jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shao-Kang Qian | Hui Lv | Alaaddin M. M. Saeed | Jinsong Shen | Wen-Huan Qiao | Yiming Shen | Guiping Cao
Source: Materials Today Communications (article, 2025-04-17)
Field: Materials Science — Polymers and Plastics

Machine learning-driven exploration of static dielectric constants in PPO oligomers: Decoding quantitative structure-property relationships and underlying physical mechanisms Materials science ; Property (philosophy) ; Dielectric ; Decoding methods ; Physical property ; Chemical physics ; Biological system ; Composite material ; Optoelectronics ; Computer science ; Algorithm Conducting polymers and applications ; Organic Electronics and Photovoltaics ; Machine Learning in Materials Science
Authors: Edward Ng | Shi Jun Ang | Hui Yang | Yingqian Chen | Ming Wah Wong
Source: Computational Materials Today (article, 2025-10-21)
Field: Materials Science — Ceramics and Composites

Accelerated and efficient modeling of low-κ organosilicate glass with the M3GNet machine learning interatomic potentials Accurate modeling of low-κ dielectric materials is essential for advancing next-generation microelectronic devices. However, the inherent structural and chemical disorder in amorphous organosilicate glasses presents significant challenges for conventional molecular dynamics (MD) simulations, particularly when using empirical forcefields calibrated on crystalline phases. In this study, we explore the application of the Materials 3-body Graph Network (M3GNet), a machine learning interatomic potential, for high-fidelity modeling of amorphous organosilicate systems. By integrating M3GNet into extended MD workflows, we generated a chemically diverse set of low-κ organosilicate glass structures and computed their Young’s moduli. The resulting predictions of mechanical properties show excellent agreement with experimental data and outperform static deformation approaches based on density functional theory (DFT) and MD simulations using conventional force fields. These findings underscore the accuracy and transferability of M3GNet for disordered systems and demonstrate its utility in accelerating the structural and property prediction of amorphous low-κ materials. • M3GNet applied for accurate modeling of amorphous low-κ organosilicate glasses • Extended MD workflows generate chemically diverse amorphous glass structures • Young’s moduli predictions show excellent agreement with experimental data • M3GNet outperforms DFT and conventional force fields for disordered systems • Demonstrates M3GNet’s accuracy and transferability in materials property prediction Amorphous solid ; Molecular dynamics ; Transferability ; Dielectric ; Interatomic potential ; Experimental data ; Characterization (materials science) ; Microelectronics Glass properties and applications ; Phase-change materials and chalcogenides ; X-ray Diffraction in Crystallography
Authors: Yue Wang | Caijuan Yang | Xiaoyan Sun | Feng Li | Wenying Zhao | Xia Li | Shuguang Xiang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-driven models toward polymer glass transition temperature embedded in structures Monte Carlo method ; Test set ; Kriging ; Linear regression ; Gaussian process ; Glass transition ; Gaussian ; Regression ; Property (philosophy) Machine Learning in Materials Science ; Material Dynamics and Properties ; Polymer crystallization and properties
Authors: Alex X. Liu | Haomin Zhuang | Olaf Wiest | Ying Cheng | Xiangliang Zhang
Source:  (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

