jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ilgar Baghishov | Jan Janßen | Graeme Henkelman | Danny Pérez
Source: arXiv (Cornell University) (preprint, 2025-06-06)
Field: Materials Science — Materials Chemistry

Application-specific machine-learned interatomic potentials: exploring the trade-off between DFT convergence, MLIP expressivity, and computational cost Machine-learned interatomic potentials (MLIPs) are revolutionizing computational materials science and chemistry by offering an efficient alternative to {\em ab initio} molecular dynamics (MD) simulations. However, fitting high-quality MLIPs remains a challenging, time-consuming, and computationally intensive task where numerous trade-offs have to be considered, e.g., How much and what kind of atomic configurations should be included in the training set? Which level of {\em ab initio} convergence should be used to generate the training set? Which loss function should be used for fitting the MLIP? Which machine learning architecture should be used to train the MLIP? The answers to these questions significantly impact both the computational cost of MLIP training and the accuracy and computational cost of subsequent MLIP MD simulations. In this study, we use a configurationally diverse beryllium dataset and quadratic spectral neighbor analysis potential. We demonstrate that joint optimization of energy versus force weights, training set selection strategies, and convergence settings of the {\em ab initio} reference simulations, as well as model complexity can lead to a significant reduction in the overall computational cost associated with training and evaluating MLIPs. This opens the door to computationally efficient generation of high-quality MLIPs for a range of applications which demand different accuracy versus training and evaluation cost trade-offs. Interatomic potential ; Convergence (economics) ; Range (aeronautics) ; Computational complexity theory ; Quadratic equation ; Function (biology) ; Set (abstract data type) ; Task (project management) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Electrocatalysts for Energy Conversion
Authors: Deriyan Senjaya | Stephen Ekaputra Limantoro
Source: arXiv (Cornell University) (preprint, 2025-12-19)
Field: Materials Science — Materials Chemistry

Enhancing Reconstruction Capability of Wavelet Transform Amorphous Radial Distribution Function via Machine Learning Assisted Parameter Tuning 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 reconstructing the first and second Radial Distribution Function (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. The shortcoming arises from improper parameter (a, b, Kf, C, and Λ)) selection, as the parameters intrinsically represent atomic interactions within amorphous materials. This study addresses the issue by optimizing WT-RDF parameters using a machine learning approach via learnable parameter optimization, parameter bounding, and selective loss, producing the enhanced WT-RDF+ framework. WT-RDF+ improves the precision of peak reconstructions and outperforms benchmark Machine Learning (ML) models, including Radial Basis Function (RBF) and Long Short-term Memory (LSTM), when trained on only 25% of the binary dataset. Specifically, the machine learning benchmarks are defined as regressors with radial distance r input and G(r) output taken from Ab Initio Molecular Dynamics (AIMD) RDF simulation, not the reduced structure factor SR(q) to G(r) inversions. These results demonstrate that WT-RDF+ is a robust and reliable model for RDF reconstruction of Ge-Se and Ag-Ge-Se family. 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: Eymana Maria (22121739) | Jonathan J. Denney (11401725) | Yusu Wang (4344916) | Peter G. Khalifah (1568605) | Katsuyo Thornton (1356255)
Source: Figshare (article, 2025-08-26)
Field: Materials Science — Materials Chemistry

Understanding theEffect of Sample Geometry on TemperatureDistribution during Optical Floating Zone Crystal Growth in VacuumEnvironment through Heat Transfer Modeling Optical floating zone furnaces (OFZ) have had a transformative impact on fundamental science due to their ability to rapidly produce large single crystals of a wide variety of complex materials. However, a quantitative understanding of the OFZ growth environment is generally lacking due to the difficulty of measuring the local sample temperatures during OFZ growth, as well as to the general lack of information about the temperature-dependent physical parameters needed to model heat transfer. To overcome these challenges, we apply a physics-based heat transfer model, parametrized by measurements from synchrotron experiments and a machine-learning (ML) algorithm, to simulate the temperature distributions of samples heated in an OFZ furnace in a vacuum environment. This model is used to quantitatively understand how the sample maximum temperature and temperature gradient (key parameters that influence the success of crystal growth) are affected by the rod size, rod shape, and heat-zone position on the rod. The results of this study can be applied to make informed decisions on how crystal growth parameters can be tuned to modify temperature profiles and to optimize crystal growth outcomes even when data on internal sample temperature profiles (e.g., those obtained through in situ synchrotron experiments) are not accessible. Temperature gradient ; Synchrotron ; Sample (material) ; Crystal growth ; Crystal (programming language) ; Heat transfer ; Position (finance) Solidification and crystal growth phenomena ; Glass properties and applications ; Solid State Laser Technologies
Authors: Damiano Ricciarelli | Simona Filice | Viviana Scuderi | Giuseppe Fisicaro | Gaetano Calogero | Ioannis Deretzis | Ignazio Vacante | Simona Crispi | Rosario G. Viglione | Silvia Scalese | Antonino La Magna
Source:  (article, 2025-09-24)
Field: Materials Science — Materials Chemistry

