jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jian-Tao Wang | Peitao Liu | Heyu Zhu | Mingfeng Liu | Hui Ma | Yun Chen | Yan Sun | Xing‐Qiu Chen
Source: Physical Review Materials (article, 2025-05-16)
Field: Materials Science — Materials Chemistry

Efficient moment tensor machine-learning interatomic potential for accurate description of defects in Ni-Al Alloys Combining the efficiency of semiempirical potentials with the accuracy of quantum mechanical methods, machine-learning interatomic potentials (MLIPs) have significantly advanced atomistic modeling in computational materials science and chemistry. This necessitates the continual development of MLIP models with improved accuracy and efficiency, which enable long-time scale molecular dynamics simulations to unveil the intricate underlying mechanisms that would otherwise remain elusive. Among various existing MLIP models, the moment tensor potential (MTP) model employs a highly descriptive rotationally covariant moment tensor to describe the local atomic environment, enabling the use of even linear regression for model fitting. Although the current MTP model has achieved state-of-the-art computational performance for similar accuracy, there is still room for optimizing the contraction process of moment tensors. In this paper, we propose an effective genetic algorithm-based optimization scheme that can significantly reduce the number of independent moment tensor components and intermediate tensor components. This leads to a speedup of nearly one order of magnitude in evaluation and also improved accuracy compared to the traditional MTP model for intricate basis sets. We have applied our improved MTP model to predicting the energetic and dynamical properties of various point and planar defects in Ni-Al alloys, showing overall good performance and in general outperforming the semiempirical potentials. This paper paves the way for fast and accurate atomistic modeling of complex systems and provides a useful tool for modeling defects in Ni-Al alloys. Materials science ; Moment tensor ; Interatomic potential ; Tensor (intrinsic definition) ; Moment (physics) ; Condensed matter physics ; Computational chemistry ; Molecular dynamics ; Classical mechanics ; Composite material ; Geometry ; Physics ; Mathematics Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels
Authors: Pengxin Pu | Xin Song | Hu Ding | Yuan Deng | Haisong Feng | Xin Zhang
Source: The Journal of Physical Chemistry Letters (article, 2025-04-21)
Field: Materials Science — Materials Chemistry

Fast-Track to Catalyst Stability: Machine Learning Optimized Predictions for M1/M2-N<sub>6</sub>-Gra Catalysts Graphene-based dual-atom catalysts M1/M2-N<sub>6</sub>-Gra have shown significant potential in various reactions, although their stabilities are debated. Therefore, developing an efficient and accurate approach to screen thermodynamically stable M1/M2-N<sub>6</sub>-Gra is significant. Herein, we designed a rational machine learning (ML) scheme based on 143 DFT calculated samples to predict the formation energies (<i>E</i><sub><i>f</i></sub>) of 1134 possible M1/M2-N<sub>6</sub>-Gra. A well performing multilayer perceptron model with test set <i>R</i><sup>2</sup> <i>=</i> 0.98 was obtained after feature engineering, model training, data supplementation, and transfer learning. This model successfully screened 604 thermodynamic stable M1/M2-N<sub>6</sub>-Gra with <i>E</i><sub><i>f</i></sub> < 0 eV. Feature importance, predictions distribution, and energy decomposition revealed that the coordination number significantly influences <i>E</i><sub><i>f</i></sub>, with cohesive energy dominating low-coordination catalysts and binding energy between metal and substrate being more critical in higher-coordination catalysts. This work highlights the potential of ML and developed effective approaches to screen thermodynamically stable catalysts and reveals the laws of stability for various materials. Catalysis ; Track (disk drive) ; Stability (learning theory) ; Computer science ; Fast track ; Artificial intelligence ; Machine learning ; Chemistry ; Operating system ; Organic chemistry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions
Authors: Qinghe Gao | Lukas Schulze Balhorn | Alessandro Laera | Raoul Meys | Jonas Goßen | Jana M. Weber | Gregor Wernet | Artur M. Schweidtmann
Source: Computers & Chemical Engineering (article, 2025-08-28)
Field: Materials Science — Materials Chemistry

