jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Miguel Nouman (21557202) | Richard B. Canty (19337513) | Brent A. Koscher (1449973) | Matthew A. McDonald (10766331) | Klavs F. Jensen (722008)
Source: Figshare (article, 2025-06-17)
Field: Materials Science — Materials Chemistry

General ChemicallyIntuitive Atom- and Bond-LevelDFT Descriptors for Machine Learning Approaches to Reaction ConditionPrediction We demonstrate the usefulness of general atom- and bond-level density functional theory (DFT) descriptors to enhance the performance of neural networks for general reaction condition prediction. We treat condition prediction as a multiclass classification task and report the performance of neural networks and random forests as evaluated by 5-fold cross-validation on a 69,935 reaction data set with 296 distinct single-component reaction condition classes and varying input embedding compositions. We show that by combining structural and general DFT descriptors, models with up to 71% fewer trainable parameter than their purely structural counterparts can provide comparable or superior weighted precision, top-1 and top-3 accuracies. Moreover, we report improvements of up to 5, 10, and 11% in weighted precision, top-1 accuracy and <i>F</i><sub>1</sub> score, respectively, for neural networks trained on hybrid representations which combine general DFT and structural descriptors, when compared to structural models with equivalent architectures and input sizes. Remarkably, the best performing neural network trained on hybrid embeddings outperforms the best purely structural model investigated despite the latter benefiting from of an embedding strategy with 267 times more data points than the one used for generating and embedding hybrid descriptors, with both strategies being unsupervised learning algorithms that share considerable conceptual and architectural similarities. Embedding ; Artificial neural network ; Set (abstract data type) ; Pattern recognition (psychology) ; Task (project management) ; Basis (linear algebra) ; Multiclass classification Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Le Zong | Lingxin Li | Lantian Zhang | Xuecheng Jin | Y K Zhang | Wenfeng Yang | Pengfei Liu | Bin Gan | Liujie Xu | Yuanshen Qi | Wenwen Sun
Source: International Journal of Minerals Metallurgy and Materials (article, 2025-12-22)
Field: Materials Science — Materials Chemistry

Processing map for oxide dispersion strengthening Cu alloys based on experimental results and machine learning modelling Artificial neural network ; Dispersion (optics) ; Microstructure ; Particle swarm optimization ; Oxide ; Backpropagation ; Thermomechanical processing Fusion materials and technologies ; Advanced ceramic materials synthesis ; Microstructure and mechanical properties
Authors: Da Bean Han (22402318) | Gyoung S. Na (8367210) | Hyun Woo Kim (1543174)
Source: Figshare (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Learning MultidimensionalElectronegativity of SelectedAtom Types in Organic Molecules Using Graph Neural Networks Electronegativity can be considered a data-driven concept that has been widely used since Pauling proposed this property. However, updating the electronegativity based on the vast amount of high-quality experimental and computational data has been overlooked. Thus, advances in artificial intelligence (AI), with its ability to manage large data sets and identify underlying patterns, necessitate reconsidering data-driven concepts such as electronegativity. In this work, we present a data-driven method to generate more informative multidimensional electronegativity of atoms in organic molecules using graph neural networks. Although this electronegativity can be extended to any dimension, we focused on 2D electronegativity to do a more detailed classification of the atoms and their covalent bonds. By replacing the conventional electronegativity with the newly proposed one, we observed performance improvement in molecular machine learning tasks. We believe that our findings will be useful in understanding electronegativity and chemical bonds by applying AI-driven methods to chemical studies. Electronegativity ; Artificial neural network ; Atom (system on chip) ; Graph ; Molecular graph ; Molecule Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Surface Chemistry and Catalysis
Authors: Xuxin Kang | Wenjing Zhou | Ziyuan Li | Zhaoqin Chu | Haihong Yin | Shan Gao | Aijun Du | Xiangmei Duan
Source: ArXiv.org (preprint, 2025-04-22)
Field: Materials Science — Materials Chemistry

