jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhudan Chen | Hao Li | Xiaowei Xu | ZhuoZheng Wang | Yan Jiang | Yi Luo | Libo Wang | Weishen Yang
Source: Dalton Transactions (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

The structure–property relationship of metallocene-based ethylene oligomerization catalysts using DFT and graph neural networks Ethylene oligomerization catalysts have been extensively studied in both experimental and simulation contexts, yet a molecular-level understanding of structure-property relationship remains far from full understanding. Herein, an applicable strategy for the design of ligands of ethylene oligomerization catalysts is proposed. Density functional theory (DFT) and 3D graph neural networks (3D GNNs) have been combined to establish the relationship between the catalyst structure and its property. A series of titanium-based metallocene catalysts with different ligands were designed and calculated using DFT to establish a dataset. The catalyst prediction model was constructed using 3D GNNs, and a weighted removal approach was used to compare output results and study the impact of different ligand structures on the oligomerization selectivity represented by the energy barrier difference between β-hydrogen transfer and the fourth ethylene insertion. The <i>R</i><sup>2</sup> values of the energy barrier difference predictions by four 3D GNNs were 0.93-0.96, indicating good predictive accuracy of the graph network models. Using the graph neural network explanation algorithms, we investigated the influence of different substructures within the ligands on trimerization selectivity. Based on the training and explanation results of the model, an external validation set is designed, and the <i>R</i><sup>2</sup> is 0.92, suggesting the generalization ability of the model. This enabled a molecular-level study of the relationship between the structure of the titanocene catalyst and its properties, providing guidance for the design of new catalyst structures. Metallocene ; Catalysis ; Ethylene ; Artificial neural network ; Materials science ; Property (philosophy) ; Graph ; Chemistry ; Polymer chemistry ; Organic chemistry ; Computer science ; Polymerization ; Artificial intelligence ; Theoretical computer science ; Polymer Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Organometallic Complex Synthesis and Catalysis
Authors: Geoffrey R. Weal | Maryam Nurhuda | Justin M. Hodgkiss | Paul Hume | Daniel M. Packwood
Source: The Journal of Chemical Physics (article, 2025-07-10)
Field: Materials Science — Materials Chemistry

Graph neural networks to predict atomic transition charges and exciton couplings in organic semiconductors Exciton couplings between molecules in organic semiconductors are important parameters for simulating exciton diffusion, but they are time-consuming to compute from first-principles. Previous works have developed machine-learned models to predict exciton couplings, but most of these models are restricted to specific molecules and cannot generalize over databases of organic materials. In this paper, we present a graph neural network (GNN) that can predict exciton couplings between organic molecules by using atomic transition charges as an intermediary. Our GNN is shown to predict exciton couplings between important fused-ring electron acceptors (FREAs), as well as many other molecules found in the Cambridge Crystallographic Data Center crystal database, with good accuracy. We also show that the predicted couplings can be used for accurate simulations of exciton diffusion. This work, therefore, overcomes the key limitation of previous machine-learned models for exciton couplings and thereby brings us closer to the possibility of performing high-throughput virtual screening of organic materials for photovoltaic applications. Exciton ; Molecule ; Organic semiconductor ; Organic molecules ; Biexciton ; Artificial neural network ; Chemical physics ; Materials science ; Computer science ; Physics ; Chemistry ; Statistical physics ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Conducting polymers and applications ; Organic Electronics and Photovoltaics
Authors: Xiaorui Liu | Jianghao Liang | Ziyi Wang | Qingyu Li | Yida Deng | Haozhi Wang
Source: Advanced Energy Materials (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Building Catalyst Exploration Highways by Integrating High‐Throughput and Machine Learning Technologies ABSTRACT There is growing interest in highly active catalysts to meet the increasing requirements of high‐efficiency energy conversion and storage. However, catalyst investigation through traditional trial‐and‐error experiments is impeded by the long duration and high cost involved. Recently, high‐throughput (HT) technologies that enable rapid catalyst screening in a vast parameter space within days (or even hours) have revolutionized catalyst research. Machine learning (ML) is a powerful tool for extracting knowledge from large datasets generated by HT experiments and computational calculations. The integration of HT with ML technologies demonstrates great promise for facilitating the discovery of advanced catalysts. From this perspective, this study highlights the use of frameworks integrating HT experimental/computational techniques with ML methodologies for catalyst research, with emphasis on the progress in such frameworks in identifying optimal candidates, elucidating reaction mechanisms, and determining critical descriptors. Furthermore, future directions for accelerating high‐performance catalyst discovery using automated HT synthesis platforms, advanced in situ characterization, and intelligent ML algorithms are outlined. This work is expected to establish a closed‐loop workflow toward the design of highly active catalysts and provide a new avenue for fundamental theory and practical applications in catalyst research. Workflow ; Catalysis ; Work (physics) ; Duration (music) ; Emerging technologies ; Energy (signal processing) Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; CO2 Reduction Techniques and Catalysts
Authors: Hongkai Zhang | Chunjian Pan | Qi Liang | Longchun Zhong | Wei‐Ping Pan
Source: Journal of Chemical Information and Modeling (article, 2025-12-12)
Field: Chemistry — Inorganic Chemistry

Multiobjective Optimization of Metal–Organic Framework Structural Properties and Synthesis Costs through Machine Learning Metal-organic frameworks (MOFs) are a novel class of porous materials characterized by high surface area, high porosity, and tunable structure, possessing immense application potential. Many synthesis methods have been developed for MOFs, such as chemical, hydrothermal, mechanochemical, and microwave-assisted methods. However, their commercialization faces significant hurdles due to the high synthesis costs that mainly include the material costs of organic ligands, metal precursors, solvents, and energy consumption during prolonged reaction times. The synthesis conditions and material selections also impact the MOF structural properties, such as specific surface area and porosity. Hence, in this paper, we propose a machine learning-based multiobjective framework to identify optimal MOF synthesis conditions while achieving desired structural properties. Leveraging synthesis data from the SynMOF database and large-scale commercial raw material cost data, we developed machine learning predictive models. Pareto-optimal synthesis routes maximizing SSA while minimizing cost were identified using the nondominated sorting genetic algorithm II (NSGA-II). Subsequently, k-Nearest Neighbors (k-NN) was employed to retrieve practical optimal synthesis conditions by matching the optimized abstract features with those of metal types, solvent types, and ligand descriptors. Finally, a Bagging-integrated positive-unlabeled learning model was employed to screen solutions with a production feasibility probability exceeding 0.7. This comprehensive framework provides a strategic pathway for synthesizing high-performance, low-cost MOFs. Sorting ; Commercialization ; Multi-objective optimization ; Genetic algorithm ; Matching (statistics) ; Production (economics) ; Raw material Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications ; Multicomponent Synthesis of Heterocycles
Authors: Eric S. Toberer | Andrew C. Novick | Elif Ertekin
Source: MRS Bulletin (article, 2025-08-01)
Field: Materials Science — Materials Chemistry

High throughput and machine learning approaches for thermoelectric materials Throughput ; Thermoelectric effect ; Materials science ; Computer science ; Nanotechnology ; Engineering physics ; Engineering ; Physics ; Telecommunications ; Thermodynamics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Shiyun Liu | Jacqueline M. Cole
Source: Journal of Chemical Information and Modeling (article, 2025-08-13)
Field: Chemistry — Spectroscopy

Automated Determination of the Molecular Substructure from Nuclear Magnetic Resonance Spectra Using Neural Networks Nuclear magnetic resonance (NMR) spectroscopy is an indispensable tool for determining the structural characteristics of a molecule by analyzing its chemical shifts. A wealth of NMR spectra therefore exists and continues to amass on a daily basis, at an ever-increasing rate owing to the progressive automation of chemical analysis. This growth and automation have led to the data analysis step in NMR spectroscopy becoming the main bottleneck in the structural characterization of a new chemical compound. In particular, the data interpretation step is slow and prone to error as it requires manual examination by a suitably trained scientist. Machine learning (ML) methods could overcome this bottleneck, pending that they can automatically correlate the collection of peaks in an NMR spectrum with the substructure of its subject molecule. This study explores the art of the possible using three types of ML methods that are based on neural-network architectures: a multilayer perceptron (MLP) + long short-term memory (LSTM) neural network, a convolutional neural network (CNN), and an MLP + recurrent neural network (RNN). NMR spectrum-structure correlations were encoded into each type of neural network using two forms of molecular representation, one employing functional groups and the other using a novel neighbor-based method. These models were trained on 34,503 and 17,311 experimental <sup>13</sup>C and <sup>1</sup>H NMR spectra, respectively. The influence of incorporating metadata about experimental conditions (NMR field strength, temperature, and solvent) into the neural-network model was also investigated. The models incorporated coupling constants as a proxy for spectral intensities in the case of <sup>13</sup>C NMR spectra. We found that the MLP + LSTM model achieved the highest accuracy (88%) when trained on <sup>13</sup>C NMR spectra and incorporating experimental metadata (compared to 77% without incorporating it). While the CNN model performance was slightly lower (86% accuracy), it determined molecular substructures in one-third of the computational run time compared to the MLP + LSTM model. Thus, the CNN model emerged as the practically best model when considering performance, time, and cost. Artificial neural network ; Bottleneck ; Artificial intelligence ; Nuclear magnetic resonance spectroscopy ; Computer science ; Convolutional neural network ; Chemical shift ; Pattern recognition (psychology) ; Biological system ; NMR spectra database ; Nuclear magnetic resonance ; Chemistry ; Spectral line ; Physics Molecular spectroscopy and chirality ; Metabolomics and Mass Spectrometry Studies ; Spectroscopy and Chemometric Analyses
Authors: Meysam Akbari | Liang Wang | Qigui Wang
Source: International Journal of Metalcasting (article, 2025-06-10)
Field: Materials Science — Materials Chemistry

Material Characterization of Aluminum Castings Using Machine Learning Techniques The emergence of machine learning (ML) techniques has significantly improved the accuracy and efficiency in materials characterization. This paper reviews the application of ML algorithms in microstructural analysis and defect detection processes of aluminum castings. By leveraging ML methods, multiple ML models were trained to automatically identify and classify different types of casting defects and microstructural features. Advanced image processing techniques, combined with convolutional neural networks (CNNs), enable the detection of casting defects such as shrinkage porosity, oxides and multiscale microstructure features (i.e., eutectic phases and secondary dendrite arm spacing of aluminum). This study highlights the advantages of the developed ML models in the accuracy and reduction of measurement time in the lab and reducing the reliance on manual analysis and subjective judgment. The findings emphasize the significant impact of ML techniques on metallurgical research and industrial applications, enhancing the reliability and performance of material analysis tools. Materials science ; Structural material ; Characterization (materials science) ; Aluminium ; Metallurgy ; Nanotechnology Machine Learning in Materials Science ; Aluminum Alloy Microstructure Properties ; Additive Manufacturing Materials and Processes
Authors: Rui-Jia Sun | Bijun Tang | Zheng Liu
Source: Materials Today Chemistry (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Explainable machine learning for 2D material layer group prediction with automated descriptor selection Artificial intelligence ; Selection (genetic algorithm) ; Layer (electronics) ; Group (periodic table) ; Computer science ; Machine learning ; Feature selection ; Materials science ; Composite material ; Chemistry Machine Learning in Materials Science ; Advanced X-ray and CT Imaging ; Computational Drug Discovery Methods
Authors: Evan R. Antoniuk | Shehtab Zaman | Tal Ben‐Nun | Peggy Li | James Diffenderfer | Sahin, Busra | Obadiah Smolenski | Tim Hsu | Anna M. Hiszpanski | Kenneth Chiu | Bhavya Kailkhura | Brian Van Essen
Source: ArXiv.org (preprint, 2025-05-03)
Field: Materials Science — Materials Chemistry

BOOM: Benchmarking Out-Of-distribution Molecular Property Predictions of Machine Learning Models Data-driven molecular discovery leverages artificial intelligence/machine learning (AI/ML) and generative modeling to filter and design novel molecules. Discovering novel molecules requires accurate out-of-distribution (OOD) predictions, but ML models struggle to generalize OOD. Currently, no systematic benchmarks exist for molecular OOD prediction tasks. We present $\mathbf{BOOM}$, $\mathbf{b}$enchmarks for $\mathbf{o}$ut-$\mathbf{o}$f-distribution $\mathbf{m}$olecular property predictions: a chemically-informed benchmark for OOD performance on common molecular property prediction tasks. We evaluate over 150 model-task combinations to benchmark deep learning models on OOD performance. Overall, we find that no existing model achieves strong generalization across all tasks: even the top-performing model exhibited an average OOD error 3x higher than in-distribution. Current chemical foundation models do not show strong OOD extrapolation, while models with high inductive bias can perform well on OOD tasks with simple, specific properties. We perform extensive ablation experiments, highlighting how data generation, pre-training, hyperparameter optimization, model architecture, and molecular representation impact OOD performance. Developing models with strong OOD generalization is a new frontier challenge in chemical ML. This open-source benchmark is available at https://github.com/FLASK-LLNL/BOOM Benchmark (surveying) ; Property (philosophy) ; Generalization ; Hyperparameter ; Benchmarking ; Representation (politics) ; Deep learning ; Data modeling Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Dexin Zhu | Mingshuo Nie | Hong‐Hui Wu | Chunlei Shang | Jiaming Zhu | Xiaoye Zhou | Yuan Zhu | Feiyang Wang | Binbin Wang | Shuize Wang | Junheng Gao | Haitao Zhao | Chaolei Zhang | Xinping Mao
Source: Journal of Materials Informatics (article, 2025-02-10)
Field: Materials Science — Materials Chemistry

