jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Marwan A. Hassan | Cristian Gabellini | Hatem Helal | Dominique Beaini | Kirill Neklyudov
Source: ArXiv.org (preprint, 2025-06-02)
Field: Materials Science — Materials Chemistry

Self-Refining Training for Amortized Density Functional Theory Density Functional Theory (DFT) allows for predicting all the chemical and physical properties of molecular systems from first principles by finding an approximate solution to the many-body Schrödinger equation. However, the cost of these predictions becomes infeasible when increasing the scale of the energy evaluations, e.g., when calculating the ground-state energy for simulating molecular dynamics. Recent works have demonstrated that, for substantially large datasets of molecular conformations, Deep Learning-based models can predict the outputs of the classical DFT solvers by amortizing the corresponding optimization problems. In this paper, we propose a novel method that reduces the dependency of amortized DFT solvers on large pre-collected datasets by introducing a self-refining training strategy. Namely, we propose an efficient method that simultaneously trains a deep-learning model to predict the DFT outputs and samples molecular conformations that are used as training data for the model. We derive our method as a minimization of the variational upper bound on the KL-divergence measuring the discrepancy between the generated samples and the target Boltzmann distribution defined by the ground state energy. To demonstrate the utility of the proposed scheme, we perform an extensive empirical study comparing it with the models trained on the pre-collected datasets. Finally, we open-source our implementation of the proposed algorithm, optimized with asynchronous training and sampling stages, which enables simultaneous sampling and training. Code is available at https://github.com/majhas/self-refining-dft. Minification ; Sampling (signal processing) ; Energy (signal processing) ; Asynchronous communication ; Density functional theory ; Code (set theory) ; Computation ; Dependency (UML) ; Representation (politics) Machine Learning in Materials Science ; Creativity in Education and Neuroscience
Authors: Chengchen Jin | Kai Xiong | Zhongqian Lv | Congtao Luo | Hui Fang | Jiankang Zhang | Aimin Zhang | Shunmeng Zhang | Yong Mao | Yingwu Wang
Source: Advanced Theory and Simulations (article, 2025-12-29)
Field: Materials Science — Materials Chemistry

Front Cover: High‐Throughput Calculation and Machine Learning‐Assisted Prediction of the Mechanical Properties of Refractory Multi‐Principal Element Alloys (Adv. Theory Simul. 1/2026) Refractory (planetary science) ; Finite element method ; Element (criminal law) ; Front (military) ; Work (physics) Machine Learning in Materials Science ; Metallurgy and Material Forming ; Model Reduction and Neural Networks
Authors: Hong-Li Yang (22130857) | De-Guang Liu (13906012) | Ke Duan (5139722) | Ying Wang (11406) | Haoke Qiu (18360304) | Jianwei Zhang (13799) | Yao Fu (386864) | Zhao-Yan Sun (1468582)
Source: Figshare (article, 2025-08-27)
Field: Materials Science — Polymers and Plastics

Machine Learning-GuidedDesign of Bio-Based Furan-ContainingPolyimides with Tunable Dielectric Properties The development of biobased furan-containing polyimides with tailored dielectric properties is critical for sustainable electronics, yet their structure–property relationships remain underexplored. Here, we combine density functional theory (DFT) calculations and machine learning to predict the dielectric performance of furan-based polyimides and systematically compare them with conventional analogues. We first employed high-throughput computational techniques based on DFT and density functional perturbation theory to establish a high-frequency dielectric constant data set comprising 3032 furan-based polyimides and 2061 phenyl-based polyimides. Using this data set, we trained a graph neural network model, which achieved an <i>R</i><sup>2</sup> greater than 0.82 in predicting the average high-frequency dielectric constant on the test set. A comparison of the model’s predictions with existing experimental results showed similar trends, further validating the predictive accuracy of our model. Then we evaluated the impact of furan substitution for phenyl groups on the dielectric constant across the entire chemical space, achieving wider dielectric constant distribution of furan-containing polyimides. Our study reveals the importance of furan-based polymers in the application of electronics. Dielectric ; Permittivity ; Density functional theory ; Constant (computer programming) ; Perturbation (astronomy) ; Perturbation theory (quantum mechanics) ; Artificial neural network Synthesis and properties of polymers ; Dielectric materials and actuators ; Synthesis and Properties of Aromatic Compounds
Authors: Deriyan Senjaya | Stephen Ekaputra Limantoro
Source: ArXiv.org (article, 2025-12-19)
Field: Materials Science — Materials Chemistry

