jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fatma Demirkaya Miloğlu | Burak Bayrak | Hanife Kucuk | İbrahim Yücel Özbek | Meltem Çetin
Source: Journal of Analytical Science & Technology (article, 2025-11-18)
Field: Chemistry — Analytical Chemistry

Bioinformatics and machine learning-based simultaneous spectrophotometric identification of Montelukast sodium and Desloratadine in a commercial film-coated tablet Abstract Desloratadine (DL) and montelukast sodium (MTS) are combined in a pharmaceutical preparation for the treatment of allergic rhinitis and asthma. DL is a competitive H1-receptor antagonist and helps the management of allergic reactions and relieves allergy symptoms, while MTS is a leukotriene receptor antagonist that inhibits the effects of inflammatory mediators. This study introduces a machine learning-assisted UV-Vis spectrophotometric method for the simultaneous quantification of DL and MTS in a commercial film-coated tablet, addressing limitations due to spectral overlap. Five machine learning regression models (Support Vector Regression, Ridge Regression, Lasso Regression, Elastic Net, and Linear Regression) were evaluated. Ridge Regression (λ = 0.1) was selected for its balance of accuracy, computational efficiency, and robustness. The method was applied using Aircomb ® film-coated tablets, containing 5 mg DL and 10.4 mg MTS, ensuring high precision. The developed method demonstrated high recovery rates (99.25% for DL and 101.0% for MTS with minimal relative standard deviation (≤ 1.59%). Sustainability assessments using Analytical GREEnness Metric (AGREE) and Complex Green Analytical Procedure Index (ComplexGAPI) confirmed its alignment with green analytical chemistry principles. Ridge Regression (λ = 0.1) provided accurate and reproducible results, making it suitable for routine pharmaceutical analysis. This study highlights the potential of machine learning-assisted UV-Vis spectrophotometry as a cost-effective and environmentally friendly alternative for pharmaceutical quality control. The method minimizes solvent consumption while ensuring analytical precision. Future research may explore non-linear models, such as artificial neural networks, to enhance predictive performance and broaden its applicability. Support vector machine ; Lasso (programming language) ; Desloratadine ; Linear regression ; Regression analysis ; Spectrophotometry ; Partial least squares regression ; Metric (unit) Analytical Methods in Pharmaceuticals ; Computational Drug Discovery Methods ; Pharmaceutical Quality and Counterfeiting
Authors: Hao Li | Yeda Lian | Zhuobin Ma | Leike Yang | Jundong Wang | Lanjie Niu
Source: Silicon (article, 2025-12-11)
Field: Materials Science — Materials Chemistry

BPNN-Based Strength Prediction of Single-Crystal Silicon at High Strain Rates Split-Hopkinson pressure bar ; Finite element method ; Silicon ; Artificial neural network ; Microelectromechanical systems ; Strain rate ; Backpropagation ; Strain (injury) High-Velocity Impact and Material Behavior ; Boron and Carbon Nanomaterials Research ; Nonlocal and gradient elasticity in micro/nano structures
Authors: Tatyana Krivobokova | Razvan-Andrei Morariu | Gianluca Finocchio | Boris Maryasin
Source: ChemRxiv (preprint, 2025-05-07)
Field: Materials Science — Materials Chemistry

Modelling and estimation of chemical reaction yields from high-throughput experiments Machine learning (ML) and artificial intelligence (AI) techniques are transforming the way chemical reactions are studied today. Valuable datasets from high-throughput experimentation (HTE) are increasingly being generated to better understand reaction conditions that are crucial for outcomes such as yields and selectivities. However, it is often overlooked that data from such designed experiments possess a very specific structure, which can be captured by appropriate statistical models. Ignoring these underlying data structures when applying ML/AI algorithms can result in completely misleading conclusions. In contrast, leveraging knowledge about the data-generating process together with suitable estimation approaches yields reliable, interpretable, and comprehensive insights into the chemical reaction mechanisms. A particularly complex dataset is available for the Buchwald-Hartwig amination. Using this dataset, an appropriate statistical model for HTE-generated chemical data is introduced, and a suitable parameter estimation algorithm is developed. Based on the estimated model, new insights into the Buchwald-Hartwig amination are thoroughly discussed. Our approach is directly applicable to a wide range of HTE-generated data for chemical reactions and beyond Throughput ; Estimation ; Computer science ; Engineering ; Systems engineering ; Telecommunications Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation
Authors: Yun Zeng | Ruijie Liu | Yongquan Jiang | Zigang Deng
Source: Advances in Engineering Technology Research (article, 2025-04-07)
Field: Materials Science — Materials Chemistry