An optimized strategy for density prediction of intermetallics across varied crystal structures via graph neural network Intermetallic compounds are crucial in modern industry due to their exceptional properties, where density is identified as a critical parameter determining their potentiality for lightweight applications. In this study, over 7,000 density data points are collected for binary intermetallic compounds from different crystal structures. A new intermetallics graph neural network (IGNN) model is developed to perform regression and classification tasks for density prediction. Compared to traditional machine learning models, the IGNN model demonstrated superior capability in capturing crystal structure and effectively addressing challenges posed by polymorphism. The interpretability of the IGNN model classification process is enhanced through the t-distributed stochastic neighbor embedding (t-SNE) visualization method. Additionally, the IGNN model exhibited excellent performance in predicting the density of multicomponent complex intermetallic compounds, indicating its robustness and generalizability. This study presents a graph neural network (GNN) method suitable for multi-crystal structure data modeling, providing a novel computational framework for density prediction in intermetallic compounds. This advancement represents a significant contribution to this field, paving the way for more targeted material selection and application in lightweight technologies. Artificial neural network ; Intermetallic ; Crystal structure prediction ; Graph ; Computer science ; Materials science ; Artificial intelligence ; Pattern recognition (psychology) ; Crystal structure ; Theoretical computer science ; Crystallography ; Chemistry ; Metallurgy Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Intermetallics and Advanced Alloy Properties
Authors: Gufeng Yu | Xi Wang | Yugong Luo | Guanlin Li | Rui Ding | Runhan Shi | Xiaohong Huo | Yang Yang
Source: Journal of Chemical Information and Modeling (article, 2025-01-02)
Field: Materials Science — Materials Chemistry

Machine Learning for Reaction Performance Prediction in Allylic Substitution Enhanced by Automatic Extraction of a Substrate-Aware Descriptor Despite remarkable advancements in the organic synthesis field facilitated by the use of machine learning (ML) techniques, the prediction of reaction outcomes, including yield estimation, catalyst optimization, and mechanism identification, continues to pose a significant challenge. This challenge arises primarily from the lack of appropriate descriptors capable of retaining crucial molecular information for accurate prediction while also ensuring computational efficiency. This study presents a successful application of ML for predicting the performance of Ir-catalyzed allylic substitution reactions. We introduce SubA, an innovative substrate-aware descriptor that is inspired by the fact that specific atoms or motifs in reactants drive the reaction outcomes. By employing graph matching algorithms for molecular backbone identification and incorporating atomic and molecular properties derived from density functional theory calculations, SubA extracts essential information at both the atomic level and the molecular level. Compared to four mainstream descriptors, SubA achieves reduced dimensionality and enhanced prediction accuracy with over 2% mean absolute error reduction in both random and scaffold splitting evaluations. It also demonstrates better generalization when confronted with previously unreported substrate combinations in extended experiments. Furthermore, an interpretable analysis of SubA shows that the predictor focuses on key molecular and atomic features, offering insights into reaction mechanisms. Computer science ; Artificial intelligence ; Generalization ; Dimensionality reduction ; Substrate (aquarium) ; Machine learning ; Chemistry ; Biological system ; Mathematics Machine Learning in Materials Science ; Asymmetric Hydrogenation and Catalysis ; Computational Drug Discovery Methods
Authors: Kyohei Kawashima | Takumi Sato | Kei-ichi Okazaki | Kang Kim | Nobuyuki Matubayasi | Toshifumi Mori
Source: APL Machine Learning (article, 2025-03-01)
Field: Materials Science — Materials Chemistry

Investigating the hyperparameter space of deep neural network models for reaction coordinates Identifying reaction coordinates (RCs) is a key to understanding the mechanism of reactions in complex systems. Deep neural network (DNN) and machine learning approaches have become a powerful tool to find the RC. On the other hand, the hyperparameters that determine the DNN model structure can be highly flexible and are often selected intuitively and in a non-trivial and tedious manner. Furthermore, how the hyperparameter choice affects the RC quality remains obscure. Here, we explore the hyperparameter space by developing the hyperparameter tuning approach for the DNN model for RC and investigate how the parameter set affects the RC quality. The DNN model is built to predict the committor along the RC from various collective variables by minimizing the cross-entropy function; the hyperparameters are automatically determined using the Bayesian optimization method. The approach is applied to study the isomerization of alanine dipeptide in vacuum and in water, and the features that characterize the RC are extracted using the explainable AI (XAI) tools. The results show that the DNN models with diverse structures can describe the RC with similar accuracy, and furthermore, the features analyzed by XAI are highly similar. This indicates that the hyperparameter space is multimodal. The electrostatic potential from the solvent to the hydrogen H18 plays an important role in the RC in water. The current study shows that the structure of the DNN models can be rather flexible, while the suitably optimized models share the same features; therefore, a common mechanism from the RC can be extracted. Hyperparameter ; Artificial neural network ; Space (punctuation) ; Artificial intelligence ; Computer science ; Deep neural networks ; Machine learning Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Data Processing Techniques