Machine Learning Assisted Parameter Tuning on Wavelet Transform Amorphous Radial Distribution Function Understanding atomic structures is crucial, yet amorphous materials remain challenging due to their irregular and non-periodic nature. The wavelet-transform radial distribution function (WT-RDF) offers a physics-based framework for analyzing amorphous structures, reliably predicting the first and second RDF peaks and overall curve trends in both binary Ge 0.25 Se 0.75 and ternary Ag x(Ge 0.25 Se 0.75)100-x (x=5,10,15,20,25) systems. Despite these strengths, WT-RDF shows limitations in amplitude accuracy, which affects quantitative analyses such as coordination numbers. This study addresses the issue by optimizing WT-RDF parameters using a machine learning approach, producing the enhanced WT-RDF+ framework. WT-RDF+ improves the precision of peak predictions and outperforms benchmark ML models, including RBF and LSTM, even when trained on only 25 percent of the binary dataset. These results demonstrate that WT-RDF+ is a robust and reliable model for structural characterization of amorphous materials, particularly Ge-Se systems, and support the efficient design and development of phase-change thin films for next-generation electronic devices and components. Ternary operation ; Amorphous solid ; Benchmark (surveying) ; Binary number ; Function (biology) ; Amplitude ; Pattern recognition (psychology) ; Radial distribution function Phase-change materials and chalcogenides ; X-ray Diffraction in Crystallography ; Machine Learning in Materials Science
Authors: Chernovol, Pavel Ivanovich | Verkhovykh, Anastasiya Vladimirovna | Mirzoev, Alexandr Aminulaevich
Source: National repository of research data in engineering (article, 2025-12-05)
Field: Materials Science — Materials Chemistry

A comparative analysis of DeePMD and MTP models for short-range order prediction in Fe-V solid solutions Body-centered cubic Fe-V alloys and their derived steels are extensively utilized in industry due to their outstanding mechanical properties, which include high strength, hardness, wear resistance, and improved impact toughness at elevated temperatures. Recent research has increasingly focused on the investigation of short-range order (SRO) in these alloys, particularly after the observation that the Cowley SRO parameter in a related Fe-Cr system changes with concentration. At low chromium concentrations, SRO is negative, indicating a propensity for short-range atomic ordering. Conversely, it becomes positive above 10 atomic percent, suggesting a tendency for segregation. Analyzing SRO in disordered alloys using molecular dynamics simulations requires the careful selection of highly accurate interatomic potentials to accurately represent the system’s behavior. This study compares interatomic potentials derived from two prominent machine learning techniques: the Moment Tensor Potential (MTP) and the Deep Potential method (DeePMD). These potentials are applied to the Fe-V system across a vanadium concentration range of 0 – 25 atomic percent. Their performance is evaluated through error and efficiency analyses, concentrating on their ability to predict material properties such as the Cowley parameter, equilibrium volume, and lattice constants. This assessment aims to determine the effectiveness of these potentials in capturing the characteristics of Fe-V alloys, thereby providing a foundation for further investigations into their behavior. Interatomic potential ; Solid solution ; Vanadium ; Molecular dynamics ; Lattice (music) ; Toughness ; Chromium Machine Learning in Materials Science ; Hydrogen embrittlement and corrosion behaviors in metals ; High Temperature Alloys and Creep
Authors: Chris Maloney | Lucas G. Taliaferro | Jonathan Ventura | Long Wang
Source:  (article, 2025-05-05)
Field: Materials Science — Materials Chemistry

