jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shigetoshi Mimura | Taichi Masuda | Souta Miyamoto | Katsuaki Tanabe
Source: Chemical Engineering Journal Advances (article, 2025-10-25)
Field: Materials Science — Materials Chemistry

Explainable artificial intelligence for chemical process informatics: Energy-efficient design of styrene monomer production • Global optimization of styrene plant design with explainable AI. • Bayesian optimization efficiently identified low-energy production conditions. • SHapley Additive Explanations (SHAP) guided interpretation of the process model. • SHAP-based feature selection improved model generalization performance. Parameter optimization in process design for chemical products is ideally performed at the system-wide level. However, such optimization presents a multivariate, multi-objective problem, requiring an extensive number of simulations. Here we optimized the parameters of the styrene monomer production process using an adaptive experimental design based on Bayesian optimization. As a result, we efficiently explored low-energy-consumption regions and successfully identified energy-efficient design points with a reduced number of simulations. Additionally, we analyzed the machine learning model using the SHapley Additive exPlanation (SHAP) method to visualize exploration trends and provide phenomenological interpretations. To further explore applications of SHAP analysis, we conducted feature selection and parameter tuning based on SHAP values. Feature selection using SHAP values significantly improved the generalization performance of the model, while parameter adjustments based on SHAP values further reduced energy consumption from the initially identified optimal points. This study represents the first attempt to integrate global optimization of overall chemical process design parameters with explainable artificial intelligence. Nevertheless, we also identified several challenges, such as the balance between exploration and exploitation and the limitations of local representations in the machine learning model. The framework combining Bayesian optimization and SHAP-based analysis developed in this study is applicable to various chemical processes. Our technical scheme has the potential to advance research on machine learning-driven process design and contribute to its industrial implementation in the future. Bayesian optimization ; Feature selection ; Process (computing) ; Generalization ; Process design ; Feature (linguistics) ; Selection (genetic algorithm) ; Engineering design process ; Chemical process Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Business Process Modeling and Analysis
Authors: Ryuga Kunisada | Manami Hayashi | Tabea Rohlfs | T. NAGANO | Koki Sano | Naoto Inai | Naoki Noto | Takuya Ogaki | Yasunori Matsui | Hiroshi Ikeda | Olga Garcı́a Mancheño | Takeshi Yanai | Susumu Saito
Source: Chemical Science (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Data-driven approach to elucidate the correlation between photocatalytic activity and rate constants from excited states Even though excited-state properties play a crucial role in photocatalysis, directly correlating these with photocatalytic activity remains challenging. Herein, we propose a method to elucidate the correlations between the catalytic activity of organic photosensitizers and the rate constants of various excited-state processes through integrating machine learning (ML), quantum chemical calculations, and chemical experiments. This approach was applied to interpolative predictions of the yield of the nickel/photocatalytic formation of C-O bonds and radical additions to alkenes using various organic photosensitizers with satisfactory accuracy (<i>R</i> <sup>2</sup> = 0.83 and 0.77 on the test set, respectively). The calculated rate constants obtained through quantum chemical calculations proved to be comparable or even superior to the experimentally measured excited-state lifetimes as descriptors. SHAP-based visual analysis revealed that the rate constants corresponding to transitions from the T<sub>1</sub> state provide significant contributions to the interpolative prediction of photocatalytic activity. Additionally, the non-radiative decay process between the S<sub>1</sub> and S<sub>0</sub> states helps describe the low catalytic activity of poorly emissive photosensitizers. These findings highlight the potential of the proposed method to provide insights into photocatalytic properties that are difficult to obtain using conventional approaches. Photocatalysis ; Reaction rate constant ; Quantum chemical ; Excited state ; Catalysis ; Yield (engineering) ; Quantum yield ; Quantum Machine Learning in Materials Science ; Radical Photochemical Reactions ; Advanced Photocatalysis Techniques
Authors: Rasmus Fromsejer | Bjørn Maribo‐Mogensen | Georgios M. Kontogeorgis | Xiaodong Liang
Source: Computational Materials Science (article, 2025-02-27)
Field: Materials Science — Materials Chemistry