Leveraging Hybrid Representations for Robust Molecular Property Prediction in Low-Data Regimes Molecular machine learning faces a persistent tradeoff between interpretability and predictive accuracy. Descriptors and fingerprints provide chemically meaningful features but limited predictive power, while learned representations from graph neural networks (GNNs) or SMILES-based models achieve high accuracy at the expense of transparency. In this study, we restrict experiments to descriptors and fixed fingerprints, leaving embeddings as a future extension. We conduct a systematic evaluation of hybrid molecular representations that combine descriptors with fingerprints for property prediction across classification and regression tasks. On the BBBP (blood-brain barrier permeability), ESOL (aqueous solubility), and FreeSolv (hydration free energy) datasets, hybrids yield up to 7 % higher ROC-AUC than descriptors and reduce RMSE by up to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{4 8 \%}$</tex> relative to fingerprints, with the largest gains in low-data regimes <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{(10-25\%}$</tex> of training data). Ablation studies show that descriptors and fingerprints provide complementary signals, and feature analyses confirm that interpretability is preserved. By quantifying robustness under both full-data and data-scarce settings, this study demonstrates that hybrid feature fusion is an effective and reliable strategy for molecular property prediction. The complete framework is integrated into the Hands-On Data Science for Chemists platform to support reproducibility and adoption. Property (philosophy) ; Stability (learning theory) ; Representation (politics) ; Noise (video) ; Robustness (evolution) ; Key (lock) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Saeed Safari | Paul D. Wilcox | Mahmoud Mostafavi | David Knowles
Source:  (article, 2025-07-20)
Field: Materials Science — Materials Chemistry

Data-Driven Multiscale Modelling of History-Dependent Plasticity Using Recurrent Neural Operators Abstract Multiscale modelling in materials science, which connects and translates information across multiple length scales, has become increasingly prominent for its ability to link macroscopic behaviour with underlying microscale phenomena. This is crucial for structural integrity assessment of safety-critical components in industrial applications since a minor change to the microstructure can significantly affect the macroscopic results. Concurrent multiscale models, where mesoscale models are directly coupled to a macroscale model and run concurrently, could be a solution. However, they are prohibitively computationally expensive. In this work, we explore an alternative to this costly approach using a surrogate for mesoscale solutions based on a deep learning model. The deep learning model is a recurrent neural operator that learns the underlying history-dependent states of material via its differential form. It leverages large datasets to learn material deformation in homogenised macroscale from sampled mesoscale representative simulations. The model input is only strain, the output is stress, and the internal state variables learned during training control the evolution of the material. The performance of the surrogate model is evaluated using examples ranging from one-dimensional to full 3D cases of material elastoplastic cyclic deformation, incorporating combined isotropic and kinematic hardening. The results show the effects of model architecture on its predictive capability and the routes to use it for uncertainty quantification. Furthermore, the procedure of mesoscale crystal plasticity simulations to generate datasets for model training is explained. Finally, the model is applied to learn the homogenised mesoscale behaviour of an untextured material case. Mesoscale meteorology ; Microscale chemistry ; Isotropy ; Operator (biology) ; Kinematics ; Multiscale modeling ; Plasticity ; Range (aeronautics) Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Model Reduction and Neural Networks
Authors: Wenjie Wang | K. Jia | Yuwen Cheng
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Prediction the effect of oxygen vacancies and single transition metal doping on CO2RR activity of Ta2CO2 MXene based on first principles and machine learning MXenes ; Transition metal ; Catalysis ; Valence (chemistry) ; Dopant ; Oxygen ; Doping ; Electronic structure MXene and MAX Phase Materials ; Catalysis and Hydrodesulfurization Studies ; Advanced Photocatalysis Techniques
Authors: Jing Tian | Yuan Li | Min Guan | Jijie Zheng | Jie Chu | Yong Liu | Gaorong Han
Source: Journal of Materiomics (article, 2025-08-08)
Field: Materials Science — Materials Chemistry