Computational Methodologies for the Design and Investigation of Graphene Oxide-Based Sensors We present an ab initio modeling approach for graphene oxide (GO), augmented by machine learning based structural simulations, to extract the optical and electrical properties of the material at different scale levels. The approach addresses a longstanding challenge in GO modeling, crucial for the domain of sensor: capturing the impact on electrical properties provided by variable amount and typology of oxygen functional groups, complex amorphous structure and molecular adsorption of gases and molecules. We show the validation of our model against experimental absorption spectra and photoluminescence data obtained during laser irradiation processes, and we discuss its extension to simulate electrical properties with larger cells of tens of nanometers size, obtained with machine learning interatomic potentials, following preliminary tests we conducted. Breaking with conventional approaches, our framework has the potential to enable electro-optical property predictions on large scales under varying physicochemical scenarios. Those include variable oxidation level, pore formation, and binding with supramolecular species, all of which are common in sensor fabrication processes or during the sensing itself. While this is, indeed, of paramount importance for the mentioned field, further domains of application could be environment, optoelectronics, energy storage, and microelectronics. Graphene ; Noise (video) ; Field (mathematics) ; Process (computing) Graphene research and applications ; Advanced Sensor and Energy Harvesting Materials ; Carbon Nanotubes in Composites
Authors: Yongxian Wu (16948698) | Ray Luo (141237)
Source: Figshare (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

Grid-Context ConvolutionalModel for Efficient MolecularSurface Construction from Point Clouds Accurate and efficient molecular surface representation is essential in computational chemistry, impacting applications such as enzymology, rational drug design, and molecular recognition. Traditional approaches, including solvent-accessible surface (SAS) and solvent-excluded surface (SES), are widely used but often suffer from computational inefficiencies and limited adaptability to complex molecular structures. More recently, grid-independent machine learning methods have accelerated surface construction; however, these approaches rely solely on local single-grid information and fail to capture grid-context interactions, leading to inaccuracies in modeling complex interior surface regions. In this work, we introduce Con2SES, a novel framework that explicitly incorporates grid-context interactions to improve the accuracy and robustness of molecular surface construction. By leveraging learnable convolutional operators in both two-dimensional (2D) and three-dimensional (3D) space, Con2SES mitigates the limitations of grid-independent methods and ensures a more continuous and accurate surface construction. Extensive evaluations on protein molecules and diverse molecular collections demonstrate that Con2SES significantly outperforms grid-independent approaches in accuracy while maintaining computational efficiency comparable to that of the fastest grid-independent method, achieving approximately 99% accuracy and a 28-fold speedup compared to the classical SES implementation in the AMBER software package. Furthermore, Con2SES has been integrated into the AMBER platform and released as an open-source tool, providing a high-performance solution for large-scale molecular surface analysis. Robustness (evolution) ; Surface (topology) ; Point cloud ; Representation (politics) ; Speedup ; Software ; Computational complexity theory ; Computational model Machine Learning in Materials Science ; Protein Structure and Dynamics ; Surface Chemistry and Catalysis
Authors: Mijas Tiwari
Source: Himalayan Physics (article, 2025-09-05)
Field: Materials Science — Materials Chemistry

