jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shivani Singh | Sourov Mondal | Juan Carlos Nieves | Vicen¸ Torra
Source:  (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Evaluation of Machine-Learning Models in Polymer Chemistry with Prediction of Not Reported Measurements This research has been conducted in the project “Understanding and designing Block Copolymers using AI” funded by the KEMPE foundation. This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation. Software ; Work (physics) ; Polymer ; Block (permutation group theory) ; Copolymer Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Gaussian Processes and Bayesian Inference
Authors: Ignacio Migliaro | Markus G. S. Weiss | Alistair J. Sterling
Source: Journal of Chemical Theory and Computation (article, 2026-02-10)
Field: Materials Science — Materials Chemistry

ChemRefine: An Open-Source Automated and Interoperable Platform for Machine Learning and Quantum Chemistry Simulations The role of machine learning in computational chemistry is growing rapidly, promising expedited tasks while improving prediction accuracy. However, the interface of newly developed machine-learned interatomic potentials (MLIPs) with existing computational chemistry workflows based on quantum mechanics (QM) remains a challenge. Here, we introduce ChemRefine as a modular automated computational chemistry platform that enables researchers to seamlessly integrate the next generation of MLIPs into both complex and high-throughput computational workflows. We road-test ChemRefine on multiple real-world computational tasks, including host-guest binding for transmembrane anion transporter design, redox and photophysical property predictions for enzyme cofactors and photocatalysts, conformational sampling of organometallic complexes, and transition state finding for "click" reaction design. MLIPs can be automatically trained, fine-tuned, and deployed during workflows, accelerating simulations while maintaining prediction accuracy. In all cases, multiple computational tasks are condensed into a single reproducible workflow. To simplify the use of ChemRefine, we also introduce a custom large language model, ChemRefineGPT, that generates all required input and configuration files based on a natural language description of the desired workflow. Workflow ; Interoperability ; Modular design ; Interface (matter) ; Quantum chemistry ; Computational complexity theory ; Abstraction ; Molecular dynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Scientific Computing and Data Management
Authors: Jhovanna Garcia | Paul Humrickhouse | Steven J. Zinkle | Rinkle Juneja
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

A machine learning informed decay heat prediction framework for fusion structural materials Decay heat ; Benchmark (surveying) ; Neutron ; Fusion ; Nuclear data ; Exponential decay ; Scalability ; Heat flux ; Fusion power ; Kernel (algebra) Machine Learning in Materials Science ; Fusion materials and technologies ; Nuclear Physics and Applications
Authors: Zhengda Chen | Jianhua Wang | Xing Fan | Guoxin Zhang | Yupeng Wei | Mengxiao Han | Yuhong Huang | Yongfeng Wang | Haiping Lin
Source: Journal of Materials Chemistry A (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Same but different: how permutation degeneracy enhances machine learning in single-atom catalyst discovery Incorporating structural permutation degeneracy into machine learning descriptors significantly improves the accuracy and generalizability of catalytic activity predictions in single-atom catalysts. Degeneracy (biology) ; Permutation (music) ; Generalizability theory ; Pattern recognition (psychology) ; Matching (statistics) Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Hongjun Chu | Dandan Zhang | Hancong Sun | Wenzhi Li | Zhaohui Song
Source: IEEE Antennas and Wireless Propagation Letters (article, 2026-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Diffractive Neural Network-Based Intelligent Multiband Metasurface Antennas for Gain-Bandwidth Synthesis The growing demand for high-capacity wireless connectivity in industrial systems has driven the adoption of millimeter-wave and multi-standard networks, necessitating intelligent multiband antennas for adaptive spectrum use. However, conventional designs rely on expert-driven optimization, limiting agility and scalability. Here, we present a physics-informed, deep learning-enabled framework for automated antenna synthesis using diffractive neural networks (DNNs). By employing cascaded metasurfaces composed of Huygens unit-cells to emulate multilayer neural networks, this framework enables parametrical mapping of learned weights to spatial amplitude and phase modulation of electromagnetic waves, allowing the automated generation of customized antenna gain-bandwidth profiles across operating bands without manual tuning. For demonstration, we design three DNN-based antennas that establish single gain band with a peak at 60 GHz, double gain bands with peaks at 50 GHz and 70 GHz, and triple gain bands with peaks at 50 GHz, 60 GHz, and 70 GHz, respectively. The out-of-band rejection is larger than 20dB. Measurement results demonstrate the gain-bandwidth shaping capability in good agreement with simulations. This work offers a pathway for intelligent antenna design in industrial wireless networks. Artificial neural network ; Directional antenna ; Diffraction ; Reflection (computer programming) ; Antenna (radio) ; Bandwidth (computing) Metamaterials and Metasurfaces Applications ; Neural Networks and Reservoir Computing ; Advanced Wireless Communication Technologies
Authors: Lu Sun | Sheng Zhang | Zhijia Qin | Linli Zhu | Jiaqi Zhu | Dongfeng Li
Source: Mechanics of Materials (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