Data-augmented machine learning models for oxynitride glasses via Wasserstein generative adversarial network with gradient penalty and content constraint Data-driven machine learning methods have been proven highly successful in predicting glass properties, but hampered when dealing with small datasets, such as oxynitride glasses with excellent mechanical properties and chemical stability. Here, a data augmentation method based on the Wasserstein Generative Adversarial Network with Gradient Penalty (GP) and Content Constraint Penalty (CP) terms, a generative deep-learning model via the adversarial training of a generator and a discriminator, was established, in which the GP and CP terms ensure training stability and the physical rationality of the generated samples. The results indicate that the generated samples improve the performance of the oxynitride glass composition-property models trained with the XGBoost algorithm in terms of prediction accuracy and generalization capability. Furthermore, the augmented models outperform the general glass prediction model, GlassNet, over 101 experimental samples not included in the training datasets. Based on SHAP's single feature analysis and feature interaction analysis, the interpretability study further sheds light on the contributions of elements and the interactive effects of element pairs on the properties of oxynitride glasses. These achievements not only provide reliable models for the composition-property studies of oxynitride glasses but also offer a novel strategy for developing high-performance data-driven models under data scarcity scenarios. • Introducing a Content Constraint Penalty (CP) into WGAN-GP ensures that the synthetic samples are physically reasonable. • Augmented data improved XGBoost models in terms of prediction accuracy and generalization capability. • Augmented XGBoost models outperformed the previously reported deep neural network model on 101 unseen experimental samples. • SHAP analysis revealed the elemental contributions and pairwise interactions of oxynitride glasses. Constraint (computer-aided design) ; Generative adversarial network ; Generative grammar ; Computer science ; Content (measure theory) ; Adversarial system ; Artificial intelligence ; Mathematical optimization ; Deep learning ; Mathematics ; Mechanical engineering ; Engineering ; Mathematical analysis Phase-change materials and chalcogenides ; Machine Learning in Materials Science ; Glass properties and applications
Authors: Amber J. Day | Natalie Klein | Adam R. Altenhof | Harris E. Mason | Michael W. Malone | Sinead A. Williamson
Source: Data Science in Science (article, 2025-12-13)
Field: Materials Science — Materials Chemistry

Complex-Valued Signal Denoising Neural Networks for Nuclear Quadrupole Resonance-Based Detection of Fentanyl In many scientific applications, measured time series are corrupted by noise or distortions. Traditional denoising techniques often fail to recover the signal of interest, particularly when the signal-to-noise ratio is low. In this work, we demonstrate that deep learning-based denoising methods can outperform traditional denoising techniques and exhibit greater robustness to variation in noise and signal characteristics. Our motivating example is the detection of materials with Nuclear Quadrupole Resonance (NQR) spectroscopy. Specifically, we aim to detect the presence of radio frequency signals emitted as a result of interactions between the nuclear quadrupole moments and the electric field gradients in fentanyl hydrocholoride and eventually other narcotics. These are short-duration and low-amplitude signals often obscured by strong interference, making detection with traditional methods difficult. We explore real-valued and complex-valued deep learning architectures to process and denoise inherently complex-valued NQR signals. Our findings demonstrate that complex-valued neural networks are superior to their real-valued counterparts and more effective than traditional denoising methods, offering a powerful new approach for reliable fentanyl detection using NQR spectroscopy. Noise reduction ; Robustness (evolution) ; Pattern recognition (psychology) ; SIGNAL (programming language) ; Artificial neural network ; Noise (video) ; Nuclear quadrupole resonance Solid-state spectroscopy and crystallography ; Advanced NMR Techniques and Applications ; NMR spectroscopy and applications
Authors: Anees A. Khadom | N. Ahmed | Ahmed A. Al‐Amiery | Hameed B. Mahood
Source: Total Chemistry (article, 2025-08-11)
Field: Materials Science — Materials Chemistry

On the corrosion inhibition optimization of mild steel in 1M hydrochloric acid: Mathematical statistical analysis and machine learning approaches Hydrochloric acid ; Corrosion ; Computer science ; Chemistry ; Materials science ; Metallurgy Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals ; Concrete Corrosion and Durability
Authors: Yusuke Kawamura | Helmut Pfützner | Georgi Shilyashki
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Classification of Spike Domains in Grain-Oriented Electrical Steel Using Neural Networks Artificial neural network ; Spike (software development) ; Domain (mathematical analysis) ; Electrical steel ; Pattern recognition (psychology) ; Fourier series ; Magnetic domain ; Fourier transform Magnetic Properties and Applications ; Metallic Glasses and Amorphous Alloys ; Electromagnetic Simulation and Numerical Methods
Authors: Shinya Sakuma | Shunsaku Yasumura | Kenichiro Saita | Tetsuya Taketsugu | Masaru Ogura
Source: The Journal of Physical Chemistry C (article, 2025-09-24)
Field: Materials Science — Materials Chemistry

