jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jingjing Hu | Dan Guo | Zhan Si | Deguang Liu | Yunfeng Diao | Jing Zhang | Jinxing Zhou | Meng Wang
Source: Proceedings of the AAAI Conference on Artificial Intelligence (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

MOL-Mamba: Enhancing Molecular Representation with Structural &amp; Electronic Insights Molecular representation learning plays a crucial role in various downstream tasks, such as molecular property prediction and drug design. To accurately represent molecules, Graph Neural Networks (GNNs) and Graph Transformers (GTs) have shown potential in the realm of self-supervised pretraining. However, existing approaches often overlook the relationship between molecular structure and electronic information, as well as the internal semantic reasoning within molecules. This omission of fundamental chemical knowledge in graph semantics leads to incomplete molecular representations, missing the integration of structural and electronic data. To address these issues, we introduce MOL-Mamba, a framework that enhances molecular representation by combining structural and electronic insights. MOL-Mamba consists of an Atom &amp; Fragment Mamba-Graph (MG) for hierarchical structural reasoning and a Mamba-Transformer (MT) fuser for integrating molecular structure and electronic correlation learning. Additionally, we propose a Structural Distribution Collaborative Training and E-semantic Fusion Training framework to further enhance molecular representation learning. Extensive experiments demonstrate that MOL-Mamba outperforms state-of-the-art baselines across eleven chemical-biological molecular datasets. Representation (politics) ; Chemistry ; Computer science ; Zoology ; Biology Machine Learning in Materials Science ; Various Chemistry Research Topics ; Advanced biosensing and bioanalysis techniques
Authors: Wei Wang | Mingrong Lu | Fei Wei | Shuju Fang | Guizhen Li | Xuewen Tan | Jianjun Wang | Yi Tang
Source: Separation and Purification Technology (article, 2025-07-22)
Field: Materials Science — Materials Chemistry

Machine learning-guided optimization of Nb2CTx MXene preparation modified with tetrabutylammonium hydroxide: comprehensive structural characterization and enhanced performance analysis Characterization (materials science) ; Hydroxide ; Tetrabutylammonium hydroxide ; Chemistry ; Materials science ; Chemical engineering ; Chromatography ; Nanotechnology ; Organic chemistry ; Engineering MXene and MAX Phase Materials ; Ferroelectric and Negative Capacitance Devices ; 2D Materials and Applications
Authors: Dimitris Kalatzis | Alkmini D. Nega | Yiannis Kiouvrekis
Source: Eng—Advances in Engineering (article, 2025-07-01)
Field: Chemistry — Analytical Chemistry

Raman Spectra Classification of Pharmaceutical Compounds: A Benchmark of Machine Learning Models with SHAP-Based Explainability Raman spectroscopy has become an indispensable analytical technique in pharmaceutical research, offering non-invasive, rapid, and chemically specific insights into pharmaceutical compounds. In this study, we present a comprehensive benchmark of machine learning models for classifying 32 pharmaceutical compounds based on their Raman spectral signatures. A diverse array of algorithms—including Support Vector Machines (SVMs), Random Forests, k-Nearest Neighbors (k-NN), Gradient Boosting (XGBoost, LightGBM), and 1D Convolutional Neural Networks (CNNs)—were evaluated on a publicly available dataset. The results demonstrate outstanding classification performance across models, with linear SVM achieving the highest accuracy of 99.88%, followed closely by CNN (99.26%). Ensemble methods such as Random Forest and XGBoost also yielded high accuracies above 98.3%. In addition to strong predictive performance, SHAP (SHapley Additive exPlanations) analysis was employed to interpret model decisions. CNN models, in particular, revealed well-localized and chemically meaningful spectral regions critical to classification. This combination of high accuracy and interpretability highlights the promise of explainable AI in pharmaceutical analysis and quality control, offering robust, transparent, and scalable solutions for real-world applications. Benchmark (surveying) ; Raman spectroscopy ; Computer science ; Machine learning ; Artificial intelligence ; Optics ; Physics ; Cartography ; Geography Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Computational Drug Discovery Methods
Authors: S. J. Davis Hans | M. Muthukumaran | K. Kumaresan | V. G. Pradeep Kumar | Dayanand M. Goudar | Subraya Krishna Bhat
Source: Scientific Reports (article, 2025-11-26)
Field: Materials Science — Polymers and Plastics