Boosting Superconductor Discovery: CrystalGAT Achieves High-Accuracy Critical Temperature Prediction Through Local-Global Crystal Graph Learning This study aims to predict the critical temperature (Tc) of superconducting materials and determine their superconducting state using our novel graph neural network model, CrystalGAT. This model skillfully integrates graph attention networks and convolutional neural layers to effectively capture local interactions between atoms and global material properties. Our research is based on the 3DSC_MP dataset, which contains three-dimensional crystal structures of superconductors and their corresponding Tc data. The CrystalGAT model processes atomic features, bond features, angular features, and chemical composition features (MAGPIE features) to learn complex interactions between atoms and bonds, as well as global properties of the entire chemical composition. Experimental results demonstrate that CrystalGAT achieves significant improvements in predictive performance compared to traditional models, exhibiting lower error metrics and higher R² values. This strongly highlights the immense potential of integrating detailed atomic-level and structural characteristics into superconductor prediction models. Our research provides new insights and tools for the discovery and design of high-temperature superconducting materials. Boosting (machine learning) ; Graph ; Superconductivity ; Computer science ; Artificial intelligence ; Condensed matter physics ; Machine learning ; Materials science ; Physics ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; High-pressure geophysics and materials
Authors: Yunyang Li | Lin Huang | Zhihao Ding | Chu Wang | Xinran Wei | Han Yang | Zun Wang | Chang Liu | Yu Shi | Peng Jin | Qin Tao | Mark Gerstein | Jia Zhang
Source: ArXiv.org (preprint, 2025-01-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

E2Former: An Efficient and Equivariant Transformer with Linear-Scaling Tensor Products Equivariant Graph Neural Networks (EGNNs) have demonstrated significant success in modeling microscale systems, including those in chemistry, biology and materials science. However, EGNNs face substantial computational challenges due to the high cost of constructing edge features via spherical tensor products, making them impractical for large-scale systems. To address this limitation, we introduce E2Former, an equivariant and efficient transformer architecture that incorporates the Wigner $6j$ convolution (Wigner $6j$ Conv). By shifting the computational burden from edges to nodes, the Wigner $6j$ Conv reduces the complexity from $O(|\mathcal{E}|)$ to $ O(| \mathcal{V}|)$ while preserving both the model's expressive power and rotational equivariance. We show that this approach achieves a 7x-30x speedup compared to conventional $\mathrm{SO}(3)$ convolutions. Furthermore, our empirical results demonstrate that the derived E2Former mitigates the computational challenges of existing approaches without compromising the ability to capture detailed geometric information. This development could suggest a promising direction for scalable and efficient molecular modeling. Scalability ; Computer science ; Political science ; Humanities ; Mathematics ; Philosophy ; Database Magnetism in coordination complexes ; Metal-Catalyzed Oxygenation Mechanisms ; Organometallic Complex Synthesis and Catalysis
Authors: Jaehoon Cha | Steven Tendyra | Alvin J. Walisinghe | Adam Hill | S. C. Basak | Peter R. Spackman | Michael W. Anderson | Jeyan Thiyagalingam
Source: Communications Physics (article, 2025-07-02)
Field: Materials Science — Materials Chemistry

Disentangling autoencoders and spherical harmonics for efficient shape classification in crystal growth simulations Controlling crystal growth is a challenge across numerous industries, as the functional properties of crystalline materials are determined during formation and often depend on particle shape. Current approaches rely on expensive, time-consuming experimental studies complemented by exhaustive parameter space simulations, creating significant computational and analytical burdens. Despite machine learning advances in crystal growth for structure-property relationships, applications targeting morphological control remain underdeveloped. Here, we demonstrate how disentangling autoencoders combined with particle aspect ratio and spherical harmonics descriptors can enhance simulation workflows for crystal growth. This approach reveals continuous transformation pathways between different crystal morphologies whilst preserving underlying crystallographic principles. Our method significantly reduces data analytics burdens, shortens design study timelines, and deepens understanding of crystal shape control. This framework enables more efficient exploration of possible crystal morphologies, facilitating the targeted design of crystalline materials with specific functional properties. Crystal (programming language) ; Spherical harmonics ; Materials science ; Property (philosophy) ; Harmonics ; Crystal growth ; Computer science ; Workflow ; Artificial intelligence ; Particle (ecology) ; Biological system ; Nanotechnology ; Crystallography ; Chemistry ; Physics ; Database Machine Learning in Materials Science ; Crystallization and Solubility Studies ; Calcium Carbonate Crystallization and Inhibition
Authors: Jeetha Raj j | C. Justin Dhanaraj | T. Rasappan
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Chemistry — Organic Chemistry

