jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aymar Tchagoue | Véronique Églin | Jean-Marc Petit | Sébastien Pruvost | Jannick Duchet‐Rumeau | Jean‐François Gérard
Source: Journal of Chemical Information and Modeling (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

Dual Embedding: A Fine-Tuned Language Model Approach for Accurate Polymer Glass Transition Temperature Prediction Recent years have witnessed major advances in polymer informatics, yet accurately predicting polymer properties, such as the glass transition temperature (<i>T</i><sub>g</sub>), remains a challenge. Language models like BERT have been leveraged to derive embeddings from polymer representations (e.g., SMILES). However, similarity between embedding vectors in these latent spaces primarily reflects chemical structural similarity, with limited alignment to physicochemical properties. Here, we introduce a dual-embedding framework that enhances <i>T</i><sub>g</sub> prediction by combining a conventional BERT-based embedding with a fine-tuned counterpart explicitly trained so that vector similarity reflects proximity in <i>T</i><sub>g</sub> values. We evaluate our approach across four benchmarks: a heterogeneous data set compared against 25 machine learning baselines, along with three additional data sets focused on homopolymers and polyimides. The dual embedding outperforms standard BERT-based embeddings, achieving up to a 20% reduction in RMSE and surpassing alternative models such as graph-based and descriptor-based approaches. These results demonstrate that embedding molecular properties directly into representations can advance polymer informatics beyond structure-centric paradigms. Embedding ; Similarity (geometry) ; Dual (grammatical number) ; Set (abstract data type) ; Polymer ; Language model ; Glass transition Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Advanced Graph Neural Networks
Authors: Edgar Olehnovics | Yifei Michelle Liu | Nada Mehio | Ahmad Y. Sheikh | Michael R. Shirts | Matteo Salvalaglio
Source: ChemRxiv (preprint, 2025-04-08)
Field: Chemistry — Analytical Chemistry

Lattice free energies of molecular crystals using normalizing flow Ranking computationally predicted molecular crystal polymorphs by thermodynamic stability requires anharmonic free energy calculations that are scalable and robust. Although classical free energy perturbation (FEP) methods provide very accurate lattice free energy estimates, their scalability in large polymorphic landscapes is limited by the need to ergodically sample one chain of overlapping alchemical Boltzmann distributions per polymorph. In contrast, targeted free energy perturbation (TFEP) can converge free energies of the same accuracy directly on the existing physical data sampled in each polymorph. The only major challenge in TFEP is obtaining an accurate bijective mapping between each polymorph and a common reference distribution. To achieve a general mapping strategy, we turn to normalising flow neural networks. In this work, we demonstrate the feasibility of normalising-flow enhanced TFEP in molecular crystals. Specifically, we describe a normalising flow architecture, based on spline-coupling, that can achieve a significant overlap between a Boltzmann distribution of a molecular crystal supercell and the artificial distribution learned by a probabilistic generative model (PGM) trained by example on relatively small amounts of MD data. The accuracy and efficiency of this approach were assessed by comparing PGM-based FE estimates to the reference FE estimates from the Einstein crystal method (ECM). These two methods were applied to three molecules of practical relevance in the pharmaceutical industry: succinic acid, a salt former and starting material, and two drug molecules, Veliparib and Mivebresib. A compelling agreement in accuracy between the PGM and the ECM was observed in the polymorphic landscapes for these three compounds across all supercell sizes that were considered. In addition to showing that normalising flow can provide reliable free energy estimates in systems that are significantly more complex compared to those previously studied at a significantly lower cost than ECM, we also discuss how one can further improve the cost-effectiveness of this approach. Lattice (music) ; Materials science ; Crystallography ; Mathematics ; Physics ; Chemistry Petroleum Processing and Analysis
Authors: A Zhang | Chengke Bao | Z. A. Zhu | Weidong Ji
Source: Computational Materials Science (article, 2025-05-16)
Field: Materials Science — Materials Chemistry

A quantum-transformer hybrid architecture for polymer property prediction: Addressing data sparsity issues Transformer ; Quantum ; Architecture ; Polymer ; Computer science ; Materials science ; Engineering ; Electrical engineering ; Physics ; Composite material ; Quantum mechanics ; Voltage Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Machine Learning and ELM
Authors: Hongwei Du | Jian Hui | Lanting Zhang | Hong Wang
Source: ArXiv.org (preprint, 2025-02-14)
Field: Materials Science — Materials Chemistry