Mechanochemistry and mechanical properties of carbon nanotubes: Advances in experimental, theoretical, and computational understanding This comprehensive review examines the mechanochemistry and mechanical properties of carbon nanotubes (CNTs) through integrated analysis of experimental and theoretical research. Experimental studies reveal exceptional mechanical characteristics including Young's moduli up to 1.8 TPa, tensile strengths exceeding 100 GPa, and unique deformation mechanisms like reversible buckling and "sword-in-sheath" fracture. Theoretical frameworks establish structure-property relationships, demonstrating how Stone-Wales defects, chirality, and functionalization govern mechanical behavior at atomic scales. Cross-validation shows remarkable alignment between atomistic simulations and empirical measurements, particularly for elastic moduli and defect-mediated failure. Modern approaches employing irradiation-induced crosslinking and machine learning demonstrate significant enhancements in load transfer efficiency and property prediction accuracy. Critical research gaps persist in understanding chirality-specific fracture dynamics and scalable composite integration, while emerging techniques combining machine learning with multiscale modeling offer promising pathways for predictive nanomaterial design. This synthesis provides fundamental insights for advancing CNT-based applications in high-performance composites and nanoelectromechanical systems. Mechanochemistry ; Carbon nanotube ; Surface modification ; Deformation (meteorology) ; Nanoelectromechanical systems ; Carbon fibers ; Deformation mechanism ; Nanomaterials ; Ultimate tensile strength Carbon Nanotubes in Composites ; Graphite, nuclear technology, radiation studies ; Boron and Carbon Nanomaterials Research
Authors: Xiaoting Xu | Ridong Zhang | Kaiqi Zhang | Yunliang Qi | Zhi Wang
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Deep Neural Network-Based Approach for Optimizing Ammonia-Hydrogen Combustion Mechanism Mechanism (biology) ; Artificial neural network ; Ammonia ; Combustion ; Computer science ; Hydrogen ; Process engineering ; Chemistry ; Biochemical engineering ; Artificial intelligence ; Engineering ; Organic chemistry ; Physics Catalytic Processes in Materials Science ; Industrial Technology and Control Systems ; Advanced Combustion Engine Technologies
Authors: Ahmetcan Alkoç | Afife Binnaz Hazar Yoruç | Adem Can Uşak | C.M. Aras | Mete Bakır
Source: SSRN Electronic Journal (preprint, 2025-01-01)
Field: Materials Science — Materials Chemistry

Artificial Neural Network-Based Modeling of Sustainable Plasma Process Parameters for Predicting Wettability of Aircraft Composite Surfaces Artificial neural network ; Wetting ; Composite number ; Process (computing) ; Materials science ; Artificial intelligence ; Computer science ; Process engineering ; Engineering ; Composite material High voltage insulation and dielectric phenomena
Authors: Hubert Beck | Pavol Simko | Lars L. Schaaf | Ondřej Maršálek | Christoph Schran
Source: The Journal of Chemical Physics (article, 2025-12-15)
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 modeling. While models continue to become more generalizable, an open challenge relates to efficient uncertainty predictions for active learning and robust error analysis. In this work, we utilize 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, in particular 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 ; Model Reduction and Neural Networks
Authors: S. P. Pronin | Anastasia G. Zryumova | Alexander А. Piletsky | V. I. Belyaev
Source: Engineering Technologies and Systems (article, 2025-09-29)
Field: Chemistry — Analytical Chemistry

Intelligent Assessment of Wheat Yield through the Variable Potential of Seeds Introduction. Evaluation of wheat seed quality is an integral part of the technological process of its production since it increases the yield. The yield is affected by many different factors. The evaluation methods are constantly being improved taking into account new factors, physical methods and technical means. Currently seeds and crops sowing quality intelligent evaluation methods are developing very rapidly. The electrophysical method allows evaluating the soil influence on seeds by the variable potential. Aim of the Study. The study is aimed at examining changes in the variable potential of wheat seeds of a known crop yields during seed swelling in solutions with different potassium and sodium ratios and creating a convolutional neural network to estimate potential crop yields through the variable potential and known potassium and sodium ratios. Materials and Methods. The studies were carried out using seeds of two varieties of spring wheat with different yields. To simulate soil quality, there were used solutions with diffe­rent potassium chloride and sodium chloride ratios. The variable potential was measured using a device based on the data acquisition board LA50-USB. The yield was estimated using wavelet transform and deep convolutional neural network with ResNet groups. Results. There have been developed the experimental graphs of the variable potential change depending on the potassium and sodium ratio in a solution simulating soil quality. The neural network was used to classify the potential yield of wheat seeds through wavelet transforms of the variable potential, and potassium and sodium ratios. There has been compiled a table of neural network responses to test variable potentials. Discussion and Conclusion. The developed graphs of the variable potential change depending on potassium change in the external environment were compared with the results of studies by other authors. The results qualitatively coincide. The developed neural network can classify the potential yield of wheat seeds through the variable potential, and potassium and sodium ratios. The conducted study is useful for agricultural enterprises and farmers. The proposed methodology for assessing potential crop yields through variable potential and water extract will allow optimizing the process of potassium application to the soil. Yield (engineering) ; Potassium ; Sowing ; Crop yield ; Sodium ; Variable (mathematics) ; Convolutional neural network ; Crop Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Sonakshi Gupta | Akhlak Mahmood | Shivank Shukla | Rampi Ramprasad
Source: arXiv (Cornell University) (preprint, 2025-06-02)
Field: Materials Science — Materials Chemistry

