jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Malte Grunert | Max Großmann | Erich Runge
Source: arXiv (Cornell University) (preprint, 2025-01-10)
Field: Materials Science — Materials Chemistry

Discovery of sustainable energy materials via the machine-learned material space Does a machine learning model actually gain an understanding of the material space? We answer this question in the affirmative on the example of the OptiMate model, a graph attention network trained to predict the optical properties of semiconductors and insulators. By applying the UMAP dimensionality reduction technique to its latent embeddings, we demonstrate that the model captures a nuanced and interpretable representation of the materials space, reflecting chemical and physical principles, without any user-induced bias. This enables clustering of almost 10,000 materials based on optical properties and chemical similarities. Beyond this understanding, we demonstrate how the learned material space can be used to identify more sustainable alternatives to critical materials in energy-related technologies, such as photovoltaics. These findings demonstrate the dual utility of machine learning models in materials science: Accurately predicting material properties while providing insights into the underlying materials space. The approach demonstrates the broader potential of leveraging learned materials spaces for the discovery and design of materials for diverse applications, and is easily applicable to any state-of-the-art machine learning model. Sustainable energy ; Space (punctuation) ; Energy (signal processing) ; Computer science ; Engineering ; Physics ; Renewable energy ; Electrical engineering Machine Learning in Materials Science
Authors: Kiho Matsubara | Tooru Nirazuka | Kei Takahashi | Takeshi Matsuda | Minoru Kuroiwa | Masaaki Omichi | Noriaki Seko | Ryohei Kakuchi
Source: Materials Today Chemistry (article, 2025-03-07)
Field: Chemistry — Organic Chemistry

Computationally complemented insights into new generation solvents for radiation-induced graft polymerization The adoption of next-generation solvents has lagged behind that of conventional solvents, primarily due to the operational and economic advantages associated with the latter. These advantages stem from the well-established body of knowledge regarding their chemical applications. This disparity creates a reinforcing cycle, where the widespread use of conventional solvents limits the integration of next-generation alternatives. To address this issue, this study focuses on optimizing solvents in radiation-induced graft polymerization (RIGP) processes to advance sustainable materials chemistry. By leveraging machine learning approaches, the study provides practical guidelines for facilitating the transition from conventional to next-generation solvents, effectively transferring chemical insights from established systems to emerging alternatives. • Machine learning optimizes solvents in radiation-induced graft polymerization. • Practical guidelines enable a shift from conventional to next-generation solvents. • Sustainable chemistry is feasible through transferable chemical insights. Polymerization ; Materials science ; Radiation ; Chemical engineering ; Process engineering ; Polymer chemistry ; Composite material ; Polymer ; Engineering ; Physics ; Optics Photopolymerization techniques and applications ; Advanced Polymer Synthesis and Characterization ; biodegradable polymer synthesis and properties
Authors: Wencai Yi | Jiping Xiong | Xingang Jiang | YuQiu Zhang | Chaozheng He | Xiaobing Liu
Source: Materials Genome Engineering Advances (article, 2025-12-31)
Field: Materials Science — Materials Chemistry

GASCAP: Accelerating Adsorption Evaluation Using Graph Voronoi Diagram and Machine Learning Method ABSTRACT Adsorption on a solid surface is a significant chemical process in the fields of gas sensors, solid catalysts, hydrogen storage materials, and ion batteries. Here, we develop a high‐throughput computing package, termed as gas sensors and catalysts automatically screening package (GASCAP), to accelerate the evaluation of adsorption on solid surfaces using integrated computational materials engineering. The aims of GASCAP are to detect unequal adsorption sites, construct coadsorption structures, analyze adsorption energies, calculate work functions, and clarify charge interaction in high‐throughput ways. The regulation of CO adsorption on the Pt (111) surface is used as a benchmark to demonstrate the effectiveness of GASCAP. Additionally, the GASCAP is interfaced with the machine learning interatomic potentials (MILP), to accelerate the adsorption energy computations. The calculated results reveal that the MILP can effectively accelerate the adsorption energy screening at 220 times when the calculation accuracy is reliable. To expand the application, a database is built with 5914 adsorbates and substrates. Considering the fast development of high‐throughput calculations, the GASCAP will be a promising simulation platform for the future development in solid surface science. Adsorption ; Voronoi diagram ; Process (computing) ; Benchmark (surveying) ; Surface (topology) ; Energy (signal processing) ; Solid surface ; Graph Machine Learning in Materials Science ; Electrocatalysts for Energy Conversion ; Ammonia Synthesis and Nitrogen Reduction
Authors: Yachun Wang | Luca Capriotti | Fidelma Giulia Di Lemma | Daniele Salvato | Fei Xu | Yalei Tang | Kyle M. Paaren | Alexander Lee Swearingen | David Porter
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