Temperature dependent strength prediction of nanotwinned nickel-based single crystal superalloys by integrating atomistic simulation and machine learning Artificial neural network ; Reliability (semiconductor) ; Softening ; Support vector machine ; Single crystal ; Correlation coefficient ; Deformation (meteorology) ; Pearson product-moment correlation coefficient Machine Learning in Materials Science ; High Temperature Alloys and Creep ; Boron and Carbon Nanomaterials Research
Authors: Maya Ramesh Holkar | Dhanashri Amar Khaladkar
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2026-03-31)
Field: Materials Science — Materials Chemistry

Artificial Intelligence in Polymer Chemistry: Opportunities and Challenges Abstract Artificial intelligence (AI) has emerged as a powerful tool for accelerating scientific discovery across chemistry and materials science. In polymer chemistry, AI techniques such as machine learning, deep learning, and data-driven modeling are transforming traditional experimental workflows by enabling rapid prediction of polymer properties, optimization of synthesis conditions, and discovery of new polymeric materials. Traditional polymer development often relies on time-consuming trial-and-error approaches, whereas AI techniques such as machine learning (ML), deep learning (DL), and reinforcement learning can analyze large datasets and identify complex structure–property relationships. This paper reviews the opportunities provided by AI in polymer chemistry and discusses the key challenges that must be addressed to realize its full potential in materials innovation. Workflow ; Key (lock) ; Deep learning ; Applications of artificial intelligence ; Reinforcement learning Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Polymer Synthesis and Characterization
Authors: Shuai Li | Dongrong Liu | Shu Li | Minghua Chen
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

A Materials Discovery Method Considering the Trade-Off Phenomenon in Machine Learning Prediction Capabilities between Interpolation and Extrapolation: Case Study on Multi-Objective Mg-Zn-Al Alloy Design The exploration of high-performance materials presents a fundamental challenge in materials science, particularly in predicting properties for materials beyond the known range of target property values (extrapolation). This study formally investigated the interpolation-extrapolation trade-off phenomenon in the prediction capabilities of machine learning (ML) models. A new ML scheme was proposed, featuring a newly developed ML model and forward cross-validation-based hyperparameter optimization, which demonstrated superior extrapolation prediction across multiple materials datasets. Based on this ML scheme, multi-objective optimization was performed to systematically identify lightweight Mg-Zn-Al alloys with both high bulk modulus and high Debye temperature. Subsequently, the designed alloys were validated through density functional theory calculations. Furthermore, a three-category classification strategy was summarized through the dual-driven approach combining domain knowledge and data, emphasizing their synergistic potential for materials discovery. The practical framework developed in this study provides a novel research perspective for exploring high-performance materials. Extrapolation ; Interpolation (computer graphics) ; Hyperparameter ; Range (aeronautics) ; Property (philosophy) ; Perspective (graphical) ; Domain (mathematical analysis) ; Kriging Machine Learning in Materials Science ; Magnesium Alloys: Properties and Applications ; Titanium Alloys Microstructure and Properties
Authors: Hongyu Gao | Marc Honecker
Source: The Journal of Chemical Physics (article, 2026-01-02)
Field: Materials Science — Materials Chemistry

