jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ohmin Kwon | Marco Gibaldi | Kasturi Nagesh Pai | Arvind Rajendran | Tom K. Woo
Source: ACS Central Science (article, 2025-07-07)
Field: Chemistry — Inorganic Chemistry

Identification of Metal–Organic Frameworks for near Practical Energy Limit CO <sub>2</sub> Capture from Wet Flue Gases: An Integrated Atomistic and Process Simulation Screening of Experimental MOFs Metal-organic framework (MOF) materials have attracted significant attention as solid sorbents for low energy CO<sub>2</sub> capture with adsorption-based gas separation processes. In this work, an integrated screening workflow combining a series of atomistic and process simulations was applied to identify promising MOFs for a 4-step pressure-vacuum swing adsorption (P/VSA) process at three different CO<sub>2</sub> flue gas compositions (6%, 15% and 35%). Starting from 55,818 unique experimentally characterized MOFs, ∼19k porous MOFs were investigated via atomistic grand canonical Monte Carlo (GCMC) simulations and machine learning model-based process optimizations to accelerate the screening of a large candidate database. Thousands of MOFs were identified for each of the CO<sub>2</sub> compositions tested that could achieve within 4% of the practical energy limit of dry CO<sub>2</sub> capture for the P/VSA process while still meeting the 95% CO<sub>2</sub> purity and 90% recovery constraints. From this pool, 3D MOFs without open metal sites were subjected to the multicomponent (CO<sub>2</sub>/N<sub>2</sub>/H<sub>2</sub>O) GCMC simulations at 40% relative humidity. Based on these simulations, hundreds of MOFs were identified at each CO<sub>2</sub> composition that could retain 90% of their CO<sub>2</sub> capture at this humidity while also adsorbing a minimal amount of water. A geometric analysis of these high performing materials revealed that narrow, straight 1D-channels were a common structural motif for low energy wet flue gas CO<sub>2</sub> capture with P/VSA. Flue gas ; Metal-organic framework ; Process (computing) ; Limit (mathematics) ; Process engineering ; Identification (biology) ; Environmental science ; Materials science ; Energy (signal processing) ; Detection limit ; Computer science ; Waste management ; Chemistry ; Adsorption ; Engineering ; Physics ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Carbon Dioxide Capture Technologies ; Industrial Gas Emission Control
Authors: Swapan Maity | Akshita Upreti | Shantanu Chowdhury | Dipesh Kumar Dubey | Hitesh Harsukhbhai Chandpa | Jairam Meena | Manas Kumar Santra | Pralay Maiti
Source: Small (article, 2025-09-18)
Field: Materials Science — Materials Chemistry

Doxorubicin Intercalated Co‐Al Layered Double Hydroxide Nanocarrier With pH‐Responsive Controlled Release and Selective Melanoma Suppression: Toward Next‐Generation Nanotherapeutics Clinical oncology grapples with persistent challenges, particularly the rapid onset of tumor resistance and the systemic limitations of conventional chemotherapeutics such as doxorubicin (Dox). Despite its potency, Dox suffers from poor solubility, non-specific distribution, and severe toxicity, often compromising therapeutic efficacy. Addressing these obstacles, this study explores cobalt-aluminium layered double hydroxides (Co-Al LDHs) as smart nanocarriers for targeted melanoma therapy. Capitalizing on their tunable structure, high drug-loading capacity, and biocompatibility, Co-Al LDHs facilitate enhanced encapsulation, sustained release, and selective tumor accumulation of Dox. Density Functional Theory (DFT) analyses confirm robust molecular interactions between Dox and the LDH matrix, ensuring structural stability and favorable energetics for drug delivery. In vitro assays reveal significant cytotoxicity (≈80%) against melanoma cells and with minimal toxicity (≈8%) to normal muscle cells. Furthermore, in vivo evaluations using luciferase-tagged B16-F10 melanoma models demonstrate pronounced tumor inhibition and excellent systemic biocompatibility. Augmented by machine learning-guided force field modelling, this platform also offers a predictive framework for engineering next-generation nanotherapeutics. Together, these findings position Co-Al LDHs as a promising frontier in nanomedicine, merging targeted delivery, controlled release, and computational precision to overcome current therapeutic barriers in melanoma treatment and beyond. Nanocarriers ; Melanoma ; Doxorubicin ; In vivo ; Cytotoxicity ; Controlled release ; Layered double hydroxides ; Drug ; In vitro Layered Double Hydroxides Synthesis and Applications ; Bone Tissue Engineering Materials ; MXene and MAX Phase Materials
Authors: Xiao Luo | Yue Li | Yunze Liu | Jinlei Mu | J.J. Quan
Source: Construction and Building Materials (article, 2025-01-24)
Field: Materials Science — Materials Chemistry