Hybrid experimental and machine learning approach for optimizing abrasive wear of microcrystalline cellulose modified hemp/bamboo fiber composites In this work, hemp/bamboo hybrid fabric-epoxy composites reinforced with 0-9 wt% microcrystalline cellulose (µCC) is examined for their abrasive wear behavior. Compression molding was used to create composites with 0, 3, 6, and 9 wt% µCC. In accordance with ASTM G65 guidelines, wear tests were conducted under controlled dry sand abrasion. Using a Taguchi L<sub>16</sub> design, the effects of applied load (5-20 N), abrading distance (250-1000 m), and µCC content on wear loss were assessed. To predict abrasive wear and examine the role of µCC filler, several machine learning models were used, including Linear Regression, K-Nearest Neighbors, Artificial Neural Networks, Random Forest, Gradient Boosting, and eXtreme Gradient Boosting. By increasing the hardness and load-bearing capacity of the composite, µCC mechanistically increases wear resistance and lessens material removal during abrasion. According to ANOVA results, wear loss was most affected by abrading distance (44.08%), load (34.21%), and µCC content (18.01%). The Random Forest model had the lowest error (RMSE = 0.045) and the highest predictive accuracy (R<sup>2</sup> = 0.942). Abrading distance is the main factor influencing wear resistance, followed by load and µCC content, according to feature importance analysis. Accurately forecasting abrasive wear and creating high-performance, sustainable hybrid composites can be accomplished by combining machine learning and experimental data. Abrasive ; Microcrystalline cellulose ; Taguchi methods ; Random forest ; Compression molding ; Molding (decorative) ; Fiber Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Aluminum Alloys Composites Properties
Authors: Robin Curth | Theodor E Röhrkasten | Carolin Müller | Julia Westermayr
Source: Scientific Data (article, 2025-07-26)
Field: Materials Science — Materials Chemistry

Surface Hopping Nested Instances Training Set for Excited-state Learning Theoretical studies of molecular photochemistry and photophysics are essential for understanding fundamental natural processes but rely on computationally demanding quantum chemical calculations. This complexity limits both direct simulations and the development of machine learning (ML) models trained on this data. To address this, we introduce SHNITSEL, a data repository containing 418,870 ab-initio data points of nine organic molecules in their ground and electronically excited states. Each data point includes high-accuracy quantum chemical properties such as energies, forces, and dipole moments in the ground state and electronically excited singlet or triplet states as well as properties that arise from the coupling of electronic states, namely nonadiabatic couplings, transition dipoles, or spin-orbit couplings. Generated with state-of-the-art methods, SHNITSEL provides a robust benchmark for ML models and facilitates the development of ML-based approaches for excited state properties. Excited state ; Ab initio ; Surface hopping ; Dipole ; Singlet state ; Ground state ; Potential energy surface ; Physics ; Chemistry ; Chemical physics ; Statistical physics ; Computer science ; Atomic physics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Spectroscopy and Quantum Chemical Studies
Authors: Sean Lynch | Jacob LaMountain | Bo Fan | Jie Bu | Amogh Raju | Daniel Wasserman | Anuj Karpatne | Viktor A. Podolskiy
Source: ACS Photonics (article, 2025-07-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Physics-Guided Hierarchical Neural Networks for Maxwell’s Equations in Plasmonic Metamaterials While machine learning (ML) has found multiple applications in photonics, traditional "black box" ML models typically require prohibitively large training data sets. Generation of such data, as well as the training processes themselves, consume significant resources, often limiting practical applications of ML. Here, we demonstrate that embedding Maxwell's equations into ML design and training significantly reduces the required amount of data and improves the physics-consistency and generalizability of ML models, opening the road to practical ML tools that do not need extremely large training sets. The proposed physics-guided machine learning (PGML) approach is illustrated on the example of predicting complex field distributions within hyperbolic meta-material photonic funnels, based on multilayered plasmonic-dielectric composites. The hierarchical network design used in this study enables knowledge transfer and points to the emergence of effective medium theories within neural networks. Metamaterial ; Plasmon ; Physics ; Maxwell's equations ; Artificial neural network ; Statistical physics ; Theoretical physics ; Classical mechanics ; Quantum mechanics ; Artificial intelligence ; Computer science Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Neural Networks and Reservoir Computing
Authors: Andreas Penz | Jakob Gamper | Josef M. Gallmetzer | Felix R. S. Purtscher | Thomas S. Hofer
Source: Journal of Computational Chemistry (article, 2025-05-26)
Field: Chemistry — Inorganic Chemistry

Parametrization of Zirconium for DFTB3/3OB: A Pathway to Study Complex Zr‐Compounds for Biomedical and Material Science Applications This work presents the extension of the semi-empirical density functional tight binding method, DFTB3, to include zirconium for biomedical and material science applications. The parametrization of Zr has been carried out in consistency with already established 3OB parameters including the elements C, H, N, O, S, P, Mg, Zn, Na, K, Ca, F, Cl, Br, and I. Zirconium-ligand association and reaction energies have been compared with results from quantum chemical calculations obtained at MP2 and DFT (PBE and B3LYP) level of theory, as well as with those from the semi-empirical methods DFTB2/PTBP and GFN2-xTB. A structural validation has been carried out on 1,897 compounds reported in the Cambridge Structural Database, revealing an average root mean square deviation comparable to that obtained at semi-empirical (DFTB2/PTBP and GFN2-xTB) level of theory and via the novel neural network potential MACE-MP-0. To provide a critical validation of the newly derived parameters, the structure of the biomedically relevant Zr-DFO complex has been evaluated with respect to a DFT (B3LYP) reference calculation. In addition, extensive DFTB3 MD simulations of the two prominent metal-organic frameworks UiO-66 and UiO-67 have been performed. The results demonstrate the applicability of the newly developed parameters for the study of zirconium-containing metal-organic frameworks, when compared to experimental measurements, as well as computational approaches. Zirconium ; Density functional theory ; Parametrization (atmospheric modeling) ; Computational chemistry ; Metal-organic framework ; Metal ; Materials science ; Quantum chemistry ; Chemistry ; Physical chemistry ; Thermodynamics ; Molecule ; Physics ; Supramolecular chemistry ; Adsorption ; Organic chemistry ; Quantum mechanics Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography ; Inorganic Fluorides and Related Compounds
Authors: Hang Yu | H.C. Chen | Ganesh D. Sharma | Yen‐Ju Cheng | Chain‐Shu Hsu | Ta‐Ya Chu | Jianping Lu | Fang‐Chung Chen
Source: Archives of Computational Methods in Engineering (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

Computational Modeling of Indoor Organic Photovoltaics: Dataset Curation, Predictive Analysis, and Machine Learning Approaches This study presents a comprehensive dataset that encompasses the indoor device performance of organic photovoltaic (OPV) materials, their corresponding SMILES codes, and frontier molecular orbital (FMO) energy levels. This dataset comprises a total of 128 subsets and features 64 pairs of donors and acceptors. We demonstrate that traditional models, such as the Shockley–Queisser limit and Scharber’s model, are insufficient for accurately predicting the behavior of indoor OPVs based on the molecular orbitals of these materials. In contrast, we explore the predictive capabilities of four machine learning (ML) models for estimating the power conversion efficiencies (PCEs) of indoor OPVs, utilizing molecular structure information and FMO data from the dataset we compiled. The trained ML models exhibit strong predictive performance with high correlation coefficients (r > 0.8) for indoor PCE values; notably, the support vector regression (SVR) model achieves the highest r of 0.878. The generalization capabilities of the models are also assessed using previously unseen materials, and the results demonstrate high accuracy rates. The SVR algorithm reaches the best average accuracy of 92.1%, underscoring its potential for efficiently screening materials for indoor applications. Our findings suggest that this dataset, with opportunities for future expansion, could significantly facilitate material design and accelerate computer-aided materials screening, reducing the need for extensive experimental testing in the development of indoor OPVs. Computer science ; Photovoltaics ; Machine learning ; Artificial intelligence ; Organic solar cell ; Predictive modelling ; Photovoltaic system ; Engineering Machine Learning in Materials Science ; Photovoltaic System Optimization Techniques
Authors: Manuel S. Drehwald | Asma Jamali | Rodrigo A. Vargas–Hernández
Source: The Journal of Chemical Physics (article, 2025-02-28)
Field: Materials Science — Materials Chemistry