Predicting Newtonian viscosity using machine learning trained on equilibrium molecular dynamics Newtonian viscosity (η0) defines the reference state for liquid rheology and serves as a cornerstone for constitutive modeling. Equilibrium molecular dynamics (EMD) provides a rigorous route to η0 through the Green-Kubo [M. S. Green, J. Chem. Phys. 22, 398-413 (1954) and R. Kubo, J. Phys. Soc. Jpn. 12, 570-586 (1957)] formalism, yet accurate evaluation of stress autocorrelations requires prohibitively long simulations. Here, we integrate EMD with machine learning to model the relationship between η0 and the stress correlation time (τ) for liquid n-hexadecane. A fine-tuned three-layer artificial neural network trained on EMD data captures the onset and evolution of the viscosity plateau across thermodynamic conditions. The model reproduces direct EMD results and aligns with extrapolated non-equilibrium predictions, offering a physically consistent and computationally efficient framework for determining equilibrium viscosity and supporting the development of rate-dependent viscosity models. Viscosity ; Rheology ; Artificial neural network ; Non-Newtonian fluid ; Newtonian fluid ; Stress (linguistics) ; Experimental data Machine Learning in Materials Science ; Phase Equilibria and Thermodynamics ; Material Dynamics and Properties
Authors: Shogo Wakazaki | Yuki Sakai | Kazuya Takahashi | H. Das | Masaki Azuma
Source: Scientific Reports (article, 2026-04-10)
Field: Materials Science — Materials Chemistry

Large-scale molecular dynamics simulations of negative thermal expansion in Bi1−xLaxCoO3 using a pretrained machine learning force field Negative Thermal Expansion (NTE) is a key property for achieving dimensional stability in advanced technologies, yet understanding its underlying mechanisms, particularly the dynamic processes of phase transitions, remains a formidable challenge. In this study, we applied large-scale molecular dynamics simulations using the Crystal Hamilton Graph Network (CHGNet), a pretrained universal machine learning force field (MLFF), to elucidate the atomic-level mechanisms of the NTE phenomenon in the perovskite oxide system Bi1−xLaxCoO3. CHGNet achieved a computational speed approximately 20,000 times faster than first-principles calculations and qualitatively reproduced the chemical trend of the NTE transition temperature systematically decreasing as La substitution increased. Leveraging this computational efficiency, we successfully visualized the dynamic process of the NTE phase transition at the atomic level, which had previously been unobservable. The results revealed a nucleation and propagation mechanism wherein the phase transition initiates heterogeneously in La-rich regions and propagates to Bi-rich regions. These findings demonstrate that universal MLFFs are powerful tools for more quickly elucidating complex phase transition phenomena and open up new possibilities for computational science-driven exploration of new materials. Phase transition ; Negative thermal expansion ; Molecular dynamics ; Force field (fiction) ; Nucleation ; Stability (learning theory) ; Field (mathematics) ; Computational model ; Process (computing) ; Phase (matter) Thermal Expansion and Ionic Conductivity ; Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices
Authors: Matthew C. Carson | Alice Wu | Kalyana B. Duggal | Madeline E. Rotella | Marisa C. Kozlowski
Source: Angewandte Chemie (article, 2026-03-09)
Field: Chemistry — Organic Chemistry

Photocatalytic Cross‐Coupling of Phenols and Heteroaryl Halides With Machine Learning‐Guided Reaction Prediction ABSTRACT Developing sustainable methods for C(sp 2 )─C(sp 2 ) bond formation that avoid transition‐metals and prefunctionalized substrates remains a central goal in synthetic chemistry. Phenols and N ‐heteroarenes (azines) are abundantly available, yet their cross‐coupling is hindered by mismatched redox properties and chemoselectivity issues. Herein, we report a photochemical strategy that couples phenols with heteroaryl halides under redox‐neutral conditions using an organic dye photocatalyst and base. Concurrent oxidation of the phenol component and reduction of the azine component generates complementary radicals that cross‐couple efficiently, delivering moderate to high yields (up to 91%) with high functional group tolerance. Mechanistic experiments and density functional theory (DFT) studies elucidate the radical reaction pathways, while substrate clustering, high‐throughput experimentation (HTE), and machine learning (ML) enable prediction of C–C versus S N Ar reactivity across broad chemical space. Chemoselectivity ; Phenols ; Phenol ; Halide ; Photocatalysis ; Radical ; Reactivity (psychology) ; Functional group ; Substrate (aquarium) Radical Photochemical Reactions ; Catalytic C–H Functionalization Methods ; Catalytic Cross-Coupling Reactions
Authors: Chunjie Wei | Yuhan Liu | Hongyou Cui | Yujiao Xie | Feng Song | Yuan Zhang | Hongzi Tan
Source: Green Chemistry (article, 2026-01-01)
Field: Materials Science — Biomaterials

