jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Dawid Falkowski | Alicja Mikolajczyk | Piotr Skurski | Jakub Brzeski | Tomasz Puzyn
Source: International Journal of Hydrogen Energy (article, 2026-03-24)
Field: Materials Science — Materials Chemistry

Machine learning-driven SSbD strategy for discovering next-generation anodes in solid oxide electrolysis cells Anode ; Oxide ; Electrolysis ; Density functional theory ; Cluster analysis ; Rational design Machine Learning in Materials Science ; Advancements in Solid Oxide Fuel Cells ; Molten salt chemistry and electrochemical processes
Authors: Rui Zhang | Yinkang Zhao | Yilin Zhang | Qiaochuan Chen | Yuexing Han | Na Song | Peng Ding | Cailian Zhu
Source: SSRN Electronic Journal (preprint, 2026-01-01)
Field: Materials Science — Materials Chemistry

Prediction of Flexural Strength of Carbon Fiber Reinforced Polymers,Based on Literature Mining Sorting ; Process (computing) ; Identification (biology) ; Flexural strength ; Random forest ; Key (lock) ; Field (mathematics) ; Test data Machine Learning in Materials Science ; Fiber-reinforced polymer composites ; Mechanical Behavior of Composites
Authors: Pawan Bohane | Reyas Ali | V. B. Rajkumar | Pallavi Deshmukh | Trushar B. Gohil | Ajeet K. Srivastav
Source: Advanced Engineering Materials (article, 2026-03-17)
Field: Materials Science — Materials Chemistry

Data‐Driven Approach for Simultaneous Optimization of Strength and Conductivity in Al Alloys Achieving an optimal balance between the mechanical and electrical properties of aluminium alloys through conventional trial‐and‐error methods has posed a significant challenge. This study proposes a machine learning‐driven alloy design strategy by identifying key features through correlation screening, recursive elimination, and exhaustive feature selection techniques, followed by Bayesian optimization (BO) for iterative composition design. Out of various regression models trained, the random forest exhibited the best predictive performance. Based on a comprehensive dataset of aluminium alloys, the most influential factors affecting ultimate tensile strength (UTS) were identified as volume size factor, second ionization energy, and Pauling electronegativity. In comparison, electrical conductivity (EC) was primarily influenced by volume size factor, absolute electronegativity, and work function. Importantly, for the first time, the volume size factor and the electron work function were both identified as key features influencing UTS and EC. Predictive models for UTS and EC were developed, with errors of less than 8% and 9%, respectively. A new aluminium alloy composition, Al‐3.16Cu‐3 Mg‐0.23Cr‐0.21Si‐0.18 Mn (wt.%), was designed through BO and experimentally validated. The alloy showed an excellent combination of properties with a UTS of 278 MPa and EC of 52.68% international annealed copper standard (IACS), demonstrating the effectiveness of the proposed method in simultaneously improving mechanical and electrical performance. Alloy ; Aluminium ; Aluminium alloy ; Ultimate tensile strength ; Work (physics) ; Electrical resistivity and conductivity ; 6063 aluminium alloy ; Volume (thermodynamics) Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Corrosion Behavior and Inhibition
Authors: Dongming Huan | Chenkai Gu | Yawei Gu | Bo Shen | Rujing Hou | Jing Zhong | Yichang Pan | Weihong Xing
Source: ChemRxiv (article, 2026-03-08)
Field: Chemistry — Inorganic Chemistry

