jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muhammad Shalahuddin Al Ja’farawy | Vo Thi Nhat Linh | ChaeWon Mun | Jun-Young Yang | Jun Young Kim | Rowoon Park | Sung‐Gyu Park | Dongho Kim | Min‐Young Lee | Ho Sang Jung
Source: Advanced Science (article, 2025-08-01)
Field: Chemistry — Spectroscopy

Plasmonic Molecular Entrapment for Label‐Free Methylated DNA Detection and Machine‐Learning Assisted Quantification (Adv. Sci. 29/2025) LabeL-Free DiagnosisThe artwork highlights a breakthrough method for detecting and quantifying global DNA methylation.This highly sensitive approach leverages in situ surface growth of plasmonic particles to enable molecular trapping and label-free sensing.Combined with machine learning analysis, it allows precise quantification of global methylation levels, offering a promising strategy for early cancer diagnosis and comprehensive epigenetic profiling.More details can be found in article number 2503257 by Min-Young Lee, Ho Sang Jung, and co-workers Plasmon ; Nanotechnology ; Chemistry ; Computational biology ; Materials science ; Computer science ; Biology ; Optoelectronics Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Advanced biosensing and bioanalysis techniques
Authors: S. Chandrasekhar | Shiva Raj Pokhrel | Navneet Singh
Source: ArXiv.org (preprint, 2025-10-08)
Field: Materials Science — Materials Chemistry

Adapting Quantum Machine Learning for Energy Dissociation of Bonds Accurate prediction of bond dissociation energies (BDEs) underpins mechanistic insight and the rational design of molecules and materials. We present a systematic, reproducible benchmark comparing quantum and classical machine learning models for BDE prediction using a chemically curated feature set encompassing atomic properties (atomic numbers, hybridization), bond characteristics (bond order, type), and local environmental descriptors. Our quantum framework, implemented in Qiskit Aer on six qubits, employs ZZFeatureMap encodings with variational ansatz (RealAmplitudes) across multiple architectures Variational Quantum Regressors (VQR), Quantum Support Vector Regressors (QSVR), Quantum Neural Networks (QNN), Quantum Convolutional Neural Networks (QCNN), and Quantum Random Forests (QRF). These are rigorously benchmarked against strong classical baselines, including Support Vector Regression (SVR), Random Forests (RF), and Multi-Layer Perceptrons (MLP). Comprehensive evaluation spanning absolute and relative error metrics, threshold accuracies, and error distributions shows that top-performing quantum models (QCNN, QRF) match the predictive accuracy and robustness of classical ensembles and deep networks, particularly within the chemically prevalent mid-range BDE regime. These findings establish a transparent baseline for quantum-enhanced molecular property prediction and outline a practical foundation for advancing quantum computational chemistry toward near chemical accuracy. Quantum machine learning ; Quantum ; Ansatz ; Support vector machine ; Artificial neural network ; Quantum algorithm ; Quantum chemistry ; Robustness (evolution) ; Perceptron Machine Learning in Materials Science
Authors: Michael S. Chen | Alan Robledo | Mark E. Tuckerman
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

<i>(Keynote)</i> Beating the Viscosity-Conductivity Inverse Relation Barrier to Create a Breakthrough Electrolyte for Emerging Battery Applications: Quantum Mechanics Holds the Key Candidate systems for next-generation battery electrolyte materials, such as deep eutectic solvents and ionic liquids, often suffer from the limitation of an inverse relation that exists between viscosity and conductivity, known as Walden’s rule, which can suppress rates of charge transport and limit their performance characteristics. A strategy for circumventing this problem draws its inspiration from the world of fuel-cell based ion exchange membranes and the types of charge transport processes operative in these systems. In this talk, I will describe a project aimed at leveraging machine learning and Feynman path-integral based quantum simulation strategies, in combination with experimental synthesis and characterization, to develop a novel class of battery electrolytes that demonstrates an ability to escape the limitations of Walden’s rule. In particular, I will describe how the charge transport processes in this new class electrolytes achieve breakthrough performance by harnessing their unusual quantum character to drive the structural diffusion or Grotthuss diffusion mechanism, a phenomenon discoverable due to the power of machine learning. I will discuss the selection of candidate chemical species for each of the component processes, protocols for combining these components into a high-performance electrolyte, current results, and next steps in the evolution of the project. This work will serve to illustrate both the power of modern computational and machine learning approaches in the design of electrochemical systems but also to broaden the perspective on what constitutes a “breakthrough” electrolyte. Figure 1 Battery (electricity) ; Electrolyte ; Quantum ; Charge (physics) ; Relation (database) ; Diffusion ; Power (physics) ; Work (physics) Machine Learning in Materials Science ; Advanced Battery Materials and Technologies ; Thermal Expansion and Ionic Conductivity
Authors: Jia-kuan Ren | Yuze Ji | Lijuan Zhu | Peng Wang | Nan Ji | Zhihong Li | Jing Fang | Xuewei Xing | Chun Feng
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