An Integrated Approach to Examining Fuel-Cladding Chemical Interaction in Ht9/U-10zr Metallic Fast Reactor Fuels: Coupling Machine Learning with Electron Microscopy and Local Mechanical Properties Analysis Cladding (metalworking) ; Coupling (piping) ; Nuclear engineering ; Materials science ; Composite material ; Engineering Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Engineering Thermal-Hydraulics
Authors: Johannes Karwounopoulos | Jasper De Landsheere | Leonard Galustian | Tobias Jechtl | Esther Heid
Source: ChemRxiv (preprint, 2025-07-21)
Field: Materials Science — Materials Chemistry

Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states The accurate prediction of reaction barrier heights is crucial for understanding chemical reactivity and guiding reaction design. Recent advances in machine learning (ML) models, particularly graph neural networks, have shown great promise in capturing complex chemical interactions. Here, directed message-passing neural networks (D-MPNNs) on graph overlays of the reactant and product structures were shown to provide promising accuracies for reaction property prediction. They rely solely on molecular graph changes as input and thus require no additional information during inference. However, the reaction barrier height intrinsically depends on the conformations of the reactants, transition state, and products, which are not taken into account in standard D-MPNNs. In this work, we present a hybrid approach where we combine the power of D-MPNNs predicting barrier heights with generative models predicting transition state geometries on-the-fly for organic reactions. The resulting model thus only requires two-dimensional graph information as input, while internally leveraging three-dimensional information to increase accuracy. We furthermore evaluate the influence of additional physical features on D-MPNN models of reaction barrier heights, where we find that additional features only marginally enhance predictive accuracy and are especially helpful for small datasets. In contrast, our hybrid graph/coordinate approach reduces the error of barrier height predictions for the two investigated datasets RDB7 and RGD1. Graph ; On the fly ; Transition (genetics) ; Computer science ; Econometrics ; Mathematics ; Chemistry ; Theoretical computer science Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Mass Spectrometry Techniques and Applications
Authors: Riccardo Farris | Emanuele Telari | Nongnuch Artrith | Konstantin M. Neyman | Albert Bruix
Source: ArXiv.org (preprint, 2025-09-23)
Field: Materials Science — Materials Chemistry

Bayesian Neural Networks versus deep ensembles for uncertainty quantification in machine learning interatomic potentials Neural-network-based machine learning interatomic potentials have emerged as powerful tools for predicting atomic energies and forces, enabling accurate and efficient simulations in atomistic modeling. A key limitation of traditional deep learning approaches, however, is their inability to provide reliable estimates of predictive uncertainty. Such uncertainty quantification is critical for assessing model reliability, especially in materials science, where often the model is applied on out-of-distribution data. Different strategies have been proposed to address this challenge, with deep ensembles and Bayesian neural networks being among the most widely used. In this work, we introduce an implementation of Bayesian neural networks with variational inference in the aenet-PyTorch framework. To evaluate their applicability to machine learning interatomic potentials, we systematically compare the performance of variational BNNs and deep ensembles on a dataset of 7,815 TiO$_{2}$ structures. The models are trained on both the full dataset and a subset to assess how variations in data representation influence predictive accuracy and uncertainty estimation. This analysis provides insights into the strengths and limitations of each approach, offering practical guidance for the development of uncertainty-aware machine learning interatomic potentials. Artificial neural network ; Inference ; Uncertainty quantification ; Bayesian probability ; Bayesian inference ; Deep learning ; Representation (politics) ; Bayesian optimization Machine Learning in Materials Science ; Model Reduction and Neural Networks ; Computational Drug Discovery Methods
Authors: Johannes Karwounopoulos | Jasper De Landsheere | Leonard Galustian | Tobias Jechtl | Esther Heid
Source: ChemRxiv (preprint, 2025-08-28)
Field: Materials Science — Materials Chemistry

Graph-based prediction of reaction barrier heights with on-the-fly prediction of transition states The accurate prediction of reaction barrier heights is crucial for understanding chemical reactivity and guiding reaction design. Recent advances in machine learning (ML) models, particularly graph neural networks, have shown great promise in capturing complex chemical interactions. Here, directed message-passing neural networks (D-MPNNs) on graph overlays of the reactant and product structures were shown to provide promising accuracies for reaction property prediction. They rely solely on molecular graph changes as input and thus require no additional information during inference. However, the reaction barrier height intrinsically depends on the conformations of the reactants, transition state, and products, which are not taken into account in standard D-MPNNs. In this work, we present a hybrid approach where we combine the power of D-MPNNs predicting barrier heights with generative models predicting transition state geometries on-the-fly for organic reactions. The resulting model thus only requires two-dimensional graph information as input, while internally leveraging three-dimensional information to increase accuracy. We furthermore evaluate the influence of additional physical features on D-MPNN models of reaction barrier heights, where we find that additional features only marginally enhance predictive accuracy and are especially helpful for small datasets. In contrast, our hybrid graph/coordinate approach reduces the error of barrier height predictions for the two investigated datasets RDB7 and RGD1. Graph ; Transition (genetics) ; On the fly ; Computer science ; Activation barrier ; Econometrics ; Theoretical computer science ; Mathematics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Mass Spectrometry Techniques and Applications
Authors: Fabrizio Mastrolorito | Fulvio Ciriaco | Orazio Nicolotti | Francesca Grisoni
Source: ChemRxiv (preprint, 2025-04-24)
Field: Materials Science — Materials Chemistry

Enhancing deep chemical reaction prediction with advanced chirality and fragment representation This work introduces a fragment-based chemical language -- fragSMILES -- into reaction prediction with deep learning. FragSMILES encodes molecular substructures and chirality, enabling compact and expressive string representations. In a systematic comparison with well-established molecular notations — Simplified Molecular Input Line Entry System (SMILES), Self-Referencing Embedded Strings (SELFIES), and Sequential Attachment-based Fragment Embedding (SAFE) — fragSMILES achieved the highest performance across forward- and retro-synthesis prediction, with superior recognition of stereochemical reaction information. Moreover, fragSMILES enhances the capacity to capture stereochemical complexity -- a key challenge in synthesis planning. Our results demonstrate that chirality-aware and fragment-level representations can advance current computer-assisted synthesis planning efforts. Fragment (logic) ; Representation (politics) ; Chirality (physics) ; Artificial intelligence ; Chemistry ; Computer science ; Stereochemistry ; Physics ; Political science ; Algorithm ; Nuclear physics ; Chiral symmetry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Chemical Synthesis and Analysis
Authors: Tianyi Zhang | Yuhui Wu | Ye Tian | Youxiang Wang | Peng Zhang | Qiannuan Shi | Qun Fang | Jianzhang Pan | Qiao Jin | Jian Ji
Source: Bioactive Materials (article, 2025-12-17)
Field: Chemistry — Organic Chemistry