Molecular Gradient Evolution Optimizer (Mogeo) – Based Artificial Neural Network (Ann) Optimization, Dft, Molecular Docking and Pharmacokinetic Studies of Ruthenium(Iii) and Vanadium(V) Complexes Ruthenium ; Vanadium ; Artificial neural network ; Docking (animal) ; Molecular dynamics ; Computer science ; Computational chemistry ; Chemistry ; Materials science ; Artificial intelligence ; Catalysis ; Organic chemistry ; Medicine Inorganic and Organometallic Chemistry ; Computational Drug Discovery Methods
Authors: Tongjun Niu | Ross Lee | Sebastian Lam | Joseph Hafen | Steven Lukow | Nan Li | Peter Hosemann | Jonathan Gigax
Source: JOM (article, 2025-01-16)
Field: Materials Science — Materials Chemistry

A Novel Machine Learning-Driven Approach to High Throughput Mechanical Testing Throughput ; Computer science ; Machine learning ; Artificial intelligence ; Operating system Machine Learning in Materials Science ; Integrated Circuits and Semiconductor Failure Analysis ; Force Microscopy Techniques and Applications
Authors: BAO, QIANG | HE, JIANNING | LI, ZHIXUAN | Bu, Yanyan | Wang, Xiangfu
Source: Open MIND (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Supplementary document for Machine Learning Prediction and Efficient Screening Method for Thermally Induced Fluorescence Enhancement of Er³+ Doped Materials - 7678828.pdf Supplement 1 Doping ; Fluorescence ; Pattern recognition (psychology) ; Training set Lanthanide and Transition Metal Complexes ; Carbon and Quantum Dots Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: BAO, QIANG | HE, JIANNING | LI, ZHIXUAN | Bu, Yanyan | Wang, Xiangfu
Source: Figshare (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Supplementary document for Machine Learning Prediction and Efficient Screening Method for Thermally Induced Fluorescence Enhancement of Er³+ Doped Materials - 7678828.pdf Supplement 1 Doping ; Fluorescence ; Pattern recognition (psychology) ; Training set Lanthanide and Transition Metal Complexes ; Carbon and Quantum Dots Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Lujun Li | Haibin Yu
Source: Engineering (article, 2025-08-20)
Field: Materials Science — Materials Chemistry

A Multi-Scale Graph Neural Network for the Prediction of Multi-Component Gas Adsorption Metal–organic frameworks (MOFs) hold great potential for gas separation and storage, and graph neural networks have proven to be a powerful tool for exploring material structure–property relationships and discovering new materials. Unlike traditional molecular graphs, crystal graphs require consideration of periodic invariance and modes. In addition, MOF structures such as covalent bonds, functional groups, and global structures impact adsorption performance in different ways. However, redundant atomic interactions can disrupt training accuracy, potentially leading to overfitting. In this paper, we propose a multi-scale crystal graph for describing periodic crystal structures, modeling interatomic interactions at different scales while preserving periodicity invariance. We also propose a multi-head attention crystal graph network in multi-scale graphs (MHACGN-MS), which learns structural characteristics by focusing on interatomic interactions at different scales, thereby reducing interference from redundant interactions. Using MOF adsorption for gases as an example, we demonstrate that MHACGN-MS outperforms traditional graph neural networks in predicting multi-component gas adsorption. We also visualize attention scores to validate effective learning and demonstrate the model’s interpretability. Component (thermodynamics) ; Artificial neural network ; Adsorption ; Scale (ratio) ; Graph ; Computer science ; Data mining ; Artificial intelligence ; Chemistry ; Theoretical computer science ; Thermodynamics ; Physics ; Geography ; Cartography ; Physical chemistry Machine Learning in Materials Science ; Machine Learning and ELM ; Topic Modeling
Authors: Sang Ho Lee | Jong Dae Jang | Jun-Hyung Bae | Tae‐Hwan Kim
Source: Research Square (preprint, 2025-04-25)
Field: Materials Science — Materials Chemistry

