jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ruiqi Gao | Pinchen Xie | Roberto Car
Source: ArXiv.org (preprint, 2025-11-27)
Field: Materials Science — Materials Chemistry

A Machine Learning Model for the Chemistry of a Solvated Electron In molecular simulations, machine-learning force fields can achieve ab initio accuracy at a lower cost but remain limited in the explicit modeling of electrons. In this work, we develop an electron-aware machine-learning force field, in which an excess electron of interest is modeled quantum mechanically, while the remaining short-range interactions and long-range Coulombic forces are machine-learned to reproduce a density functional theory calculation. We demonstrate the method on the solvated electron in bulk water and its reaction with a hydronium ion. We identify a proton transfer mechanism by which the excess proton recombines with the electron. We determine the forward reaction rates between 350 K and 450 K from first-passage survival functions, which yield an Arrhenius relationship with an activation energy of 3.2 kcal$\cdot$mol$^{-1}$, in good agreement with experiment. From an enhanced sampling simulation, we determine the equilibrium constant, and thus the reaction free energy, which is also consistent with experimental measurements. Proton ; Solvated electron ; Hydronium ; Density functional theory ; Yield (engineering) ; Ab initio ; Arrhenius equation ; Electron ; Ab initio quantum chemistry methods ; Quantum Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Advanced Physical and Chemical Molecular Interactions
Authors: Oleh Yasniy | Vladyslav Demchyk
Source: Measuring and computing devices in technological processes (review, 2025-05-21)
Field: Materials Science — Materials Chemistry

SHAPE MEMORY ALLOYS AND MACHINE LEARNING: A REVIEW Shape memory alloys (SMAs) have found widespread application in various fields of science and technology due to their unique properties, such as superelasticity and shape memory effect. These alloys retain their initial form by memorising it between two transformation phases, which is temperature or magnetic field-dependent. The application of such materials is straightforward. The alloy can be deformed by force and recover to its initial shape or size after heating over a specific temperature. There are a lot of various kinds of SMA, for instance, Fe–Mn–Si, Cu–Zn–Al, and Cu–Al–N, and every type of SMA is applied specifically, though Nitinol Ni-Ti is ubiquitous because of its stable properties SMAs are widely used in medicine, the aerospace industry, motor building, civil engineering, dentistry, etc. During their operation, structural elements made of SMAs undergo long-term cyclic loading that can lead to premature loss of functional properties, exhaustion of lifetime, and subsequent failure. Therefore, ensuring sufficient functional properties and endurance of SMA is necessary. Often, the experiments are quite costly and time-consuming and require expert knowledge. Therefore, it is crucial to model the functional and structural properties of SMAs by employing AI (Artificial intelligence) and machine learning (ML) methods. AI can be employed to model SMA behaviour. AI is actively used in material science and fracture mechanics ML is a part of AI that can efficiently solve complicated tasks. This study aims to perform a comprehensive review of the application of ML methods to estimate various properties of shape memory alloys. A comprehensive analysis of ML methods was performed as applied to modelling various properties of SMAs. Several studies concern the application of methods of AI and ML to solve such problems. In general, AI and ML methods are promising and powerful tools to model the SMAs properties. Nevertheless, there is always room for improvement and further elaboration of the aforementioned methods and approaches for modelling the functional and structural properties of SMAs Shape-memory alloy ; SMA* ; Pseudoelasticity ; Computer science ; Field (mathematics) ; Transformation (genetics) ; Materials science ; Mechanical engineering ; Artificial intelligence ; Martensite ; Composite material ; Mathematics ; Engineering ; Algorithm ; Chemistry Shape Memory Alloy Transformations
Authors: Mohammed Saleh Alshaikh
Source: International Journal of Emerging Research in Engineering Science and Management (article, 2025-12-30)
Field: Materials Science — Materials Chemistry