Universal Machine Learning Interatomic Potentials are Ready for Solid Ion Conductors With the rapid development of energy storage technology, high-performance solid-state electrolytes (SSEs) have become critical for next-generation lithium-ion batteries. These materials require high ionic conductivity, excellent electrochemical stability, and good mechanical properties to meet the demands of electric vehicles and portable electronics. However, traditional methods like density functional theory (DFT) and empirical force fields face challenges such as high computational costs, poor scalability, and limited accuracy across material systems. Universal machine learning interatomic potentials (uMLIPs) offer a promising solution with their efficiency and near-DFT-level accuracy.This study systematically evaluates six advanced uMLIP models (MatterSim, MACE, SevenNet, CHGNet, M3GNet, and ORBFF) in terms of energy, forces, thermodynamic properties, elastic moduli, and lithium-ion diffusion behavior. The results show that MatterSim outperforms others in nearly all metrics, particularly in complex material systems, demonstrating superior accuracy and physical consistency. Other models exhibit significant deviations due to issues like energy inconsistency or insufficient training data coverage.Further analysis reveals that MatterSim achieves excellent agreement with reference values in lithium-ion diffusivity calculations, especially at room temperature. Studies on Li3YCl6 and Li6PS5Cl uncover how crystal structure, anion disorder levels, and Na/Li arrangements influence ionic conductivity. Appropriate S/Cl disorder levels and optimized Na/Li arrangements enhance diffusion pathway connectivity, improving overall ionic transport performance. Electrical conductor ; Ion ; Materials science ; Chemical physics ; Computer science ; Engineering physics ; Nanotechnology ; Physics ; Quantum mechanics ; Composite material Machine Learning in Materials Science
Authors: Juhi Jannat Mim | Saifuddun Rakib | Shahana Akter | Jannat Rosul Nisha | Safiullah Khan | S M Maksudur Rahman | Mehedi Hasan Manik | Nayem Hossain
Source: Journal of Nanoparticle Research (article, 2025-10-25)
Field: Materials Science — Materials Chemistry

The emerging role of machine learning in nanomaterials research: applications, challenges, and future directions Context (archaeology) ; Generative grammar ; Software deployment ; Process (computing) ; Reinforcement learning ; Property (philosophy) Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Yuma Iwasaki
Source: Vacuum and Surface Science (article, 2025-06-09)
Field: Materials Science — Materials Chemistry

Autonomous Materials Search for Magnetic Materials In recent years, “Materials Informatics,” which leverages data science for the exploration of novel materials, has gained significant attention. Among various approaches, “autonomous materials discovery,” which integrates robotics and materials simulations with machine learning, has emerged as a promising method for efficiently navigating vast material spaces. In this study, we employed a simulation-based autonomous materials discovery approach that combines first-principles calculations with Bayesian optimization to explore a broad space of magnetic alloys. As a result, we successfully discovered and synthesized novel alloys with high magnetization. Materials science ; Computer science ; Nanotechnology Machine Learning in Materials Science ; Optimization and Search Problems ; Minerals Flotation and Separation Techniques
Authors: Man Li | Lingyun Dai | Huan Wu | Yan Yan | Joon Sang Kang | Sophia C. King | Patricia E. McNeil | Danielle M. Butts | Tiphaine Galy | Michal Marszewski | Esther H. Lan | Bruce Dunn | Sarah H. Tolbert | Laurent Pilon | Yongjie Hu
Source: Materials Today Physics (article, 2025-01-24)
Field: Materials Science — Materials Chemistry

