jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zhen Liu | Jessica L. Vinskus | Yue Fu | Peng Liu | Kevin J. T. Noonan | Olexandr Isayev
Source: JACS Au (article, 2025-10-13)
Field: Materials Science — Materials Chemistry

Fast and Accurate Ring Strain Energy Predictions with Machine Learning and Application in Strain-Promoted Reactions Ring strain energy (RSE) is crucial for understanding molecular reactivity, with broad implications in polymerization, click chemistry, drug discovery and beyond. However, quantitatively determining RSE through experiments or quantum mechanics (QM) is resource-intensive, limiting its application on a large scale. We present a machine learning (ML)-based workflow that enables the reliable and efficient prediction of RSE, entirely bypassing traditional QM calculations. Our workflow employs AIMNet2 machine learning interatomic potentials and Auto3D for the identification of low-energy conformers and RSE computation. Remarkably, it achieves an <i>R</i> <sup>2</sup> of 0.997 and a mean absolute error (MAE) of 0.896 kcal/mol when benchmarked against the ωB97M-D4/Def2-TZVPP method, while running orders of magnitude faster than DFT calculations. To demonstrate the utility of our workflow, we successfully differentiated reactive from nonreactive molecules in copper-free click chemistry, [3 + 2] cycloaddition reaction and ring-opening metathesis polymerization, underscoring its transferability to diverse molecular systems. Additionally, we compiled the RSE Atlas, a computational database encompassing 16,905 single-ring molecules, offering a valuable resource for investigating factors influencing RSE. Our approach transforms RSE into a readily computable property, facilitating its integration into reaction designs. Workflow ; Identification (biology) ; Energy (signal processing) ; Cycloaddition ; Ring (chemistry) ; Probably approximately correct learning ; Limiting ; Potential energy surface ; Fragment (logic) Machine Learning in Materials Science ; Chemical Synthesis and Analysis ; Computational Drug Discovery Methods
Authors: Takeo Sudo | Satoki Ishiai | Yuuki Ishiwatari | T. Yokoyama | Kenji Yasuoka | Noriyoshi Arai
Source: Next research. (article, 2025-01-18)
Field: Chemistry — Organic Chemistry

Machine learning-enabled exploration of mesoscale architectures in amphiphilic-molecule self-assembly The physical and chemical properties of functional materials exhibit significant diversity, attributable to both the chemical structures of the constituent molecules and their self-assembled configurations. Consequently, designing molecules to achieve specific functions remains a pivotal focus in engineering and materials science. Recent advances in machine learning and molecular simulation have demonstrated effectiveness in materials informatics. However, a direct method for precisely correlating the functionality of materials with their chemical structures has not yet been established. Functional materials are complex because they contain distinct mesoscale molecular assemblies that bridge the molecular-scale chemical structure and the macroscale material properties. Therefore, multiscale predictive models are essential. Furthermore, these assemblies undergo significant alterations in response to thermodynamic conditions and compositional ratios, making it challenging to predict material properties from molecular structures. This study focuses on molecular aggregates, employing molecular simulation and machine learning to predict aggregate structures quantitatively. Prior research has classified regular crystal structures, such as ice and metals. However, more comprehensive studies are needed to predict disordered soft-matter structures using limited information. We will use graph neural networks to predict self-assembled structures from local structural data. This methodology applies to typical self-assembled structures within theoretical frameworks, such as the critical packing parameter that includes micelles, vesicles, and bilayers. Furthermore, extending this model allows us to quantitatively treat complex higher-order structures common in multicomponent systems like medical products and cosmetics. The extended model will undoubtedly advance materials science. Mesoscale meteorology ; Amphiphile ; Amphiphilic molecule ; Nanotechnology ; Self-assembly ; Molecule ; Materials science ; Chemistry ; Geography ; Polymer ; Copolymer ; Organic chemistry ; Meteorology Advanced Polymer Synthesis and Characterization ; Supramolecular Self-Assembly in Materials ; Block Copolymer Self-Assembly
Authors: José Ferraz-Caetano | Filipe Teixeira | M. Natália D. S. Cordeiro
Source: Journal of Chemical Information and Modeling (article, 2025-06-24)
Field: Materials Science — Materials Chemistry

Optimizing Vanadium-Catalyzed Epoxidation Reactions: Machine-Learning-Driven Yield Predictions and Data Augmentation Catalytic epoxidations are key chemical processes serving as essential steps in the synthesis of commercially valuable compounds. This study presents an innovative supervised machine learning (ML) model to predict the reaction yield of the vanadium-catalyzed epoxidation of small alcohols and alkenes. Our framework uncovers relevant chemical characteristics for structure design, offering a pathway for automated optimization of epoxidation reactions. The study also incorporates the concept of data augmentation, handling experimental variability by generating synthetic reactions to densify under-represented data segments. Trained on a curated data set of 273 experimental epoxidation reactions with vanadyl catalyst groups, the model achieved a predictive <i>R</i><sup>2</sup> test score of 90%, with a mean absolute yield prediction error of 4.7%. The ML model offers a high degree of explainability, as descriptor analysis identified key experimental and chemical descriptors that influence catalytic reaction predictions. This represents a significant development in catalytic epoxidation studies, highlighting the critical role of data science in reaction research and catalyst optimization. Catalysis ; Yield (engineering) ; Vanadium ; Experimental data ; Computer science ; Chemical reaction ; Chemistry ; Biochemical engineering ; Biological system ; Reaction conditions ; Machine learning ; Materials science ; Organic chemistry ; Mathematics ; Engineering Machine Learning in Materials Science ; Polyoxometalates: Synthesis and Applications ; Catalysis and Oxidation Reactions
Authors: Nicholas Casetti | Dylan M. Anstine | Olexandr Isayev | Connor W. Coley
Source: Journal of Chemical Theory and Computation (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Anticipating the Selectivity of Intramolecular Cyclization Reaction Pathways with Neural Network Potentials Reaction mechanism search tools have demonstrated the ability to provide insights into likely products and rate-limiting steps of reacting systems. However, reactions involving several concerted bond changes─as can be found in many key steps of natural product syntheses─can complicate the search process. To mitigate these complications, we present a mechanism search strategy particularly suited to help expedite exploration of an exemplary family of such complex reactions, cyclizations. We provide a cost-effective strategy for identifying relevant elementary reaction steps by combining graph-based enumeration schemes and machine learning techniques for intermediate filtering. Key to this approach is our use of a neural network potential (NNP), AIMNet2-rxn, for computational evaluation of each candidate reaction pathway. In this article, we evaluate the NNP's ability to estimate activation energies, demonstrate the correct anticipation of stereoselectivity, and recapitulate complex enabling steps in natural product synthesis. Artificial neural network ; Key (lock) ; Anticipation (artificial intelligence) ; Mechanism (biology) ; Natural product ; Product (mathematics) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Microbial Natural Products and Biosynthesis
Authors: Chong Yao | Qianjun Zhang | Hao Lu | Xinhui Zhang | Yuanjing Fan | Jie Luo | Rubo Fang | Hao Liu | Jinghui Lyu | Feng Feng | Lili Lin | Chunshan Lu | Ying Zheng | Jianguo Wang | Qingtao Wang | Qunfeng Zhang | Xiaonian Li
Source: Journal of the American Chemical Society (article, 2025-12-20)
Field: Materials Science — Materials Chemistry

Deep Learning Guided Exploration of Transition Metal Oxide Catalysts in Acetylene Selective Hydrogenation The development of empirical materials, hindered by the complex interplay between material properties and reaction mechanisms, typically necessitates an extensive trial-and-error process to identify optimal catalysts. In our study, we integrate density functional theory (DFT) predictions to generate mappings of electronic and molecular adsorption properties, which are then employed to identify novel materials using deep learning algorithms. These newly identified materials were synthesized and assessed for their catalytic performance in the selective hydrogenation of acetylene. Notably, CuTiO<sub>3</sub> catalysts demonstrated exceptional performance, achieving acetylene conversion exceeding 99% and ethylene selectivity greater than 99% at a relatively low temperature of 75 °C. Additionally, CuO-doped TiO<sub>2</sub> was observed to form strong acetylene adsorption sites and weaker ethylene adsorption sites (Cu-O-Ti). The p-π hybridized coupling between the oxygen p-orbitals and the π-electrons of acetylene in the Cu-O-Ti structure was found to play a critical role in facilitating the conversion of acetylene to ethylene. Acetylene ; Catalysis ; Adsorption ; Selectivity ; Ethylene ; Transition metal ; Oxide ; Ethylene oxide Catalytic Processes in Materials Science ; Synthesis and Characterization of Pyrroles ; Catalysis for Biomass Conversion
Authors: Yanhao Deng | Li Yan | Gopalakrishnan Sai Gautam | Bonan Zhu | Zeyu Deng
Source: Journal of Materials Chemistry A (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accelerating the discovery of disordered multi-component solid-state electrolytes using machine learning interatomic potentials Machine learning interatomic potentials, fine-tuned for complex solid-state electrolytes, enable accurate modeling and discovery of novel compositions with enhanced ion transport for next-generation batteries. Component (thermodynamics) ; Electrolyte ; Materials science ; Solid-state ; State (computer science) ; Interatomic potential ; Computer science ; Chemical physics ; Statistical physics ; Chemistry ; Physics ; Engineering physics ; Molecular dynamics ; Thermodynamics ; Computational chemistry ; Physical chemistry ; Electrode ; Algorithm Machine Learning in Materials Science ; Solid-state spectroscopy and crystallography ; Advanced Thermoelectric Materials and Devices
Authors: Shulong Li | Hongyuan Zhou | Zuhui Zhou | Li‐Yong Gan | Fanggong Cai | Yong Zhao | Jianping Long | Liang Qiao
Source: Small Methods (article, 2025-08-12)
Field: Materials Science — Materials Chemistry

Synergizing Machine Learning with High‐Throughput DFT to Design Efficient Single‐Atom Catalysts for Hydrogen Evolution Reaction The development of efficient single-atom catalysts (SACs) for electrocatalytic hydrogen evolution (HER) has garnered significant attention within the scientific community. However, the extensive scope of material experimentation, coupled with high research and development costs and prolonged research cycles, severely hampers the efficient advancement of related materials. In this study, the HER activity of 90 types of SACs is systematically investigated, which consist of single transition metal (TM) and/or nonmetal (NM) atoms bonded in graphyne (TM-NM-GY), by synergizing machine learning algorithms with high-throughput DFT computations. The findings reveal that the HER catalytic activity of Fe-GY, Fe-B-GY, Ni-B-GY, Pd-B-GY, Sc-N-GY, Co-N-GY, Y-N-GY, and Pd-N-GY surpasses that of commercial Pt/C catalysts. Moreover, non-metallic B or N atom doping can effectively modulate the HER performance of SACs. Furthermore, it is confirmed that HER activity correlates with characteristic factors such as the bond length of the coordinating atoms, d-band center, metal binding height, charge transfer, and ICOHP. Finally, machine learning stacking models have proven efficient in predicting and designing superior HER SACs. It is anticipated that these insights will accelerate the prediction and design of corresponding SACs. Throughput ; Catalysis ; Atom (system on chip) ; Hydrogen atom ; Hydrogen ; Computer science ; Nanotechnology ; Materials science ; Chemistry ; Parallel computing ; Organic chemistry ; Operating system Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Catalysis and Hydrodesulfurization Studies
Authors: Shiqiang Hao | Prashant Singh | A. V. Smirnov | D. D. Johnson | David E. Alman | Michael C. Gao
Source: Journal of Applied Physics (article, 2025-08-22)
Field: Materials Science — Materials Chemistry

Developing reliable machine learning interatomic potential for Fe–Cr–Ni austenitic alloys Gaining atomistic understanding of mechanical behavior of heat-resistant structural materials such as Fe–Cr–Ni-based alloys requires an approach with an accuracy close to density functional theory (DFT) that considers the intrinsic properties of the bulk lattice and important defects such as stacking faults, grain boundaries, and surfaces. This work aims to develop reliable machine learning interatomic potential (MLIAP) at cross-scale for Fe–Cr–Ni ternary alloys with a focus on the face-centered-cubic (fcc) solid solution structure. Leveraging the advantages of moment tensor potentials, which typically necessitate a relatively small training dataset and enable rapid calculations using the large-scale atomic/molecular massively parallel simulator package, we ensure the stability and accuracy of the trained potentials. Important defects such as stacking faults, grain boundaries, and surfaces for wide-range compositions are investigated. Structural, thermal, elastic, and defect properties are determined from molecular dynamics simulations comprising several thousand atoms, generated via canonical Monte Carlo simulations guided by the trained potential. The trained potential allows efficient atomic simulations of structural, thermal, and mechanical properties of fcc Fe–Cr–Ni solid solution alloys as a function of composition and temperature. Therefore, the MLIAP approach represents a major advancement from DFT calculations that are limited to small simulation sizes and traditional molecular dynamics simulations using relatively low accuracy potentials. Furthermore, this work outlines a practical foundation for further investigating the structural evolution and mechanical behavior of austenitic stainless steel and nickel-based alloys in a wide array of applications in extreme environments. Austenite ; Materials science ; Metallurgy ; Interatomic potential ; Chemistry ; Microstructure ; Molecular dynamics ; Computational chemistry Machine Learning in Materials Science ; Metal and Thin Film Mechanics ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Leonid Komissarov | Nenad Manevski | Katrin Groebke Zbinden | Lisa Sach-Peltason
Source: Journal of Chemical Information and Modeling (article, 2025-07-17)
Field: Materials Science — Materials Chemistry

Explainable Graph Neural Networks in Chemistry: Combining Attribution and Uncertainty Quantification Graph Neural Networks (GNNs) are powerful tools for predicting chemical properties, but their black-box nature can limit trust and utility. Explainability through feature attribution and awareness of prediction uncertainty are critical for practical applications, for example in iterative lab-in-the-loop scenarios. We systematically evaluate different posthoc feature attribution methods and study their integration with uncertainty quantification in GNNs for chemistry. Our findings reveal a strong synergy: attributing uncertainty to specific input features (atoms or substructures) provides a granular understanding of model confidence and highlights potential data gaps or model limitations. We evaluated several attribution approaches on aqueous solubility and molecular weight prediction tasks, demonstrating that methods like Feature Ablation and Shapley Value Sampling can effectively identify molecular substructures driving prediction and its uncertainty. This combined approach significantly enhances the interpretability and actionable insights derived from chemical GNNs, facilitating the design of more useful models in research and development. Interpretability ; Computer science ; Artificial intelligence ; Machine learning ; Attribution ; Uncertainty quantification ; Feature (linguistics) ; Graph ; Black box ; Data mining ; Theoretical computer science Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Explainable Artificial Intelligence (XAI)
Authors: Yu Jin | Lujie Jin | Yujin Ji | Youyong Li
Source: Materials Chemistry Frontiers (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Data-driven composition-only machine learning for high-performance solid-state electrolytes A composition-only machine learning model was trained on the experimental ionic conductivity database, and the model predicted lithium solid-state electrolytes exhibiting high ionic conductivity with ab initio molecular dynamics validation. Composition (language) ; Electrolyte ; Materials science ; Solid-state ; State (computer science) ; Chemical engineering ; Computer science ; Engineering physics ; Chemistry ; Engineering ; Physical chemistry ; Electrode Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Advancements in Battery Materials
Authors: Adithya Ranjith Kartha | Dhanush P. Ajayakumar | Muhammad Zaffwan Idris | Gopi Ragupathy
Source: ACS Omega (article, 2025-04-28)
Field: Materials Science — Materials Chemistry