&lt;p&gt;Prediction of Strength and Toughness for High-Strength Titanium Alloys Used for Oil And Gas Drill by Feature Engineering Directed Machine Learning Algorithms&lt;/p&gt; Toughness ; Titanium alloy ; Drill ; Feature selection ; Boosting (machine learning) ; Gradient boosting ; Stability (learning theory) ; Feature (linguistics) ; Titanium Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Welding Techniques and Residual Stresses
Authors: Wei Xue | Junli Wang | Wenbo Luo | Yanke Guo | Youhua Yan | Wen Chen | Shaoxiang Li | Andreas Züttel
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Accelerated discovery of low-cost Mg-based solid-state H2 storage alloys for enhanced hydrogen des-/absorption behavior via machine learning-guided binary-element minor doping Hydrogen storage ; Electronegativity ; Desorption ; Valence (chemistry) ; Hydrogen ; Dielectric ; Softmax function ; Extrapolation Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Spacecraft and Cryogenic Technologies
Authors: Kirill Kulaev | Bogdan Protsenko | Weiren Cheng | Qinghua Liu | Д. Н. Горбунов | Mikhail Lifar | В. Г. Власенко | А. С. Бурлов | С. А. Гуда | Mikhail A. Soldatov | Alexander A. Guda | Alexander Soldatov
Source: ChemRxiv (preprint, 2025-08-04)
Field: Materials Science — Materials Chemistry

DeepFit: physically and chemically informed XAS-Structure fitting made simple Whilst X-ray Absorption Near Edge Structure (XANES) spectroscopy is a valuable tool to unravel local atomic and electronic structure of catalysts' active sites under working conditions, its quantitative analysis is a complex ill-posed problem. Main limitations of currently existing methods for XANES quantitative analysis are (1) high demand of computational resources needed, (2) lack of universality of ML models between absorption edges, (3) absence of chemical and physical constraints, (4) low specificity for real catalytical cases. To address them simultaniously in an unified framework we introduce the DeepFit, a deep learning approach for physically and chemically informed on-the-fly XANES spectra analysis. By constructing the tmXAS—a comprehensive database of 67,000 possible local atomic environments and their corresponding Ab-initio K-edge XAS spectra for all 3d and 4d transition metals—we leverage a state-of-the-art universal deep learning model for rapid spectral prediction. This approach incorporates physical constraints through equivariance, ensuring consistency with fundamental symmetries. Then, combining quantum chemistry and DeepFit neural network differentiation, we implement an approach for chemically informed atomic structure refinement, capable of $3D$ structure prediction considering both spectroscopic and energetic favorability. Domain-specific spectroscopic solutions were put in the tmXAS database and DeepFit approach to better work with often overlooked in analysis methods and highly relevant cases of homogeneous and single-site/single-atom catalysts. The method's validity for both structure refinement and spectra prediction is rigorously demonstrated with several benchmarks, including structure unraveling of the Rh-complex homogeneous hydroformylation catalyst and well-defined 3d/4d metal coordination compounds (Cu, Co, Zn, Ni), where quantitative agreement with single-crystal XRD and EXAFS is met. By unifying physical constraints with computational efficiency, DeepFit establishes a new universal method for rapid XANES analysis with chemical plausibility and spectroscopic agreement being systematically encoded into efficient gradient-driven optimization. XANES ; X-ray absorption spectroscopy ; Leverage (statistics) ; Computer science ; Absorption spectroscopy ; Ab initio ; Chemistry ; Spectral line ; Statistical physics ; Artificial intelligence ; Physics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Spectroscopy and Fluorescence Analysis ; Electron and X-Ray Spectroscopy Techniques
Authors: Juliette Fenogli | Laurence Grimaud | Rodolphe Vuilleumier | Philippe Schwaller
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2025-09-16)
Field: Materials Science — Materials Chemistry