Environmental impacts prediction using graph neural networks on molecular graphs Artificial neural network ; Computer science ; Graph ; Artificial intelligence ; Theoretical computer science ; Machine learning Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Joseph Han | In Kim | Namjung Cho | Kuang-Ming Yang | Jin Suk Myung | Jaeseong Park | Seong Hun Kim | Woo Jin Choi
Source: Materials Genome Engineering Advances (article, 2025-08-04)
Field: Materials Science — Materials Chemistry

Toward accurate machine learning‐driven prediction of polymeric composites properties based on experimental data Abstract In response to climate change, there has been a focus on developing lightweight and environmentally friendly materials, with active research aimed at enhancing the energy efficiency of electric and hybrid vehicles. In this context, the development of polymer composites with superior thermal conductivity (TC) has been recognized as critical to meeting mechanical property requirements. This paper presents a machine learning model that utilized 1774 experimental data points to predict various properties of polymer composites, such as density, heat deflection temperature, flexural modulus, flexural strength, tensile yield strength, impact strength, and TC. Various data representation methods for composition data are employed, and the XGBoost model is trained, achieving high accuracy with an average R 2 score of 0.95. This machine learning model, informed by experimental data, is a useful tool for predicting and optimizing the properties of polymer composites. Composite material ; Materials science ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Advanced Battery Technologies Research
Authors: C. X. Lin | Lin Zhang | Yi Dong
Source: Journal of Energy Storage (article, 2025-05-09)
Field: Materials Science — Materials Chemistry

Compositional machine learning and high-throughput screening aided discovery of novel anti-perovskite solid-state electrolytes High-throughput screening ; Drug discovery ; Throughput ; Solid-state ; Perovskite (structure) ; State (computer science) ; Computer science ; Materials science ; Nanotechnology ; Chemistry ; Engineering ; Biology ; Engineering physics ; Bioinformatics ; Operating system ; Crystallography Machine Learning in Materials Science ; Advancements in Battery Materials ; Perovskite Materials and Applications
Authors: Jie Xiao | Guodong Fang | Yisheng Zhang | Xiaoqiang Qin | Hongyue Wang | Changqing Hong | Songhe Meng
Source: Composites Science and Technology (article, 2025-04-21)
Field: Materials Science — Materials Chemistry

Material response model with artificial neural networks for ablation analysis of lightweight silicone-modified phenolic matrix nanocomposites Materials science ; Composite material ; Nanocomposite ; Silicone ; Ablation ; Matrix (chemical analysis) Carbon Nanotubes in Composites ; Silicone and Siloxane Chemistry ; Gas Dynamics and Kinetic Theory
Authors: Dennis Delali Kwesi Wayo
Source: Archives of Computational Methods in Engineering (article, 2025-08-14)
Field: Materials Science — Materials Chemistry

Ensembles of Graph and Physics-Informed Machine Learning for Scientific Modeling in Materials Science: A Review Ensemble learning ; Robustness (evolution) ; Ensemble forecasting ; Benchmark (surveying) ; Uncertainty quantification ; Graph ; Property (philosophy) ; Artificial neural network Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Mingyu Yu | Isaiah A. Moses | Wesley F. Reinhart | Stephanie Law
Source: ACS Applied Materials & Interfaces (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Multimodal Machine Learning Analysis of GaSe Molecular Beam Epitaxy Growth Conditions Autonomous synthesis platforms integrating machine learning with in situ diagnostics have the potential to revolutionize thin-film growth by enabling real-time process optimization and reducing the need for manual tuning. However, their application to molecular beam epitaxy (MBE) remains underdeveloped. Here, we present a machine learning-guided framework for MBE growth of GaSe films, leveraging reflection high-energy electron diffraction (RHEED) as an in situ diagnostic alongside <i>ex situ</i> characterization via X-ray diffraction and atomic force microscopy. Unsupervised learning on RHEED patterns reveals a well-defined boundary between high- and low-quality samples, capturing physically meaningful features. Mutual information analysis shows a strong correlation between RHEED embeddings and rocking curve full-width at half-maximum (fwhm), while the correlation with AFM root-mean-square (RMS) roughness is weak. Among key growth conditions, growth rate most strongly influences fwhm, whereas the Se/Ga flux ratio primarily affects RMS roughness and the RHEED embeddings. Supervised learning models trained to predict fwhm and RMS roughness demonstrate moderate accuracy, with significant improvement achieved by incorporating RHEED embeddings. Furthermore, anomaly detection via residual analysis in supervised learning aligns well with unsupervised classification from RHEED, reinforcing the reliability of the predictive models. This study establishes a data-driven framework for machine learning-assisted MBE, paving the way for real-time process control and accelerated optimization of thin-film synthesis. Materials science ; Molecular beam epitaxy ; Epitaxy ; Nanotechnology ; Optoelectronics ; Engineering physics ; Engineering Machine Learning in Materials Science ; 2D Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: Miho Tanaka | Kai Sasaki | Jesada Punyafu | Mayu Muramatsu | Mitsuhiro Murayama
Source: Scientific Reports (article, 2025-04-25)
Field: Materials Science — Metals and Alloys

Machine-learning-aided analysis of relationship between crystal defects and macroscopic mechanical properties of TWIP steel Establishing efficient methods to obtain quantitative data on crystal defect evolution is vital for understanding material properties. Dynamic Transmission Electron Microscopy (TEM) captures crystal defects in materials undergoing plastic deformation, generating vast datasets with high temporal and spatial resolution. However, manual analysis of these images is labor-intensive, and automated, unbiased analysis remains a challenge. In this study, we developed a U-net-based machine learning approach to analyze TEM videos of crystal defect evolution in Twinning-Induced Plasticity (TWIP) steels with different grain sizes. The method overcame challenges like field-of-view translation and nonuniform defect motion. This approach quantitatively measured defect evolution as a function of time and strain with the same temporal resolution as the original videos, detecting even minor changes with high accuracy. We use this technique to quantitatively reveal the switch of the dominant plastic deformation mechanism with grain size and the relaxation of elastic strain due to the rapid increase in stacking faults. Our results validate the use of U-net models for efficient semantic segmentation of TEM videos, enabling accurate quantitative analysis. This work advances TEM video analysis and provides new insights into the deformation mechanisms of materials. Twip ; Crystal twinning ; Materials science ; Deformation (meteorology) ; Dislocation ; Computer science ; Crystal (programming language) ; Artificial intelligence ; Deformation mechanism ; Plasticity ; Segmentation ; Machine learning ; Microstructure ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Force Microscopy Techniques and Applications
Authors: Hari Prasad Bhatta | Hyo‐Kyung Han | Ravi Maharjan | Seong Hoon Jeong
Source: Pharmaceutics (review, 2025-09-24)
Field: Chemistry — Analytical Chemistry

Recent Techniques to Improve Amorphous Dispersion Performance with Quality Design, Physicochemical Monitoring, Molecular Simulation, and Machine Learning Amorphous solid dispersions (ASDs) represent a promising formulation strategy for improving the solubility and bioavailability of poorly water-soluble drugs, a major challenge in pharmaceutical development. This review provides a comprehensive analysis of the physicochemical principles underlying ASD stability, with a focus on drug-polymer miscibility, molecular mobility, and thermodynamic properties. The main manufacturing techniques including hot-melt extrusion, spray drying, and KinetiSol<sup>®</sup> dispersing are discussed for their impact on formulation homogeneity and scalability. Recent advances in excipient selection, molecular modeling, and in silico predictive approaches have transformed ASD design, reducing dependence on traditional trial-and-error methods. Furthermore, machine learning and artificial intelligence (AI)-based computational platforms are reshaping formulation strategies by enabling accurate predictions of drug-polymer interactions and physical stability. Advanced characterization methods such as solid-state NMR, IR, and dielectric spectroscopy provide valuable insights into phase separation and recrystallization. Despite these technological innovations, ensuring long-term stability and maintaining supersaturation remain significant challenges for ASDs. Integrated formulation design frameworks, including PBPK modeling and accelerated stability testing, offer potential solutions to address these issues. Future research should emphasize interdisciplinary collaboration, leveraging computational advancements together with experimental validation to refine formulation strategies and accelerate clinical translation. The scientists can unlock the full therapeutic potential with emerging technologies and a data-driven approach. Quality by Design ; Characterization (materials science) ; Stability (learning theory) ; Amorphous solid ; Quality (philosophy) ; Pharmaceutical sciences Analytical chemistry methods development ; Analytical Chemistry and Chromatography ; Microfluidic and Capillary Electrophoresis Applications
Authors: Ali Shabani | Marieh Aminaei | Mohammad Reza Toroghinejad
Source: Computational Materials Science (article, 2025-06-12)
Field: Materials Science — Materials Chemistry

Data-driven prediction of ultimate tensile strength in low alloy steel: A machine learning approach Ultimate tensile strength ; Alloy ; Materials science ; Metallurgy ; Composite material Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Saurabh Tiwari | K. Dash | Nokeun Park | N.S. Reddy
Source: Coatings (article, 2025-07-31)
Field: Materials Science — Materials Chemistry

Machine Learning-Based Prediction of Atmospheric Corrosion Rates Using Environmental and Material Parameters Atmospheric corrosion significantly impacts infrastructure worldwide, with traditional assessment methods being time-intensive and costly. This study developed a comprehensive machine learning framework for predicting atmospheric corrosion rates using environmental and material parameters. Three regression models (Linear Regression, Random Forest, and Gradient Boosting) were trained on a scientifically informed synthetic dataset incorporating established corrosion principles from ISO 9223 standards and peer-reviewed literature. The Gradient Boosting model achieved superior performance with cross-validated R2 = 0.835 ± 0.024 and RMSE = 98.99 ± 16.62 μm/year, significantly outperforming the Random Forest (p &lt; 0.001) and Linear Regression approaches. Feature importance analysis revealed the copper content (30%), exposure time (20%), and chloride deposition (15%) as primary predictors, consistent with the established principles of corrosion science. Model diagnostics demonstrated excellent predictive accuracy (R2 = 0.863) with normally distributed residuals and homoscedastic variance patterns. This methodology provides a systematic framework for ML-based corrosion prediction, with significant implications for protective coating design, material selection, and infrastructure risk assessment, pending comprehensive experimental validation. Random forest ; Gradient boosting ; Corrosion ; Linear regression ; Predictive modelling ; Regression ; Environmental science ; Boosting (machine learning) ; Feature selection ; Computer science ; Homoscedasticity ; Machine learning ; Artificial intelligence ; Statistics ; Materials science ; Mathematics ; Metallurgy ; Heteroscedasticity Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Machine Learning in Materials Science
Authors: Ye Sheng | Yabei Wu | Chang Bo Jiang | Xiaowen Cui | Yuanqing Mao | Caichao Ye | Wenqing Zhang
Source: Journal of Materials Informatics (article, 2025-02-08)
Field: Materials Science — Materials Chemistry

Interpretable model of dielectric constant for rational design of microwave dielectric materials: a machine learning study Machine learning (ML) has advantages in studying fundamental properties of materials and comprehending structure-property correlations. In this study, we employed sure independence screening and sparsifying operator (SISSO) method (ML technique) to explore the experimental dielectric constant, temperature coefficient of frequency resonator, and quality factor of inorganic oxide microwave dielectric materials. Among the constructed white-box models, the highest accuracy, with a coefficient of determination (R2) of 0.8, was observed in predicting the dielectric constants of the quaternary materials. Additionally, we proposed a straightforward strategy to merge the ternary and quaternary datasets in a single training, aiming to address the issue of data scarcity in ML research. Although this strategy slightly compromises the model accuracy, it has the advantage of creating a more unified trained model for structural-property relationship understanding. Using the unified and interpretable model trained with the merged dataset, we derived a general rule governing the dielectric constant of materials. Our ML findings regarding the dielectric property provide fundamental insights for designing microwave dielectric materials with diverse dielectric constants. Dielectric ; Merge (version control) ; Ternary operation ; Microwave ; Materials science ; Computer science ; Artificial intelligence ; Algorithm ; Optoelectronics Ferroelectric and Piezoelectric Materials ; Microwave Dielectric Ceramics Synthesis ; X-ray Diffraction in Crystallography
Authors: Luke D. Gibson | Rajni Chahal | Vyacheslav S. Bryantsev
Source: Chemical Science (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Computing chemical potentials with machine-learning-accelerated simulations to accurately predict thermodynamic properties of molten salts The successful design and deployment of next-generation nuclear technologies heavily rely on thermodynamic data for relevant molten salt systems. However, the lack of accurate force fields and efficient methods has limited the quality of thermodynamic predictions from atomistic simulations. Here we propose an efficient free energy framework for computing chemical potentials, which is the central free energy quantity behind many thermodynamic properties. We accelerate our simulations without sacrificing accuracy by using machine learning interatomic potentials trained on density functional theory (DFT) data. Using lithium chloride as our model system, we compute chemical potentials with DFT-accuracy for solid and liquid phases by transmuting ions into noninteracting particles. Notably, in the liquid phase, we demonstrate consistency whether we transmute one ion pair or the entire system into ideal gas particles. By locating the temperature where the chemical potential of solid and liquid phases cross, we predict a melting point of 880 ± 18 K for lithium chloride, which is remarkably close to the experimental value of 883 K. With this successful demonstration, we lay the foundation for high-throughput thermodynamic predictions of many properties that can be derived from the chemical potentials of the minority and majority components in molten salts. Computer science ; Thermodynamics ; Chemistry ; Physics Machine Learning in Materials Science ; Metallurgical Processes and Thermodynamics ; Molten salt chemistry and electrochemical processes
Authors: Jiajun Jiang | Yamin Xue | Zehui Xiong | Yao Shen | Zebang Cheng | Wenjing Hu | Duoduo Zhang | Guoliang Zhang | Renjie Zhu | Liliang Zha | Ziqiu Wang | Lin Peng | Tingting Shi | Yufeng Zhang | Jing Chen | Xiaolin Liu | Jia Lin
Source: The Journal of Chemical Physics (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Accelerating the exploration of material space for hydride double perovskite superconductors under ambient pressure through machine learning In the field of hydride superconductors, a great challenge is to achieve superconducting states under ambient pressure conditions rather than the extreme high-pressure environments that have been required in experiments. Achieving this goal is crucial for advancing the practical applications of high-temperature superconducting materials. We discover a family of compounds (hydride double perovskite superconductors with space group Fm3̄m and chemical formula A2MM'H6) to achieves this goal. A machine-learning-accelerated approach is utilized to search for hydride double perovskite superconductors under ambient pressure within an extensive dataset comprising over 106 535 hypothetical compounds. 15 stable hydride double perovskite superconductors are discovered under ambient pressure, with the highest superconducting transition temperature (Tc) reaching 18.7 K. The structural stability, electronic properties, and superconducting behavior of these materials have been comprehensively analyzed. Phonon dispersion analysis has highlighted the critical role of lattice vibrations in electron-phonon coupling (EPC), where the contribution of H atom vibrations is essential for facilitating electron pairing and the onset of superconductivity. This demonstrates that the machine-learning-accelerated approach is a highly effective method and can be easily extended to other compounds. Superconductivity ; Ambient pressure ; Hydride ; Perovskite (structure) ; Condensed matter physics ; Phonon ; Pairing ; Materials science ; Chemistry ; Physics ; Thermodynamics ; Crystallography ; Metallurgy ; Metal Electronic and Structural Properties of Oxides ; Machine Learning in Materials Science ; Inorganic Chemistry and Materials
Authors: Linbing Jiang | Youshi Zeng | Ke Deng | Yang Guo | Huiqin Yin | Jie Qiu | Xi-Jun Wu | Wenguan Liu
Source: International Journal of Hydrogen Energy (article, 2025-09-11)
Field: Materials Science — Materials Chemistry

Solute-driven modulation of tritium behavior in FLiBe molten salts: A machine learning molecular dynamics approach Tritium ; Redox ; Molten salt ; Molecular dynamics ; Beryllium ; Uranium ; Thermal diffusivity ; Chromium Nuclear Materials and Properties ; Fusion materials and technologies ; Hydrogen Storage and Materials