A machine-learning framework for interpretable prediction of cellulose degree of polymerization retention in green solvents Machine learning-assisted prediction of the degree of polymerization retention of cellulose during dissolution and degradation in green solvents. Degree of polymerization ; Cellulose ; Polymerization ; Dissolution ; Degree (music) ; Degradation (telecommunications) Advanced Cellulose Research Studies ; Machine Learning in Materials Science ; Polymer Science and PVC
Authors: Sunuk Choe | Taegeun Song | Donghun Lee | Ara Go | Nojoon Myoung
Source: Current Applied Physics (article, 2026-04-06)
Field: Materials Science — Materials Chemistry

Machine learning-assisted advances in graphene and 2D materials Machine learning (ML) is becoming an enabling layer for graphene and two-dimensional (2D) material research, helping to manage the growing complexity of spectroscopy, electron transport, and multiscale simulation data. This brief review surveys recent advances across four representative domains: (i) ML-assisted characterization of defects and nanostructures, (ii) ML-based inference of local material properties, (iii) ML approaches for interpreting phase-coherent quantum transport in mesoscopic devices, and (iv) ML-accelerated simulations/modeling and inverse-design workflows. Across these areas, ML is highlighted as a complementary methodology that enhances parameter inference and automates analysis in regimes where conventional approaches are limited. We conclude by outlining challenges for reliable deployment of ML methods in graphene and 2D materials research, and by discussing promising directions toward more robust ML-integrated discovery and design in graphene and related 2D material platforms. • Reviews ML-assisted advances in graphene and 2D materials research. • Covers defect analysis, local inference, quantum transport, and modeling. • Emphasizes physics-guided interpretation beyond black-box prediction. • Highlights ML for spectroscopy, microscopy, and multiscale simulations. • Outlines challenges in data quality, transferability, and interpretability. Graphene ; Carbon nanotube ; Graphene nanoribbons ; Quantum dot Graphene research and applications ; 2D Materials and Applications ; Machine Learning in Materials Science
Authors: Zongfan Wei | Yong Xie | Waqas Akhtar | Nan Qu | Yong Liu | Ling Sun | Baoan Wu | Jingchuan Zhu
Source: Physica Scripta (article, 2026-02-16)
Field: Materials Science — Materials Chemistry

Modeling and optimization of composition-property relationships in copper alloys using explainable machine learning Abstract To address the inherent trade-off between strength and electrical conductivity in copper alloys, this study proposes an interpretable modeling framework based on automated machine learning. Utilizing a dataset of 465 experimental records containing both tensile strength and electrical conductivity measurements, with chemical composition as the sole input variable, we systematically compared multiple regression algorithms using the PyCaret platform. Interpretability and visualization were achieved through SHAP and t-SNE analyses. Results demonstrate that nonlinear ensemble models significantly outperform traditional linear models, with the Gradient Boosting Regressor achieving determination coefficients (R 2 ) of 0.94 and 0.97 for strength and conductivity predictions on the test set, respectively, while maintaining low error rates. SHAP analysis revealed that elements such as Ni and Si contribute positively to strength, whereas Cu, Ni, and Sn are critical factors influencing conductivity. t-SNE dimensionality reduction further identified performance distribution characteristics of typical alloy systems: Cu-Cr-Zr alloys excel in medium-strength and high-conductivity regions, Cu-Ni-Si systems exhibit high-strength potential, and Cu-Zn alloys occupy low-performance zones. This work provides an efficient and interpretable approach for modeling composition-property relationships in copper alloys, offering theoretical support and data-driven insights for the rapid design and optimization of next-generation high-strength, high-conductivity copper alloys. Interpretability ; Dimensionality reduction ; Boosting (machine learning) ; Curse of dimensionality ; Gradient boosting ; Copper ; Linear regression ; Support vector machine ; Conductivity ; Nonlinear system Machine Learning in Materials Science ; Microstructure and mechanical properties ; Corrosion Behavior and Inhibition
Authors: Hajar Lamouadene | Majid EL Kassaoui | Manal Benyoussef | M. El Yadari | A. El Kenz | A. Benyoussef
Source: Materials Chemistry and Physics (article, 2026-01-14)
Field: Materials Science — Materials Chemistry

Advanced deep learning models for accurate and synergistic prediction of photocatalytic properties in doped BaTiO3 materials Photocatalysis ; Deep learning ; Hydrogen production ; Band gap ; Doping ; Dopant ; Barium titanate ; Valence (chemistry) Machine Learning in Materials Science ; Advanced Photocatalysis Techniques ; TiO2 Photocatalysis and Solar Cells
Authors: Martin Callsen | Tai-Ting Lee | Mei-Yin Chou
Source: ArXiv.org (article, 2026-01-29)
Field: Materials Science — Materials Chemistry

Dynamically training machine-learning-based force fields for strongly anharmonic materials Machine learning (ML) force fields have emerged as a powerful tool for computing materials properties at finite temperatures, particularly in regimes where traditional phonon-based perturbation theories fail or cannot be extended beyond the harmonic approximation. These approaches offer accuracy comparable to ab initio molecular dynamics (MD), but at a fraction of the computational cost. However, their reliability critically depends on the quality and representativeness of the training data. In particular, static training datasets often lead to failure when the force field encounters previously unseen atomic configurations during MD simulations. In this work, we present a framework for dynamically training ML force fields and demonstrate its effectiveness across materials with varying degrees of anharmonicity, including cubic boron arsenide (c-BAs), silicon (Si), and tin selenide (SnSe). Our method builds on the conventional lattice dynamics expansion of total energy and incorporates Bayesian error estimation to guide adaptive data acquisition during simulation. Specifically, we show that trajectory-averaged Bayesian errors enable efficient and targeted exploration of the configuration space, significantly enhancing the robustness and transferability of the resulting force fields. We further demonstrate how Bayesian error estimation can be applied to determine the convergence of the dynamic training without requiring additional ab initio data. This proposed framework offers a practical and easily implementable scheme to improve the training process, which is the most critical step in developing reliable ML force fields. Force field (fiction) ; Ab initio ; Bayesian probability ; Robustness (evolution) ; Anharmonicity ; Bayesian inference ; Convergence (economics) Machine Learning in Materials Science ; Thermal properties of materials ; Inorganic Chemistry and Materials
Authors: Krunal Jagdishbhai Koshiya | Gianluca Rizzello | Paul Motzki
Source: Advanced Intelligent Systems (article, 2026-01-30)
Field: Materials Science — Materials Chemistry

A Neural Network‐Based Self‐Sensing Embedded Position Control System for Shape Memory Alloy Wire Actuators Shape memory alloy (SMA) wire transducers undergo a phase change in their crystal lattice when heated above a certain transformation temperature, which results in an actuation strain on the order of 4–6% as well as a change in the material electrical properties. The latter opens up the possibility of performing self‐sensing, namely estimation of the wire mechanical deformation during actuation based on electrical measurements only. However, accurate SMA self‐sensing is challenging because the electromechanical and thermal characteristics are generally hysteretic and depend on various factors like actuation frequency, biasing mechanism, external load, and electrical heating strategy. To practically address this issue, in this work, an artificial‐intelligence‐based approach is proposed for SMA self‐sensing. The proposed neural network consists of a combination of recurrent and simple neuron types to reconstruct the position of an SMA actuator in real‐time, using applied electrical power and SMA wire's electrical resistance as inputs. After training and validation, the neural network is implemented on a microcontroller‐based embedded system to develop a self‐sensing closed‐loop position control system. The accuracy of the self‐sensing control scheme is validated against a sensor‐based control solution, showing root mean squared errors of 0.028 and 0.021 mm respectively over a displacement range of 2 mm. Shape-memory alloy ; SMA* ; Actuator ; Artificial neural network ; Control theory (sociology) ; Displacement (psychology) ; Position (finance) ; Transducer ; Control system Shape Memory Alloy Transformations ; Aeroelasticity and Vibration Control ; Piezoelectric Actuators and Control