Benchmarking Large Language Models for Polymer Property Predictions Machine learning has revolutionized polymer science by enabling rapid property prediction and generative design. Large language models (LLMs) offer further opportunities in polymer informatics by simplifying workflows that traditionally rely on large labeled datasets, handcrafted representations, and complex feature engineering. LLMs leverage natural language inputs through transfer learning, eliminating the need for explicit fingerprinting and streamlining training. In this study, we finetune general purpose LLMs -- open-source LLaMA-3-8B and commercial GPT-3.5 -- on a curated dataset of 11,740 entries to predict key thermal properties: glass transition, melting, and decomposition temperatures. Using parameter-efficient fine-tuning and hyperparameter optimization, we benchmark these models against traditional fingerprinting-based approaches -- Polymer Genome, polyGNN, and polyBERT -- under single-task (ST) and multi-task (MT) learning. We find that while LLM-based methods approach traditional models in performance, they generally underperform in predictive accuracy and efficiency. LLaMA-3 consistently outperforms GPT-3.5, likely due to its tunable open-source architecture. Additionally, ST learning proves more effective than MT, as LLMs struggle to capture cross-property correlations, a key strength of traditional methods. Analysis of molecular embeddings reveals limitations of general purpose LLMs in representing nuanced chemo-structural information compared to handcrafted features and domain-specific embeddings. These findings provide insight into the interplay between molecular embeddings and natural language processing, guiding LLM selection for polymer informatics. Workflow ; Natural language ; Key (lock) ; Property (philosophy) ; Leverage (statistics) ; Benchmarking ; Generative model ; Benchmark (surveying) ; Natural language understanding Machine Learning in Materials Science
Authors: Shweta Sharma (41033) | Natan Kaminsky (21437948) | Kira Radinsky (9701284) | Lilac Amirav (1269921)
Source: Figshare (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Predicting OxidationPotentials with DFT-Driven MachineLearning We introduce OxPot, a comprehensive open-access data set comprising over 15 thousand chemically diverse organic molecules. Leveraging the precision of DFT-derived highest occupied molecular orbital energies (<i>E</i><sub>HOMO</sub>), OxPot serves as a robust platform for accelerating the prediction of oxidation potential (<i>E</i><sub>ox</sub>). Using the PBE0 hybrid functional and cc-pVDZ basis set, we establish a strong near-linear correlation between <i>E</i><sub>HOMO</sub> and experimental <i>E</i><sub>ox</sub> values, achieving an exceptional correlation coefficient (<i>R</i><sup>2</sup>) of 0.977 and a low root-mean-square error (RMSE) of 0.064. The correlation highlights the accuracy of OxPot as a machine learning (ML)-ready resource for <i>E</i><sub>ox</sub> prediction. To further facilitate future development of ML models, we extensively tested various algorithms and conducted a thorough feature importance analysis. This analysis offers valuable insights into the key molecular descriptors that influence <i>E</i><sub>ox</sub> predictions, thereby enhancing model interpretability and guiding the design of more effective predictive models. Furthermore, the computational efficiency of the methodology ensures rapid predictions of <i>E</i><sub>ox</sub> for additional chemically similar molecules, thereby increasing its applicability for large-scale molecular screening and broader applications in chemical research. Interpretability ; Set (abstract data type) ; Molecular descriptor ; Feature (linguistics) ; Correlation coefficient ; Correlation ; Training set ; Key (lock) ; Experimental data Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Machine Learning in Bioinformatics