jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sergei Manzhos | Manabu Ihara
Source: Advanced Intelligent Discovery (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

Gaussian Process Regression–Neural Network Hybrid with Optimized Redundant Coordinates: A New Simple Yet Potent Tool for Scientist's Machine Learning Toolbox Recently, a Gaussian process regression–neural network (GPRNN) hybrid machine learning method was proposed, which is based on additive‐kernel GPR in redundant coordinates constructed by rules [J. Phys. Chem. A 127 (2023) 7823]. The method combines the expressive power of an NN with the robustness of linear regression, in particular, with respect to overfitting when the number of neurons is increased beyond optimal. We introduce opt‐GPRNN, in which the redundant coordinates of GPRNN are optimized with a Monte Carlo algorithm, and show that when combined with optimization of redundant coordinates, GPRNN attains the lowest test set error with much fewer terms/neurons and retains the advantage of avoiding overfitting when the number of neurons is increased beyond optimal value. The method, opt‐GPRNN, possesses an expressive power closer to that of a multilayer NN and could obviate the need for deep NNs in some applications. With optimized redundant coordinates, a dimensionality reduction regime is also possible. Examples of application to machine learning, an interatomic potential and materials informatics are given. Overfitting ; Toolbox ; Robustness (evolution) ; Artificial neural network ; Gaussian process ; Curse of dimensionality ; Process (computing) ; Convolutional neural network ; Dimensionality reduction Machine Learning in Materials Science ; Gaussian Processes and Bayesian Inference ; Advanced Graph Neural Networks
Authors: Jesse Ahlbrecht | Marius D. R. Lutz | Vera Jost | Michael Färber | Stefan Bräse | Georg Wuitschik
Source: ChemRxiv (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Which Reaction Conditions Work on Drug-Like Molecules? Lessons from 66,000 High-Throughput Experiments High-throughput experimentation (HTE) accelerates chemical discovery by shortening the lead times for molecule synthesis. The choice of initial reaction conditions directly influences the outcome and length of any reaction optimization. But human involvement in plate design and data analysis remains a significant cost factor and is accompanied with biases. Therefore, making the most out of past reaction outcomes is crucial. While advances in machine learning allow to generate promising reaction conditions, this approach is often not suitable because not enough relevant reaction data is available or it is of insufficient quality. Herein we introduce a robust statistical method using z-scores to analyze 66,000 internal HTE reactions on complex molecules. Additionally, we publish the underlying data as well as a tool to analyze and draw actionable conclusions from this data set. We exemplify the power of this method for the widely employed Buchwald-Hartwig and Suzuki-Miyaura cross-coupling reactions. The results reveal optimal conditions that differ significantly from literature-based guidelines. These data-driven insights provide high-quality starting points for optimization campaigns, improving their overall efficiency. Work (physics) ; Outcome (game theory) ; Lead (geology) ; Experimental data ; Data point ; Statistical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Cell Image Analysis Techniques
Authors: Haotian Yu | Zeming Yuan | Qiang Han | Jiaxin Li | Tao Li
Source: International Journal of Hydrogen Energy (article, 2026-01-28)
Field: Materials Science — Materials Chemistry

Magnesium-based hydrogen storage materials: Design and performance optimization of single-component and multi-component systems review Hydrogen storage ; Dehydrogenation ; Hydrogen ; Hydrogen economy ; Catalysis ; Dissociation (chemistry) ; Energy storage ; Carbon nanotube Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Metalloenzymes and iron-sulfur proteins
Authors: P. D. Varuna S. Pathirage | Konstantinos D. Vogiatzis
Source: ArXiv.org (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

DDCCNet: Physics-enhanced Multitask Neural Networks for Data-driven Coupled-cluster We present the data-driven coupled-cluster deep network (DDCCNet), a family of multitask, physics-enhanced deep learning architectures designed to predict coupled-cluster singles and doubles (CCSD) amplitudes and correlation energies from lower-level electronic structure methods. The three DDCCNet variants (termed as v1, v2, and v3) progressively incorporate architectural refinements ranging from parallel subnetworks for t_1 and t_2 amplitudes to feature-partitioned blocks and physics-enhanced intermediate prediction layers that are structured in accordance with coupled-cluster equations to enhance physical consistency and multitask learning efficiency. These models jointly learn correlated amplitude patterns while embedding symmetry and orbital-level interactions directly into the network structure. Applied to methanol conformers, CO2 clusters, and small organic molecules, DDCCNet_v2 delivered the most accurate and transferable performance, achieving chemically precise correlation energies across diverse molecular systems. Collectively, DDCCNet establishes a scalable, physically grounded framework that unifies machine learning and ab initio theory for efficient, data-driven electronic structure prediction. Artificial neural network ; Consistency (knowledge bases) ; Embedding ; Deep learning ; Amplitude ; Ab initio ; Correlation ; Pairing Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Crystallography and molecular interactions
Authors: Tang, Siwen | Li, Jiajia | Cao, Yun | Zhou, Guobing
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-02-28)
Field: Materials Science — Materials Chemistry