Exploring chemical reactivity with explainable Machine Learning Feature (linguistics) ; Reactivity (psychology) ; Training set ; Key (lock) ; Set (abstract data type) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Explainable Artificial Intelligence (XAI)
Authors: Coffin, Jean-Baptiste
Source: theses.fr (ABES) (article, 2025-10-10)
Field: Chemistry — Analytical Chemistry

Perfumes comparison by combination of machine learning and analytical chemistry Les travaux menés dans le cadre de cette thèse portent sur l'application de l'apprentissage automatique à l'analyse de mélanges complexes par chromatographie gazeuse couplée à la spectrométrie de masse (GC-MS). Face aux limitations des approches conventionnelles nécessitant l'identification et la quantification préalables des composés, cette étude explore des approches computationnelles innovantes pour l'évaluation de la similarité moléculaire. Le travail de recherche s'articule autour de deux axes principaux. Le premier axe repose sur l'adaptation et l'évaluation d'outils existants de comparaison spectrale au contexte spécifique des composés volatils odorants. Cette démarche s'appuie sur l'analyse de bases de données spécialisées en GC-MS dans le but d'optimiser les mesures de similarité entre spectres de masse pour des molécules d'intérêt dans le secteur des parfums et arômes. Le second axe porte sur les possibilités d'automatisation de l'analyse de mélanges complexes à partir de données de chimie analytique. L'objectif est de développer une nouvelle méthodologie permettant de s'affranchir des étapes d'identification manuelle des constituants tout en préservant la pertinence analytique des mesures de similarité. L'évaluation de la performance des modèles proposés est réalisée à travers des cas d'application concrets, notamment dans les domaines de l'olfaction et du contrôle qualité des matières premières. Les résultats obtenus mettent en évidence le potentiel d'intégration de techniques d'apprentissage automatique dans l'analyse spectrale pour optimiser les pratiques analytiques et valoriser des ensembles de données complexes à grande échelle. Cette recherche contribue à l'avancement des connaissances dans le domaine de l'analyse automatisée des mélanges odorants. Context (archaeology) ; Spectral analysis ; Statistical analysis Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Computational Drug Discovery Methods
Authors: Sejun Kim
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Deep Learning Model for Autonomous Laboratory The growing demand for artificial intelligence (AI) technologies that can accurately predict material properties is particularly critical in the field of battery research. To address this need, it is essential to expand material databases beyond the limitations of density functional theory (DFT) and develop deep learning (DL) models that can be effectively trained with limited high-quality data, enabling more efficient autonomous laboratories for battery material discovery. In this presentation, I introduce PROFiT-Net (PRoperty-networking Orbital Field maTrix-convolutional neural Network), a deep learning model that uses a crystal structure representation based on the orbital field matrix (OFM) and incorporates additional information about elemental properties and valence electron configurations. This model captures the relationships between elemental properties within the crystal structure, tailored specifically for battery materials. PROFiT-Net demonstrates exceptional accuracy in predicting experimental data relevant to battery materials, such as band gaps, formation enthalpies, and electrochemical properties like ion diffusion and capacity retention. It outperforms or matches other leading deep learning models, providing results comparable to DFT or hybrid functional calculations. Additionally, the model identifies key physical trends and correlations that are important for optimizing battery performance, while maintaining scalability for large datasets. With its strong predictive capabilities, PROFiT-Net has the potential to significantly advance autonomous battery research by bridging the gap between theoretical predictions and experimental outcomes, accelerating the discovery of high-performance materials for next-generation batteries. Deep learning ; Bridging (networking) ; Battery (electricity) ; Scalability ; Field (mathematics) ; Multiscale modeling ; Artificial neural network ; Experimental data Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Guohai Liu | Huahu Xu | Hao Zhang | Yuzhe Huang
Source:  (article, 2025-04-11)
Field: Materials Science — Materials Chemistry