Research on the prediction of mechanical properties of magnesium-silicon-based cement and the mechanism of element interaction based on machine learning Mechanism (biology) ; Magnesium ; Materials science ; Cement ; Silicon ; Element (criminal law) ; Composite material ; Metallurgy Magnesium Oxide Properties and Applications ; Concrete and Cement Materials Research ; Innovative concrete reinforcement materials
Authors: Fernando Fajardo-Rojas | Ryther Anderson | Mingwei Li | Remco Chang | Diego A. Gómez‐Gualdrón
Source: Chemistry of Materials (article, 2025-07-17)
Field: Chemistry — Inorganic Chemistry

Data-Driven Insights on the Impact of Functionalization on Metal–Organic Framework Free Energies Functionalization is poised to play a prominent role in MOF development as it could become the to-go strategy to bestow extant MOF with new properties and to control the MOF pore shape and size by modulating polymorph selection. Thus, to speed up MOF development through computational work, a better (predictive) understanding of how functionalization impacts MOF synthesizability is needed. Here, we use a data-driven approach where molecular dynamics simulations on 5000+ MOFs are used to shed light on how functionalization affects MOF free energy, as the latter has been largely tied to MOF synthesizability and polymorph selection. More consistently, in MOFs with higher void fractions, we find that functionalization generally reduces free energy, with entropy contributing significantly to this thermodynamic stabilization. Although with some functionalizations (–CF3, –F, –Br, –SH, and–OH), the role of entropy is more apparent than with others (–CN, –CH3, –NO2, and –NH3). Through uneven stabilization of polymorphs, we also find functionalization (more often with –Br, –CN, and –CF3) as capable of altering polymorph (topology) selection relative to original nonfunctionalized polymorphic families. However, no switch in polymorph stability occurred when the original (unfunctionalized) polymorphs were separated by more than 1.42 kJ/mol per MOF atom. We show that machine learning can predict the functionalization-induced free energy change of a parent MOF with a mean absolute error of 0.17 kJ/mol per atom, using only the physical properties of the parent MOF and the functional group as inputs. The ML-based SHAP analysis agrees with the human analysis on the functionalization of molecular mass and the hydrogen fraction of the parent MOF being among the factors that influence change in free energy the most. Finally, we present a publicly accessible dynamic interface to visualize and navigate the free-energy data, thereby encouraging the research community to engage with and utilize the data to help uncover new insights. Surface modification ; Metal-organic framework ; Materials science ; Nanotechnology ; Chemistry ; Organic chemistry ; Physical chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Advancements in Battery Materials
Authors: Z.Y. Zhang | Yuliang Shi | Farnaz A. Shakib
Source: APL Materials (article, 2025-09-01)
Field: Materials Science — Materials Chemistry

Machine learning prediction of thermodynamic stability and electronic properties of 2D layered conductive metal–organic frameworks Two-dimensional layered electrically conductive metal–organic frameworks (EC MOFs) have emerged as promising materials for electronic and energy applications due to their tunable electronic properties and structural versatility. Here, we employ machine learning (ML) models to predict the thermodynamic stability and electronic properties of EC MOFs, significantly reducing the dependence on costly ab initio calculations. We construct different feature sets by integrating compositional features from generic statistical reduction methods along with structural descriptors curated from the EC-MOF database (termed GD, M-GD, and A-GD features). Various ML models, including linear, tree-based, and ensemble learning approaches, are benchmarked for predicting formation energies of EC MOFs, as one measure of synthesizability, as well as predicting their electrically conductive nature. The density functional theory data pool spans the 536 monolayer systems in the EC-MOF database, divided into 90% training and 10% test sets. Our results demonstrate that ML models treated by proper feature engineering can achieve very high accuracy for formation energy prediction, with the coefficient of determination, R2, being as high as 0.96. Via proper feature engineering, we also report up to 92% accuracy in predicting the metallicity of EC MOFs and 82% in bandgap classification using the extra tree classifier. The trained ML models are further applied to a new class of EC MOFs, which are neither part of the training nor members of the EC-MOF database, showcasing their predictive power and transferability. This work establishes the foundations of a data-driven framework for accelerating the discovery of novel EC MOFs in future research. Materials science ; Metal-organic framework ; Electrical conductor ; Chemical stability ; Metal ; Stability (learning theory) ; Nanotechnology ; Chemical engineering ; Metallurgy ; Physical chemistry ; Machine learning ; Composite material ; Adsorption ; Computer science Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; X-ray Diffraction in Crystallography
Authors: Y.W. Zhao | Chao Zhao | Xiao Wang | Feng Ding
Source: Small Methods (article, 2025-04-29)
Field: Materials Science — Materials Chemistry

Stability of sp <sup>3</sup> Hybridized Amorphous Carbon and its Transformation to Nanodiamond A newly synthesized carbon allotrope, fully sp<sup>3</sup>-hybridized amorphous carbon (sp<sup>3</sup>-AC), has generated widespread research interest, especially in terms of structural stability and evolution. Here, the molecular simulations with machine learning force field to explore the origin of its stability and the structural transformation from sp<sup>3</sup>-AC to diamond is performed. It is found that the diffusion of carbon in the covalently bonded sp<sup>3</sup>-AC is extremely slow. Although the sp<sup>3</sup>-AC exhibits a higher energy than diamond, the nucleation of diamond within it remains thermodynamically unfavorable. The transformation from sp<sup>3</sup>-AC to diamond is impeded by an exceptionally high nucleation energy barrier of ≈17 eV, contributing to its kinetic stability. Analysis indicates that diamond nucleation within sp<sup>3</sup>-AC induces a localized density reduction, which may serve as the underlying mechanism that inhibits nucleation and suppresses continuous growth. As a result, annealing the sp<sup>3</sup>-AC at high temperature and high pressure leads to a specific type of composite, where randomly oriented diamond nanocrystals are evenly distributed in sp<sup>3</sup>-AC. The specific composite of sp<sup>3</sup>-AC-nanodiamond serves as an intermediate between sp<sup>3</sup>-AC and diamond, which reveals the underlying mechanism of recent experimental observation of sp<sup>3</sup>-AC-nanodiamond composite. Nanodiamond ; Nucleation ; Diamond ; Materials science ; Annealing (glass) ; Amorphous carbon ; Carbon fibers ; Amorphous solid ; Composite number ; Chemical engineering ; Nanotechnology ; Chemical physics ; Crystallography ; Chemistry ; Composite material ; Organic chemistry Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Carbon Nanotubes in Composites
Authors: David Medina-Ortiz | Diego Álvarez-Saravia | Nicole Soto-García | Diego Sandoval-Vargas | Jacqueline Aldridge | Sebastián Rodríguez | Bárbara A. Andrews | Juan A. Asenjo | Anamaria Sanchez–Daza
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-02-10)
Field: Materials Science — Biomaterials

Protein language models accelerate the discovery of Plastic-Degrading Enzymes Abstract Plastic pollution presents a critical environmental challenge, necessitating innovative and sustainable solutions. In this context, biodegradation using microorganisms and enzymes offers an environmentally friendly alternative. This work introduces an AI-driven frame-work that integrates machine learning (ML) and generative models to accelerate the discovery and design of plastic-degrading enzymes. By leveraging pre-trained protein language models and curated datasets, we developed seven ML-based binary classification models to identify enzymes targeting specific plastic substrates, achieving an average accuracy of 89%. The framework was applied to over 6,000 enzyme sequences from the RemeDB to classify enzymes targeting diverse plastics, including PET, PLA, and Nylon. Besides, generative learning strategies combined with trained classification models in this work were applied for de novo generation of PET-degrading enzymes. Structural bioinformatics validated potential candidates through in-silico analysis, highlighting differences in physicochemical properties between generated and experimentally validated enzymes. Moreover, generated sequences exhibited lower molecular weights and higher aliphatic indices, features that may enhance interactions with hydrophobic plastic substrates. These findings highlight the utility of AI-based approaches in enzyme discovery, providing a scalable and efficient tool for addressing plastic pollution. Future work will focus on experimental validation of promising candidates and further refinement of generative strategies to optimize enzymatic performance. Enzyme ; Computer science ; Chemistry ; Computational biology ; Biochemistry ; Biology biodegradable polymer synthesis and properties ; Enzyme Catalysis and Immobilization ; Biochemical and Structural Characterization
Authors: Espen Fritschka | Gabriele Sadowski
Source: Molecular Pharmaceutics (article, 2025-07-03)
Field: Chemistry — Spectroscopy

The Hidden Crux of Correctly Determining Octanol–Water Partition Coefficients The partitioning of molecules between an aqueous and an organic medium is of major interest for pharmaceutical development and the chemical industry. It characterizes the impact of substances to the environment and to humans, e.g., their accumulation in living organisms. It is usually quantified in terms of the octanol-water partition coefficient <i>K</i><sub>OW</sub> of these substances. Although this is a clearly defined thermodynamic property, different experimental approaches exist for its estimation. Using active pharmaceutical ingredients (APIs) as examples, we demonstrate the large scatter in experimentally determined partition coefficients reported in the literature. This is especially serious for weak bases or weak acids, which account for around 95% of all APIs. In some cases, reported <i>K</i><sub>OW</sub> values for the same substance differ by even several orders of magnitude. This is particularly worrying because this property is crucial for approval procedures of APIs and is also used as input for a whole range of estimation methods, such as machine-learning algorithms. In this work, we discuss the physical reasons for the unusually high variety of reported <i>K</i><sub>OW</sub> values. Using physicochemical laws, it is shown that the large scatter of the data is not caused by analytical uncertainties but by the extrapolation of the experimental data to a solute concentration of zero. Based on this, we propose a new approach for evaluating experimental data on partition coefficients. This approach involves extrapolating experimentally determined distribution coefficients with respect to pH rather than concentration. We will show that this reduces the uncertainty of the experimentally obtained <i>K</i><sub>OW</sub> values, narrowing the difference between the highest and the lowest value for the same substance of currently about 2.4 to about 0.5 logarithmic units. The new approach can be combined with any existing experimental method for concentration analysis. Moreover, the obtained data agree very well with theoretical values obtained from thermodynamic modeling explicitly considering solute ionization, thus validating the proposed approach. Partition coefficient ; Octanol ; Chemistry ; Partition (number theory) ; Mathematics ; Chromatography ; Combinatorics Analytical Chemistry and Chromatography ; Chemical and Physical Properties in Aqueous Solutions ; Crystallization and Solubility Studies
Authors: Rashid Khan | Ali M. Mohsen | As’ad Alizadeh | Narinderjit Singh-Sawaran-Singh | Shaymaa Abed Hussein | Abdellatif M. Sadeq | Husam Rajab | Khalil Hajlaoui
Source: Case Studies in Thermal Engineering (article, 2025-09-17)
Field: Materials Science — Materials Chemistry

AI-integrated design of nanofluid-based PVT systems containing nano-enhanced PCM: Combining optimized GPR models with grasshopper optimization and the CoCoSo algorithm This study introduces a novel, multi-stage framework designed to optimize the performance of photovoltaic thermal (PVT) systems incorporating nanofluid-based working fluids and nanoparticle-enhanced phase change materials (NPCMs). Addressing a key gap in the literature, the proposed methodology synergistically integrates machine learning (ML), artificial intelligence (AI)-based optimization, and multi-criteria decision-making (MCDM), elements traditionally studied in isolation. The four-phase approach comprises: (1) data preprocessing and analysis, (2) predictive modeling using Gaussian process regression (GPR), (3) multi-objective optimization via the grasshopper optimization algorithm (GOA), and (4) decision-making using the CoCoSo method. GPR model hyperparameters were tuned using both genetic algorithm (GA) and stochastic paint optimizer (SPO), with GA-GPR achieving superior accuracy for electrical power (EP) and thermal power (TP) prediction (R > 0.998). The superior GA-optimized GPR models demonstrated distinct structural configurations: a pure quadratic–rational quadratic kernel with low sigma and moderate regularization for EP prediction, and a constant–Matern 5/2 kernel with higher sigma and regularization for TP. These optimized structures enhanced robustness, adaptability, and accuracy across the two performance objectives. Optimization results revealed that thinner PCM layers (1.5–1.7 cm), higher mass fraction of nanomaterials in PCM (11–12 wt%), and moderate flow rates (≈58.6 kg/h) led to optimal TP values (>373 W/m 2 ), while thicker PCMs enhanced EP (∼137 W/m 2 ). The CoCoSo method demonstrated superior effectiveness in ranking Pareto-optimal solutions compared with traditional MCDM techniques, particularly under varying priority scenarios, thereby highlighting the framework’s adaptability to diverse energy objectives. This integrated strategy offers a scalable and accurate pathway for designing high-efficiency, application-specific PVT systems for sustainable energy infrastructures. Kriging ; Soft sensor ; Gaussian process ; Support vector machine ; Regularization (linguistics) ; Modular design ; Adaptability ; Optimization problem ; Kernel (algebra) High voltage insulation and dielectric phenomena ; Solar Thermal and Photovoltaic Systems ; Power Transformer Diagnostics and Insulation
Authors: Sachin Jagdale | Babita Agarwal | Simran Karekar | Pavan B. Rathi | Vahid Jouyban‐Gharamaleki | Dmitriy M. Makarov | Abolghasem Jouyban
Source: Journal of Molecular Liquids (article, 2025-04-30)
Field: Materials Science — Materials Chemistry

Solubility, thermodynamic properties and predictive modeling of glimepiride in various solvents Glimepiride ; Solubility ; Thermodynamics ; Hildebrand solubility parameter ; Chemistry ; Materials science ; Organic chemistry ; Medicine ; Physics ; Internal medicine Crystallization and Solubility Studies ; Chemical Thermodynamics and Molecular Structure ; Chemical and Physical Properties in Aqueous Solutions
Authors: Beibei Xie | Xinhui Liu | Zhiwei Wang
Source: Measurement (article, 2025-07-08)
Field: Chemistry — Analytical Chemistry

Multimodal feature fusion network based on image and NIR spectroscopy for diesel fuel brand identification Identification (biology) ; Diesel fuel ; Feature (linguistics) ; Fusion ; Pattern recognition (psychology) ; Image (mathematics) ; Artificial intelligence ; Spectroscopy ; Image fusion ; Near-infrared spectroscopy ; Computer science ; Computer vision ; Materials science ; Automotive engineering ; Engineering ; Optics ; Physics ; Botany Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: Yan Han | Xi He | Hailiang Xiong | Anping Liu | Haiyang Shi | Wang Li | Yingzhou Huang
Source: Microchemical Journal (article, 2025-05-21)
Field: Chemistry — Analytical Chemistry

Optoplasmonic MOF film for SERS-AI detection of flavor from Baijiu Flavor ; Chemistry ; Materials science ; Food science Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Advanced Chemical Sensor Technologies
Authors: Peichen Zhong | Xuefang Dai | Bowen Deng | Gerbrand Ceder | Kristin A. Persson
Source: Materials Horizons (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Crystal structure prediction with host-guided inpainting generation and foundation potentials Unconditional crystal structure generation with diffusion models faces challenges in identifying symmetric crystals as the unit cell size increases. We present the crystal host-guided generation (CHGGen) framework to address this challenge through conditional generation using an inpainting method, which optimizes a fraction of atomic positions within a predefined and symmetrized host structure to improve the success rate for symmetric structure generation. By integrating inpainting structure generation with a foundation potential for structure optimization, we demonstrate the method on the ZnS-P<sub>2</sub>S<sub>5</sub> and Li-Si chemical systems, where the inpainting method generates a higher fraction of symmetric structures than unconditional generation. The practical significance of CHGGen extends to enabling the structural modification of crystal structures, particularly for systems with partial occupancy or intercalation chemistry. The inpainting method also allows for seamless integration with other generative models, providing a versatile framework for accelerating materials discovery. Foundation (evidence) ; Host (biology) ; Materials science ; Crystal (programming language) ; Inpainting ; Crystal structure ; Crystallography ; Nanotechnology ; Artificial intelligence ; Computer science ; Biology ; Chemistry ; Ecology ; Geography ; Image (mathematics) ; Archaeology Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Nuclear Materials and Properties