jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Saba Taheri | Dmitry Volodkin | André G. Skirtach | Bogdan V. Parakhonskiy
Source: Expert Opinion on Drug Delivery (article, 2025-11-19)
Field: Materials Science — Biomaterials

Calcium carbonate in drug delivery: functional carrier design, applications, and data-driven perspectives Calcium carbonate, especially vaterite, offers a tunable, biocompatible platform for controlled and sustainable delivery. Hybrid CaCO₃-hydrogel systems address the limits of purely inorganic or organic carriers. Machine learning and explainable AI will accelerate synthesis and formulation optimization. Future progress depends on scalable, eco-friendly production and clear regulatory frameworks. Biocompatible material ; Calcium ; Drug ; Calcium carbonate ; Drug delivery ; Drug carrier Calcium Carbonate Crystallization and Inhibition ; Polymer Surface Interaction Studies ; Marine Sponges and Natural Products
Authors: Rafael
F. Verı́ssimo (21214570) | Pedro H. F. Matias (21214561) | Mateus R. Barbosa (21214564) | Flávio O. S. Neto (21214567) | Brenno A. D. Neto (8138031) | Heibbe C. B. de Oliveira (9226241)
Source: Figshare (article, 2025-04-29)
Field: Materials Science — Materials Chemistry

Integrating MachineLearning and SHAP Analysis toAdvance the Rational Design of Benzothiadiazole Derivatives with TailoredPhotophysical Properties 2,1,3-Benzothiadiazole (BTD) derivatives show promise in advanced photophysical applications, but designing molecules with optimal desired properties remains challenging due to complex structure–property relationships. Existing computational methods have a high cost when predicting precise photophysical characteristics. Machine learning with Morgan fingerprints was employed to forecast BTD derivative maximum absorption and emission wavelengths. Three flavors of machine learning models were applied, namely, Random Forest, LigthGBM, and XGBoost. Random forest achieved <i>R</i><sup>2</sup> values of 0.92 for absorption and 0.89 for emission, validated internally with 10-fold cross-validations and externally with recent experimental data. SHapley Additive exPlanations (SHAP) analysis revealed critical design insights, highlighting the tertiary amine presence and solvent polarity as key drivers of red-shifted emissions. By the development of a web-based predictive tool, the potential of machine learning to accelerate molecular design is demonstrated, providing researchers a powerful approach to engineer BTD derivatives with enhanced photophysical properties. Rational design ; Solvent polarity ; Key (lock) ; Random forest ; Absorption (acoustics) ; Derivative (finance) Luminescence and Fluorescent Materials ; Computational Drug Discovery Methods ; Machine Learning in Materials Science
Authors: Alptuğ Ulugöl | Jessi Bückmann | Ruizhi Yang | Roy Hoitink | Alfons van Blaaderen | Frank Smallenburg | Laura Filion
Source: ArXiv.org (preprint, 2025-09-08)
Field: Materials Science — Materials Chemistry

Comparing unsupervised learning methods for local structural identification in colloidal systems Quantifying local structures in self-assembled systems is a central challenge in soft matter and materials science. When no a priori knowledge of the relevant structures is available, traditional order parameters often fall short. Unsupervised machine learning provides a convenient route to autonomously uncover structural motifs directly from particle configurations. In this work, we systematically compare three popular dimensionality reduction techniques; Principal Component Analysis (PCA), Autoencoders (AE), and Uniform Manifold Approximation and Projection (UMAP), for classifying local environments in self-assembled systems. We first apply these methods to fluid and crystal configurations of hard and charged spheres. Thereafter, we apply it to an icosahedral arrangement of spheres that self-assembled in spherical confinement, both from simulations as well as from experiments. We demonstrate that UMAP consistently outperforms the other methods in capturing complex structural features, offering a robust tool for structural classification without supervision. Unsupervised learning ; Dimensionality reduction ; A priori and a posteriori ; Principal component analysis ; Pattern recognition (psychology) ; Projection (relational algebra) ; Curse of dimensionality ; Nonlinear dimensionality reduction ; Identification (biology) Pickering emulsions and particle stabilization ; Material Dynamics and Properties ; Block Copolymer Self-Assembly
Authors: M. Uchida | Mei Toji | Keito Oya | Yoshihisa KANEKO