Cgip-Deepreac+ Model for Organic Chemical Reactions Based on Deep Active Learning In synthetic organic chemistry, finding and optimising reaction conditions is a complex and time-consuming process. With the development of deep and active learning techniques, we have tools to predict and guide this complex process. The CGIP-DeepReac+ model introduced in this paper is a framework that combines deep learning and active learning strategies, designed to solve the problem of reaction prediction and optimisation in synthetic organic chemistry. CGIP-DeepReac+ is a synthetic organic chemistry prediction model based on deep active learning, which employs a graph neural network to deal with complex molecular structures, and combines with active learning strategies to Efficient selection of training data significantly improves the prediction performance. The model performs well in a number of organic chemical reactions, accurately predicting reaction results and optimising reaction conditions, providing a powerful tool for synthetic chemists. With data accumulation and model optimisation, CGIP-DeepReac+ is expected to play an important role in synthetic chemistry. The contribution of this paper is to demonstrate the potential of CGIP in enhancing the performance of the DeepReac+ model and to provide a new perspective on chemical reaction prediction. Future work will focus on further optimising the model structure, extending the dataset and exploring the application of CGIP in other chemical image analysis tasks. Computer science ; Deep learning ; Active learning (machine learning) ; Artificial intelligence Machine Learning in Materials Science ; Machine Learning and Algorithms
Authors: Wei Zhang | Guobao Xu | Xiao Li | Jian Cong | Peng Wang | Yang Xu | Bo Wei
Source: SAR and QSAR in environmental research (article, 2025-08-03)
Field: Chemistry — Spectroscopy

Unravelling phosphorylation-induced impacts on inhibitor-CDK2 through multiple independent molecular dynamics simulations and deep learning Phosphorylation plays an important role in the activity of CDK2 and inhibitor binding, but the corresponding molecular mechanism is still insufficiently known. To address this gap, the current study innovatively integrates molecular dynamics (MD) simulations, deep learning (DL) techniques, and free energy landscape (FEL) analysis to systematically explore the action mechanisms of two inhibitors (SCH and CYC) when CDK2 is in a phosphorylated state and bound state of CyclinE. With the help of MD trajectory-based DL, key functional domains such as the loops L3 loop and L7 are successfully identified. The results of FEL analysis show that the binding of CyclinE significantly enhances conformational stability of key functional regions of CDK2 (such as the L3 loop, L7 loop, and αC helix), while phosphorylation modification increases conformational diversity of the CDK2-related system. Further verification by quantum mechanics/molecular mechanics-generalized Born surface area (QM/MM-GBSA) calculations shows that binding of CyclinE can enhance the binding ability of inhibitors, while phosphorylation weakens this binding effect. Residue-based free energy estimation reveals the hot spot regions of inhibitor-CDK2 binding, providing crucial target information for structure-based drug design. This study provides theoretical foundations for the development of highly selective CDK2 inhibitors and might be of great significance for cancer targeted therapy. Molecular dynamics ; Phosphorylation ; Stability (learning theory) ; Deep learning ; Mechanism (biology) ; Cancer therapy ; Energy landscape ; Cyclin-dependent kinase 2 ; Binding site Advanced NMR Techniques and Applications ; Protein Structure and Dynamics ; Enzyme Structure and Function
Authors: Feiying You (22119041) | Wenhao Wang (515852) | Jun Zhu (84054)
Source: Figshare (article, 2025-08-25)
Field: Chemistry — Organic Chemistry

PredictingDinitrogen Activation and Coupling withCarbon Dioxide and Other Small Molecules by Methyleneborane: A CombinedDFT and Machine Learning Study The capture of carbon dioxide is extremely important due to the increasingly severe greenhouse effect, and the conversion of dinitrogen into high-value N–C compounds is of great significance. Here, we predict through density functional theory calculations that the coupling of dinitrogen with carbon dioxide by methyleneborane becomes favorable both thermodynamically and kinetically. Machine learning analysis suggests that increasing the HOMO–LUMO gap or the charge on the boron atom or decreasing the charge of the nitrogen atom will reduce the reaction energies. N<sub>2</sub> coupling is examined as a concerted step, as CO<sub>2</sub> is a σ donor and a π acceptor. Besides, N<sub>2</sub> coupling with other small molecules including formaldehyde, sulfur dioxide, acetonitrile, and <i>N</i>-ethylidenemethylamine was also examined in both thermodynamics and kinetics. Our findings highlight a crucial role of main group species in N<sub>2</sub> coupling chemistry. Density functional theory ; Coupling (piping) ; Carbon dioxide ; Molecule ; Atom (system on chip) ; Nitrogen ; Small molecule ; Charge (physics) Organoboron and organosilicon chemistry ; Carbon dioxide utilization in catalysis ; Machine Learning in Materials Science
Authors: Enhao Liu | Youkang Duan | Yu Li | Cong Peng | Wei Feng
Source: Electrochemical Energy Reviews (article, 2025-12-01)
Field: Chemistry — Inorganic Chemistry