Understanding the thermal conductivity of inorganic solid electrolyte interphase (SEI) components LiF and Li2O using machine learning-based interatomic potential Interphase ; Thermal conductivity ; Electrolyte ; Interatomic potential ; Conductivity ; Thermal ; Thermal conduction ; Ionic conductivity Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Haibin Wen | Zhe Zhao | Fanfu Wang | Tianyi Xu | Hao Zhang | Chao Yang | Ye Wei
Source: ArXiv.org (article, 2026-03-10)
Field: Materials Science — Materials Chemistry

Logos: An evolvable reasoning engine for rational molecular design The discovery and design of functional molecules remain central challenges across chemistry,biology, and materials science. While recent advances in machine learning have accelerated molecular property prediction and candidate generation, existing models tend to excel either in physical fidelity without transparent reasoning, or in flexible reasoning without guarantees of chemical validity. This imbalance limits the reliability of artificial intelligence systems in real scientific design workflows.Here we present Logos, a compact molecular reasoning model that integrates multi-step logical reasoning with strict chemical consistency. Logos is trained using a staged strategy that first exposes the model to explicit reasoning examples linking molecular descriptions to structural decisions, and then progressively aligns these reasoning patterns with molecular representations. In a final training phase, chemical rules and invariants are incorporated directly into the optimization objective, guiding the model toward chemically valid outputs. Across multiple benchmark datasets, Logos achieves strong performance in both structural accuracy and chemical validity, matching or surpassing substantially larger general-purpose language models while operating with a fraction of their parameters. Beyond benchmark evaluation, the model exhibits stable behaviour in molecular optimization tasks involving multiple, potentially conflicting constraints. By explicitly exposing intermediate reasoning steps, Logos enables human inspection and assessment of the design logic underlying each generated structure. These results indicate that jointly optimizing for reasoning structure and physical consistency offers a practical pathway toward reliable and interpretable AI systems for molecular science, supporting closer integration of artificial intelligence into scientific discovery processes. Benchmark (surveying) ; Consistency (knowledge bases) ; Matching (statistics) ; Property (philosophy) ; Model-based reasoning ; Fidelity ; Reasoning system ; Automated reasoning ; Scientific reasoning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Emma Ghiara | Zeyu Wu | Mahel Voulhoux | Ona Mola Bertran | Vittorio Bertini | Carla Torres | Aiswarya Kethamkuzhi | G. T. Telles | Victor Fuentes | Elzbieta Pach | Cornelia Pop | Eduardo Solano | Kapil Gupta | Takanobu Kiss | Xavier Obradors | Teresa Puig
Source: Advanced Materials Technologies (article, 2026-02-19)
Field: Materials Science — Materials Chemistry

High‐Throughput Screening of REBCO Superconducting Thin Films Fabricated Via Combinatorial Inkjet Printing and TLAG Process ABSTRACT High‐throughput screening has revolutionized material design, enabling the accelerated development and optimization of materials through data‐driven approaches. This study employs drop‐on‐demand inkjet printing to fabricate combinatorial superconducting (REBCO, ) thin films with a controlled compositional gradient, allowing a systematic investigation of Rare Earth composition effects on the Transient Liquid Assisted Growth (TLAG) process. A methodological framework is established for fabricating and characterizing combinatorial REBCO thin films. Automated and synchrotron‐based techniques—including Interferometry, EDX, XRD, and Scanning Hall Probe Microscopy (SHPM)—are used to build comprehensive property maps, revealing composition‐driven variations in crystal growth and superconducting properties. Furthermore, SEM and STEM confirm REBCO crystal quality and provide deeper material insights at specific combinatorial sample locations. This framework also lays the groundwork for integrating machine learning approaches into the understanding of the TLAG process and predicting the final quality of REBCO superconductive thin films. By bridging combinatorial synthesis, advanced characterization, and data‐driven analysis, this study establishes a workflow that can be leveraged in the future to facilitate the scaling‐up of TLAG technology for industrial applications. Thin film ; Superconductivity ; Inkjet printing ; Process (computing) ; Characterization (materials science) ; Workflow Machine Learning in Materials Science ; Physics of Superconductivity and Magnetism ; Electronic and Structural Properties of Oxides
Authors: Divyanshu Dubey
Source: MetArXiv (OSF Preprints) (preprint, 2026-03-02)
Field: Materials Science — Materials Chemistry