Performance assessment of high-throughput Gibbs free energy predictions of crystalline solids Crystalline solids are integral to a broad range of natural science and engineering applications and their Gibbs free energy G is an important parameter in modeling their thermodynamics. However, predicting G for solids remains a difficult task and an under-explored field in high-throughput thermochemistry. In this work, we benchmark the performance of the newest generation of machine learning (ML) predictions, machine learning interatomic potentials (MLIPs), and density functional theory in predicting G within the harmonic and quasi-harmonic approximations against experimental data from 100–2500 K and for up to 784 compounds. Furthermore, these calculations are fed to a reaction network (RN) from which experimentally informed predictions can be made. We find that predictions of G made by MLIPs display promising performance but with the help of the RN, simpler methods show similar or better performance. Nonetheless, we show that much of the calculated and experimental data for G still lack the accuracy and precision required for some thermodynamic modeling applications. Finally, we apply the RN to predict the room temperature Gibbs free energy of formation and find that it performs satisfactorily but that improvements need to be made before these predictions can be used as reliable indicators of thermodynamic stability in general applications. • Models for the Gibbs free energy of numerous crystalline solids are investigated. • Model predictions are compared to experimental data for temperatures up to 2500 K. • The capabilities of several machine learning interatomic potentials are examined. • Reaction network predictions show promise for near calculation-free predictions. • Applications are limited by the accuracy and precision of predictions and experiment. Gibbs free energy ; Throughput ; Materials science ; Thermodynamics ; Statistical physics ; Chemistry ; Computer science ; Physics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; nanoparticles nucleation surface interactions
Authors: Yucheng Ouyang | Ying Liu | Honghui Shang | Zhenchuan Chen | Jiahao Shan | Huimin Cui | Xiaobing Feng | Xin Chen | Xingyu Gao | Lifang Wang | Haifeng Song | Rongfen Lin | Fang Li
Source:  (article, 2025-02-28)
Field: Materials Science — Materials Chemistry

TensorMD: Molecular Dynamics Simulation with Ab Initio Accuracy of 50 Billion Atoms Molecular dynamics simulation emerges as an important area that HPC+AI helps to investigate the physical properties, with machine-learning interatomic potentials (MLIPs) being used. General-purpose machine-learning (ML) tools have been leveraged in MLIPs, but they are not perfectly matched with each other, since many optimization opportunities in MLIPs have been missed by ML tools. This inefficiency arises from the fact that HPC+AI applications work with far more computational complexity compared with pure AI scenarios. This paper has developed an MLIP, named TensorMD, independently from any ML tool. TensorMD has been evaluated on two supercomputers and scaled to 51.8 billion atoms, i.e., ~ 3× compared with state-of-the-art. Molecular dynamics ; Ab initio ; Computer science ; Ab initio quantum chemistry methods ; Statistical physics ; Dynamics (music) ; Computational chemistry ; Computational science ; Chemistry ; Physics ; Molecule ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Nuclear Physics and Applications
Authors: Andreas Niederquell | Barbora Vraníková | Martin Kuentz
Source: International Journal of Pharmaceutics (article, 2025-07-02)
Field: Chemistry — Spectroscopy