Machine Learning–Based Prediction of Organic Solar Cell Performance Using Molecular Descriptors The performance of Organic Solar Cells (OSCs) is intrinsically linked to the molecular, electronic, and structural properties of donor and acceptor materials. This study employs various machine learning techniques, namely the Generalized Regression Neural Network (GRNN), Support Vector Machine (SVM), and Tree Boost, to predict key performance metrics of OSCs, including power conversion efficiency (PCE), short-circuit current density (JSC), open-circuit voltage (VOC), and fill factor (FF). The models are trained and evaluated using an experimentally reported dataset compiled by Sahu et al. Correlation analysis demonstrates that material characteristics such as polarizability, bandgap, dipole moment, and charge transfer are statistically associated with OSC performance. The predictive performance of the GRNN model is compared with that of the SVM and Tree Boost models, showing consistently lower prediction errors within the considered dataset. In addition, sensitivity analysis is performed to assess the relative importance of the predictor variables and to examine the influence of kernel functions on GRNN performance. The results indicate that machine learning models, particularly GRNN, can serve as effective data-driven tools for predicting the performance of organic solar cells and for supporting computational screening studies. Support vector machine ; Tree (set theory) ; Kernel (algebra) ; Decision tree ; Artificial neural network ; Pattern recognition (psychology) ; Sensitivity (control systems) ; Correlation Machine Learning in Materials Science ; Organic Electronics and Photovoltaics ; Conducting polymers and applications
Authors: Vladyslav Demchyk | Oleh Yasniy
Source: Scientific journal of the Ternopil national technical university (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Modelling of functional properties of shape memory alloy by machine learning methods This study deals with the modelling of NiTi shape memory alloy dissipated energy by means of supervised machine learning methods, considering the loading frequency. Shape memory alloys are materials of high interest both to science and industry. These materials are enjoying wide popularity due to their two peculiar properties: unique effect of shape memory and superplasticity, caused by direct austenite-martensite phase transformation and reverse martensite-austenite transformation. The traditional deterministic methods of assessment of material properties are often costly, time-consuming, and demand a well-trained workforce and laboratory equipment. On the contrary, in recent years, the methods of artificial intelligence have gained widespread attention due to their ability to reveal hidden insights from existing data. Machine learning is a subset of artificial intelligence. It allows training based on the available data and becomes better with time without the explicit need to be programmed. The experimental dataset was taken from open scientific sources. It contained the hysteresis curves for six loading frequencies of 0.1, 0.5, 1, 5, 7, and 10 Hz. The input data consisted of the next features: stress s (MPa), cycle number N, and loading frequency f (Hz). Based on these data, for each loading cycle, and for each loading cycle, the dissipated energy was calculated. To remove noise, Locally Weighted Scatterplot Smoothing (LOWESS) smoother in the nonparametric package of statsmodels was utilized. After that, the trapezoid numerical integration method was employed to calculate the area enclosed by the hysteresis loop of the respective cycle, that is, the dissipated energy. To augment the dataset, its points were interpolated using the modified Akima interpolation method (makima). Four models were built using the methods of Random Forest, AdaBoost, Gradient Boosting, and Neural Network. The best results were shown by the ensemble methods, such as AdaBoost, and Random Forest. For instance, the MAPE of AdaBoost was just 0.074, whereas the MAPE of Random Forest was 0.144. It was found that the Gradient Boosting method and Neural Network are not suitable for such a dataset, since the errors are quite large and, therefore, these methods are not good enough to be employed for solving such a problem. Shape-memory alloy ; Hysteresis ; Smoothing ; Dissipation ; Energy (signal processing) ; Interpretability ; Transformation (genetics) ; Artificial neural network Shape Memory Alloy Transformations ; Machine Learning in Materials Science ; Metallurgical and Alloy Processes
Authors: Ryo Fujita | Yoshifumi Amamoto | Jun Kikuchi
Source: Research Square (preprint, 2025-04-25)
Field: Materials Science — Materials Chemistry

Bayesian Optimization of Biodegradable Polymers via Machine Learning Driven Features from Low-Field NMR Data Bayesian probability ; Bayesian optimization ; Field (mathematics) ; Biodegradable polymer ; Polymer ; Artificial intelligence ; Computer science ; Machine learning ; Materials science ; Mathematics ; Composite material Machine Learning in Materials Science ; Nuclear Physics and Applications ; Fuel Cells and Related Materials
Authors: Deborah Braide | Eslam G. Al-Sakkari | Christopher Panaritis | Longzhe Li | Ntumba Kabongo Etienne | Laura Lavigne | Alexander Abanobi | Ahmed Ragab | Gregory S. Patience | Daria C. Boffito
Source: Journal of environmental chemical engineering (article, 2025-11-06)
Field: Chemistry — Inorganic Chemistry

Ultrasound accelerates zeolite synthesis: Predictive modelling via machine learning Crystallinity ; Yield (engineering) ; Agglomerate ; Hydrothermal synthesis ; Zeolite ; Hydrothermal circulation ; Particle size ; Ultrasound Zeolite Catalysis and Synthesis ; Catalysis for Biomass Conversion ; Catalysis and Oxidation Reactions
Authors: David A. Egger | Manuel Grumet | Tomáš Bučko
Source: ArXiv.org (preprint, 2025-06-24)
Field: Materials Science — Materials Chemistry