AI-guided precise design of antimicrobial polymers through high-throughput screening technology on an automated platform Antimicrobial peptides (AMPs)-mimicking antimicrobial polymers show great potential as therapeutic alternatives to antibiotics in the looming "post-antibiotic era". However, the discovery of new AMP-mimicking antimicrobial polymers is challenging due to the vast chemical space of side-chain combinations. The advancement of AI-guided high-throughput screening enables more efficient, precise, and intelligent material design. Herein, we integrate combinatorial chemistry, machine learning, and automated high-throughput synthesis and characterization platforms to establish a new paradigm for the design of antimicrobial polymers with excellent biocompatibility. Starting with a library of 13,728 combinations, a seed dataset of 400 structures is generated, followed by four Design-Build-Test-Learn iterations using a new machine learning model. 7 top-performing candidates are screened with a minimum inhibitory concentration (MIC) ≤ 8 μg/mL and an inhibitory concentration causing 20 % cell death (IC<sub>20</sub>) ≥ 64 μg/mL. The highest-performing polymer (MIC 2 μg/mL, IC<sub>20</sub> 256 μg/mL) shows similar in vivo therapeutic efficacy with ceftazidime. Overall, the integration of AI-guided high-throughput screening and combinatorial chemistry accelerates the discovery of new antimicrobial polymers, which provides a scalable strategy for developing novel antimicrobial agents. Antimicrobial ; Polymer ; Antimicrobial peptides ; Scalability ; High-throughput screening ; Chemical space Antimicrobial agents and applications ; Antimicrobial Peptides and Activities ; Microbial Natural Products and Biosynthesis
Authors: A. A. Tsygankov
Source: High Energy Chemistry (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Rapid Algorithm for Creation of Machine Learning Potential for Viscosity Calculation of Liquid Ni30Fe70 The use of machine learning methods in materials modeling is a powerful tool for reproducing the properties of a medium with an accuracy close to ab initio calculations. Describing the properties of even binary disordered systems is a nontrivial task due to the complexity of finding the global minimum of potential energy to describe the medium. This problem can be solved using machine learning methods. Using the deep learning method implemented in the DeePMD package, we demonstrate that a machine-learned potential can be obtained for calculating the viscosity of Ni30Fe70 melt using classical molecular dynamics. Moreover, the accuracy of viscosity calculation will be close to the experimental one. This result opens up prospects for developing procedures to rapidly obtain potentials for modeling the dynamic properties of binary alloys. Binary number ; Viscosity ; Energy (signal processing) ; Task (project management) ; Ab initio ; Potential energy Machine Learning in Materials Science ; Material Dynamics and Properties ; Advanced Physical and Chemical Molecular Interactions
Authors: Sravan Kumar Josyula | Y. Noiman | E. J. Payton | Tommaso Giovannelli
Source: Computational Materials Science (article, 2025-10-10)
Field: Materials Science — Materials Chemistry