Prediction of CO2 reduction reaction intermediates and products on transition metal-doped r-GeSe monolayers:A combined DFT and machine learning approach The electrocatalytic CO2 reduction reaction (CO2RR) is a complex multi-proton-electron transfer process that generates a vast network of reaction intermediates. Accurate prediction of free energy changes (G) of these intermediates and products is essential for evaluating catalytic performance. We combined density functional theory (DFT) and machine learning (ML) to screen 25 single-atom catalysts (SACs) on defective r-GeSe monolayers for CO2 reduction to methanol, methane, and formic acid. Among nine ML models evaluated with 14 intrinsic and DFT-based features, the XGBoost performed best (R2 = 0.92 and MAE = 0.24 eV), aligning closely with DFT calculations and identifying Ni, Ru, and Rh@GeSe as prospective catalysts. Feature importance analysis in free energy and product predictions highlighted the significance of CO2 activation with O-C-O and IPC-O1 as the key attributes. Furthermore, by incorporating non-DFT-based features, rapid predictions became possible, and the XGBoost model retained its predictive performance with R2 = 0.89 and MAE = 0.29 eV. This accuracy was further validated using Ir@GeSe. Our work highlights effective SACs for CO2RR, and provides valuable insights for efficient catalyst design. Catalysis ; Reduction (mathematics) ; Density functional theory ; Formic acid ; Process (computing) ; Feature (linguistics) ; Work (physics) ; Product (mathematics) Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts
Authors: Li-Hong Lin (20999061) | Jin-Jin Li (1493293) | Yun-Xiang Pan (1477735) | Fangyou Yan (1530637) | Zheng-Hong Luo (1493296) | Yin-Ning Zhou (1493299)
Source: Figshare (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Accelerating PolyesterIntelligence: Machine-Learning-AssistedPrediction of Glass Transition Temperature and Virtual Molecules Screening Rapid development of the economy and society has resulted in a need for polyesters that are tailored to diverse performance requirements. Unfortunately, the innovation of polyester materials is mainly dependent on experience and intuitive guidance. Herein, we propose various interpretable quantitative-structure–property relationship (QSPR) models based on machine-learning-assisted approaches, which can accurately predict polyesters’ glass transition temperatures (<i>T</i><sub>g</sub>) and facilitate the exploration of novel polyesters. Initially, 695 polyesters with <i>T</i><sub>g</sub> values are collected to establish multiple QSPR models using three different algorithms, which undergo both internal and external validation. The relative coefficient (<i>R</i><sup>2</sup>) values of the best deep neural network (DNN) model on the training set and testing set reach 0.9588 and 0.9314, respectively, which is among the better levels in related studies. The use of Morgan fingerprint with frequency (MFF) descriptors and associated Shapley Additive Explanations analysis does reveal a couple of interesting physical trends associated with variation of <i>T</i><sub>g</sub> with the substructure beyond what was reported before. To better widen the chemical space of the existing polyester material family, a virtual polyester library is constructed using a retrosynthetic strategy. Furthermore, this workflow identifies 20 novel polyesters with low synthetic complexity by high-throughput screening and validates these polyesters through molecular dynamics simulations, which show an average absolute error of 9.42 °C between the model-predicted and MD-simulated values. Machine-learning-assisted approach not only improves the efficiency of polyester material discovery but also provides a promising perspective for understanding the thermal properties of polyesters from a microscopic chemical structural viewpoint. Polyester ; Glass transition ; Set (abstract data type) ; Chemical space ; Workflow ; Substructure ; Characterization (materials science) ; Fingerprint (computing) Machine Learning in Materials Science ; Polymer crystallization and properties ; Material Dynamics and Properties
Authors: Mikkel Ohm Sauer | Peder Lyngby | Kristian S. Thygesen
Source: ArXiv.org (preprint, 2025-04-08)
Field: Materials Science — Materials Chemistry