Machine Learning Accelerates Raman Computations from Molecular Dynamics for Materials Science Raman spectroscopy is a powerful experimental technique for characterizing molecules and materials that is used in many laboratories. First-principles theoretical calculations of Raman spectra are important because they elucidate the microscopic effects underlying Raman activity in these systems. These calculations are often performed using the canonical harmonic approximation which cannot capture certain thermal changes in the Raman response. Anharmonic vibrational effects were recently found to play crucial roles in several materials, which motivates theoretical treatments of the Raman effect beyond harmonic phonons. While Raman spectroscopy from molecular dynamics (MD-Raman) is a well-established approach that includes anharmonic vibrations and further relevant thermal effects, MD-Raman computations were long considered to be computationally too expensive for practical materials computations. In this perspective article, we highlight that recent advances in the context of machine learning have now dramatically accelerated the involved computational tasks without sacrificing accuracy or predictive power. These recent developments highlight the increasing importance of MD-Raman and related methods as versatile tools for theoretical prediction and characterization of molecules and materials. Anharmonicity ; Raman spectroscopy ; Context (archaeology) ; Computation ; Characterization (materials science) ; Thermal ; Perspective (graphical) ; Molecular vibration Machine Learning in Materials Science
Authors: Faezeh Bahrami | Alireza Salimi | Zahrasadat Momenzadeh Abardeh | Artem R. Oganov
Source: CrystEngComm (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

Prediction of the crystal structure of avadomide using machine learning potentials and crystal engineering Today, machine learning (ML) and crystal structure prediction (CSP) guided by crystal engineering (CE) are principal tools in computational materials discovery. Crystal structure prediction ; Crystal structure ; Crystal (programming language) ; Principal (computer security) ; Crystal engineering ; Principal component analysis Machine Learning in Materials Science ; Crystallography and molecular interactions ; X-ray Diffraction in Crystallography
Authors: Snehitha Srirangam | Swastik Paul | Siddharth Deshpande
Source: Journal of the Indian Institute of Science (article, 2025-11-14)
Field: Materials Science — Materials Chemistry

Machine Learning Approaches for Tackling Atomic-Scale Surface-Driven Complexities in Heterogeneous Catalysis Key (lock) ; Focus (optics) ; Heterogeneous catalysis ; Catalysis ; Computational model Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Electrocatalysts for Energy Conversion
Authors: Zibo Wu | Bohan Zhang | Wei‐Hai Fang | Zhendong Li
Source: ArXiv.org (preprint, 2025-07-25)
Field: Materials Science — Materials Chemistry

Hybrid tensor network and neural network quantum states for quantum chemistry Neural network quantum states (NQS) have emerged as a powerful and flexible framework for addressing quantum many-body problems. While successful for model Hamiltonians, their application to molecular systems remains challenging for several reasons. In this work, we introduce three innovations to overcome some of the key limitations. (1) We propose two novel ansätzet hat hybridize tensor network and neural network states for addressing initialization challenges and enhancing the expressivity of tensor networks. First, we develop a bounded-degree graph recurrent neural network (BDG-RNN) ansatz that leverages graph-based updates, enabling applications to molecular electronic structure problems. Second, we introduce restricted Boltzmann machine (RBM) inspired correlators to further enhance expressivity and improve accuracy, without dramatically modifying the underlying variational Monte Carlo (VMC) optimization framework. (2) We introduce a semi-stochastic algorithm for local energy evaluation, which significantly reduces computational cost while maintaining high accuracy. Combining these advances, we demonstrate that our approaches can achieve chemical accuracy in challenging systems, including the one-dimensional hydrogen chain H50, the iron-sulfur cluster [Fe2S2(SCH3)4]^{2-}, and a three-dimensional $3 \times 3 \times 2$ hydrogen cluster H18. These methods are implemented in an open-source package - PyNQS (https://github.com/Quantum-Chemistry-Group-BNU/PyNQS) to advance NQS methodologies for quantum chemistry. Ansatz ; Artificial neural network ; Quantum computer ; Tensor (intrinsic definition) ; Quantum ; Initialization ; Boltzmann machine ; Qubit ; Key (lock) Machine Learning in Materials Science ; Quantum many-body systems ; Quantum, superfluid, helium dynamics
Authors: Tülay Yıldırım | Hüseyin Zengin
Source: Metals (article, 2025-10-24)
Field: Materials Science — Biomaterials