Advanced Machine Learning Models for Predicting Perovskite Formation Energy: Ensemble Methods and Neural Networks The prediction of formation energy in perovskite materials (ABX₃) is crucial for accelerating material discovery in energy-related applications. This study applied six machine learning models—Extra Trees, Gradient Boosting Regressor (GBR), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Deep Neural Networks (DNN), and a hybrid Gradient Boosting Recurrent Neural Network (GBRNN)—to model the complex relationships between formation energy and elemental properties such as electronegativity, atomic radius, and ionization energy. The models were trained and evaluated on computational datasets using metrics including R², Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Robustness was ensured through 5-fold and 10-fold cross-validation, while Bayesian optimization fine-tuned hyperparameters such as learning rate, tree depth, batch size, and dropout rate. SHAP analysis identified bond variability and B-site cation properties as the most significant predictors, providing insights into structural characteristics influencing material stability. Among the models, the GBRNN hybrid demonstrated the best performance, achieving an R² of 0.9818 and effectively capturing complex non-linear interactions in the data. This study underscores the value of combining machine learning with feature interpretability and hyperparameter tuning to predict formation energy with high accuracy, offering actionable insights for designing stable perovskite materials and optimizing their properties for energy applications. Interpretability ; Artificial neural network ; Gradient boosting ; Mean squared error ; Hyperparameter ; Boosting (machine learning) ; Robustness (evolution) ; Decision tree ; Naive Bayes classifier Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Ammonia Synthesis and Nitrogen Reduction
Authors: Indian Institute of Technology Madras | Divyanshu Dubey
Source: Zenodo (CERN European Organization for Nuclear Research) (preprint, 2026-03-03)
Field: Materials Science — Materials Chemistry

Advanced Machine Learning Models for Predicting Perovskite Formation Energy: Ensemble Methods and Neural Networks The prediction of formation energy in perovskite materials (ABX₃) is crucial for accelerating material discovery in energy-related applications. This study applied six machine learning models—Extra Trees, Gradient Boosting Regressor (GBR), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Deep Neural Networks (DNN), and a hybrid Gradient Boosting Recurrent Neural Network (GBRNN)—to model the complex relationships between formation energy and elemental properties such as electronegativity, atomic radius, and ionization energy. The models were trained and evaluated on computational datasets using metrics including R², Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Robustness was ensured through 5-fold and 10-fold cross-validation, while Bayesian optimization fine-tuned hyperparameters such as learning rate, tree depth, batch size, and dropout rate. SHAP analysis identified bond variability and B-site cation properties as the most significant predictors, providing insights into structural characteristics influencing material stability. Among the models, the GBRNN hybrid demonstrated the best performance, achieving an R² of 0.9818 and effectively capturing complex non-linear interactions in the data. This study underscores the value of combining machine learning with feature interpretability and hyperparameter tuning to predict formation energy with high accuracy, offering actionable insights for designing stable perovskite materials and optimizing their properties for energy applications. Interpretability ; Artificial neural network ; Gradient boosting ; Mean squared error ; Hyperparameter ; Boosting (machine learning) ; Robustness (evolution) ; Decision tree ; Naive Bayes classifier Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Ammonia Synthesis and Nitrogen Reduction
Authors: Hemant Gupta | Hemang Seth | Sunny Kaushik | Rohit Mogli | Riddhika Mahalanabis | B. Ashok
Source: Journal of Computational Science (article, 2026-03-05)
Field: Materials Science — Materials Chemistry

Predicting glass transition temperature using molecular structure factors: Organic molecular compounds &amp; polymers Glass transition ; Polymer ; Molecule ; Organic molecules ; Molecular conformation ; Transition temperature Material Dynamics and Properties ; Polymer crystallization and properties ; Advanced Physical and Chemical Molecular Interactions
Authors: Qinrui Liu | Michael Forrester | Eric W. Cochran | George A. Kraus | Scott Broderick
Source: Computational Materials Today (article, 2026-03-21)
Field: Materials Science — Materials Chemistry

Improved data-driven modeling of sustainable high Tg polymers through topological feature analysis and enhanced property distribution The reliable quantitative prediction of polymer glass transition temperature (Tg) is critical for designing polymeric materials with high thermal stability. In this study, we develop a machine learning framework that balances predictive accuracy and model interpretability for Tg prediction. Building on our previous work, which had uneven distribution of data with a bias towards low-Tg polymers, we have expanded the dataset to cover a broader range of polymer chemistries, especially those with Tg values above 100 °C. This overcomes the issue in the prior work which was that in order to model Tg, any interpretability or understanding of the governing factors was lost. By expanding the dataset, interpretive models with equivalent accuracy are developed, and the comparison of this enhanced model allows us to better understand the factors which promote high Tg behavior. As a demonstration of the benefit of this approach, these key factors are used to computationally design new polymer chemistries with expected improvements in Tg. After expanding the dataset to include more high-Tg polymers, the model developed in this study achieved a training R² of 0.899 and a substantially improved test R² of 0.869. Property (philosophy) ; Feature (linguistics) ; Distribution (mathematics) ; Topology (electrical circuits) ; Polymer Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Topological and Geometric Data Analysis
Authors: Kiarash Farajzadehahary | Shaghayegh Hamzehlou | Nicholas Ballard | José M. Asua
Source: Computers & Chemical Engineering (article, 2026-02-19)
Field: Chemistry — Organic Chemistry