Ballistic transport from propagating vibrational modes in amorphous silicon dioxide: Thermal experiments and atomistic-machine learning modeling Understanding thermal transport in amorphous materials is critical for a wide range of applications, including buildings, vehicles, aerospace, and acoustic technologies. Despite its importance, the fundamental behavior of heat carriers in amorphous structures remains poorly understood and is often attributed to localized vibrational modes with mean free paths of about 1 nm, posing significant challenges for engineering their thermal functionalities. In this study, we present experimental measurements on mesoporous silica and atomistic analyses using Monte Carlo simulations and machine learning models to quantify the relationship between nanoarchitecture and effective thermal conductivity. Through rational chemical synthesis and ultrafast spectroscopy measurements, a strong size dependence within the sub-10 nm regime is observed, where the classical Fourier heat conduction theory fails to account for the effects of porosity and pore size. This deviation from diffusive transport is attributed to the significant contribution of propagating vibrational modes, in addition to non-propagating modes, revealing unexpectedly long mean free paths and ballistic thermal transport for heat carriers in amorphous silica. The fundamental vibrational modes in amorphous silica are further investigated using spectral-dependent Boltzmann transport equation simulations and molecular dynamics with machine learning potentials, showing good agreement with experimental results. This study provides valuable insights into nanoscale-modulated thermal transport properties in mesoporous silica and opens new opportunities for the rational design of thermally insulating materials. Materials science ; Amorphous solid ; Thermal ; Silicon dioxide ; Silicon ; Molecular vibration ; Chemical physics ; Composite material ; Crystallography ; Optics ; Optoelectronics ; Thermodynamics ; Raman spectroscopy ; Physics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thin-Film Transistor Technologies
Authors: Jinhyeok Jeon | ChaeWon Mun | Daehyeon Kang | Jaewon Na | Hye-Jin Park | Jong-Joo Rha | Jae Woo Kim | Sung‐Gyu Park
Source: Journal of Hazardous Materials (article, 2025-11-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep learning-integrated SERS platform for accurate identification of diverse phthalate ester subtypes Phthalate esters (PAEs) are widely employed as plasticizers to enhance the durability, flexibility, and processability of polymer-based materials. Because PAEs do not form covalent bonds with polymer matrices, they can readily migrate into the environment. This raises significant ecological issues and human health concerns such as endocrine disruption and developmental toxicity. In this study, we developed a surface-enhanced Raman spectroscopy (SERS)-based analytical platform integrated with deep learning algorithms for the rapid and accurate identification and classification of seven representative PAEs. Plasmonic gold nanopillar (AuNP) substrates were engineered to form vertically and horizontally oriented nanogap structures that generate intense localized electromagnetic hotspots, substantially amplifying the Raman signals of adsorbed PAEs. Comprehensive spectral datasets were collected through SERS detection using a portable Raman spectrometer. Deep neural network (DNN) models trained on these data achieved robust classification performance with an accuracy of 99.4 % across all PAE species. Furthermore, SHapley Additive exPlanations (SHAP) analysis provided interpretable insights into the most discriminative Raman spectral features driving the model predictions. The platform was successfully applied to detect PAEs in commercially available consumer products at concentrations near the regulatory threshold of 0.1 % (w/w) established by regulatory bodies such as the European Union's RoHS Directive and the US CPSIA. These findings demonstrate that the integration of nanostructure-enhanced SERS and deep learning constitutes a powerful, high-throughput, and field-deployable approach for reliable PAE detection, with broad applicability in environmental monitoring, consumer product safety evaluation, and regulatory compliance. Raman spectroscopy ; Identification (biology) ; Deep learning ; Plasmon ; Phthalate ; Chemometrics Gold and Silver Nanoparticles Synthesis and Applications ; Effects and risks of endocrine disrupting chemicals ; Microplastics and Plastic Pollution
Authors: Berat İlhan-Ceylan | Ali Aydın | Mümin Hüsamioğlu | Olcay Bölükbaşı-Yalçınkaya | Muhammet Köse | Tarık Küçükdeniz | Selcan Karakuş | Yasemin Kurt
Source: Inorganic Chemistry Communications (article, 2025-08-25)
Field: Chemistry — Inorganic Chemistry

Dose-dependent multifunctional effects of oxovanadium(IV) thiosemicarbazone complexes: A molecular modeling approach Semicarbazone ; Chemistry ; Molecular model ; Stereochemistry Vanadium and Halogenation Chemistry ; Metal complexes synthesis and properties ; Lanthanide and Transition Metal Complexes
Authors: H. Kang | Tenglong Lu | Zhao Qi | Jiandong Guo | Sheng Meng | Miao Liu
Source: AI for Science (article, 2025-06-01)
Field: Materials Science — Materials Chemistry

FastTrack: a fast method to evaluate mass transport in solid leveraging universal machine learning interatomic potential We introduce a rapid, accurate framework for computing atomic migration barriers in crystals by combining universal machine‐learning force fields (MLFFs) with 3D potential‐energy‐surface sampling and interpolation. Our method suppresses periodic self‐interactions via supercell expansion, builds a continuous potential energy surface (PES) from MLFF energies on a spatial grid, and extracts minimum‐energy pathways without predefined nudged elastic band (NEB) images. For a benchmark set of twelve electrode and electrolyte materials, including LiCoO _2 , LiFePO _4 , and Li _10 GeP _2 S _12 , our MLFF‐derived barriers lie within tens of meV of density functional theory (DFT) and experiment values, while achieving a ∼100-fold speedup over standard DFT‐NEB calculations. We benchmark GPTFF, CHGNet, and MACE, showing that fine‐tuning on PBE/PBE + U data further enhances accuracy. Ultimately, we introduce an open‐source package for high‐throughput materials screening and interactive PES visualization. Benchmark (surveying) ; Energy (signal processing) ; Surface (topology) ; Supercell ; Interatomic potential ; Work (physics) ; Deep learning ; Potential energy Machine Learning in Materials Science
Authors: Can Leng | Xuguang Chen | Jie Liu | Chunye Gong | Bo Yang | Zhuo Tang | Wangdong Yang | Wei‐Qing Huang | Yi‐Ge Zhou | Maosong Mo | Kenli Li | Keqin Li
Source: Computational Materials Science (article, 2025-02-23)
Field: Materials Science — Materials Chemistry

Fully automated high-throughput computer-based catalytic material screening framework and its application on the new-generation Tianhe supercomputer Supercomputer ; Throughput ; Computer science ; Catalysis ; Computer architecture ; Computational science ; Parallel computing ; Nanotechnology ; Materials science ; Chemistry ; Operating system Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Konstantina Pantelidou | Stavros Devrelis | Antonios Lalas | Konstantinos Votis | Dimitrios Tzovaras
Source: Key engineering materials (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

Enhancing Hansen Solubility Predictions: A Combined Approach Using Integrated Datasets and Optimized Machine Learning Techniques Accurate prediction of Hansen Solubility Parameters (HSPs) is important for understanding chemical compatibility in fields like pharmaceuticals, cosmetics and chemical engineering. This study aims to enhance HSP prediction by employing machine learning techniques and using a large, extended dataset from the Hansen Solubility Parameter in Practice (HSPiP) software. Models like XGBoost, CatBoost, LightGBM, as well as ensemble methods, were used for regression, optimized through hyperparameter tuning, feature selection and evaluated using Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and R-squared (R 2 ) metrics. The results indicated that using a wide variety of molecular components improves prediction accuracy and enhances the model’s applicability across different compounds. The findings additionally show that advanced machine learning methods can significantly improve HSP prediction accuracy, facilitating more precise solubility estimates and advancing applications in chemical and materials science. Solubility ; Feature selection ; Mean absolute error ; Mean squared error ; Hyperparameter ; Mean squared prediction error ; Compatibility (geochemistry) ; Ensemble learning Crystallization and Solubility Studies
Authors: Kunyang Cheng | Xiujuan Cheng | Mingyang Shi | Xuying Zhou | Jiahao Deng | Gang Jiang | Jiguang Du
Source: Physical review. E (article, 2025-05-19)
Field: Materials Science — Materials Chemistry