MOLPIPx: An end-to-end differentiable package for permutationally invariant polynomials in Python and Rust In this work, we present MOLPIPx, a versatile library designed to seamlessly integrate permutationally invariant polynomials with modern machine learning frameworks, enabling the efficient development of linear models, neural networks, and Gaussian process models. These methodologies are widely employed for parameterizing potential energy surfaces across diverse molecular systems. MOLPIPx leverages two powerful automatic differentiation engines-JAX and EnzymeAD-Rust-to facilitate the efficient computation of energy gradients and higher-order derivatives, which are essential for tasks such as force field development and dynamic simulations. MOLPIPx is available at https://github.com/ChemAI-Lab/molpipx. Computer science ; Python (programming language) ; Invariant (physics) ; End-to-end principle ; Differentiable function ; Computation ; Artificial neural network ; Gaussian ; Theoretical computer science ; Computational science ; Artificial intelligence ; Algorithm ; Programming language ; Mathematics ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Shaofeng Shao | Liangwei Yan | Jiale Li | Yizhou Zhang | Jun Zhang | Hyoun Woo Kim | Sang Sub Kim
Source: ACS Sensors (article, 2025-03-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Harnessing Transfer Deep Learning Framework for the Investigation of Transition Metal Perovskite Oxides with Advanced p-n Transformation Sensing Performance Gas sensing materials based on transition metal perovskite oxides (TMPOs) have garnered extensive attention across various fields such as air quality control, environmental monitoring, healthcare, and national defense security. To overcome challenges encountered in traditional research, a deep learning framework combining natural language processing technology (Word2Vec) and crystal graph convolutional neural network (CGCNN) was adopted in this study, proposing a predictive method that incorporates a comprehensive data set consisting of 1.2 million literature abstracts and 110,000 crystal structure data entries. This method assessed the optimal combination of zinc-cobalt bimetallic ions complexed with ligands as precursors for perovskite oxides. The regulatory function of ligand concentration on the p-n transformation of zinc-cobalt oxide sensing performance was identified, and optimization strategies were provided. The Zn(II)/Co(III)/1-methyl-1<i>H</i>-imidazole-2-carboxylic acid complex was synthesized and demonstrated exceptional sensitivity and selectivity toward volatile organic compounds (VOCs), particularly 3-hydroxy-2-butanone (3H-2B). The p-n transformation mechanism of sensing performance was deeply analyzed through the construction of the hyper-synergistic ligand interaction matrix model for n-type sensors (HSLIM-n) and the parametrized surface-ligand resonance model for p-type sensors (PSLRM-p), enhancing the fundamental understanding of the sensing material properties. Even in highly interfering environments, the functionalized perovskite oxides exhibited outstanding sensitivity and selectivity toward 3H-2B gas, with a low detection limit of 25 parts per billion (ppb). This comprehensive research approach has facilitated the construction of a transfer learning-enhanced deep learning framework, which has shown high efficiency in predicting the performance and precise design of perovskite oxides, and its effectiveness was meticulously verified through detailed experimental validation. Perovskite (structure) ; Materials science ; Transformation (genetics) ; Transition metal ; Transfer of learning ; Nanotechnology ; Chemical engineering ; Computer science ; Chemistry ; Artificial intelligence ; Catalysis ; Engineering Magnetic and transport properties of perovskites and related materials ; Electronic and Structural Properties of Oxides ; Perovskite Materials and Applications
Authors: Venkata Subbaiah Munagapati | Hsin-Yu Wen | Gutha Yuvaraja | Jet‐Chau Wen | Sada Venkateswarlu | Vijaya Yarramuthi | Anjani R.K. Gollakota | Chi‐Min Shu | E. A. Lohith | N.V.V. Jyothi
Source: Coordination Chemistry Reviews (article, 2025-09-04)
Field: Chemistry — Analytical Chemistry

Advances in biocompatible chitosan composites for chromium, arsenic, and radionuclide remediation: experimental and computational insights Chemistry ; Biocompatible material ; Environmental remediation ; Chitosan ; Arsenic ; Chromium ; Radionuclide ; Radiochemistry ; Nanotechnology ; Contamination ; Biomedical engineering ; Organic chemistry ; Nuclear physics Analytical chemistry methods development ; Chromium effects and bioremediation ; Adsorption and biosorption for pollutant removal
Authors: Jason D. Josephson | John Paul Pezacki | Masaya Nakajima
Source: Journal of Physical Organic Chemistry (article, 2025-01-17)
Field: Chemistry — Organic Chemistry