Fluoride-Ion Batteries: A Review of Recent Advances and Future Opportunities Scope (computer science) ; Leverage (statistics) ; Battery (electricity) ; Electrochemical energy storage ; Energy demand ; Anode Inorganic Fluorides and Related Compounds ; Fiber-reinforced polymer composites ; Advancements in Battery Materials
Authors: Chao Sun | Bin Zhang
Source: Research Square (preprint, 2025-05-05)
Field: Materials Science — Materials Chemistry

Insights and Analysis of Machine Learning for Benzene Hydrogenation to Cyclohexene Cyclohexene ; Yield (engineering) ; Catalysis ; Benzene ; Variable (mathematics) ; Machine learning ; Computer science ; Chemistry ; Materials science ; Mathematics ; Organic chemistry ; Composite material Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Catalysis and Hydrodesulfurization Studies
Authors: S. Binotto | L. Brunelli | M. Barusco | C. Masiero | G. A. Susto
Source: Padua Research Archive (University of Padova) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Deep Learning Approach for Aluminum Corrosion Recognition in Semiconductor Manufacturing Deep learning ; Aluminium ; Semiconductor device fabrication ; Deep drawing ; Corrosion Machine Learning in Materials Science ; Advanced Neural Network Applications ; Industrial Vision Systems and Defect Detection
Authors: P. Semnami | Florence H. Vermeire | Hugo da Silva Marque | Joris Thybaut | Pedro Freitas Mendes
Source: Ghent University Academic Bibliography (Ghent University) (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning for catalyst informatics : decoding the role of data Informatics ; Decoding methods ; Health informatics ; Key (lock) Machine Learning in Materials Science ; Scientific Computing and Data Management ; Big Data and Digital Economy
Authors: Katzberger, Paul | Pultar, Felix | Riniker, Sereina
Source: Repository for Publications and Research Data (ETH Zurich) (article, 2025-01-27)
Field: Materials Science — Materials Chemistry

Transferring Knowledge from MM to QM: A Graph Neural Network-Based Implicit Solvent Model for Small Organic Molecules The conformational ensemble of a molecule is strongly influenced by the surrounding environment. Correctly modeling the effect of any given environment is, hence, of pivotal importance in computational studies. Machine learning (ML) has been shown to be able to model these interactions probabilistically, with successful applications demonstrated for classical molecular dynamics. While first instances of ML implicit solvents for quantum-mechanical (QM) calculations exist, the high computational cost of QM reference calculations hinders the development of a generally applicable ML implicit solvent model for QM calculations. Here, we present a novel way of developing such a general machine-learned QM implicit solvent model, which relies neither on QM reference calculations for training nor experimental data, by transferring knowledge obtained from classical interactions to QM. This strategy makes the obtained graph neural network (GNN) based implicit solvent model (termed QM-GNNIS) independent of the chosen functional and basis set. The performance of QM-GNNIS is validated on NMR and IR experiments, demonstrating that the approach can reproduce experimentally observed trends unattainable by state-of-the-art implicit-solvent models. Artificial neural network ; Graph ; Solvent models ; Solvent effects ; Molecular dynamics ; Molecule ; Basis (linear algebra) ; Solvent ; Graph theory Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Molecular spectroscopy and chirality
Authors: Bo Huang | Fan Fan | Bin Xi | Xiaofeng Meng | Han Wang | Bowen Zhang | Qingbo Xu | Wei Feng | Xiaoman Wang | Hongbo Zhang | Feng Zhou | Zhenming Liu | Wenbiao Zhou
Source: Research Square (preprint, 2025-01-19)
Field: Materials Science — Materials Chemistry

Assessing Conformation Validity and Rationality of Deep Learning-Generated 3D Molecules Rationality ; Psychology ; Artificial intelligence ; Cognitive science ; Computer science ; Epistemology ; Philosophy Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics