jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hugo Marques | Simon Müller
Source: Fluid Phase Equilibria (article, 2025-01-08)
Field: Materials Science — Materials Chemistry

Self-solvation energies: Extended open database and GNN-based prediction • A self-solvation energy dataset merging DIPPR and Yaws databases is presented, covering 5420 compounds and 71656 data points across various temperatures. • A novel approach predicts self-solvation energies across various temperatures, overcoming the limitations of standard condition models (298.15 K). • This study highlights the potential of machine learning in self-solvation energy prediction, enabling broader applications in chemical synthesis and separation processes. Solvation energies play a crucial role in various chemical processes, ranging from chemical synthesis to separation techniques. To optimize these processes, it is essential to accurately predict solvation energies across different temperatures and solvents. However, most existing studies primarily focus on the standard temperature of 298.15 K. In this work, we address this limitation by creating an extensive database, which combines the DIPPR and Yaws databases. Our comprehensive dataset includes 5420 pure compounds, resulting in 71656 data points spanning a wide range of temperatures. Additionally, besides the development of this novel database, another key contribution of this work is the coupling of the well-known Graph Convolutional Neural Network Chemprop, with our database with the aim of predicting self-solvation energies across diverse temperatures for the first time. The results presented here demonstrate the overall effectiveness of the model, evidenced by a low Mean Absolute Error (MAE) of 0.09 kcal mol −1 and a high Determination Coefficient (R²) of 0.992. Additionally, the Average Relative Deviation (ARD) of the data is 2.2%, further confirming the accuracy of the model. In fact, the model demonstrates robust predictive performance across data of varying quality, including a significant fraction of pseudo-experimental values derived from predictive schemes. However, it is worth noting that some groups of compounds, such as small sized compounds and low-numbered ring structures, exhibited somewhat larger deviations than expected. This suggests areas for further refinement and indicates that while the model is robust, there is still room for improvement in specific cases. This approach represents an overall improvement over previous models and offers enhanced versatility for practical applications in chemical synthesis and separation processes. Chemistry ; Solvation ; Computational chemistry ; Database ; Molecule ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Phase Equilibria and Thermodynamics
Authors: Md. Jamal Uddin | Jitang Fan
Source: Polymer Testing (article, 2025-09-03)
Field: Materials Science — Materials Chemistry

Machine learning framework to predict the mechanical properties of photopolymer gyroid lattices at various strain rates Photopolymer gyroid lattices are advanced materials owing to their exceptional mechanical performance and impact energy absorption capability. However, determining their mechanical properties is challenging because of the complex interactions between their intricate shapes, material properties, and manufacturing defects. In this work, a machine learning (ML) framework was developed to forecast the mechanical properties, including Young’s modulus, yield strength, fracture strength, fracture strain, and toughness, of photopolymer gyroid lattices at low computational costs. This framework employs a recursive feature elimination technique to reduce less important features, followed by a grid search method to optimize the hyperparameters of the model. Also, we applied random forest, extra tree, artificial neural network, and long short-term memory techniques and evaluated their performance using coefficients of determination ( ), mean absolute error (MAE), and root mean square error (RMSE). The extra tree and random forest algorithms produced the best results. In addition, we examined the effects of strain rate and porosity on the mechanical properties. The SHapley Additive exPlanations (SHAP) procedure was applied to show the effects of the individual attributes on the modeling results. This framework provides a cost-effective and time-efficient alternative to experimental procedures, enabling precise prediction of mechanical properties of photopolymer gyroid lattices. • Photopolymer gyroid lattices are advanced materials owing to their exceptional mechanical performance and impact energy absorption capability. • To crack the challenge of determining the mechanical properties of photopolymer gyroid lattices, machine learning (ML) framework is developed. • Recursive feature elimination technique is applied to reduce less important features and then grid search method is employed to optimize the hyperparameters. • Random forest, extra tree, artificial neural network, and long short-term memory techniques are applied to forecast various mechanical properties. • Effects of strain rate and porosity on mechanical properties are examined and effects of individual attributes on modeling results are analyzed. Gyroid ; Materials science ; Photopolymer ; Strain (injury) ; Composite material ; Polymer science ; Nanotechnology ; Copolymer ; Polymer ; Polymerization Machine Learning in Materials Science ; Drilling and Well Engineering
Authors: Bohayra Mortazavi | Fazel Shojaei | Masoud Shahrokhi | Xiaoying Zhuang
Source: Carbon (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Machine learning-assisted first-principles study of structural, electronic, optical, thermal, and mechanical properties of novel s-triazine-based organic framework monolayers Nanoporous carbon-nitride covalent organic frameworks (CN-COFs) represent an emerging class of nanomaterials with tunable architectures and scalable synthesis routes. Recent advances introduced four novel CN-COFs nanosheets, featuring hybrid benzene/s-triazine cores. Using these advances as a foundation, we designed four CN-COFs with identical structures but slightly different chemistry. We evaluated structural stability, electronic/optical properties, mechanical strength, and thermal transport using a hybrid approach combining density functional theory (DFT) and machine learning interatomic potentials (MLIPs). Complex stable atomic configurations, along with thermal and mechanical properties, were efficiently identified using MLIPs, while electronic and optical properties were accurately analyzed through single-step DFT calculations. Structurally, four frameworks exhibit Kagome lattice symmetry, hosting unique electronic features like flat and Dirac bands, while corrugated configurations induce modified band structures. These semiconductors display band gaps ranging from 2.73 to 3.72 eV, allowing strong photon absorption across the UV-visible spectrum and aligning well with water redox potentials, making them promising for optoelectronic and photocatalytic applications. Despite their highly porous nature, CN-COFs demonstrate impressive mechanical resilience, sustaining strain levels up to around 0.3 and tensile strengths exceeding 10 GPa, significantly surpassing conventional polymers. Additionally, fine-tuning the chemistry of the linkages allows for the occurrence of out-of-plane corrugations, resulting in ultralow lattice thermal conductivity, which is particularly attractive for thermoelectric and thermal insulating applications. Our comprehensive findings confirm the stability, mechanical robustness, ultralow thermal conductivity and appealing semiconducting nature of CN-COFs, highlighting their application prospects in flexible, high-performance optoelectronics and energy storage and conversion systems. Nanomaterials ; Nanoporous ; Density functional theory ; Semiconductor ; Band gap ; Thermoelectric effect ; Thermoelectric materials ; Thermal conductivity ; Photonics ; Thermal Covalent Organic Framework Applications ; Solar-Powered Water Purification Methods ; Thermal properties of materials
Authors: Sanjay Chakraborty | Jonas Björk | Martin Dahlqvist | Johanna Rosen | Fredrik Heintz
Source: Computer Science Review (article, 2025-10-17)
Field: Materials Science — Materials Chemistry