Machine Learning Enabling the Prediction of Activation Energies of SPAAC ABSTRACT This study leverages machine learning to predict the activation energies of strain‐promoted azide‐alkyne cycloaddition (SPAAC) reactions. Using DFT calculations, 631 sets of bond angles and Mulliken charges from two acyclic alkynes were collected. Multiple machine learning models were trained on these data, achieving high accuracy ( R 2 &gt; 0.95). Both bond angle and charge were crucial for reliable predictions. The models successfully predicted activation energies for SPAAC reactions with unseen cycloalkynes, within certain limitations. Chemistry ; Machine learning ; Chemical physics ; Computer science Click Chemistry and Applications ; Advanced Biosensing Techniques and Applications ; Chemical Synthesis and Analysis
Authors: Yannic Rath | George H. Booth
Source: Nature Communications (article, 2025-02-26)
Field: Materials Science — Materials Chemistry

Interpolating numerically exact many-body wave functions for accelerated molecular dynamics While there have been many developments in computational probes of both strongly-correlated molecular systems and machine-learning accelerated molecular dynamics, there remains a significant gap in capabilities in simulating accurate non-local electronic structure over timescales on which atoms move. We develop an approach to bridge these fields with a practical interpolation scheme for the correlated many-electron state through the space of atomic configurations, whilst avoiding the exponential complexity of these underlying electronic states. With a small number of accurate correlated wave functions as a training set, we demonstrate provable convergence to near-exact potential energy surfaces for subsequent dynamics with propagation of a valid many-body wave function and inference of its variational energy whilst retaining a mean-field computational scaling. This represents a profoundly different paradigm to the direct interpolation of potential energy surfaces in established machine-learning approaches. We combine this with modern electronic structure approaches to systematically resolve molecular dynamics trajectories and converge thermodynamic quantities with a high-throughput of several million interpolated wave functions with explicit validation of their accuracy from only a few numerically exact quantum chemical calculations. We also highlight the comparison to traditional machine-learned potentials or dynamics on mean-field surfaces. Interpolation (computer graphics) ; Wave function ; Statistical physics ; Computer science ; Molecular dynamics ; Convergence (economics) ; Electronic structure ; Potential energy ; Physics ; Classical mechanics ; Quantum mechanics ; Artificial intelligence ; Motion (physics) Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Shao-Kang Qian | Yiming Shen | Jinsong Shen | Gui‐Ping Cao
Source: Materials Today Communications (article, 2025-04-08)
Field: Materials Science — Polymers and Plastics

Regulated synthesis and investigated properties of polyphenylene oxide: Status and perspectives Materials science ; Oxide ; Chemical engineering ; Nanotechnology ; Metallurgy Synthesis and properties of polymers ; Silicone and Siloxane Chemistry ; Advanced Polymer Synthesis and Characterization
Authors: Hyerim Kim | Woonbae Sohn | Jung Hun Lee | Seok Joo Yang | Young-Seok Shim | Cheon Woo Moon | Hyojung Kim
Source: Journal of the Korean Ceramic Society (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

Recent advances in MXene materials for resistive switching memory devices Neuromorphic engineering ; Resistive random-access memory ; Memristor ; Non-volatile memory ; Electronics ; Leverage (statistics) ; Key (lock) ; Resistive touchscreen MXene and MAX Phase Materials ; Advanced Memory and Neural Computing ; Ferroelectric and Negative Capacitance Devices
Authors: Kai Qian | Lingyang Meng | Guanhua Zhang | Ji Li | Jinze Lyu
Source: Journal of environmental chemical engineering (article, 2025-06-26)
Field: Materials Science — Materials Chemistry

Neural network-like CeO2-loaded polyvinylidene fluoride membrane for tri-phase interface catalytic ozonation of antibiotics Polyvinylidene fluoride ; Catalysis ; Membrane ; Fluoride ; Phase (matter) ; Interface (matter) ; Chemical engineering ; Materials science ; Chemistry ; Adsorption ; Inorganic chemistry ; Organic chemistry ; Engineering Catalytic Processes in Materials Science ; Gas Sensing Nanomaterials and Sensors ; Advanced Photocatalysis Techniques