Deep learning-based microstructure reconstruction for characterizing the electromechanical property of nanomaterial networks Various microscopic imaging techniques, such as optical microscopy, scanning electron microscopy, and X-ray microscopy, are essential for characterizing multi-scale material behavior and understanding structure-property relationships. However, establishing a large microstructural image library of materials through experimental imaging can be expensive, time-consuming, and inaccessible. There is an emerging trend in using computational techniques (e.g., traditional statistical approaches and machine learning) to more efficiently generate microstructural images to be utilized in material studies. This study focuses on developing deep learning approaches to reconstruct and artificially generate microstructures of strain-sensing nanomaterial networks based on microscope imagery. Here, the nanomaterial networks were fabricated using corona-enabled electrostatic printing, which leverages high voltage discharged in the air to attract feedstock material particles – specifically carbon nanotubes – onto substrates. The fabricated carbon nanotube networks were previously found to exhibit strain-sensing performance with high linearity. To better understand this electromechanical behavior, optical microscope images of carbon nanotube networks under different tensile strains were experimentally obtained and used for developing deep learning models. In this study, we employ a Generative Adversarial Network (GAN), incorporating skip-layer excitation and differentiable augmentation. The GAN model is conditioned on specific microstructure metrics, such as porosity, and uses the Learned Perceptual Image Patch Similarity as part of its loss function to ensure realistic image generation. The model is trained on 2048×2048 images, allowing it to accurately replicate complex microstructural patterns. Overall, this study establishes a technique based on deep learning for generating a large database for characterizing the electrical properties of nanomaterial networks at micro-scale in a low-cost and efficient manner. Microstructure ; Nanomaterials ; Property (philosophy) ; Materials science ; Deep learning ; Computer science ; Nanotechnology ; Artificial intelligence ; Composite material Machine Learning in Materials Science
Authors: Hubert Beck | Pavol Simko | Lars L. Schaaf | Ondrej Marsalek | Christoph Schran
Source: ArXiv.org (preprint, 2025-08-13)
Field: Materials Science — Materials Chemistry

Multi-head committees enable direct uncertainty prediction for atomistic foundation models Machine learning potentials have become a standard tool for atomistic materials modelling. While models continue to become more generalisable, an open challenge relates to efficient uncertainty predictions for active learning and robust error analysis. In this work, we utilise MACE and its multi-head mechanism to implement a committee neural network potential for message-passing architectures, where the committee comprises multiple output modules attached to the same atomic environment descriptors. As with traditional committees of independent networks, the standard deviation of the predictions functions as an estimate of the model's uncertainty. We show for a range of datasets in custom-build models that the uncertainty of the force predictions correlates well with the true errors. We subsequently apply this concept to foundation models, specifically MACE-MP-0, where we train only the newly attached output heads while keeping the remaining part of the model fixed. We use this approach in an active learning workflow to condense the training set of the foundation model to just 5\% of its original size. The foundation model multi-head committee trained on the condensed training set enables reliable uncertainty estimation without any substantial decrease in prediction accuracy. Foundation (evidence) ; Set (abstract data type) ; Artificial neural network ; Uncertainty quantification ; Range (aeronautics) ; Workflow Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Machine Learning and Algorithms
Authors: Omee, Sadman Saadeed | Wei, Lai | Hu, Jianjun
Source: Scholar Commons (University of South Carolina) (article, 2025-09-26)
Field: Materials Science — Materials Chemistry