Predicting Catalytic Activity for CH <sub>4</sub> Combustion on Pd-Exchanged Zeolite Catalysts Using Automated Reaction Route Mapping While computational predictions of catalytic activity are highly desired, conventional methods have difficulty capturing the complexity of reactions on solid catalyst surfaces. To address this issue, we employed a novel approach combining neural network potentials (NNPs) with automated reaction route mapping to explore reaction mechanisms of CH4 combustion on Pd-exchanged zeolites (Pd-CHA, Pd-beta, and Pd-MOR). The predicted reaction map of CH4 combustion over Pd2+ site revealed partially oxidized species such as CH2O, HCOOH, and bicarbonate as the potential intermediates toward CO2 + 2H2O. Activation energies (Ea) of the rate-determining step (RDS) were evaluated, revealing the order of Ea as Pd-MOR < Pd-beta < Pd-CHA. A kinetic analysis using rate constant matrix contraction (RCMC) method estimated the catalytic activities of these catalysts. No reaction intermediates with significant lifetimes were observed on Pd-beta and Pd-MOR, whereas stable bicarbonate intermediates were present on Pd-CHA, decreasing the formation rate of CO2 + 2H2O. Kinetic analysis further predicted the pseudo CO2 formation rate with activity order Pd-MOR > Pd-beta > Pd-CHA, aligning with experimental results. These findings demonstrate the potential of the automated reaction route mapping with NNP for predicting the catalytic activity of solid catalysts, enabling their efficient prescreening and rational design. Catalysis ; Reaction rate constant ; Combustion ; Zeolite ; Bicarbonate ; Reaction rate ; Order of reaction ; Kinetic energy Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Zeolite Catalysis and Synthesis
Authors: Shuai Li | Sree Sourav Das | Haobo Wang | Sujit Bati | Prasanna V. Balachandran | Junichiro Shiomi | Jerrold A. Floro | Mona Zebarjadi
Source: ArXiv.org (preprint, 2025-03-27)
Field: Materials Science — Materials Chemistry

High thermoelectric power factor in Ni-Fe alloy for active cooling applications Metallic thermoelectric materials are promising candidates for active cooling applications, where high thermal conductivity and a high thermoelectric power factor are essential to maximize effective thermal conductivity. While metals inherently possess high thermal and electrical conductivities, they typically exhibit low Seebeck coefficients. In this work, we create a database and apply machine learning techniques to identify metallic binary alloys with large Seebeck coefficients. Specifically, we identify Ni-Fe as a promising candidate for active cooling. We then fabricate Ni-Fe ingots and demonstrate thermoelectric power factor values as high as 120 μW/{cm.K^2} at 200 K for these stable alloys, which are composed of cost-effective and abundant elements. Furthermore, we show that the effective thermal conductivity of these alloys, under small temperature differences, can exceed that of pure copper at temperatures above 250 K. Thermoelectric effect ; Thermoelectric materials ; Thermal conductivity ; Thermoelectric cooling ; Seebeck coefficient ; Power factor ; Thermoelectric generator ; Alloy Advanced Thermoelectric Materials and Devices ; Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science
Authors: Tong Wu | Mingzi Sun | Bolong Huang
Source: Small Methods (review, 2025-06-24)
Field: Materials Science — Materials Chemistry

A Review of Automated Workflow Pipelines for Computational Chemists Modern computational chemistry is a powerful tool for chemists to probe into material properties and to gain insight into the experimental results. In recent years, the development in artificial intelligence (AI) and machine learning (ML) has gained remarkable interest in computational chemistry. However, the accuracy of ML models highly depends on the fed data source. As a result, substantial high quality computational results from ab initio methods are required first to explore the potentials of AI and ML better. The extensive data demands from ML training lead to the appearance of high-throughput quantum chemistry approach, where thousands of or tens of thousands of computation tasks are required. Batch processing of model creation and data processing by leveraging dedicated programs and codes is of significant importance to save the scientists from repeating laborious computer operations. This review focuses on the assistive tools and codes on automated workflows especially for high-throughput quantum chemistry approaches. Workflow ; Computer science ; Computation ; Throughput ; Computational science ; Artificial intelligence ; Data science ; Programming language ; Database Machine Learning in Materials Science ; Scientific Computing and Data Management ; Computational Drug Discovery Methods
Authors: Wei Lai | Rongzhi Dong | Nihang Fu | Sadman Sadeed Omee | Jianjun Hu
Source: ArXiv.org (preprint, 2025-03-29)
Field: Materials Science — Materials Chemistry

TCSP 2.0: Template Based Crystal Structure Prediction with Improved Oxidation State Prediction and Chemistry Heuristics Crystal structure prediction remains a major challenge in materials science, directly impacting the discovery and development of next-generation materials. We introduce TCSP 2.0, a substantial evolution of our template-based crystal structure prediction framework that advances predictive capabilities through synergistic integration of several key techniques into its major components. Building upon TCSP 1.0's template-matching foundation, this enhanced version implements three critical innovations: (1) replacement of Pymatgen with deep learning-based BERTOS model for oxidation state prediction with superior performance, (2) implementation of sophisticated element embedding distance metrics for improved chemical similarity assessment, and (3) development of a robust majority voting mechanism for space group selection that reduces prediction uncertainty. TCSP 2.0 also expands its template base by incorporating template structures from Materials Cloud, C2DB, and GNoME databases alongside the original Materials Project repository, creating a more comprehensive structural foundation. Rigorous validation across 180 diverse test cases of the CSPBenchmark demonstrates TCSP 2.0's exceptional performance, achieving 83.89% space-group success rate and 78.33% structural similarity accuracy for top-5 predictions, substantially outperforming both its predecessor and the competing modern CSP algorithms including CSPML and EquiCSP. Heuristics ; Embedding ; Similarity (geometry) ; Key (lock) ; Template ; Base (topology) ; Crystal structure prediction ; Selection (genetic algorithm) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Inorganic Chemistry and Materials
Authors: Shen Wang | Yining Liu | Weiren Zhao | Yang Li
Source: The Journal of Organic Chemistry (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

Optimizing Model Learning Performance on a Challenging Heck Reaction Yield Data Set The development of machine learning (ML) in organic synthesis is limited due to the lack of available data sets. The ML-compatible literature data set for Heck reaction yields, named HeckLit, has been established. With 10,002 cases, the data set spans multiple reaction subclasses and covers a larger chemical space compared to high-throughput experimentation data sets, including nearly 3.6 × 10<sup>12</sup> accessible cases. HeckLit accelerates the advancement of ML-driven synthesis research; however, it suffers from the same dilemma as other literature-based data sets, sparse distribution and high-yield preference, leading to limited model learning ability on the test set, with <i>R</i><sup>2</sup> = 0.318. Thus, feature distribution smoothing (FDS) and subset splitting training strategy (SSTS) are utilized to tackle this issue. Despite no improvement when using FDS, SSTS boosted the <i>R</i><sup>2</sup> to 0.380. This optimization approach relies on the subset division. Therefore, we suggest a criterion for splitting. The SSTS opens up a new avenue for tackling the challenge of learning from large-scale data sets. Yield (engineering) ; Heck reaction ; Computer science ; Data set ; Set (abstract data type) ; Artificial intelligence ; Data science ; Machine learning ; Chemistry ; Materials science ; Programming language Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis