jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Da Long | Zhihao Li | Xin Xu | Chengchun Liu | Hui Zhang | Xiaoyu Cao | Liulin Yang | Xinchang Wang | Fanyang Mo
Source: Scientific Data (article, 2025-10-14)
Field: Chemistry — Spectroscopy

Computed ECD spectral data for over 10,000 chiral organic small molecules Determining the absolute configuration of chiral molecules is of fundamental importance in the fields of natural products chemistry, asymmetric catalysis, and pharmaceutical development. A widely adopted approach involves the comparison of experimental and theoretical electronic circular dichroism (ECD) spectra, which has proven to be a reliable method for absolute configuration assignment. However, the generation of theoretical ECD spectra via time-dependent density functional theory (TD-DFT) remains the rate-limiting step in this workflow, making its acceleration both essential and challenging. Although recent advances in deep learning offer promising strategies for establishing structure-spectrum relationships and expediting theoretical spectrum prediction, the lack of standardized and comprehensive ECD spectral datasets continues to hinder progress. This study presents the Chiral Molecular Circular Dichroism Spectral (CMCDS) dataset, a systematically structural benchmark dataset that addresses the fragmentation of existing ECD data. Characterized by high standardization, scalability, and broad molecular diversity, CMCDS facilitates deep learning applications in ECD analysis and fosters data-driven discovery of chiral molecules. Expediting ; Circular dichroism ; Molecule ; Fragmentation (computing) ; Small molecule ; Absolute configuration ; Density functional theory Molecular spectroscopy and chirality ; Crystallography and molecular interactions ; Spectroscopy and Quantum Chemical Studies
Authors: Ignacio Migliaro | Merle Yahr Weiss | Alistair J. Sterling
Source: ChemRxiv (article, 2025-09-30)
Field: Materials Science — Materials Chemistry

ChemRefine: An open-source automated and interoperable platform for machine learning and quantum chemistry simulations Workflow ; Interface (matter) ; Modular design ; Interoperability ; Computational model ; Computational complexity theory ; Feature (linguistics) ; Cheminformatics Machine Learning in Materials Science
Authors: Nishamol Kuriakose
Source:  (preprint, 2025-04-22)
Field: Chemistry — Inorganic Chemistry

Feature engineering and machine learning-based prediction of hydrogen adsorption for MOF materials Feature (linguistics) ; Adsorption ; Feature engineering ; Artificial intelligence ; Machine learning ; Hydrogen ; Computer science ; Materials science ; Pattern recognition (psychology) ; Chemistry ; Physical chemistry ; Deep learning ; Organic chemistry ; Philosophy Metal-Organic Frameworks: Synthesis and Applications
Authors: Hexiang Qi (14850668) | Yangqiu Liu (8928269) | Xiaofan Shi (18188931) | Zhewei Li (8928272) | Ming Lei (161082)
Source: Figshare (article, 2025-07-19)
Field: Materials Science — Materials Chemistry

Layered MolecularEditor for Automatic Constructionof Chemical Structure Data Sets and Its Application Herein, the layered molecular editor (LME) method is developed to automatically construct chemical structures by modifying various moieties of molecules in chemical reactions, such as substituent groups. With an open application programming interface (API), LME could be widely applied in the screening of chemical reactions and the modification effect of substituent groups, integrating with other computational chemistry programs. By means of LME, data sets of Diels–Alder (DA) and Claisen rearrangement reactions with different substituent groups have been created from templates of substrates, and the relationships between their chemical activities and corresponding chemical structures are investigated by automatic geometric structure optimization and transition state searching using GFN2-xTB, iEIP, and machine learning methods. This shows the scaling ability and reliability of the developed LME method. Substituent ; Scaling ; Template ; Molecule ; Chemical structure ; Context (archaeology) ; State (computer science) ; Interface (matter) Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Andrés Felipe Usuga | C. S. Praveen | Aleix Comas‐Vives
Source: Research Square (preprint, 2025-09-23)
Field: Materials Science — Materials Chemistry

Explainable GNN Framework Guided by Local Chemical Features to Predict Binding Energies in Bimetallic Alloys Bimetallic strip ; Computation ; Artificial neural network ; Denticity ; Catalysis ; Limiting ; Binding energy ; Cluster analysis Machine Learning in Materials Science ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Bing Yan | Shijia Wang | Gui Xiao | Xinpeng Xing | Qishun Guo | Chengang Cao | Jian Li | Tao Jiang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Organic Chemistry

Qsar-Driven Digital Catalyst Models: High-Throughput Design of Polyolefin Elastomers Polyolefin ; Elastomer ; Catalysis ; Throughput ; Materials science ; Polymer science ; Computer science ; Process engineering ; Organic chemistry ; Chemistry ; Nanotechnology ; Composite material ; Engineering Organometallic Complex Synthesis and Catalysis ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Syahgindo, Muhammad Riva'i | Putera, Zefrika Gauraf | Rahmad Arifin | Maulana, Aldi
Source: OSF Preprints (OSF Preprints) (article, 2025-01-01)
Field: Chemistry — Inorganic Chemistry

INVESTIGATION OF FORMULAS AND SURFACE MODELLING OF MOF-5 USING COMPUTATIONAL CHEMISTRY AND MACHINE LEARNING Metal–Organic Frameworks (MOFs) are porous crystalline materials with exceptionally high surface areas, offering promising applications in gas storage, separation, and catalysis. Among them, MOF-5, with the chemical formula Zn₄O(BDC)₃ (BDC = benzene-1,4-dicarboxylate), has been extensively studied as a model system. The aim of this study is to investigate the structural and energetic properties of MOF-5, focusing on bond parameters, thermodynamic stability, and its potential as a functional material.The methodology combines theoretical calculations and quantum-chemical approaches. Molecular parameters were determined using fundamental relations such as Gibbs free energy (ΔG = ΔH – TΔS), vibrational energy (E = hν), and rotational constants (B = h / 8π²cI). Structural visualization was carried out in both 2D and 3D representations to demonstrate the connectivity between Zn₄O clusters and BDC ligands, forming a cubic porous lattice. Computational analysis provided key insights into bond lengths, bond dissociation energies, and vibrational frequencies associated with Zn–O and C=O interactions.The results indicate that MOF-5 possesses a stable crystalline framework, with an average Zn–O bond length of approximately 197 pm, bond energies on the order of several hundred kJ/mol, and characteristic vibrational modes confirming strong coordination. These features highlight MOF-5 as a potential candidate for hydrogen storage, gas adsorption, and catalytic applications, while also serving as a reference system for theoretical modeling in materials chemistry. Keyword: Metal-Organic Frameworks (MOFs), MOF-5 (Zn₄O(BDC)₃), Quantum chemical analysis, Gas storage and adsorption, Thermodynamic stability Bond-dissociation energy ; Dissociation (chemistry) ; Hydrogen bond ; Chemical bond ; Gibbs free energy ; Potential energy surface ; Surface (topology) ; Bond order ; Molecule ; Potential energy Metal-Organic Frameworks: Synthesis and Applications ; Energetic Materials and Combustion ; X-ray Diffraction in Crystallography
Authors: Zhiyuan Chen | Jinyu Song | Lai Hu | Peng Xu | Zheng-Wei Sun | Xiao‐Chun Hang | Zhu Hongjun | Senqiang Zhu | Rui Liu
Source: Chinese Chemical Letters (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

Structure-aware machine learning for predicting photophysical properties of MR-TADF materials Photoluminescence ; Fluorescence ; Quantum yield ; Random forest ; Key (lock) ; Diode ; Ensemble learning ; Yield (engineering) Machine Learning in Materials Science ; Conducting polymers and applications ; Advanced Memory and Neural Computing
Authors: Raúl Pérez‐Soto | Mihai V. Popescu | Sabari Kumar | Leticia Adao Gomes | Changyeob Lee | Elijah Shore | Steven R. López | Robert S. Paton | Seonah Kim
Source: ChemRxiv (preprint, 2025-07-30)
Field: Materials Science — Materials Chemistry

A Fragment Based Approach Towards Curating, Comparing and Developing Machine Learning Models Applied in Photochemistry The development of Graph Neural Networks for the task of predicting molecular properties has gained a great deal of attention as it typically allows the correlation of quick to compute atomic and bond descriptors with overall molecular properties. With the raising interest in photochemistry and photocatalysis as sustainable alternatives to thermal reactions, curation of virtual databases of computed photophysical properties for training of machine learning models has become popular. Unfortunately, current efforts fail to consider the exciton localization onto different chromophores of the same molecule, leading to potentially large prediction errors. Here we describe a molecular fragmentation strategy that can be used to overcome this limitation, while also providing a way to compare structural diversity between different libraries. Using a newly generated a database of 46,432 adiabatic S0-T1 energy gaps (ALFAST-DB), we compare its diversity against two datasets from the literature and demonstrate that a fragment-based delta learning approach improves model generalizability while achieving accuracies matching those of traditional message passing graph neural network architectures (MPGNN) Fragment (logic) ; Artificial intelligence ; Computer science ; Algorithm Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Shilpi Chaudhary
Source: International Journal of Renewable Energy and its Commercialization (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Review on Nanotechnology for Catalysis and Solar Energy Conversion This roadmap on Nanotechnology for Catalysis and Solar Energy Conversion focuses on the application of nanotechnology in addressing the current challenges of energy conversion: 'high efficiency, stability, safety, and the potential for low-cost/scalable manufacturing.Solar-to-fuel conversion involves using nanotechnology to improve the efficiency and effectiveness of converting solar energy into chemical fuels.Solar Water Splitting Nanotechnology is applied to enhance the process of splitting water into hydrogen and oxygen using solar energy.Solar Photovoltaics The roadmap covers various types of solar cells, including dye-sensitized solar cells (DSSCs), perovskite solar cells, and organic photovoltaics, with a focus on improving their efficiency and cost-effectiveness.Bio-Catalysis Nanotechnology is explored for enhancing enzymatic reactions for bio-catalysis, which can be valuable for renewable energy applications.The smart engineering of materials and electrodes at the nanoscale is expected to improve the efficiency of solar-to-fuel conversion.Semiconductor Nanoparticles are discussed to enhance solar energy conversion, particularly in the context of DSSCs.Rapid advancements in perovskite solar cells are mentioned, including new ideas for 2D and 3D hybrid halide perovskites.Multiple Exciton Generation (MEG) from hot carriers, is introduced as a potential way to significantly boost photovoltaic efficiency by exploiting the quantization effects in semiconductor nanostructures like quantum dots, wires, or wells.Nanoscale Characterization Methods recognizes the importance of improving methods for characterizing nanoscale materials.Terahertz spectroscopy is highlighted as an example of a technique that can overcome the challenges associated with nanoscale materials characterization.Computational Science is the Computational frameworks and machine learning methods are emphasized as tools to predict structure-property relationships in materials and devices.An emphasis is placed on organic photovoltaics in this context.The "Electrochemical Leaf" is a concept is introduced as an innovation in electrochemistry and beyond, potentially contributing to advancements in various areas of science and technology.Biohybrid Approaches mentions that efficient and specific enzyme catalysts can be combined with nanomaterials for biohybrid approaches, offering unique possibilities for renewable energy applications.The goal of this research is to advance nanotechnology in catalysis and solar energy conversion to provide significant benefits to society, including more efficient and cost-effective renewable energy solutions. Solar energy conversion ; Solar energy ; Energy transformation ; Catalysis ; Applications of nanotechnology ; Photovoltaics Chemical and Physical Properties of Materials ; Electrocatalysts for Energy Conversion ; Electrophoretic Deposition in Materials Science
Authors: 颯 前田 | Hayato Maeda | Stephen Wu | 莉花 丸井 | Rika Marui | 絵里菜 吉田 | Erina Yoshida | 歓 畠山 | Kan Hatakeyama | 裕太 難波江 | Yuta Nabae | 栞 中川 | Shiori Nakagawa | 劉芽久哉 | Meguya Ryu | 亮平 石毛 | Ryohei Ishige | Yoh Noguchi | 慶浩 林 | Yoshihiro Hayashi | Masashi Ishii | Isao Kuwajima | Felix Jiang | Xuan Thang Vu | Sven Ingebrandt | 雅利 戸木田 | Masatoshi TOKITA | 淳子 森川 | Junko Morikawa | Ryo Yoshida | 晃鏡 早川 | Teruaki Hayakawa
Source: Institutional Repositories DataBase (IRDB) (article, 2025-07-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Discovery of liquid crystalline polymers with high thermal conductivity using machine learning Thermal conductivity ; Polymer ; Liquid crystalline ; Conductivity ; Thermal Liquid Crystal Research Advancements ; Advanced Sensor and Energy Harvesting Materials ; Machine Learning in Materials Science
Authors: Venkat Srinivasan
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

<i>(Invited)</i> Guiding Materials Development Utilizing Continuum-Scale Mathematical Models Continuum scale mathematical models have become an integral part of battery development, aiding in, for example, optimizing cell designs utilizing the so-called “Newman model”. Such tools reduce the number of prototypes that need to be built and accelerate development. Such models have also become an important part of guiding material development. Examples include optimizing primary and secondary particle sizes in chemistries such as the Ni-rich-Mn-rich cathodes,[1] understand impact of carbon coatings and their interplay with particle size in iron phosphate cathodes,[2] and understanding the active material-binder chemo-mechanics in silicon anodes[3] etc. In recent years, we have been advancing the use of continuum models to examine morphological changes in Li-metal anodes. Models have been developed that examine the effect of voids in the metal[4], and the chemo-mechanics at the Li|electrolyte interface.[5,6] Changing transport and mechanical properties of the electrolyte demonstrate significant variations on the nature of the morphology changes and the mechanism by which dendrites form. The model insights have been tremendously valuable in predicting material properties under which morphology changes can be minimized, providing guidance to experimentalists. In this presentation, we will summarize the learnings from the various modeling approached and the insights that have been gained. Further, the growing importance of high-performance computing to ensure that models get closer to real-world experimentation will be discussed. Finally the increasing importance of machine learning tools will be discussed. References: 1. Tewari, Deepti, et al. "Designing Particle Morphologies for Materials with Solid Transport Limitations: A Case Study of Lithium and Manganese Rich Cathode Oxides." Chemistry of Materials 36.21 (2024): 10922-10935 2. Striebel, Kathryn, et al. "Comparison of LiFePO4 from different sources." Journal of the Electrochemical Society 152.4 (2005): A664. 3. Higa, Kenneth, and Venkat Srinivasan. "Stress and strain in silicon electrode models." Journal of the Electrochemical Society 162.6 (2015): A1111. 4. Barai, Pallab, et al. "Study of void formation at the lithium| solid electrolyte interface." Chemistry of Materials 36.5 (2024): 2245-2258. 5. Barai, Pallab, Kenneth Higa, and Venkat Srinivasan. "Lithium dendrite growth mechanisms in polymer electrolytes and prevention strategies." Physical Chemistry Chemical Physics 19.31 (2017): 20493-20505. 6. Barai, Pallab, et al. "Mechanical stress induced current focusing and fracture in grain boundaries." Journal of The Electrochemical Society 166.10 (2019): A1752. Mathematical model ; Battery (electricity) ; Development (topology) ; Scale (ratio) ; Computational model ; Particle (ecology) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Block Copolymer Self-Assembly
Authors: Xiaodong Hong | Yudong Shen | Zuwei Liao | Yongrong Yang
Source: Chinese Journal of Chemical Engineering (article, 2025-07-25)
Field: Materials Science — Materials Chemistry

Machine-learning-assisted high-throughput computational screening of the n-hexane cracking initiator Throughput ; Hexane ; Cracking ; High-throughput screening ; Chemistry ; Computer science ; Organic chemistry ; Operating system Machine Learning in Materials Science ; Petroleum Processing and Analysis ; Heat transfer and supercritical fluids
Authors: Yingqi Zhao (4862782) | Kuo Zhan (21142084) | Pei-Lin Xin (12355366) | Zuyan Chen (21142087) | Shuai Li (65944) | Francesco De Angelis (8459256) | Jian-An Huang (1284306)
Source: Figshare (article, 2025-04-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Single-MoleculeSERS Discrimination of Proline fromHydroxyproline Assisted by a Deep Learning Model Discriminating low-abundance hydroxylation is a crucial and unmet need for early disease diagnostics and therapeutic development due to the small hydroxyl group with 17.01 Da. While single-molecule surface-enhanced Raman spectroscopy (SERS) sensors can detect hydroxylation, subsequent data analysis suffers from signal fluctuations and strong interference from citrates. Here, we used our plasmonic particle-in-pore sensor, occurrence frequency histogram of the single-molecule SERS spectra, and a one-dimensional convolutional neural network (1D-CNN) model to achieve single-molecule discrimination of hydroxylation. The histogram extracted spectral features of the whole data set to overcome the signal fluctuations and helped the citrate-replaced particle-in-pore sensor to generate clean signals of the hydroxylation for model training. As a result, the discrimination of single-molecule SERS signals of proline and hydroxyproline was successful by the 1D-CNN model with 96.6% accuracy for the first time. The histogram further validated that the features extracted by the 1D-CNN model corresponded to hydroxylation-induced spectral changes. Convolutional neural network ; Hydroxyproline ; Pattern recognition (psychology) ; Histogram ; Deep learning ; Interference (communication) ; Proline ; SIGNAL (programming language) Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Liudmila A. Bereznikova | I. E. Trofimov | Wenqi Jin | Qi Song | Aleksey V. Arsenin | Congwei Xie | Ivan A. Kruglov | Valentyn S. Volkov
Source: Advanced Functional Materials (article, 2025-11-20)
Field: Materials Science — Materials Chemistry

Symmetry‐Aware Equivariant Network for Discovering SHG‐Active Materials Abstract The discovery of novel materials with exceptional nonlinear optical (NLO) properties, especially high second‐harmonic generation (SHG) coefficients, is vital for advancing optical communication technologies and solid‐state laser systems. Computational and machine learning methods open up enormous opportunities for accelerating the identification and characterization of new SHG‐active materials. In this study, OptiXNet, a physics‐informed deep learning model leveraging symmetry‐aware equivariant graph neural networks is introduced, specifically designed to achieve unprecedented accuracy in predicting SHG tensors. Trained and validated on the NOEMD database, OptiXNet significantly outperforms existing state‐of‐the‐art methods in both prediction accuracy and generalization to unexplored chemical compositions. To ensure robust predictions when exploring unknown chemical spaces, an automated clustering algorithm is integrated that quantifies model confidence and flags out‐of‐distribution data points. Using this clustering‐guided approach, MgP 2 O 5 F 2 —a previously unexplored material is successfully identified with promising SHG properties—within the model's high‐confidence predictive domain. Subsequent confirmatory density functional theory (DFT) calculations validated the accuracy and effectiveness of the approach. Combining symmetry‐aware deep learning, rigorous uncertainty quantification, and reliable computational validation, the framework provides a powerful and efficient strategy for the accelerated discovery of next‐generation NLO materials. Generalization ; Artificial neural network ; Deep learning ; Cluster analysis ; Deep neural networks ; Nonlinear system ; Identification (biology) ; Training set ; Leverage (statistics) Machine Learning in Materials Science ; Crystal Structures and Properties ; Advanced Chemical Physics Studies
Authors: Tim Kircher (21246699) | Martin Votsmeier (5147435)
Source: Figshare (article, 2025-05-05)
Field: Materials Science — Materials Chemistry

Machine LearningSurrogate Models for MechanisticKinetics: Embedding Atom Balance <i>and</i> Positivity Multiscale simulations of reactive flows are critical in many fields. However, their application is often hindered by the high computational cost of solving detailed chemical kinetics. Recent advances in surrogate models for reactive chemistry offer promising speedups, but ensuring physical consistency remains challenging. In particular, machine learning models for chemical kinetics must enforce atom balance and guarantee the positivity of predicted concentrations. Here, we introduce a positivity preserving projection and a correction by linear interpolation backtracking which simultaneously guarantee both constraints. We demonstrate this using two practical examples from atmospheric chemistry and heterogeneous catalysis, as well as for a large number of random, synthetically generated reaction systems. In all cases, our approach yields exclusively positive model predictions conforming to the atom balance, without reducing the overall accuracy of the model. Surrogate model ; Embedding ; Interpolation (computer graphics) ; Projection (relational algebra) ; Atom (system on chip) ; Consistency (knowledge bases) ; Uncertainty quantification Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Quantum many-body systems