Comparison of derivative-free and gradient-based minimization for multi-objective compositional design of shape memory alloys Designing shape memory alloys (SMAs) that meet performance targets while remaining affordable and sustainable is a complex challenge. In this work, we focus on optimizing SMA compositions to achieve an arbitrary target martensitic start temperature (Ms) while minimizing cost. To do this, we use machine learning models as surrogate predictors and apply numerical optimization methods to search for suitable alloy combinations. We trained two types of machine learning models, a tree-based ensemble and a neural network, using a dataset of experimentally characterized alloys and physics-informed features. The tree-based model was used with a derivative-free optimizer (COBYLA), while the neural network, which provides gradient information, was paired with a gradient-based optimizer (TRUST-CONSTR). Our results show that while both models predict Ms with similar accuracy, the optimizer paired with the neural network finds better solutions more consistently. COBYLA often converged to suboptimal results, especially when the starting guess was far from the target. The TRUST-CONSTR method showed more stable behavior and was better at reaching alloy compositions that met both objectives. This study demonstrates a practical approach to exploring new SMA compositions by combining physics-informed data, machine learning models, and optimization algorithms. Although the scale of our dataset is smaller than simulation-based efforts, the use of experimental data improves the reliability of the predictions. The approach can be extended to other materials where design trade-offs must be made with limited data. Josyula et al. show that while a tree-based ensemble can provide slightly better accuracy than a neural network in predicting phase transformation temperatures in shape memory alloys from the elemental compositions, a gradient-based optimizer paired with the neural network finds better solutions more consistently than a derivative-free optimizer, and is therefore more useful for alloy discovery and design. • A multi-objective optimization approach is applied to shape memory alloy compositions. • Cost and transformation temperatures are simultaneously considered. • Neural networks enable gradient-based objective function minimization. Shape-memory alloy ; Artificial neural network ; SMA* ; Minification ; Reliability (semiconductor) ; Transformation (genetics) ; Genetic algorithm ; Energy minimization Shape Memory Alloy Transformations ; Topology Optimization in Engineering ; High Entropy Alloys Studies
Authors: Susan Meñez Aspera | Gerardo VALADEZ HUERTA | Yūsuke Nanba | Kaoru Hisama | Michihisa KOYAMA
Source: Journal of Computer Chemistry Japan (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Predicting CO Adsorption on Multi-elemental Alloy through Machine Learning Analysis of Ternary Components Surface-molecule interaction has always been an integral part in the analysis of reactions and surface reactivity in heterogenous catalysis. With the advent of computational resource advancement, the search for the next generation catalysts explores the combination of several metal elements in the multi-elemental nanoparticles (NP). However, with the complexity of the catalysts' surface comes the difficulty of understanding surface-molecule interaction and methods to overcome this should be considered. In our previous study, we used metal-coordination to predict CO adsorption on the ternary alloy combinations. This method describes the adsorption site by the network of metal elements interacting with the site that contributes to the change in its electronic properties. Since this network considers up to the neighbor of the first nearest neighbor of the adsorption site, in this study, we considered to use the dataset of regression coefficient of ternary alloy combinations to predict molecular adsorption on a multi-elemental NP. We tested the method on CO molecular adsorption on the quaternary RuRhIrPt NP and used the regression coefficient obtained from RuRhPt, RhIrPt, RuIrPt and RuRhIr ternary alloy. Results show that the predicted values of CO adsorption energy on the RuRhIrPt NP have comparable values of coefficient of determination (R2) and mean absolute error (MAE) with the prediction of CO adsorption on ternary alloy. Thus, this method could pave the way for predicting molecular adsorption on multi-elemental NP using only the dataset of regression coefficient from ternary alloy combinations and the atomic configurational structure of the multi-elemental NP. Ternary operation ; Adsorption ; Alloy ; Elemental analysis ; Ternary alloy ; Materials science ; Chemistry ; Chemical engineering ; Computer science ; Metallurgy ; Inorganic chemistry ; Physical chemistry ; Engineering Catalytic Processes in Materials Science
Authors: Isaías Rodríguez
Source: ArXiv.org (preprint, 2025-01-30)
Field: Materials Science — Materials Chemistry

Harnessing Artificial Intelligence for Modeling Amorphous and Amorphous Porous Palladium: A Deep Neural Network Approach Amorphous and amorphous porous palladium are key materials for catalysis, hydrogen storage, and functional applications, but their complex structures present computational challenges. This study employs a deep neural network trained on 33,310 atomic configurations from ab initio molecular dynamics simulations to model their interatomic potential. The AI-driven approach accurately predicts structural and thermal properties while significantly reducing computational costs. Validation against density functional theory confirms its reliability in reproducing forces, energies, and structural distributions. These findings highlight AI's potential in accelerating the study of amorphous materials and advancing their applications in energy and catalysis. Amorphous solid ; Artificial neural network ; Palladium ; Porosity ; Artificial intelligence ; Materials science ; Computer science ; Chemistry ; Composite material ; Catalysis ; Organic chemistry Catalytic Processes in Materials Science ; Theoretical and Computational Physics ; Metallic Glasses and Amorphous Alloys