Polymorphism Crystal Structure Prediction with Adaptive Space Group Diversity Control Crystalline materials can form different structural arrangements (i.e., polymorphs) with the same chemical composition, exhibiting distinct physical properties depending on how they are synthesized or the conditions under which they operate. For example, carbon can exist as graphite (soft, conductive) or diamond (hard, insulating). Computational methods that can predict these polymorphs are vital in materials science, which help understand stability relationships, guide synthesis efforts, and discover new materials with desired properties without extensive trial-and-error experimentation. However, effective crystal structure prediction (CSP) algorithms for inorganic polymorph structures remain limited. ParetoCSP2 is proposed, a multi-objective genetic algorithm for polymorphism CSP that incorporates an adaptive space group diversity control technique and the sustainable age-fitness Pareto optimized evolutionary mechanism, preventing over-representation of any single space group in the population guided by a neural network interatomic potential. Using an improved population initialization method and performing iterative structure relaxation, ParetoCSP2 not only alleviates premature convergence but also achieves improved convergence speed. These results show that ParetoCSP2 achieves excellent performance in polymorphism prediction, including a nearly perfect space group and structural similarity accuracy for formulas with two polymorphs but with the same number of unit cell atoms. Evaluated on a benchmark dataset, it outperforms baseline algorithms by factors of 2.46–8.62 for these accuracies and improves by 44.8–87.04% across key performance metrics for regular CSP. This source code is freely available at https://github.com/usccolumbia/ParetoCSP2. Initialization ; Crystal structure prediction ; Population ; Convergence (economics) ; Group (periodic table) ; Premature convergence ; Benchmark (surveying) ; Evolutionary algorithm Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Computational Drug Discovery Methods
Authors: Taline Kerackian | Clément Wespiser | Matthieu Daniel | Éric Pasquinet | Eugénie Romero
Source: ChemRxiv (preprint, 2025-01-31)
Field: Materials Science — Materials Chemistry

Investigation of Arenes and Heteroarenes Nitration supported by High-Throughput Experimentation and Machine Learning Access to the nitro functional group is a very common and longstanding transformation of interest in many fields of chemistry. However, the robustness and specificity of this transformation can remain challenging, particularly in the case of heteroarene nitration. From this observation, a large investigation was initiated to screen nitration conditions on various arenes and heteroarenes. The systematical and diverse study of both nitrating agents and activating reagents was conducted using high-throughput experimentation, to afford high quantity and high quality data generation. General trends have been identified and correlated to the electronic property of the heteroarene, notably the difficult nitration of electron-poor heteroarenes was highlighted. Original combinations of reagents were found to perform well in nitration reactions. The obtained data were also used to design a predictive tool relying on machine learning in order to provide the best nitration reaction conditions depending on the targeted substrate. The limited predictive efficiency obtained pointed out the importance of the diversification and the chemically relevant encoding of the data set. Nitration ; Combinatorial chemistry ; Reagent ; Computer science ; Robustness (evolution) ; Biochemical engineering ; Chemistry ; High-throughput screening ; Artificial intelligence ; Machine learning ; Organic chemistry ; Engineering ; Biochemistry Machine Learning in Materials Science ; Innovative Microfluidic and Catalytic Techniques Innovation ; Asymmetric Hydrogenation and Catalysis
Authors: Luke H. Elder (17704761) | Alexey V. Onufriev (430594)
Source: Figshare (article, 2025-07-11)
Field: Materials Science — Materials Chemistry