Dispersion-corrected Machine Learning Potentials for 2D van der Waals Materials Machine-learned interatomic potentials (MLIPs) based on message passing neural networks hold promise to enable large-scale atomistic simulations of complex materials with ab initio accuracy. A number of MLIPs trained on energies and forces from density functional theory (DFT) calculations employing semi-local exchange-correlation (xc) functionals have recently been introduced. Here, we benchmark the performance of six dispersion-corrected MLIPs on a dataset of van der Waals heterobilayers containing between 4 and 300 atoms in the moiré cell. Using various structure similarity metrics, we compare the relaxed heterostructures to the ground truth DFT results. With some notable exceptions, the model precisions are comparable to the uncertainty on the DFT results stemming from the choice of xc-functional. We further explore how the structural inaccuracies propagate to the electronic properties, and find excellent performance with average errors on band energies as low as 35 meV. Our results demonstrate that recent MLIPs after dispersion corrections are on par with DFT for general vdW heterostructures, and thus justify their application to complex and experimentally relevant 2D materials. van der Waals force ; Density functional theory ; Ab initio ; Benchmark (surveying) ; Van der Waals surface ; Artificial neural network ; Dispersion (optics) ; Similarity (geometry) Machine Learning in Materials Science ; Quantum many-body systems ; 2D Materials and Applications
Authors: Matsumura, Naoki | Yoshimoto, Yuta | Iwasaki, Yuto | Yamazaki, Meguru | Sakai, Yasufumi
Source: ArXiv.org (preprint, 2025-06-18)
Field: Materials Science — Materials Chemistry

Knowledge Distillation Framework for Accelerating High-Accuracy Neural Network-Based Molecular Dynamics Simulations Neural network potentials (NNPs) offer a powerful alternative to traditional force fields for molecular dynamics (MD) simulations. Accurate and stable MD simulations, crucial for evaluating material properties, require training data encompassing both low-energy stable structures and high-energy structures. Conventional knowledge distillation (KD) methods fine-tune a pre-trained NNP as a teacher model to generate training data for a student model. However, in material-specific models, this fine-tuning process increases energy barriers, making it difficult to create training data containing high-energy structures. To address this, we propose a novel KD framework that leverages a non-fine-tuned, off-the-shelf pre-trained NNP as a teacher. Its gentler energy landscape facilitates the exploration of a wider range of structures, including the high-energy structures crucial for stable MD simulations. Our framework employs a two-stage training process: first, the student NNP is trained with a dataset generated by the off-the-shelf teacher; then, it is fine-tuned with a smaller, high-accuracy density functional theory (DFT) dataset. We demonstrate the effectiveness of our framework by applying it to both organic (polyethylene glycol) and inorganic (L$_{10}$GeP$_{2}$S$_{12}$) materials, achieving comparable or superior accuracy in reproducing physical properties compared to existing methods. Importantly, our method reduces the number of expensive DFT calculations by 10x compared to existing NNP generation methods, without sacrificing accuracy. Furthermore, the resulting student NNP achieves up to 106x speedup in inference compared to the teacher NNP, enabling significantly faster and more efficient MD simulations. Process (computing) ; Speedup ; Inference ; Artificial neural network ; Molecular dynamics ; Range (aeronautics) ; Distillation ; Training set Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Physical and Chemical Molecular Interactions
Authors: Nour Alkhatib | Saifeldeen Abed Alrhman | Serdal Kırmızıaltın
Source: Molecular Systems Design & Engineering (article, 2025-10-07)
Field: Chemistry — Inorganic Chemistry

Machine learning highlights chemistry as the key factor in metal–organic frameworks for atmospheric water harvesting Grand Canonical Monte Carlo simulations of diverse MOFs reveal key structure–function relationships. Machine learning identifies chemical features governing water uptake and atmospheric water harvesting performance. Key (lock) ; Monte Carlo method ; Factor (programming language) ; Atmospheric chemistry Metal-Organic Frameworks: Synthesis and Applications
Authors: Sunil Kumar Srivastava | Kedari Lal Dhaker | Manvendra K. Dubey
Source: Next research. (article, 2025-12-19)
Field: Materials Science — Biomaterials