Deep-learning interatomic potentials of the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mi>ɛ</mml:mi><mml:mo>−</mml:mo><mml:mtext>Zr</mml:mtext><mml:msub><mml:mi>X</mml:mi><mml:mn>2</mml:mn></mml:msub></mml:mrow></mml:math> series <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mrow><mml:mo>(</mml:mo><mml:mi>X</mml:mi><mml:mo>=</mml:mo><mml:mi mathvariant="normal">H</mml:mi><mml:mo>,</mml:mo><mml:mo> </mml:mo><mml:mi mathvariant="normal">D</mml:mi><mml:mo>,</mml:mo><mml:mo> </mml:mo><mml:mtext>and</mml:mtext><mml:mo> </mml:mo><mml:mi mathvariant="normal">T</mml:mi><mml:mo>)</mml:mo></mml:mrow></mml:math> The ɛ-ZrH_{2} species act as an important component in zirconium-based composite hydrides, which have various applications in nuclear energy, hydrogen storage, and catalysis. In this work, deep-learning interatomic potentials for ɛ-ZrH_{2} have been developed by training density functional theory (DFT) data. The results indicate that the developed deep-learning interatomic potentials (DP) can accurately predict the structural, mechanical, and thermodynamic properties of ɛ-ZrH_{2} with DFT level accuracy. These deep-learning interatomic potentials are shown to be superior to the conventional modified embedded atom method potential. The H-isotope effect was also taken into account in constructing the deep-learning interatomic potentials, which facilitates molecular dynamic (MD) simulations under irradiation conditions. The development of these deep-learning interatomic potentials offers improved options for MD simulations of ɛ-ZrX_{2} (X=H, D, and T). Computer science ; Artificial intelligence Machine Learning in Materials Science ; Nuclear Physics and Applications ; Nuclear Materials and Properties
Authors: Muhamad Akrom | Supriadi Rustad | T. Sutojo | Wahyu Aji Eko Prabowo | Hermawan Kresno Dipojono | Ryo Maezono | Hideaki Kasai
Source: Results in Surfaces and Interfaces (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Stacking classical-quantum hybrid learning approach for corrosion inhibition efficiency of N-heterocyclic compounds This study introduces the stacking classical-quantum model (SCQM) as a novel approach to predicting N-heterocyclic compounds' corrosion inhibition efficiency (CIE). SCQM integrates classical models such as Multi-Layer Perceptron Neural Network (MLPNN) and Random Forest (RF) as base learners, with Quantum Neural Network (QNN) as the meta-learner. Experimental results demonstrate SCQM's superior performance with a coefficient of determination (R 2 ) value of 0.98 and root mean squared error (RMSE) of 0.92, outperforming classical models. Predictions of new derivatives PP1 and PP2 yielded CIE values of 95.39% and 94.05%, aligning with experimental results. The novelty lies in the hybrid framework's ability to leverage quantum feature maps, offering a groundbreaking method to explore anti-corrosion materials through quantum machine learning (QML). Stacking ; Corrosion ; Quantum ; Chemistry ; Quantum chemical ; Combinatorial chemistry ; Organic chemistry ; Physics ; Quantum mechanics ; Molecule Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; Quantum Computing Algorithms and Architecture
Authors: Jiechun Liang | J. H. L. Ling | Limin Xu | Xi Zhu
Source: Scientific Data (article, 2025-06-04)
Field: Materials Science — Materials Chemistry

A Dataset of Raman and Infrared Spectra as an Extension to the ChEMBL Raman spectroscopy and Infrared (IR) spectroscopy are two important tools in solving the structure and bond properties of molecules. With the development of deep learning methods in material science, there is a growing demand for the quantity and diversity of quantum chemistry data, so as the spectral information. However, plenty of spectra still missing in current datasets. To solve this problem, we applied Gaussian09 to construct a Raman spectrum and IR spectral dataset. In this work, currently a total of 220,000 molecules were extracted from ChEMBL. The number of molecules is increasing and is uploaded regularly. The dataset comprises optimized geometries, vibrational frequencies, IR and Raman intensities, and energies expanding both the breadth and depth of existing quantum chemistry collections. By providing high-fidelity, multidimensional feature sets, this resource enables the training and benchmarking of next-generation models including inferring substructures from spectroscopic fingerprints, assembling molecule structure from spectras, and prediction Raman or IR spectra for novel molecules. chEMBL ; Extension (predicate logic) ; Raman spectroscopy ; Infrared ; Computer science ; Biology ; Physics ; Bioinformatics ; Programming language ; Astronomy Machine Learning in Materials Science ; Nuclear Physics and Applications ; Advanced X-ray and CT Imaging
Authors: T. Manjunatha | B H Shivayogi | Maruthi Prashanth B H | Sandeepkumar Gowda | Asif Iqbal Mulla | Girish Ariga | Divijesh Puninchathaya P | Srinivasa C. S
Source: Journal of the Indian Academy of Wood Science (article, 2025-08-09)
Field: Materials Science — Polymers and Plastics

Effect of fly-ash as filler material on mechanical and thermal properties of jute-wool-epoxy hybrid composite: prediction using artificial neural network model Epoxy ; Composite material ; Filler (materials) ; Materials science ; Composite number ; Fly ash ; Artificial neural network ; Wool ; Computer science ; Machine learning Natural Fiber Reinforced Composites ; Polymer Nanocomposites and Properties ; Lignin and Wood Chemistry
Authors: Nada Alfryyan | Mudassir Hussain Tahir | Muhammad Saqib | Muhammad Yaqoob | Khadijah Mohammedsaleh Katubi | M.S. Al‐Buriahi
Source: Synthetic Metals (article, 2025-11-26)
Field: Materials Science — Materials Chemistry

Machine learning-guided screening of polycyclic aromatic compounds for thermally activated delayed fluorescence applications Boosting (machine learning) ; Gradient boosting ; Feature selection ; Training set ; Molecule ; Fluorescence ; Molecular descriptor Machine Learning in Materials Science ; Luminescence and Fluorescent Materials ; Computational Drug Discovery Methods