Source: Keisan Rikigaku Koenkai koen ronbunshu/Keisan Rikigaku Kouenkai kouen rombunshuu (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Evaluation and modeling of effect of strain rate on double yielding behavior of semi-crystalline polymer Semi-crystalline polymer typically exhibits complex viscoelastic-viscoplastic behavior including the double-yielding (DY) phenomenon. Uniaxial tensile tests at different strain rates were performed using PA specimens obtained at different annealing temperatures to evaluate the effect of the strain rate on the DY phenomenon. The slope of the stress-strain curve after the first yield and the distance between the first and second yields increased with decreasing strain rate. In contrast, the maximum stress observed during the DY phenomenon was independent of the strain rate, and uniform deformation continued for a longer strain range at lower strain rates. The viscoelastic-viscoplastic transient network model was generalized to reproduce the experimentally observed time-dependent DY phenomenon. The proposed method requires over 20 material parameters to reproduce complex mechanical behavior including time-dependent DY phenomenon. To obtain parameters accurately reproduce the experimental results, we introduced machine-learning supported Newton-Raphson identification process. The FEM simulation results with material parameters identified by the proposed method could accurately reproduce the experimentally observed mechanical responses. Strain rate ; Deformation (meteorology) ; Ultimate tensile strength ; Polymer ; Strain (injury) ; Finite element method ; Stress (linguistics) ; Annealing (glass) Polymer crystallization and properties ; Elasticity and Material Modeling ; High-Velocity Impact and Material Behavior
Authors: Wang, Connie M. | Sabino, Roberta M. | Garg, Aditya | Salih, Ahmed E. | Tadesse, Loza F. | Edelman, Elazer R.
Source: arXiv (Cornell University) (article, 2025-12-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nano-engineered surface enhanced Raman spectroscopy substrates for probing tissue-material interactions Innovation in biomaterials has brought both breakthroughs and new challenges in medicine, as implant materials have become increasingly multifunctional and complex. One of the greatest issues is the difficulty in assessing the temporal and multidimensional dynamics of tissue-implant interactions. Implant biology remains hard to decipher without a noninvasive and multiplexed technique that can accurately monitor real-time biological processes. To address this, we developed a multifunctional, self-sensing implant material composed of gold nano-columns patterned on a titanium surface (AuNC-Ti). This material acts as a nanoengineered surface-enhanced Raman spectroscopy (SERS) substrate that amplifies biological Raman signals at the tissue-implant interface, providing the ability to sense tissue-material interactions in a multiplexed and nondestructive manner. AuNC-Ti SERS substrates were fabricated using oblique angle deposition (OAD) and characterized using scanning electron microscopy (SEM) to show uniform formation of AuNCs ($360 \pm 40$ nm in length and $50 \pm 16$ nm in width). X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and contact angle measurements demonstrated biocompatible surface chemistry with ideal wettability. Biocompatibility was further demonstrated via in vitro cytotoxicity assays on human aortic endothelial cells (HAECs) cultured on AuNC-Ti surfaces. The median SERS enhancement factor (EF) was calculated to be $1.8 \times 10^5$, and spatial identification of reporter molecules and porcine tissue components on AuNC-Ti surfaces was demonstrated using confocal Raman imaging and multivariate analysis. Our approach utilizes unlabeled SERS and machine learning, promising multiplexed characterization of tissue-material interactions and subsequently enabling tissue state determination and non-invasive monitoring of implant-tissue interaction. Raman spectroscopy ; Biocompatibility ; Characterization (materials science) ; Surface-enhanced Raman spectroscopy ; X-ray photoelectron spectroscopy ; Biomolecule ; Substrate (aquarium) ; Multiplexing ; Microscopy ; Scanning electron microscope Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Laser Material Processing Techniques
Authors: Tyra Douglas | Michael J. Dzara | Andrew J. E. Rowberg | Keith A. King | Maria Syrigou | Nicholas A. Strange | Robert G. Bell | Anuj Goyal | Pin-Wen Guan | Robert B. Wexler | Joel B. Varley | Tadashi Ogitsu | Stephan Lany | Anthony H. McDaniel | Sean R. Bishop | Matthew Witman
Source: ChemRxiv (preprint, 2025-07-24)
Field: Materials Science — Materials Chemistry

Large-scale experimental validation of thermochemical water-splitting oxides discovered by defect graph neural networks Thermochemical water-splitting (TCH) based on 2-step thermal redox cycles in metal oxides is a promising approach to generating H2, but state-of-the-art (SOTA) CeO2 has several practical limitations, which has motivated continued materials discovery efforts in this field. Here, we improve upon a SOTA defect graph neural network (dGNN) surrogate model's oxygen vacancy predictions and combine them with Materials Project phase diagrams to down-select and discover structurally diverse, experimentally known metal oxides whose TCH performance was previously unknown. Amongst twelve candidates selected based on our high-throughput screening and down-selection criteria, we achieved ~80% accuracy in identifying materials with stable redox cycling and hydrogen production in stagnation flow reactor water-splitting experiments. Closer to 100% accuracy can be achieved if higher-accuracy, hybrid DFT-predicted vacancy formation energies were computed and used in lieu of the most uncertain dGNN-based screening predictions, as they correct false positives to true negatives. Notably, two discovered candidates, Sr3PrMn2O6 and Ba2Fe2O5, display hydrogen yields greater than CeO2 under specific redox conditions. These results demonstrate our ability to computationally predict and experimentally validate promising candidate TCH materials that have the potential to compete with CeO2. Scale (ratio) ; Artificial neural network ; Graph ; Computer science ; Artificial intelligence ; Theoretical computer science ; Cartography ; Geography Catalytic Processes in Materials Science ; Catalysis and Oxidation Reactions ; Advancements in Solid Oxide Fuel Cells
Authors: Wang, Connie M. | Sabino, Roberta M. | Garg, Aditya | Salih, Ahmed E. | Tadesse, Loza F. | Edelman, Elazer R.
Source: Open MIND (preprint, 2025-12-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Nano-engineered surface enhanced Raman spectroscopy substrates for probing tissue-material interactions Innovation in biomaterials has brought both breakthroughs and new challenges in medicine, as implant materials have become increasingly multifunctional and complex. One of the greatest issues is the difficulty in assessing the temporal and multidimensional dynamics of tissue-implant interactions. Implant biology remains hard to decipher without a noninvasive and multiplexed technique that can accurately monitor real-time biological processes. To address this, we developed a multifunctional, self-sensing implant material composed of gold nano-columns patterned on a titanium surface (AuNC-Ti). This material acts as a nanoengineered surface-enhanced Raman spectroscopy (SERS) substrate that amplifies biological Raman signals at the tissue-implant interface, providing the ability to sense tissue-material interactions in a multiplexed and nondestructive manner. AuNC-Ti SERS substrates were fabricated using oblique angle deposition (OAD) and characterized using scanning electron microscopy (SEM) to show uniform formation of AuNCs ($360 \pm 40$ nm in length and $50 \pm 16$ nm in width). X-ray photoelectron spectroscopy (XPS), X-ray diffraction (XRD), and contact angle measurements demonstrated biocompatible surface chemistry with ideal wettability. Biocompatibility was further demonstrated via in vitro cytotoxicity assays on human aortic endothelial cells (HAECs) cultured on AuNC-Ti surfaces. The median SERS enhancement factor (EF) was calculated to be $1.8 \times 10^5$, and spatial identification of reporter molecules and porcine tissue components on AuNC-Ti surfaces was demonstrated using confocal Raman imaging and multivariate analysis. Our approach utilizes unlabeled SERS and machine learning, promising multiplexed characterization of tissue-material interactions and subsequently enabling tissue state determination and non-invasive monitoring of implant-tissue interaction. Raman spectroscopy ; Biocompatibility ; Characterization (materials science) ; Surface-enhanced Raman spectroscopy ; X-ray photoelectron spectroscopy ; Biomolecule ; Substrate (aquarium) ; Multiplexing ; Microscopy ; Scanning electron microscope Gold and Silver Nanoparticles Synthesis and Applications ; Spectroscopy Techniques in Biomedical and Chemical Research ; Laser Material Processing Techniques
Authors: Simai Yang | Jingxiao Zhao | Pengcheng Liu | Zhao Xing | Xiaojun Liang | Xiucheng Li
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Multi-source Data Few-Shot Learning for Mechanical Property Prediction in Low-Alloy Steels Ultimate tensile strength ; Microstructure ; Stability (learning theory) ; Gradient boosting ; Pattern recognition (psychology) ; Texture (cosmology) ; Boosting (machine learning) ; Feature (linguistics) ; Binary number Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Hannes Kneiding (20319152) | David Balcells (1469065)
Source: Figshare (article, 2025-10-24)
Field: Materials Science — Materials Chemistry

tmQMg* Data Set:Excited State Properties of 74k TransitionMetal Complexes The application of machine learning approaches to meaningful problems in chemistry and materials science is still challenged by the limited availability of data. In order to close this gap, we report the tmQMg* data set, which provides excited state properties for 74k mononuclear transition metal complexes extracted from the Cambridge Structural Database. All properties were computed at the TD-DFT ωB97xd/def2SVP level of theory. The strongest electron excitations in the ultraviolet, visible, and near-infrared ranges are included, together with the wavelengths and intensities of the first 30 excited states. Further, natural transition orbitals were computed for the strongest excitations in the visible range to determine the nature of the associated charge transfers. By computing the TD-DFT spectra in both gas phase and acetone, we quantified solvatochromic effects, which are also provided with the data set, in terms of both wavelength shifts and intensity changes. The tmQMg* data set will enable the development of discriminative and generative artificial intelligence models with respect to absorption spectra, charge transfer character, and solvatochromism, enabling novel advances in the field of transition metal photochemistry. Excited state ; Atomic orbital ; Charge (physics) ; Solvatochromism ; Phase transition ; Chromophore ; Transition metal ; Field (mathematics) ; Absorption (acoustics) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Organometallic Complex Synthesis and Catalysis
Authors: Xiao Li | Ruonan Li | Luoming Hu | Lianjing Mao | Tianyu Zheng | Chen Ye | Wei Sun | Pengrui Zhang | Jinhe Sun
Source: New Journal of Chemistry (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Predicting solid–solid phase transition of quaternary ammonium salts by machine learning Predictive models are constructed through machine learning and used to determine whether a quaternary ammonium salt can undergo a solid–solid phase transition. This provides an efficient tool for the design of solid–solid phase change materials. Chemistry ; Quaternary ; Ammonium ; Phase transition ; Phase (matter) ; Solid-phase synthesis ; Organic chemistry ; Thermodynamics ; Biochemistry Crystallization and Solubility Studies
Authors: Antoni Wadowski | Anshul  D. S. Parmar | F. Kaśkosz | Jesper Byggmästar | Jan Wróbel | Mikko J. Alava | Silvia Bonfanti
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Efficient training of machine learning potentials for metallic glasses: CuZrAl validation Scalability ; Key (lock) ; Path (computing) ; Atom (system on chip) ; Density functional theory ; Interatomic potential ; Training (meteorology) ; Experimental data Machine Learning in Materials Science ; Metallic Glasses and Amorphous Alloys ; Material Dynamics and Properties
Authors: Karim Aljamal | Xiaohua Wang
Source: ArXiv.org (preprint, 2025-04-21)
Field: Chemistry — Inorganic Chemistry

Predicting Methane Adsorption in Metal-Substituted MOFs: A Comparative Study between Density Functional Theory and Machine Learning Metal-organic frameworks (MOFs) are promising materials for methane capture due to their high surface area and tunable properties. Metal substitution represents a powerful strategy to enhance MOF performance, yet systematic exploration of the vast chemical space remains challenging. In this work, we compare density functional theory (DFT) and machine learning (ML) in predicting methane adsorption properties in metal-substituted variants of three high-performing MOFs: M-HKUST-1, M-ATC, and M-ZIF-8 (M = Cu, Zn). DFT calculations reveal significant differences in methane binding energies between Cu and Zn variants of all three MOFs. On the other hand, we fine-tuned a pretrained multimodal ML model, PMTransformer, on a curated subset of hypothetical MOF (hMOF) structures to predict macroscopic adsorption properties. While the fine-tuned heat of adsorption model and uptake model qualitatively predict adsorption properties for original unaltered MOFs, they fail to distinguish between metal variants despite their different binding energetics identified by DFT. We trace this limitation to the hMOF training data generated using Grand Canonical Monte Carlo (GCMC) simulations based on classical force fields (UFF/TraPPE). Our study highlights a key challenge in ML-based MOF screening: ML models inherit the limitations of their training data, particularly when electronic effects at open metal sites significantly impact adsorption behaviors. Our findings emphasize the need for improved force fields or hybrid GCMC/DFT datasets to incorporate both geometric and electronic factors for accurate prediction of adsorption properties in metal-substituted MOFs. Adsorption ; Density functional theory ; Methane ; Key (lock) ; Monte Carlo method ; Feature (linguistics) ; TRACE (psycholinguistics) ; Surface (topology) Metal-Organic Frameworks: Synthesis and Applications ; Zeolite Catalysis and Synthesis ; Carbon Dioxide Capture Technologies
Authors: Filipp Gusev (11553135) | Evgeny Gutkin (14329442) | Francesco Gentile (500655) | Fuqiang Ban (1613203) | S. Benjamin Koby (5184893) | Fengling Li (176389) | Irene Chau (2276365) | Suzanne Ackloo (182405) | Cheryl H. Arrowsmith (246847) | Albina Bolotokova (9974699) | Pegah Ghiabi (656241) | Elisa Gibson (10061704) | Levon Halabelian (710679) | Scott Houliston (468620) | Rachel J. Harding (7847774) | Ashley Hutchinson (803479) | Peter Loppnau (273605) | Sumera Perveen (9974693) | Almagul Seitova (141148) | Hong Zeng (24161) | Matthieu Schapira (51934) | Artem Cherkasov (3636) | Olexandr Isayev (1352004) | Maria G. Kurnikova (2248756)
Source: Figshare (article, 2025-05-26)
Field: Materials Science — Materials Chemistry

Active Learning-GuidedHit Optimization for the Leucine-RichRepeat Kinase 2 WDR Domain Based on In Silico Ligand-Binding Affinities The leucine-rich repeat kinase 2 (LRRK2) is the most mutated gene in familial Parkinson’s disease, and its mutations lead to pathogenic hallmarks of the disease. The LRRK2 WDR domain is an understudied drug target for Parkinson’s disease, with no known inhibitors prior to the first phase of the Critical Assessment of Computational Hit-Finding Experiments (CACHE) Challenge. A unique advantage of the CACHE Challenge is that the predicted molecules are experimentally validated in-house. Here, we report the design and experimental confirmation of LRRK2 WDR inhibitor molecules. We used an active learning (AL) machine learning (ML) workflow based on optimized free-energy molecular dynamics (MD) simulations utilizing the thermodynamic integration (TI) framework to expand a chemical series around two of our previously confirmed hit molecules. We identified 8 experimentally verified novel inhibitors out of 35 experimentally tested (23% hit rate). These results demonstrate the efficacy of our free-energy-based active learning workflow to explore large chemical spaces quickly and efficiently while minimizing the number and length of expensive simulations. This workflow is widely applicable to screening any chemical space for small-molecule analogs with increased affinity, subject to the general constraints of RBFE calculations. The mean absolute error of the TI MD calculations was 2.69 kcal/mol, with respect to the measured <i>K</i><sub>D</sub> of hit compounds. Chemical space ; In silico ; Workflow ; Domain (mathematical analysis) ; Cheminformatics ; Affinities ; Drug discovery ; Kinase ; Cache ; Molecular dynamics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: WooChul Jung | Yong‐Seok Choi
Source: ArXiv.org (preprint, 2025-11-14)
Field: Materials Science — Materials Chemistry

Multi-View Polymer Representations for the Open Polymer Prediction We address polymer property prediction with a multi-view design that exploits complementary representations. Our system integrates four families: (i) tabular RDKit/Morgan descriptors, (ii) graph neural networks, (iii) 3D-informed representations, and (iv) pretrained SMILES language models, and averages per-property predictions via a uniform ensemble. Models are trained with 10-fold splits and evaluated with SMILES test-time augmentation. The approach ranks 9th of 2241 teams in the Open Polymer Prediction Challenge at NeurIPS 2025. The submitted ensemble achieves a public MAE of 0.057 and a private MAE of 0.082. Exploit ; Property (philosophy) ; Artificial neural network ; Graph ; Polymer ; Ensemble forecasting Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Advanced Polymer Synthesis and Characterization