Using Deep GraphNeural Networks Improves Physics-BasedHydration Free Energy Predictions Even for Molecules Outside of theTraining Set Distribution The accuracy of computational water models is crucial to atomistic simulations of biomolecules. Here we explore a decoupled framework that combines classical physics-based models with deep neural networks (DNNs) to correct residual error in hydration free energy (HFE) prediction. Our main goal is to evaluate this framework on out-of-distribution data (molecules that differ significantly from those used in training), where DNNs are known to struggle. Several common physics-based solvation models are used in the evaluation. Graph neural network architectures are tested for their ability to generalize using multiple data set splits, including out-of-distribution HFEs and unseen molecular scaffolds. Our most important finding is that for out-of-distribution data, where DNNs alone often struggle, the physics + DNN models consistently improve physics model predictions. For in-distribution data, the DNN corrections significantly improve the accuracy of physics-based models, with a final RMSE below 1 kcal/mol and a relative improvement between 40% and 65% in most cases. The accuracy of physics + DNN models tends to improve when the 6% of molecules with the highest experimental uncertainty are removed. This study provides insights into the potential and limitations of combining physics and machine learning for molecular modeling, offering a practical and generalizable strategy of using DNN as independent postprocessing correction. Artificial neural network ; Set (abstract data type) ; Training set ; Residual ; Graph ; Deep neural networks ; Energy (signal processing) Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Julia A. Stebani | Íñigo Iribarren | Gohar A. Siddiqui | Darren Wragg | Alessio Gagliardi | Angela Casini
Source: ChemRxiv (article, 2025-08-23)
Field: Materials Science — Materials Chemistry

ML-assisted Computational Workflow to the Robust Modeling of Supramolecular Metallacages in Solution We present here an integrated computational workflow to study the behavior of selected self-assembled [Pd2L4]4+ metallacages in explicit solvents (water and DMSO) and benchmark them for future in silico investigation of their host-guest chemistry, pivotal to their application as drug delivery systems. The workflow includes classical force field (FF) construction of two metallacage analogues and testing for standard and Machine Learning Interatomic Potential (MLIP)-based molecular dynamics (MD) simulations. Overall, the study provides a direct comparison of classical FF with MILPs with respect to performance vs computational cost. The proposed workflow offers a versatile framework for computationally screening and designing supramolecular systems in solution, effectively bridging the gap between quantum-level accuracy and the temporal and spatial scales needed for simulations of different functional applications. Workflow ; Supramolecular chemistry ; Computer science ; Software engineering ; Computational science ; Chemistry ; Database ; Crystallography X-ray Diffraction in Crystallography ; Machine Learning in Materials Science ; Corrosion Behavior and Inhibition
Authors: Rashid R. Valiev (1486678) | Rinat T. Nasibullin (9356746) | Dage Sundholm (1594153) | Theo Kurtén (1298547)
Source: Figshare (article, 2025-10-07)
Field: Materials Science — Materials Chemistry

Machine-LearningPredictions of Rate Constants ofInternal Conversion Using Electronic and Structural Descriptors We present a machine learning approach to predict internal conversion rate constants (<i>k</i><sub>IC</sub>) based on molecular electronic and structural descriptors. Our approach addresses the challenge of accurately estimating <i>k</i><sub>IC</sub> between the first excited singlet state (<i>S</i><sub>1</sub>) and the ground state (<i>S</i><sub>0</sub>), which is an important deactivation channel of photophysical processes. Using a cost-efficient method that assumes excitation-energy transfer to the vibrations of the X–H bonds (X = C, N, O), we generated a data set for <i>k</i><sub>IC</sub> of more than 5000 molecules, including porphyrins and [8]circulenes using parameters calculated at the time-dependent density functional theory (TDDFT) level. We have used both the CatBoost (CB) model and a neural network (NN) including transformer-based and graph-based architectures. Using the CB and NN models we obtained high precision with <i>R</i><sup>2</sup> values of about 0.99. The models successfully predict <i>k</i><sub>IC</sub> even for new molecules when they are trained with energy-augmented data sets based on electronic and structural descriptors. We also introduce graph-based X–H bond descriptors to predict rate constants for various chemical classes. The calculated rate constants indicate the promising potential of these descriptors to predict <i>k</i><sub>IC</sub> using machine learning to bypass computationally demanding quantum mechanical calculations to determine the photophysical properties. Reaction rate constant ; Excited state ; Artificial neural network ; Density functional theory ; Singlet state ; Internal conversion ; Ground state ; Constant (computer programming) Machine Learning in Materials Science ; Synthesis and Properties of Aromatic Compounds ; Molecular spectroscopy and chirality