A survey of AI-supported materials informatics The evolution from traditional artificial intelligence (AI) to advanced AI is explored in the predictive and structural analysis in materials informatics, highlighting how advancements in machine learning have revolutionised the discovery and design of new materials and molecular structures. It examines how traditional AI, with its reliance on heuristic models and empirical data, has paved the way for the emergence of generative AI, which leverages advanced machine learning frameworks to predict material properties, structural design and analysis and synthesise new materials. The work highlights key developments, compares the effectiveness of various approaches, relevant databases and software frameworks in material informatics, and discusses the transformative impact of traditional and advanced AI in accelerating materials discovery and innovation. Through a detailed analysis of recent advancements, challenges, and future prospects, this paper aims to offer valuable insights into the evolving landscape of AI-driven materials informatics. Generative grammar ; Transformative learning ; Materials informatics ; Key (lock) ; Heuristic ; Informatics Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Materials Characterization Techniques
Authors: Beatriz G. del Rio | L. González
Source: Physical Chemistry Chemical Physics (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Static, dynamic and electronic properties of some trivalent liquid rare earth metals near melting: <i>ab initio</i> and neural network simulations We report a study on several static and dynamic properties of the early trivalent liquid rare-earth metals at thermodynamic conditions near their respective melting points. It has been performed by resorting to machine learning (ML) techniques, in which the associated neural network-based interatomic potentials were derived from <i>ab initio</i> molecular dynamics simulations within Hubbard-corrected density functional theory. We report the results obtained for the static structural properties, including an analysis of the local short-range order. Single-particle and collective dynamic properties have also been obtained, from which transport coefficients and wavevector-dependent dispersion relations are evaluated. The results show a quite homogeneous behavior of the structural, dynamic, and transport properties throughout the series. The electronic properties have been obtained from the <i>ab initio</i> simulations, and show important discrepancies with respect to the low temperature solids, portraying a more band-like picture of the 4f states in the liquid. Ab initio ; Rare earth ; Molecular dynamics ; Artificial neural network ; Materials science ; Chemical physics ; Ab initio quantum chemistry methods ; Computational chemistry ; Chemistry ; Computer science ; Organic chemistry ; Molecule ; Metallurgy Chemical Thermodynamics and Molecular Structure ; Material Dynamics and Properties ; Advanced Physical and Chemical Molecular Interactions
Authors: Jiayang Zhou | Haozhe Chen | Xiangfu Wang
Source: Sensors and Actuators A Physical (article, 2025-08-18)
Field: Materials Science — Materials Chemistry

Prediction of luminescence lifetimes of Mn4+/Eu3+ doped phosphors based on interpretable machine learning Phosphor ; Luminescence ; Doping ; Materials science ; Artificial intelligence ; Computer science ; Optoelectronics Luminescence Properties of Advanced Materials ; Radiation Detection and Scintillator Technologies ; Machine Learning in Materials Science
Authors: Xiuping Zhang | Qun Liu | Rui Han
Source: Journal of Computer and Communications (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

MMGCF: Generating Counterfactual Explanations for Molecular Property Prediction via Motif Rebuild Predicting molecular properties is essential for advancing for advancing drug discovery and design. Recently, Graph Neural Networks (GNNs) have gained prominence due to their ability to capture the complex structural and relational information inherent in molecular graphs. Despite their effectiveness, the “black-box” nature of GNNs remains a significant obstacle to their widespread adoption in chemistry, as it hinders interpretability and trust. In this context, several explanation methods based on factual reasoning have emerged. These methods aim to interpret the predictions made by GNNs by analyzing the key features contributing to the prediction. However, these approaches fail to answer critical questions: “How to ensure that the structure-property mapping learned by GNNs is consistent with established domain knowledge”. In this paper, we propose MMGCF, a novel counterfactual explanation framework designed specifically for the prediction of GNN-based molecular properties. MMGCF constructs a hierarchical tree structure on molecular motifs, enabling the systematic generation of counterfactuals through motif perturbations. This framework identifies causally significant motifs and elucidates their impact on model predictions, offering insights into the relationship between structural modifications and predicted properties. Our method demonstrates its effectiveness through comprehensive quantitative and qualitative evaluations of four real-world molecular datasets. Motif (music) ; Counterfactual thinking ; Property (philosophy) ; Psychology ; Epistemology ; Philosophy ; Social psychology ; Aesthetics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Yun Deng | Ziyan Sha | Xingxing Wang | Ke Duan | Weijie Xue | Ian Beadham | Xiaolan Xiao | Changbo Zhang
Source: Gels (article, 2025-01-17)
Field: Chemistry — Spectroscopy

Exploration of Key Factors in the Preparation of Highly Hydrophobic Silica Aerogel from Rice Husk Ash Assisted by Machine Learning To expand the applications of hydrophobic silica aerogels derived from rice husk ash (HSA) through simple traditional methods (without adding special materials or processes), this paper employs machine learning to establish mathematical models to identify optimal conditions for extracting water glass and investigates how preparation conditions and heat treatment temperatures affect properties such as the porosity and hydrophobicity of HSA. The results indicate that the decision tree regression model provides the most accurate predictions for the extraction rate and modulus of water glass. Notably, the water contact angle of HSA produced using nitric acid as a catalyst can reach as high as 159.5°, classifying it as a superhydrophobic material. Additionally, while moderately increasing the concentration of the hydrophobic modifier enhances HSA's hydrophobicity, it concurrently reduces its porosity. The HSA maintained hydrophobicity until 500 °C. The pore structure of HSA collapsed gradually with the increase in heat temperature. After treatment at 700 °C, HSA lost its hydrophobicity and the porous structure was severely damaged. Compared with silica aerogel using traditional silicon sources, the damage to pore structure and the crystallization occurred at lower temperatures, but the hydrophobicity remained at higher temperatures. Aerogel ; Husk ; Materials science ; Porosity ; Contact angle ; Chemical engineering ; Crystallization ; Hydrophobic silica ; Composite material Aerogels and thermal insulation ; Advanced Cellulose Research Studies ; Mesoporous Materials and Catalysis
Authors: Mohnish Harwani | Juan C. Verduzco | Brian H. Lee | Alejandro Strachan
Source: Computational Materials Science (article, 2025-01-15)
Field: Materials Science — Materials Chemistry

Accelerating active learning materials discovery with FAIR data and workflows: A case study for alloy melting temperatures Workflow ; Alloy ; Active learning (machine learning) ; Computer science ; Materials science ; Metallurgy ; Database ; Artificial intelligence Machine Learning in Materials Science ; Mineral Processing and Grinding ; Advanced Materials Characterization Techniques
Authors: Yonghyuk Lee | Xiaobo Chen | Sabrina M. Gericke | Meng Li | Dmitri N. Zakharov | Ashley R. Head | Judith Yang | Anastassia N. Alexandrova
Source: Angewandte Chemie (article, 2025-04-22)
Field: Materials Science — Materials Chemistry

Machine‐Learning‐Driven Exploration of Surface Reconstructions of Reduced Rutile TiO <sub>2</sub> Abstract Titanium dioxide (TiO 2 ) is widely used as a catalyst support due to its stability, tunable electronic properties, and surface oxygen vacancies, which are crucial for catalytic processes such as the reverse water‐gas shift (RWGS) reaction. Reduced TiO 2 surfaces undergo complex surface reconstructions that endow unique properties but are computationally challenging to describe. In this study, we utilize machine‐learning interatomic potentials (MLIPs) integrated with an active‐learning workflow to efficiently explore reduced rutile TiO 2 surfaces. This approach enabled the prediction of a phase diagram as a function of oxygen chemical potential, revealing a variety of reconstructed phases, including a previously unreported subsurface shear plane structure. We further investigate the electronic properties of these surfaces and validate our results by comparing experimental and theoretical high‐resolution transmission electron microscopy (HRTEM). Our findings provide new insights into how extreme surface reductions influence the structural and electronic properties of TiO 2 , with potential implications for catalyst design. Rutile ; Surface (topology) ; Materials science ; Nanotechnology ; Surface modification ; Chemical engineering ; Chemistry ; Computer science ; Physical chemistry ; Engineering ; Mathematics Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Surface Roughness and Optical Measurements
Authors: Rafael Bicudo Ribeiro | Leandro R. Franco | Alexandre Holmes | Tárcius N. Ramos | Ergang Wang | Márcio T. do N. Varella | C. Moysés Araújo
Source: The Journal of Physical Chemistry B (article, 2025-05-30)
Field: Materials Science — Polymers and Plastics

Assessing Structural and Optical Properties of PTQ10-Based Donor Polymers in Solution for Eco-Friendly Photovoltaics: A Multiscale Modeling Study Aqueous-processable materials are desired to produce and commercialize eco-friendly organic solar cells. Despite the achievement of developing aqueous soluble electron donor and acceptor polymers by incorporating polar side chains (SCs), the efficiency of the greenest devices is lower than that of state-of-the-art technology processed on halogenated solvents. To investigate the impact of different substituents on structural and optical properties in solution, we considered the backbone of the PTQ10 polymer with alkyl and alkoxy SCs. We simulated oligomer chains at low and high concentration conditions via classical molecular dynamics simulations, considering both a water/ethanol mixture and chloroform as solvents. Combining an unsupervised machine learning technique and density functional theory calculations, we validated the system size for quantum calculations and investigated the impact of SCs on the excited states. Then, following the sequential QM/MM approach, we determined the absorption spectra of each polymer. From the simulations at high concentrations, we observed the stacking of different oligomers, suggesting that polymer chains already showed aggregation in solution. This is consistent with our experimental findings, as we measured a red shift of the PTQ(8bO2) spectrum when changing from a chloroform mixture to an aqueous mixture. Finally, we investigated idealized dimer interface models, whose presence of electron-donating and electron-accepting groups results in mixed signatures in the absorption spectra, widening our understanding of polymer aggregation. Environmentally friendly ; Photovoltaics ; Materials science ; Polymer ; Nanotechnology ; Process engineering ; Biochemical engineering ; Photovoltaic system ; Composite material ; Engineering ; Electrical engineering ; Ecology Conducting polymers and applications ; Polymer composites and self-healing ; Transition Metal Oxide Nanomaterials
Authors: Gbolagade Olajide | Tristan Maxson | Tibor Szilvási
Source: ACS Catalysis (article, 2025-11-09)
Field: Materials Science — Materials Chemistry

Temporal Resolution of <i>In Situ</i> TEM Limits the Characterization of Catalyst Site Dynamics In situ transmission electron microscopy (TEM) provides a means for capturing atomic-resolution micrographs under reactive environments and has been widely employed to identify active site candidates in heterogeneous catalysis. We highlight here that the limited temporal resolution of in situ TEM fundamentally restricts its ability to resolve transient surface structures that may govern catalytic activity. We demonstrate that in situ TEM cannot capture dynamic active sites that evolve on sub-nanosecond time scales by considering two systems: (i) Cu adatoms on Cu(111) at 0 and 100 bar CO pressure at 573 K and (ii) N2 decomposition on Fe(111) at 800 K. We study both systems via molecular dynamics using machine learning interatomic potentials trained on density functional theory data. Our results show that rapid surface rearrangements are averaged out in simulated TEM micrographs even under idealized imaging conditions. As a result, the resulting transient sites are rendered effectively invisible under in situ conditions. The findings of this study highlight an intrinsic limitation of in situ TEM and emphasize the necessity of additional in situ characterization as well as computational techniques to understand catalyst dynamics. Characterization (materials science) ; In situ ; Catalysis ; Molecular dynamics ; Transient (computer programming) ; Transmission electron microscopy ; Temporal resolution ; Bar (unit) ; Microscopy Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Electrocatalysts for Energy Conversion