Ranking of apparent drug affinity to mesoporous silica utilizing a chromatographic screening method and a tree-based prediction model Mesoporous silica has emerged as a promising component in bio-enabling formulation strategy. However, there is currently a lack of predictive tools for assessing drug-silica interactions in a preformulation phase, when formulators only have minimal material to guide them. This study proposes a solution: a chromatographic method to rank apparent drug-silica affinity for mesoporous formulations. Using a dataset of 52 drugs, a hydrophilic liquid interaction chromatography (HILIC) screening method was developed, with a stationary silica phase to simulate the drug carrier. Molecular descriptors were calculated for various compounds to analyze HILIC retention times using a tree-based machine learning algorithm. For silica affinity, the distribution coefficient (LogD), the molecular shape descriptor Kappa1, and the number of conjugated bonds (NCB) were identified as possible critical parameters. Additionally, an amine-modified HILIC column was evaluated to simulate a surface-modified silica carrier. The classification tree analysis revealed that Abraham's hydrogen bonding acidity, the NCB and the pKa were determinants for a qualitative assessment of drug affinity to the modified silica. The classification into low, moderate, and high affinity to the stationary phase appeared to be useful in understanding drug release from mesoporous silica formulations, highlighting its potential for future research. Chromatography ; Ranking (information retrieval) ; Drug ; Tree (set theory) ; Chemistry ; Decision tree ; Computer science ; Mathematics ; Pharmacology ; Machine learning ; Medicine ; Combinatorics Analytical Chemistry and Chromatography ; Protein purification and stability ; Microfluidic and Capillary Electrophoresis Applications
Authors: Fadhel F. Sead | Dharmesh Sur | Anupam Yadav | Suhas Ballal | Abhayveer Singh | T. Krithiga | Satvik Vats | Farrukh Yuldashev | Irfan Ahmad | Samim Sherzod
Source: Scientific Reports (article, 2025-04-22)
Field: Materials Science — Polymers and Plastics

Carbon dioxide solubility in polyethylene glycol polymer: an accurate intelligent estimation framework Polyethylene glycol (PEG), a synthetic polymer made up of repeating ethylene oxide units, is widely recognized for its broad utility and adaptable properties. Precise estimation of CO<sub>2</sub> solubility in PEG plays a vital role in enhancing processes such as supercritical fluid extraction, carbon capture, and polymer modification, where CO<sub>2</sub> serves as a solvent or transport medium. This study focuses on building advanced predictive models using machine-learning approaches, such as random forest (RF), decision tree (DT), adaptive boosting (AdaBoost), k-nearest neighbors (KNN), and ensemble learning (EL) to forecast CO<sub>2</sub> solubility in PEG across a wide range of conditions. The data utilized for model development is sourced from previously published literature, and an outlier detection method is applied beforehand to identify any suspicious data points. Additionally, sensitivity analysis is performed to evaluate the relative influence of each input parameter on the output variable. The results proved that DT model is the most performance method for estimating CO<sub>2</sub> solubility in PEG since it showed largest R-squared (i.e., 0.801 and 0.991 for test and train, respectively) and lowest error metrics (MSE: 0.0009 and AARE%: 22.58 for test datapoints). In addition, it was found that pressure and PEG molar mass directly affects the solubility in contrast to the temperature variable which has an inverse relationship. The developed DT model can be regarded accurate and robust user-friendly tool for estimating CO<sub>2</sub> solubility in PEG without needing experimental workflows which are known to be time-consuming, expensive and tedious. Polyethylene glycol ; Solubility ; Carbon dioxide ; Polymer ; Polyethylene ; Chemical engineering ; Chemistry ; Computer science ; Organic chemistry ; Engineering Polymer Foaming and Composites ; Phase Equilibria and Thermodynamics ; Adsorption, diffusion, and thermodynamic properties of materials
Authors: Vicki Stone | Elisa Moschini | Fiona Murphy | Neil Hunt | Magda Blosi | Danail Hristozov | Helinor J. Johnston | Finlay Stenton | Alicja Mikołajczyk | Agnes G. Oomen | Otmar Schmid | Georgia Tsiliki | Andrea Brunelli | Elena Badetti | Ulla Vogel | Agnieszka Gajewicz-Skrętna | Wendel Wohlleben
Source: Materials Today (article, 2025-09-06)
Field: Materials Science — Materials Chemistry

Minimising data needs to support the safer design of multicomponent nanomaterials – Application of grouping • A grouping-based strategy has been developed for design of safer materials. • Focused sub-hypotheses simplify logical evidence gathering for grouping. • Grouping sub-hypotheses address material property impacts on fate and hazard. • Enhanced properties, mixture effects are accounted for, along with transformations. • New data is generated through application of tiered testing strategies promoting the use of NAMs. There is an ongoing demand to develop options to reduce hazard testing of substances and materials on a case-by-case basis. Grouping approaches offer a way to share or re-use safety-related information between similar substances, providing insights that can inform the Safe and Sustainable by-Design (SSbD) 2 2 SSbD: Safe and Sustainable by-Design. of new materials. Here, an existing grouping hypothesis template for single-component nanomaterials (NMs) 3 3 NMs: Nanomaterials. is expanded to facilitate systematic consideration of grouping for multicomponent nanomaterials (MCNMs) 4 4 MCNMs: Multicomponent Nanomaterials. relevant to SSbD. Modifications to the template include additional information on a) the complexity of physical and chemical composition; b) the emerging properties driving the MCNM functionality; c) the potential for MCNM components to transform with different rates, leading to complex exposure scenarios; d) prioritisation and simplification of grouping decisions related to material properties (what they are), fate/toxicokinetics (where they go) and the hazard mechanisms (what they do). Existing information and data are used to formulate a matrix of sub-hypotheses that individually relate one (or more) indicators of ‘what they are’ to a single indicator of either ‘where they go’ or ‘what they do’. The resultant sub-hypotheses are easier to assess than the all-encompassing over-arching hypothesis required for regulatory application of grouping. The estimated level of impact of each indicator is used to prioritise the sub-hypothesis assessment. Accepting or rejecting each prioritised sub-hypothesis is facilitated by the application of tiered testing strategies promoting the use of relevant existing data, new approach methodologies and machine learning-based models. A case study of SiO 2 @ZnO MCNM is provided to demonstrate the template’s usefulness in an SSbD context. SAFER ; Computer science ; Risk analysis (engineering) ; Business ; Computer security Nanoparticles: synthesis and applications ; Graphene and Nanomaterials Applications ; Recycling and Waste Management Techniques
Authors: Arun Singh Bhadwal | Monika Kumari | Anil Kumar
Source: Scientific Reports (article, 2025-10-01)
Field: Materials Science — Materials Chemistry

PCF-VAE: posterior collapse free variational autoencoder for de novo drug design Generating novel molecular structures with desired pharmacological and physicochemical properties is challenging due to the vast chemical space, complex optimization requirements, predictive limitations of models, and data scarcity. This study focuses on investigating the problem of posterior collapse in variational autoencoders, a deep learning technique used for de novo molecular design. Various generative variational autoencoders were employed to map molecule structures to a continuous latent space and vice versa, evaluating their performance as structure generators. Most state-of-the-art approaches suffer from posterior collapse, limiting the diversity of generated molecules. To address this challenge, a novel approach termed PCF-VAE was introduced to mitigate the issue of posterior collapse, reduce the complexity of SMILES representations, and enhance diversity in molecule generation. In comparison to state-of-the-art models, PCF-VAE has been evaluated and compared in the MOSES benchmark at different diversity levels. Depending on the diversity level, PCF-VAE has a validity of 98.01% at D = 1, 97.10% at D = 2, and 95.01% at D = 3. It is important to note that PCF-VAE effectively generates molecules with a 100% unique structure. Both intDiv and intDiv2 are measures of internal diversity; intDiv2 ranges from 85.87 to 86.33% and intDiv ranges from 85.87 to 89.01%. Additionally, at D = 1, D = 2, and D = 3, PCF-VAE generates 93.77%, 94.71%, and 95.01% novel molecules, respectively. The results indicate that this research provides valuable insights into the challenges of molecular generation and contributes to the design of novel molecules with desired properties. Autoencoder ; Benchmark (surveying) ; Limiting ; Chemical space ; Space (punctuation) ; Generative grammar ; Drug discovery Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Generative Adversarial Networks and Image Synthesis
Authors: Marius Stieglitz | Sonja Adomeit | Matthias Müller | Karina Hettwer | Anja Schlierf | Steffen Uhlig | Sarah David | Christian Hopmann | Lisa Leuchtenberger-Engel
Source: Polymers (article, 2025-04-12)
Field: Materials Science — Materials Chemistry

