and extensible foundation for benchmarking and advancing machine learning-based polymer property prediction. The code is available at github.com/fate1997/polymon. Workflow ; Leverage (statistics) ; Benchmarking ; Training set ; Property (philosophy) ; Graph ; Predictive modelling ; Extensibility Machine Learning in Materials Science ; Advanced Polymer Synthesis and Characterization ; Block Copolymer Self-Assembly
Authors: Harshit Sethi | Markus Junttila | Orlando J. Silveira | Adam S. Foster
Source: arXiv (Cornell University) (preprint, 2026-02-23)
Field: Materials Science — Materials Chemistry

Automated structure discovery for Tip Enhanced Raman Spectroscopy Tip-Enhanced Raman Spectroscopy (TERS) provides nanoscale chemical fingerprints alongside high-resolution topographic mapping of molecules, offering a powerful tool for materials discovery. However, TERS image datasets are challenging to interpret and typically demand time-consuming, computationally intensive quantum-chemistry calculations. To overcome this problem, we present an encoder-decoder model trained and evaluated on simulated TERS images of planar molecules, enabling direct prediction of molecular structures from spectral simulated data with high accuracy. Our approach demonstrates the feasibility of automating molecular structure identification from TERS images, bypassing traditional manual analysis. These findings provide a foundation for extending machine learning methods to experimental TERS datasets, potentially accelerating molecular discovery by integrating nanoscale spectroscopy with automated computational analysis. Nanoscopic scale ; Raman spectroscopy ; Planar ; Identification (biology) ; Characterization (materials science) ; Spectroscopy ; Pattern recognition (psychology) Machine Learning in Materials Science ; Spectroscopy Techniques in Biomedical and Chemical Research ; Gold and Silver Nanoparticles Synthesis and Applications
Authors: Zihao He | Zijun Wang | Yueyao Dong | Kolesnikov Aleksei Viktorovich | Shuwei Hao | Chunhui Yang | Yuanpeng Liu | Liwei Dong
Source: Advanced Materials (article, 2026-02-05)
Field: Materials Science — Materials Chemistry

Coupling Data‐Driven and Reinforcement Learning for Material Development and Device Management in Batteries The growing demand for high-energy, safe, and durable batteries demands innovative strategies in both material discovery and device management. Conventional approaches, rooted in empirical trial-and-error and physics-based modeling, often struggle to address the complexity and dynamic nature of next-generation battery systems. Here, we highlight the emerging synergy between data-driven machine learning and reinforcement learning (RL), establishing a coupled paradigm that unifies predictive modeling with adaptive optimization. Data-driven methods enable rapid screening of cathodes, anodes, and liquid/solid electrolytes through multi-source data mining, while RL agents iteratively optimize synthesis conditions, interfacial properties, and charging protocols. Together, these approaches create closed-loop frameworks for materials development (prediction, exploration, and validation) and device management (data insight and strategy optimization), which accelerate discovery, enhance safety, and improve performance across the battery lifecycle. Finally, we further outline critical opportunities in data processing, feature engineering, and model building that can elevate this coupled paradigm from conceptual promise to industrial deployment. This integration of data-driven learning with reinforcement intelligence paves a pathway toward autonomous, high-throughput battery innovation, providing new foundations for next-generation energy technologies. Reinforcement learning ; Battery (electricity) ; Coupling (piping) ; Energy storage ; Energy management ; Reinforcement ; Feature (linguistics) Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advanced Battery Materials and Technologies
Authors: Fuchun Ge | Yuxinxin Chen | Pavlo O. Dral
Source: Journal of Chemical Theory and Computation (article, 2026-02-12)
Field: Materials Science — Materials Chemistry

Artificial Intelligence for Direct Prediction of Molecular Dynamics across Chemical Space Molecular dynamics (MD) is a powerful tool for exploring the behavior of atomistic systems, but its reliance on sequential numerical integration limits simulation efficiency. We present a novel neural network architecture, MDtrajNet, and a pretrained foundational model, MDtrajNet-1, that directly generates MD trajectories across chemical space, bypassing force calculations and integration. MDtrajNet combines equivariant neural networks with a transformer-based architecture to achieve strong accuracy and transferability in predicting long-time trajectories. This approach accelerates simulations by up to two orders of magnitude and yields better accuracy than MD propagated with established machine-learning interatomic potentials trained on the same data. Remarkably, the errors of the trajectories generated by MDtrajNet-1 for various seen and even unseen small-sized molecular systems are close to those of conventional <i>ab initio</i> MD. The current limitations of MDtrajNet-1 are attributed to the relatively small size of the chemical space in its training data; however, even for bigger, unseen systems, MDtrajNet-1 provides a good starting point for fine-tuning and obtaining system-specific models. The architecture's flexible design supports diverse application scenarios, including different statistical ensembles, boundary conditions, and interaction types. By overcoming the intrinsic speed barrier of conventional MD, MDtrajNet opens new frontiers in efficient and scalable atomistic simulations. Scalability ; Transferability ; Molecular dynamics ; Artificial neural network ; Point (geometry) ; Chemical space ; Space (punctuation) ; Boundary (topology) Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Quantum many-body systems