Advances in synthesis and characterization of bionanomaterials by using artificial intelligence and machine learning techniques: A critical review Nanocellulose ; Quality (philosophy) ; Perceptron ; Characterization (materials science) Advanced Cellulose Research Studies ; Nanocomposite Films for Food Packaging ; Hydrogels: synthesis, properties, applications
Authors: Fatemeh Parad | Soroush Mohaddes | Fahimeh Ghasemi | Mohammad Ali Faramarzi | Somayeh Mojtabavi
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Synthesis and characterization of lignin–copper nanohybrids for colorimetric acetaminophen detection: a combined physical chemistry and machine learning study Acetaminophen ranks among the most widely used pharmaceutical and personal care products today. Following consumption, the drug and its metabolites are excreted into sewage systems, wastewater treatment plants, and various aquatic environments, leading to significant ecological and health impacts. In this context, the study introduced novel and easily synthesized hybrid structures through the self-polymerization and coordination of lignin with copper (Cu@lignin·HSs) for the specific and sensitive detection of acetaminophen. The sensor exhibited high sensitivity with a detection limit (LOD) of 0.5 mM and good precision, with a relative standard deviation (RSD) of 1.1%. The recovery of acetaminophen ranged from 97.0% to 98.8%, and the method showed excellent linearity with an <i>R</i><sup>2</sup> value of 0.996, indicating high accuracy and reproducibility. The proposed portable colorimetric sensor exhibited excellent selectivity, stability, and reproducibility, with its performance in analyzing actual samples showing no significant difference compared to high-performance liquid chromatography (HPLC) (<i>p</i> > 0.05). In the machine learning analysis, ensemble models (random forest, gradient boosting, and XGBoost) effectively predicted acetaminophen detection efficiency using the prepared sensor in real wastewater. XGBoost demonstrated superior performance, achieving excellent predictive correlation and minimal error, thereby highlighting the robustness of ensemble learning for this application. In the future, the synthesized HSs could serve as a promising sensing technique for quality control in pharmaceutical wastewater. Copper ; Lignin ; Chemistry ; Characterization (materials science) ; Acetaminophen ; Nanotechnology ; Organic chemistry ; Materials science ; Biochemistry Advanced Nanomaterials in Catalysis ; Electrochemical sensors and biosensors ; Cholinesterase and Neurodegenerative Diseases
Authors: V. Cornette | Fernando S. Seijas | Rodrigo Delgado Mons | Raúl H. López
Source: Applied Surface Science (article, 2025-12-02)
Field: Materials Science — Materials Chemistry

Machine learning prediction of the regularization parameter for pore size distribution determination in nanoporous materials Nanoporous ; Regularization (linguistics) ; Adsorption ; Monte Carlo method ; Consistency (knowledge bases) ; Range (aeronautics) Mesoporous Materials and Catalysis ; Metal-Organic Frameworks: Synthesis and Applications ; Phase Equilibria and Thermodynamics
Authors: Ricardo Floriano | Payam Edalati | Gaspar Andrade | Renato Belli Strozi | Shivam Dangwal | Kaveh Edalati
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Room Temperature Hydrogen Storage Properties of Ti-Zr-Mn-Fe-Co High-Entropy Alloys Designed by a Combination of Semi-Empirical Descriptors, Thermodynamic Calculations and Machine Learning High entropy alloys ; Materials science ; Thermodynamics ; Hydrogen storage ; Entropy (arrow of time) ; Metallurgy ; Alloy ; Physics Hydrogen Storage and Materials ; Catalysis and Hydrodesulfurization Studies ; Nuclear Materials and Properties
Authors: Xin Zhou | Ze Li | Zhibo Zhang | Mengzhen Zhu | Hao Yan | Xiaobo Chen | Lianying Wu | Chaohe Yang
Source: Chemical Engineering Science (article, 2025-12-27)
Field: Chemistry — Analytical Chemistry

Molecular-Driven Multi-Objective optimization for direct crude oil catalytic cracking via Physics-Informed Neural Networks Fluid catalytic cracking ; Coke ; Sensitivity (control systems) ; Artificial neural network ; Asphaltene ; Refining (metallurgy) ; Carbon footprint ; Process (computing) ; Life-cycle assessment Petroleum Processing and Analysis ; Catalysis and Hydrodesulfurization Studies ; Heat transfer and supercritical fluids
Authors: Nakyung Lee | Jakoah Brgoch
Source: Advanced Optical Materials (article, 2025-11-12)
Field: Materials Science — Materials Chemistry

From Model to Material: Machine Learning Enabled Discovery of LED‐Compatible Phosphors Abstract Phosphor‐converted white light‐emitting diodes are transforming energy‐efficient lighting technologies. At the core of their performance are Ce 3+ and Eu 2+ activated phosphors, whose photoluminescent properties significantly impact the lighting devices’ efficiency and, equally important, govern the light quality. However, the discovery of new phosphors that meet the stringent requirements needed for commercialization has remained a challenge. Recent advances in data‐driven approaches, particularly machine learning and optimization algorithms, have begun to accelerate this process. These methods enable the accurate prediction of key photoluminescent properties and facilitate the exploration of vast compositional spaces of inorganic phosphors, guiding the targeted synthesis of new high‐performance phosphors. This review highlights the major progress in data‐driven discovery of Ce 3+ and Eu 2+ phosphors, emphasizing property prediction, materials screening, and experimental validation. It concludes with an outlook on future opportunities and challenges in the application of artificial intelligence to phosphor discovery. Phosphor ; Commercialization ; Key (lock) ; Photoluminescence ; White light ; Light-emitting diode Luminescence Properties of Advanced Materials ; Microwave Dielectric Ceramics Synthesis ; Advanced Photocatalysis Techniques
Authors: Shoeb Athar | Adrien Mecibah | Philippe Jund
Source: arXiv (Cornell University) (preprint, 2025-12-21)
Field: Materials Science — Materials Chemistry

Tackling dataset curation challenges towards reliable machine learning: a case study on thermoelectric materials Machine Learning (ML) driven discovery of novel and efficient thermoelectric (TE) materials warrants experimental TE datasets of high volume, diversity, and quality. While the largest publicly available dataset, Starrydata2, has a high data volume, it contains inaccurate data due to the inherent limitations of Large Language Model (LLM)-assisted data curation, ambiguous nomenclature and complex formulas of materials in the literature. Another unaddressed issue is the inclusion of multi-source experimental data, with high standard deviations and without synthesis information. Using half-Heusler (hH) materials as an example, this work is aimed at first highlighting these errors and inconsistencies which cannot be filtered with conventional dataset curation workflows. We then propose a statistical round-robin error-based data filtering method to address these issues, a method that can be applied to filter any other material property. Lastly, a hybrid dataset creation workflow, involving data from Starrydata2 and manual extraction, is proposed and the resulting dataset is analyzed and compared against Starrydata2. Filter (signal processing) ; Work (physics) ; Data curation ; Experimental data ; Inclusion (mineral) ; Data collection Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Ferroelectric and Negative Capacitance Devices
Authors: Taoyong Cui | Younghun Han | Haojun Jia | Chenru Duan | Qiyuan Zhao
Source: Scientific Data (article, 2025-12-04)
Field: Materials Science — Materials Chemistry

A Large Scale Molecular Hessian Database for Optimizing Reactive Machine Learning Interatomic Potentials Transition-state (TS) characterization underpins reaction modeling but conventional DFT is costly. Machine-learning interatomic potentials (MLIPs) promise quantum-level accuracy at lower cost, yet, lacking large-scale Hessian data, most are pretrained only on energies and forces, limiting TS optimization. We present HORM, the largest quantum-chemistry Hessian dataset for reactive systems: 1.84 million matrices at the ωB97x/6-31G(d) level. To exploit second-order information efficiently, we propose Hessian-informed training with stochastic row sampling, which controls the computational overhead of incorporating Hessians. Across diverse MLIP architectures and force-learning schemes, HORM yields up to 63% lower Hessian mean absolute error and up to 200× improvement in TS-search efficiency versus counterparts trained without Hessians. HORM thus fills critical data and methodological gaps, enabling more accurate, robust reactive MLIPs and scalable exploration of reaction networks. Hessian matrix ; Scalability ; Overhead (engineering) ; Scale (ratio) ; Limiting ; Exploit ; Hessian equation ; Uncertainty quantification Machine Learning in Materials Science ; Advanced Physical and Chemical Molecular Interactions ; Advanced Chemical Physics Studies
Authors: Mikhail Levenets | Andrey Chibisov
Source:  (article, 2025-10-18)
Field: Materials Science — Materials Chemistry

ACCELERATING THE SEARCH FOR AN ENERGETICALLY STABLE CRYSTAL STRUCTURE USING MACHINE LEARNING ON THE EXAMPLES OF BULK AND MONOLAYER SILICON, ZINC OXIDE, AND ALUMINUM OXIDE The paper explores the approach of accelerated search for an energetically stable crystal structure using random search and Deep MLIP (Machine-Learning Interatomic Potential) Mattersim. Crystal structure ; Zinc ; Aluminium ; Aluminum oxide ; Monolayer ; Crystal (programming language) Machine Learning in Materials Science ; MXene and MAX Phase Materials ; Advanced Electron Microscopy Techniques and Applications