Model-Based Prediction of the Tensile Properties of Polypropylene Films Made from Recycled Materials This study investigates the potential of data-driven modeling to monitor and predict mechanical properties of extruded film using recyclates. The first step is to investigate whether a process parameter can indicate the input material quality of the material, which can vary significantly due to the use of recyclate. The die pressure was shown to be a key indicator due to its strong correlation with viscosity and material degradation. The second step is to explore the ability of machine learning models-Generalized Additive Models, Linear Regression, and Random Forest-to predict film tensile strength and modulus based on extrusion process and material parameters. The results demonstrate that including Melt Flow Rate and shear viscosity in addition to pressure and feedstock type (Virgin, PIR, and PCR) significantly improves model accuracy, with Generalized Additive Models achieving the highest R<sup>2</sup> of 85.7% for tensile strength prediction. Additionally, the observed variability between different recyclate streams highlights the need for a more detailed classification of recyclates to better predict and optimize the mechanical performance of the film. While data-driven approaches for predicting properties show promise, their effectiveness remains limited by data availability and feedstock variability. Expanding datasets and improving process stability will be critical to refining predictive models for industrial application. Ultimate tensile strength ; Materials science ; Polypropylene ; Raw material ; Extrusion ; Predictive modelling ; Material properties ; Viscosity ; Process engineering ; Process (computing) ; Random forest ; Composite material ; Computer science ; Machine learning ; Engineering Machine Learning in Materials Science ; Injection Molding Process and Properties ; Recycling and Waste Management Techniques
Authors: Han Zeng | Xin Jing | Yasong Sun
Source: Case Studies in Construction Materials (article, 2025-01-10)
Field: Materials Science — Ceramics and Composites

Image-based 3D mesoscopic modeling and thermo-mechanical properties prediction of SiC/SiC composites with different preforms Ceramic matrix composites (CMCs) woven by textile technology are considered to be excellent materials for high-temperature components in new-generation aircraft engines. Therefore, it is necessary to develop models reflecting the real structure of CMCs and systematically investigate the effect of the microstructure on their macroscopic properties. In this work, based on micro-computed tomography (CT) scanning images, 3D models of the internal structure of 2D plain-woven and 2.5D SiC/SiC composites are developed using a deep learning (DL) method, and the related properties are predicted. First, CT scanning images of 3D models are segmented using an efficient and accurate DL neural network. The fabric structures of the yarns are reconstructed and real 3D descriptions are generated. Second, 3D finite element models are developed using voxel meshing and element classification programs. Finally, the stress and heat flux distributions of the two composites are calculated and analyzed, and the elastic modulus and thermal conductivity are predicted. The simulation results show that the out-of-plane properties of the plain-woven SiC/SiC composites were much lower than the in-plane ones, and the in-plane modulus and thermal conductivity were 5 and 2 times the out-of-plane ones, respectively. The difference between the out-of-plane and in-plane properties of the 2.5D composites was small, while the out-of-plane thermal properties of the 2.5D SiC/SiC composites were much larger than those of the plain-woven SiC/SiC composites. And the error between the predicted value and the experimental value is all less than 20%, making them more suitable for predicting the heat transfer and elasticity response of CMCs, while further used to predict their strength and damage behavior. Mesoscopic physics ; Materials science ; Composite material Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Cellular and Composite Structures