Physics-Constrained Artificial Intelligence for Accelerated Experimental Discovery of Metal-Organic Frameworks in Gas Separation The development of efficient adsorbents is significant but challenging for gas separation. Metal-organic frameworks (MOFs) show great potentials in cutting separation energy consumption, while suffering from the problems caused by its vast diversity. Artificial intelligence (AI) offers effective solutions, but still faces high data consumption and poor extrapolation. To address this issue, taking C 3 H 6 /C 3 H 8 as an example, we proposed a physics constrained neural network (PCNN) which integrates physical knowledge into the framework and reach a great balance between prediction accuracy and physical rationality. Meanwhile, PCNN can be well trained using much less data (< 10%) than that required by conventional models, significantly cutting the training cost. Moreover, our model can provide accurate assessments of adsorption isotherms across the full pressure range even after being trained on data from only a single pressure point, demonstrating excellent extrapolation. Finally, a series of experiments were carried out to confirm the precision of PCNN in MOF screening. MOF (CCDC ID: CUIMDZ01) with ultrahigh C 3 H 6 /C 3 H 8 selectivity (IAST selectivity of 3.5×10 5) was successfully discovered, which demonstrated the potential of our model in material discovery. This work presents an efficient workflow with broad applicability for material discovery across diverse fields. Artificial neural network ; Workflow ; Range (aeronautics) ; Work (physics) ; Energy consumption ; Energy (signal processing) Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Machine Learning in Materials Science
Authors: Dmitry Zakiryanov | Maxim I. Vlasov | Victor Bykov
Source: Modelling and Simulation in Materials Science and Engineering (article, 2026-01-19)
Field: Materials Science — Materials Chemistry

Thermochemical properties and glass transition of Na <sub>2</sub> O–Al <sub>2</sub> O <sub>3</sub> –P <sub>2</sub> O <sub>5</sub> system: a deep learning potential approach and its experimental validation Abstract Observation of glass properties including the glass transition temperature T g is one of the most challenging problems for atomistic simulations due to the complexity of composition and slow time evolution rates. To address the issue, robust machine learning potentials trained with accurate ab initio data can be employed. In this paper, a prospective glass for immobilization of nuclear waste with 0.25 Na 2 O–0.25 Al 2 O 3 –0.5 P 2 O 5 (mass fraction) composition was studied via a neural network potential. We used 42 independent ab initio calculations as a dataset. The glass was obtained by slow cooling during 96 ns from the molten state down to room temperature. We used an extrapolation technique to assess the true glass transition temperature for infinitely slow cooling and found that the resulting T g of 715 K agrees well with the experimentally observed value 682 K. The specific heat was studied for a temperature range up to T = 1500 K. Also, thermal conductivity was calculated using the non-equilibrium molecular dynamics. Among other properties, Young’s modulus, thermal diffusivity, and vibrational density of states were calculated. The reliability of the developed model was further confirmed by comparison with the experimentally measured density and Raman spectrum. Extrapolation ; Glass transition ; Thermal conductivity ; Atmospheric temperature range ; Raman spectroscopy ; Range (aeronautics) ; Thermal ; Raman scattering Thermal Expansion and Ionic Conductivity ; Glass properties and applications ; Nuclear Materials and Properties
Authors: Inhyo Lee | Hyeokjae Chae | Jongwon Park | Jihye Shin | Hugon Lee | Seunghwa Ryu
Source: Physical Chemistry Chemical Physics (article, 2026-01-01)
Field: Materials Science — Materials Chemistry

Comparative assessment of composition- and structure-based surrogate models across 2D materials databases Machine learning (ML) surrogate models are increasingly employed to accelerate materials discovery, yet their transferability across heterogeneous databases remains unclear. In this study, we benchmark the accuracy and transferability of composition-based and structure-based surrogate models using three widely adopted two-dimensional (2D) materials databases: Computational 2D Materials Database (C2DB), 2D Materials Encyclopedia (2DMatPedia), and Joint Automated Repository for Various Integrated Simulations (JARVIS-2D). We evaluate predictive performance for energy per atom and bandgap under database-to-database transfer, probe the effects of dataset size and coverage through down-sampling, and analyze error correlations across surrogate models used in this study. Energy-related properties were predicted robustly, whereas bandgap prediction proved to be substantially more difficult due to data imbalance and inconsistencies in DFT parameters across databases. Composition-based models generally exhibited more stable cross-database performance than structure-based models, underscoring that incorporating structural features does not necessarily lead to better generalization. Through down-sampling and error correlation analyses, we demonstrate that cross-database performance is primarily determined by the coverage of the training dataset and that distinct error patterns emerge from differences in the models' feature representation and architecture. Together, this study provides a systematic characterization of surrogate model robustness across 2D materials databases, offering insights into the factors that determine their transferability in materials discovery. Transferability ; Benchmark (surveying) ; Benchmarking ; Surrogate model Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; X-ray Diffraction in Crystallography
Authors: Zheng Luo | Ying Yang | Zizhen Fu | Fengqi Liu | Susu Fang | Kele Xu | Shanshan Wang
Source: Advanced Science (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics Defects in layered transition metal dichalcogenides play a crucial role in the development of high-energy-density and high-safety electrochemical devices for sustainable energy storage systems. Although transmission electron microscopy prevails as an indispensable tool for visualizing defects at the atomic scale, manual analysis in such data-intensive scenarios generally lacks both efficiency and accuracy. Fortunately, the emergence of machine learning is transforming the paradigm of electron microscopy data analytics, offering a potent tool to expedite the discovery of novel structures and knowledge. In this review, we briefly introduce the atomic structures of typical defect configurations in transition metal dichalcogenides, along with their beneficial effects on electrochemical redox kinetics and stability when used in batteries and supercapacitors. Then, the latest innovations in the defect-engineered transition metal dichalcogenides for advanced energy storage devices, and the progress made in machine learning methodologies for their application in high-throughput electron microscopy analytics are systematically summarized. Finally, this review is concluded with perspectives on the remaining challenges and future opportunities in intelligent defect characterizations and engineering toward the next-generation energy storage systems. Electrochemical energy storage ; Transition metal ; Energy storage ; Analytics ; Sustainable energy ; Energy transition ; Computer data storage ; Transmission electron microscopy 2D Materials and Applications ; Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion
Authors: Kyoichi Sawabe | Takumi Tsukiyama | Akira Oda | Atsushi Satsuma
Source: Precision Chemistry (article, 2026-03-03)
Field: Materials Science — Materials Chemistry

Adaptive Reduction of Multicollinearity for Machine Learning Prediction of CO Adsorption Energies on Single Atoms Data-driven approaches are pivotal for optimizing single-atom catalysts. However, when the training data size is limited, the prevalence of multicollinearity among physicochemical features frequently destabilizes statistical models and obscures chemical interpretation. To address this challenge, we developed a wrapper-based feature selection algorithm termed adaptive reduction of multicollinearity with retention (ARMR). This method iteratively identifies feature subsets that satisfy strict multicollinearity constraints while minimizing cross-validated prediction error. The features selected by ARMR yielded robust predictive performance in both linear and nonlinear models, clearly outperforming baselines derived from univariate correlations or standard importance rankings. The efficacy of ARMR was validated through machine learning predictions using a density functional theory dataset of CO adsorption energies on 11 transition metals embedded as a single atom on three TiO2 polymorphs (rutile, anatase, and brookite). Specifically, a gradient boosting regression model trained on ARMR-selected features achieved high predictive accuracy with a test root-mean-square error of 0.087 eV. Furthermore, analysis of selected features identified s-orbital charge density as an important electronic feature correlating with CO binding strength. By removing multicollinearity, ARMR eliminates redundant properties intrinsically correlated with metal-specific characteristics like oxidation state, retaining only robust features. ARMR provides valuable insights into metal–support interactions and offers an effective strategy for accelerating rational design of single-atom catalysts through interpretable structure–property relationships. Multicollinearity ; Feature selection ; Univariate ; Feature (linguistics) ; Boosting (machine learning) ; Linear regression ; Pattern recognition (psychology) ; Density functional theory ; Collinearity Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Air Quality Monitoring and Forecasting
Authors: Dayong Wu | Liuke Zhen | sida ma | Yameng Guo | Haikun Ma | Jie Kang | W. Li | Qian Wang | Huicong Dong | Yi Wang | Hongbiao Dong | RU SU
Source: Acta Materialia (article, 2026-01-20)
Field: Materials Science — Materials Chemistry

Interfacial atomic vibration: A key dynamic characteristic for heterogeneous nucleation substrates Nucleation ; Molecular dynamics ; Ab initio ; Structural stability ; Epitaxy ; Lattice (music) ; Force field (fiction) ; Multiscale modeling Solidification and crystal growth phenomena ; Aluminum Alloy Microstructure Properties ; Intermetallics and Advanced Alloy Properties
Authors: Renata Layse G. de Paula | Vitor S. Duarte | Giulio D. C. D’Oliveira | Mirian R. C. De Castro | Caridad N. Pérez | Jean M. F. Custódio | Allen G. Oliver | Hamilton B. Napolitano
Source: ACS Omega (article, 2026-02-03)
Field: Chemistry — Organic Chemistry

The <i>Nitro-Chloro</i> Substitution on Two Quinolinone-Chalcones: From Molecular Modeling to Antioxidant Potential The increasing demand for renewable energy has driven the search for sustainable biodiesel additives whose molecular structures exhibit antioxidant potential. This study reports the synthesis and solid-state X-ray diffraction studies of two quinolinone-chalcone derivatives, C<sub>28</sub>H<sub>19</sub>N<sub>3</sub>O<sub>7</sub>S (<b>QC-NO</b> <sub><b>2</b></sub> ) and C<sub>28</sub>H<sub>19</sub>ClN<sub>2</sub>O<sub>5</sub>S (<b>QC-Cl</b>). Density functional theory calculations and Fukui function analysis were combined with a predictive tool based on previously trained machine learning models to investigate the impact of <i>nitro</i> and <i>chloro</i> substituents on their physicochemical properties, molecular reactivity, and antioxidant potential. Theoretical results indicated that <b>QC-Cl</b> exhibits higher electronic stability, with larger energy gaps (599 kJ/mol) and greater nucleophilicity, while <b>QC-NO</b> <sub><b>2</b></sub> shows enhanced electrophilicity and electron-accepting ability. Molecular electrostatic potential maps and Fukui functions highlighted reactive sites consistent with the substituent electronic effects, particularly the strong electron-withdrawing character of the nitro group. Predictions of the hydroxyl radical scavenging rate constant (<i>k</i> <sub>OH</sub>) obtained using a tool based machine learning models demonstrated that <b>QC-NO</b> <sub><b>2</b></sub> (6.09 × 10<sup>9</sup> M<sup>-1</sup>·s<sup>-1</sup>) performs comparably to commercial antioxidants such as BHT and TBHQ. These findings underscore the relevance of structural modification in tuning antioxidant activity and suggest that chalcone-based hybrids, especially <b>QC-NO</b> <sub><b>2</b></sub> , are promising candidates to act as antioxidants in biodiesel. Fukui function ; Antioxidant ; Molecular model ; Electrophile ; Density functional theory ; Substituent ; Reactivity (psychology) ; Quantitative structure–activity relationship Free Radicals and Antioxidants ; Synthesis and biological activity ; Synthesis of Organic Compounds
Authors: Brian Lee | Linna Qiao | Samuel Gleason | Guangwen Zhou | Xiaohui Qu | Judith Yang | Jim Ciston | D. C. Lu
Source: ArXiv.org (article, 2026-01-16)
Field: Materials Science — Materials Chemistry

Predictive autoencoder-transformer model of Cu oxidation state from EELS and XAS spectra X-ray absorption spectroscopy (XAS) and electron energy-loss spectroscopy (EELS) produce detailed information about oxidation state, bonding, and coordination, making them essential for quantitative studies of redox and structure in functional materials. However, high-throughput quantitative analysis of these spectra, especially for mixed valence materials, remains challenging as diverse experimental conditions introduce noise, misalignment, broadening of the spectral features. We address this challenge by training a machine learning model consisting of an autoencoder to standardize the spectra and a transformer model to predict both Cu oxidation state and Bader charge directly from L-edge spectra. The model is trained on a large dataset of FEFF-simulated spectra and evaluates model performance on both simulated and experimental data. The results of the machine learning model exhibit highly accurate prediction across the domains of simulated and experimental XAS as well as experimental EELS. These advances enable future quantitative analysis of Cu redox processes under in situ and operando conditions. X-ray absorption spectroscopy ; Valence (chemistry) ; Spectral line ; Spectroscopy ; Absorption spectroscopy ; Analytical Chemistry (journal) ; Autoencoder ; Redox Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Wenning Chen | Zheyu Zhang | Kelvian T. Mularso | Bonghyun Jo | H-T Jung
Source: Advanced Energy Materials (article, 2026-03-27)
Field: Materials Science — Materials Chemistry

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories ABSTRACT Metal halide perovskite solar cells have been developed as a front runner in next generation photovoltaic technology. However, such rapid development is hampered by complex relationships among material compositions, process routes, device architectures, and environmental conditions. The fabrication system is strongly coupled with many variables, which implies that existing empirical optimization methods are less capable to explore design space in high dimensions. Additionally, perovskite materials have sensitive intrinsic properties to environmental factors hence facilitating reproducible optimization across the composition space is notoriously difficult. Now, in this coupled system, artificial intelligence and machine learning have driven progress toward the understanding of the nonlinear composition–process–property relationships. But their predictive reliability is limited by patchy data and low reproducibility in manual experiments. Therefore, controlling lab environments and generating reproducible data with help from autonomous laboratories is paramount to accelerate predictive design. This review highlights recent progress in data driven materials discovery and closed loop experiments in perovskite research. We also report that AI guided process optimization is capable of identifying reproducible manufacturing windows. In parallel, robotic manipulation, high throughput synthesis with in situ monitoring and autonomous characterization are enabling systematic exploration of the multi‐dimensional process space. Perovskite (structure) ; Process (computing) ; Characterization (materials science) ; Reliability (semiconductor) ; Photovoltaic system ; Process control ; Task (project management) Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chemical and Physical Properties of Materials
Authors: Brian Lee | Linna Qiao | Samuel Gleason | Guangwen Zhou | Xiaohui Qu | Judith Yang | Jim Ciston | D. C. Lu
Source: arXiv (Cornell University) (preprint, 2026-01-16)
Field: Materials Science — Materials Chemistry

Predictive autoencoder-transformer model of Cu oxidation state from EELS and XAS spectra X-ray absorption spectroscopy (XAS) and electron energy-loss spectroscopy (EELS) produce detailed information about oxidation state, bonding, and coordination, making them essential for quantitative studies of redox and structure in functional materials. However, high-throughput quantitative analysis of these spectra, especially for mixed valence materials, remains challenging as diverse experimental conditions introduce noise, misalignment, broadening of the spectral features. We address this challenge by training a machine learning model consisting of an autoencoder to standardize the spectra and a transformer model to predict both Cu oxidation state and Bader charge directly from L-edge spectra. The model is trained on a large dataset of FEFF-simulated spectra and evaluates model performance on both simulated and experimental data. The results of the machine learning model exhibit highly accurate prediction across the domains of simulated and experimental XAS as well as experimental EELS. These advances enable future quantitative analysis of Cu redox processes under in situ and operando conditions. X-ray absorption spectroscopy ; Valence (chemistry) ; Spectral line ; Spectroscopy ; Absorption spectroscopy ; Analytical Chemistry (journal) ; Autoencoder ; Redox Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; CO2 Reduction Techniques and Catalysts
Authors: Bernhard Schmiedmayer | Angela Rittsteuer | Tobias Hilpert | Georg Kresse
Source: ArXiv.org (article, 2026-02-04)
Field: Materials Science — Materials Chemistry

Scalar machine learning of tensorial quantities -- Born effective charges from monopole models Predicting tensorial properties with machine learning models typically requires carefully designed tensorial descriptors. In this work, we introduce an alternative strategy for learning tensorial quantities based on scalar descriptors. We apply this approach to the Born effective charge tensor, showing that scalar (monopole) kernel models can successfully capture its tensorial nature by exploiting the definition of the Born effective charge tensor as the derivative of the polarisation with respect to atomic displacements. We compare this method with tensorial (dipole) kernel models, as established in our previous work, in which the tensorial structure of the Born effective charge is encoded directly in the kernel and obtained via its derivative. Both approaches are then used for charge partitioning, enabling the separation of monopole and dipole contributions. Finally, we demonstrate the effectiveness of the framework by computing finite-temperature infrared spectra for a range of complex materials. Magnetic monopole ; Scalar (mathematics) ; Charge (physics) ; Kernel (algebra) ; Tensor (intrinsic definition) ; Dipole ; Discrete dipole approximation Machine Learning in Materials Science ; Quantum many-body systems ; Topological Materials and Phenomena