Unlocking the Potential of Machine Learning in Enhancing Quantum Chemical Calculations for Infrared Spectral Prediction Infrared (IR) spectroscopy is a fundamental tool for analyzing molecular structures and chemical interactions by identifying the vibrational modes of molecules. Traditional quantum mechanical methods, such as density functional theory, are highly accurate but computationally expensive and impractical for large-scale molecular systems. This project investigates the integration of machine learning (ML) techniques to predict IR spectra, offering a promising alternative that significantly reduces computational costs while maintaining high accuracy. Additionally, the project explores the utilization of IR spectra for molecular identification and classification into molecular families, enhancing the practical utility of spectral data in various scientific applications. Using TensorFlow-based ML frameworks, models were developed and trained on a data set derived from high-quality computational chemistry analyzers. These data sets, sourced from computationally optimized geometry and IR spectrum from the Gaussian 16 Program Suite, include extensive molecular geometry data, bond lengths, vibrational modes, and other quantum mechanical properties. The models aim to predict key IR spectral features, such as vibrational frequencies and intensities, while maintaining interpretability by linking chemical and quantum mechanical principles to predictions. The integration of ML with IR spectroscopy provides a scalable as well as accelerated solution for analyzing complex molecular systems. This approach holds potential in fields such as drug discovery, materials science, and chemical engineering, where rapid and accurate spectral predictions are critical. This perspective highlights the advancements achieved, the current challenges, and the future potential of ML in the context of IR spectroscopy, providing a solid foundation for further exploration at the intersection of chemistry and data science. Infrared ; Quantum chemical ; Quantum ; Computer science ; Artificial intelligence ; Machine learning ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Metabolomics and Mass Spectrometry Studies
Authors: Afaf Murtadha Kadhum | Azal S. Waheeb | Shaimaa Hassan Mallah | Masar Ali Awad | Duha M. Hasan | Hussein Ali Kadhim Kyhoiesh | Hassan E. Abd Elsalam | Islam H. El Azab
Source: Materials Chemistry and Physics (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Integrating computational chemistry and machine learning for the stability prediction of transition metal ternary compounds Stability (learning theory) ; Ternary operation ; Chemical stability ; Molecular descriptor ; Crystal structure prediction ; Polar surface area ; Cluster analysis ; Robustness (evolution) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Yue Shi | Jiaxin Tian | Haoyuan Li
Source: Journal of Chemical Information and Modeling (article, 2025-06-05)
Field: Materials Science — Materials Chemistry

MlCOFSyn: A Machine Learning Framework To Facilitate the Synthesis of 2D Covalent Organic Frameworks Two-dimensional covalent organic frameworks (2D COFs) have been historically synthesized empirically, often resulting in uncontrolled crystallization and inferior crystal sizes, which limit their performance in various applications. Recently, crystallization models tailored for 2D COFs have been developed that demonstrate great potential in facilitating their rational synthesis. Nevertheless, effective strategies to leverage these models for 2D COF synthesis remain underdeveloped, and the specialized expertise required, combined with the high computational costs of exploring the vast chemical space, poses additional barriers to their practical application. In this work, we present a machine learning framework, named MlCOFSyn, that is designed to assist in the synthesis of 2D COFs. This framework explores the application of 2D COF crystallization models by implementing three pivotal functionalities: predicting crystal sizes based on the input monomer addition sequence, reverse-engineering monomer addition sequences to achieve desired crystal sizes, and optimizing monomer addition sequences to produce larger crystals. These functionalities are critical for the controlled synthesis of 2D COFs but have been largely underexplored due to the lack of accessible theoretical tools. The MlCOFSyn framework leverages efficient machine-learning algorithms and features an intuitive graphical interface, enabling its use on consumer-grade computers by nonexperts. By addressing these gaps, the MlCOFSyn framework represents a substantial advancement in facilitating 2D COF research and synthesis. Covalent bond ; Computer science ; Covalent organic framework ; Chemistry ; Artificial intelligence ; Nanotechnology ; Materials science ; Organic chemistry Covalent Organic Framework Applications ; Metal-Organic Frameworks: Synthesis and Applications ; Advanced Photocatalysis Techniques
Authors: Ke Ma | Shiqiang Feng | Haihui Hu | Yingying Cai | Dechao Chen | Lili Han
Source: Chinese Journal of Structural Chemistry (article, 2025-03-19)
Field: Chemistry — Inorganic Chemistry

Deep learning-based workflow for atomic image denoising and chemical identification Chemistry ; Workflow ; Identification (biology) ; Artificial intelligence ; Pattern recognition (psychology) ; Image denoising ; Image (mathematics) ; Computational biology ; Computer science ; Database Radioactive element chemistry and processing ; Advanced X-ray and CT Imaging ; Machine Learning in Materials Science