Artificial intelligence simulations for critical variable tracking during polymer synthesis Variable (mathematics) ; Tracking (education) ; Computer science ; Artificial intelligence ; Psychology ; Mathematics Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Computational Drug Discovery Methods
Authors: Jisun An | Chao Qu | Yunfei Shi | XinHao Liu | Qi Tang | Fenglei Cao | Qi Yuan
Source: ArXiv.org (preprint, 2025-02-12)
Field: Chemistry — Spectroscopy

Equivariant Masked Position Prediction for Efficient Molecular Representation Graph neural networks (GNNs) have shown considerable promise in computational chemistry. However, the limited availability of molecular data raises concerns regarding GNNs' ability to effectively capture the fundamental principles of physics and chemistry, which constrains their generalization capabilities. To address this challenge, we introduce a novel self-supervised approach termed Equivariant Masked Position Prediction (EMPP), grounded in intramolecular potential and force theory. Unlike conventional attribute masking techniques, EMPP formulates a nuanced position prediction task that is more well-defined and enhances the learning of quantum mechanical features. EMPP also bypasses the approximation of the Gaussian mixture distribution commonly used in denoising methods, allowing for more accurate acquisition of physical properties. Experimental results indicate that EMPP significantly enhances performance of advanced molecular architectures, surpassing state-of-the-art self-supervised approaches. Our code is released in https://github.com/ajy112/EMPP Equivariant map ; Representation (politics) ; Position (finance) ; Computer science ; Natural language processing ; Artificial intelligence ; Linguistics ; Mathematics ; Pure mathematics ; Philosophy ; Political science ; Business Molecular spectroscopy and chirality ; Analytical Chemistry and Chromatography ; Chemical Synthesis and Analysis
Authors: Matthias Kellner (19252155) | Jacob B. Holmes (11511473) | Ruben Rodriguez-Madrid (22076282) | Florian Viscosi (22076285) | Yuxuan Zhang (1425721) | Lyndon Emsley (1261341) | Michele Ceriotti (1301079)
Source: Figshare (article, 2025-08-18)
Field: Materials Science — Materials Chemistry

A Deep LearningModel for Chemical Shieldings in MolecularOrganic Solids Including Anisotropy Nuclear Magnetic Resonance (NMR) chemical shifts are powerful probes of local atomic and electronic structure that can be used to resolve the structures of powdered or amorphous molecular solids. Chemical shift driven structure elucidation depends critically on accurate and fast predictions of chemical shieldings, and machine learning (ML) models for shielding predictions are being increasingly used as scalable and efficient surrogates for demanding ab initio calculations. However, the prediction accuracies of current ML models still lag behind those of the DFT reference methods they approximate. Here, we introduce ShiftML3, a deep-learning model that improves the accuracy of predictions of isotropic chemical shieldings in molecular solids, and does so while also predicting the full shielding tensor. On experimental benchmark sets, we find root-mean-squared errors with respect to experiment for ShiftML3 that approach those of DFT reference calculations, with RMSEs of 0.53 ppm for <sup>1</sup>H, 2.4 ppm for <sup>13</sup>C, and 7.2 ppm for <sup>15</sup>N, compared to DFT values of 0.49, 2.3, and 5.8 ppm, respectively. Ab initio ; Anisotropy ; Isotropy ; Electromagnetic shielding ; Ab initio quantum chemistry methods ; Amorphous solid ; Benchmark (surveying) ; Chemical shift Machine Learning in Materials Science ; Advanced NMR Techniques and Applications ; Synthesis and Properties of Aromatic Compounds
Authors: Wen He | Yuwei Song | Xiuya Ma | Bo Peng | Yan Zhang
Source: Journal of Chemical Education (article, 2025-05-20)
Field: Chemistry — Physical and Theoretical Chemistry

The Generalization of Named Reactions: Teaching an Organic Synthesis Course to Undergraduate Students Organic synthesis is taught either as an independent part of organic programs or as an important topic of that course. If teachers give little self-reflective guidance during the organic synthesis teaching process, students will be stuck in surface learning, which leads to poor mastery outcomes. Here we give our perspectives on deeper learning teaching design, focusing on four elements: (a) deeper learning objectives, (b) challenging learning themes, (c) deeper learning activities, and (d) continuous evaluation. Furthermore, typical teaching fragments are given as examples based on the characteristics and the teaching status of an organic chemistry course in order to provide applicable references for future relevant teaching work. Course (navigation) ; Mathematics education ; Generalization ; Chemistry ; Computer science ; Psychology ; Engineering ; Philosophy ; Epistemology Various Chemistry Research Topics ; Science Education and Pedagogy
Authors: Kshithij Mysore Nandishwara | Shuan Cheng | Pengjun Liu | Huimin Zhu | Feng Guo | Fabien Massabuau | Robert L. Z. Hoye | Shijing Sun
Source: ChemRxiv (preprint, 2025-01-15)
Field: Chemistry — Inorganic Chemistry

Vision intelligence assists microstructural optimization of Ag-Bi-I perovskite-inspired materials The slow progress in microstructural design for thin-film semiconductors remains a significant bottleneck in transitioning emerging materials into practical applications. While machine learning (ML) shows great promise in accelerating data analysis, effectively linking microstructural insights to actionable synthetic plans remains a complex challenge that surpasses the capabilities of a single model. We present the Daisy Visual Intelligence Framework, a computational platform using compound ML models for semiconductor microstructure optimization. Daisy integrates goal-oriented computer vision with an optimization agent to create a feedback loop between microstructural characterization and synthesis planning. Trained on historical synthesis and scanning electron microscopy (SEM) data for Ag-Bi-I compounds, an emerging family of perovskite-inspired materials with promise for indoor photovoltaics, the framework demonstrates the ability to learn effectively from imbalanced and heterogeneous experimental datasets. The artificial intelligence agent within Daisy identified new processing windows, which were experimentally validated to produce thin films with larger grain sizes and reduced defect densities. Our work highlights the transformative potential of agentic learning using visual information in advancing microstructural design, bridging the gap between materials discovery and application readiness. Perovskite (structure) ; Materials science ; Artificial intelligence ; Computer science ; Chemical engineering ; Engineering Pigment Synthesis and Properties ; Machine Learning in Materials Science ; Optical Imaging and Spectroscopy Techniques
Authors: Jun LI | Jiahe SONG | Wei JI | Lisheng LIU
Source: SHILAP Revista de lepidopterología (article, 2025-11-01)
Field: Materials Science — Materials Chemistry

Dynamical Mechanical Behaviors and Enhanced Ductility Mechanisms of Boron Carbide Based on Deep Potential Molecular Dynamics Simulations Boron carbide, a typical lightweight and high-strength ceramic material, has broad application prospects in national defense, military, and aerospace. However, the nanoscale amorphous shear band, which mainly arises from the destruction of icosahedra, is easily formed in boron carbide under impact, thereby causing its catastrophic shear failure. Since the formation of amorphous shear band of boron carbide significantly depends on its microstructures, molecular dynamics simulations have become a key approach to reveal the microstructural evolutions and mechanisms. However, due to the insufficient accuracy of classical atomistic potentials, classical molecular dynamics simulations face significant challenges in simulating complex material systems, such as boron carbide. In recent years, the development of machine learning methods has provided a new research paradigm for the development of atomic potentials. Among numerous machine-learning atomistic potentials, the deep potential (DP) model, which is based on deep neural networks, is particularly widely applied. This DP model can not only maintain the accuracy comparable to that of ab initio simulations, but also exhibits the efficiency comparable to that of classical molecular dynamics simulations. Thus, the DP model has become an effective strategy to examine complex material systems. In the present study, we systematically examine the research of the DP method on boron carbide ceramics. Firstly, the theoretical framework, development process of the DP model, and the construction and validation of the DP model for boron carbide are summarized. Subsequently, the mechanical responses and the localized amorphization mechanisms of boron carbide are revealed using deep potential molecular dynamics simulations. Then, some strategies are proposed to enhance the ductility of boron carbide, including microalloying, stoichiometry regulation, grain boundary engineering, and defect control. Finally, the application prospects of the DP model in the research of complex material systems, such as boron carbide, are explored. Boron carbide ; Molecular dynamics ; Amorphous solid ; Ductility (Earth science) ; Carbide ; Ceramic ; Boron Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Thermal properties of materials