Towards online control of molar mass distributions of non-linear polymers in emulsion polymerization using reinforcement learning Emulsion polymerization ; Molar mass ; Polymer ; Reinforcement learning ; Model predictive control ; Work (physics) ; Monomer ; Process (computing) ; Adhesive Advanced Polymer Synthesis and Characterization ; Machine Learning in Materials Science ; Advanced Control Systems Optimization
Authors: Ahmed  K. Al-Kamal
Source: Emerging Trends in Engineering and Sustainability (article, 2026-03-31)
Field: Materials Science — Polymers and Plastics

Simulation Approaches for Polyurethane Materials: A Multiscale Review Polyurethane (PU) products enjoy remarkable versatility due to their tunable chemistry, segmented structure, and a wide range of mechanical properties, making them useful in flexible foam products, structural systems, and biomedical applications. However, the complex multiphase morphology and the strong interaction between reaction and processing processes make experimental characterization incomprehensible on its own. In turn, computational studies have become essential to study and design PU systems at a range of spatial and temporal scales. The current review provides an overview of simulation methodologies that are relevant to polyurethane, including atomistic molecular dynamics (MD), coarse-grained (CG), and mesoscale simulations, including dissipative particle dynamics (DPD), finite element method (FEM) modeling, and computational fluid dynamics (CFD) simulations. Atomistic models provide data on molecular interactions, hydrogen bonding, and thermomechanical behavior, and CG and mesoscale methods on phase separation and morphological evolution. At the bigger length scale, nonlinear mechanical response can be predicted using FEM, whereas foaming and mold-filling processes can be predicted using CFD that is coupled with reaction kinetics and population balance equations. Its focus is on multiscale modeling strategies, which combine these apparently different approaches, hence allowing the explanation of structure-property-process links. New trends and modern issues, including the integration of machine learning and tool models of digital twins, are also mentioned, highlighting new opportunities in predictive design, based on simulations, of polyurethane materials. Dissipative particle dynamics ; Computational fluid dynamics ; Mesoscale meteorology ; Computational model ; Finite element method ; Population ; Multiscale modeling ; Nonlinear system ; Molecular dynamics ; Range (aeronautics) Polymer composites and self-healing ; Polymer Foaming and Composites ; Polymer crystallization and properties
Authors: Jesse Ahlbrecht | Marius D. R. Lutz | Vera Jost | Michael Färber | Stefan Bräse | Georg Wuitschik
Source: Repository KITopen (Karlsruhe Institute of Technology) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Which Reaction Conditions Work on Drug-Like Molecules? Lessons from 66,000 High-Throughput Experiments High-throughput experimentation (HTE) accelerates chemical discovery by shortening the lead times for molecule synthesis. The choice of initial reaction conditions directly influences the outcome and length of any reaction optimization. But human involvement in plate design and data analysis remains a significant cost factor and is accompanied by biases. Therefore, making the most out of past reaction outcomes is crucial. While advances in machine learning allow us to generate promising reaction conditions, this approach is often not suitable because not enough relevant reaction data are available or it is of insufficient quality. Herein we introduce a robust statistical method using z-scores to analyze 66,000 internal HTE reactions on complex molecules. Additionally, we publish the underlying data as well as a tool to analyze and draw actionable conclusions from this data set. We exemplify the power of this method for the widely employed Buchwald–Hartwig and Suzuki–Miyaura cross-coupling reactions. The results reveal optimal conditions that differ significantly from literature-based guidelines. These data-driven insights provide high-quality starting points for optimization campaigns, improving their overall efficiency. Work (physics) ; Outcome (game theory) ; Lead (geology) ; Data point ; Experimental data ; Statistical analysis Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Cell Image Analysis Techniques
Authors: Dan Sun | Xin Wang | Rui Li | Chuan Mo | Keyu Ren | Yi Wang | Linna Feng | Wenjie Li
Source: Journal of Alloys and Compounds (article, 2026-02-01)
Field: Materials Science — Materials Chemistry

Accelerated design and thermal stability optimization of γ-U-Mo metallic nuclear fuels Thermal stability ; Stability (learning theory) ; Differential scanning calorimetry ; Thermal ; Alloy ; Phase (matter) ; Precipitation Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; High Temperature Alloys and Creep