Predicting Corrosion Behaviour of Magnesium Alloy Using Machine Learning Approaches The primary objective of this study is to develop a machine learning-based predictive model using corrosion rate data for magnesium alloys compiled from the literature. Corrosion rates measured under different deformation rates and heat treatment parameters were analyzed using artificial intelligence algorithms. Variables such as chemical composition, heat treatment temperature and time, deformation state, pH, test method, and test duration were used as inputs in the dataset. Various regression algorithms were compared with the PyCaret AutoML library, and the models with the highest accuracy scores were analyzed with Gradient Extra Trees and AdaBoost regression methods. The findings of this study demonstrate that modelling corrosion behaviour by integrating chemical composition with experimental conditions and processing parameters substantially enhances predictive accuracy. The regression models, developed using the PyCaret library, achieved high accuracy scores, producing corrosion rate predictions that are remarkably consistent with experimental values reported in the literature. Detailed tables and figures confirm that the most influential factors governing corrosion were successfully identified, providing valuable insights into the underlying mechanisms. These results highlight the potential of AI-assisted decision systems as powerful tools for material selection and experimental design, and, when supported by larger databases, for predicting the corrosion life of magnesium alloys and guiding the development of new alloys. Corrosion ; Regression analysis ; Magnesium ; Regression ; Experimental data ; Linear regression ; Predictive modelling Magnesium Alloys: Properties and Applications ; Corrosion Behavior and Inhibition ; Machine Learning in Materials Science
Authors: N. M. Chtchelkatchev | R. E. Ryltsev | Vladimir Ankudinov | Roberto Rozas
Source: The Journal of Chemical Physics (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Machine-learning interatomic potential for barium sulfide: From thermodynamic properties to crystal growth kinetics We developed a neural network-based interatomic potential (DeePMD) for the semiconductor barium sulfide (BaS), trained on first-principles simulations of both the solid and liquid phases. Using molecular dynamics, we evaluated the bulk thermodynamic properties, anisotropic stiffnesses, and interfacial free energies for different crystallographic orientations. The performance of the DeePMD potential was compared to that of the classical Rino potential, showing improved predictions of the density and liquid structure. Growth simulations were used to estimate the crystal growth velocities over a wide temperature range. Both potentials reproduce the melting temperature and the linear growth regime near melting, whereas at lower temperatures (T < 1800 K), the DeePMD potential predicts an enhanced front velocity, potentially associated with clustering or spontaneous nucleation. By integrating the atomistic results with a kinetic phase-field model, we assessed the applicability and limitations of the existing descriptions of crystal growth kinetics. This work demonstrates the role of machine-learning potentials for predictive multiscale simulations of crystal growth. Interatomic potential ; Crystal (programming language) ; Molecular dynamics ; Crystal growth ; Work (physics) ; Barium ; Kinetic energy ; Embedded atom model Machine Learning in Materials Science ; Solidification and crystal growth phenomena ; nanoparticles nucleation surface interactions
Authors: Bo Zhu | Qian Shao
Source: The Journal of Physical Chemistry C (article, 2025-05-23)
Field: Materials Science — Materials Chemistry

Machine Learning-Directed Discovery of Ultrahigh Young’s Modulus Crystals Materials with ultrahigh Young’s modulus are essential for advanced applications such as aerospace components and energy devices. Efficiently identifying the maximum Young’s modulus of materials is a key factor in accelerating the development of advanced high-stiffness materials. In this study, we developed a model based on a crystal graph convolutional neural network, incorporating data processing and model optimization techniques. These approaches effectively address data scarcity and imbalance in high-modulus crystals while significantly enhancing prediction accuracy, reducing the mean absolute error to 30.6 GPa on the test set. Guided by the model and validated through first-principles calculations, we conducted high-throughput screening on a comprehensive data set of over 1.16 million crystals. As a result, we identified 31 ultrahigh Young’s modulus crystals, all exceeding 1000 GPa. Among them, OsO exhibited an exceptional modulus of 1557.9 GPa, making it one of the highest Young’s modulus crystals discovered through machine learning-guided screening. Furthermore, most of these crystals have formation energies below 0.4 eV/atom, indicating favorable thermodynamic stability and potential experimental synthesizability, making them promising candidates for advanced structural applications. This study presents an effective approach for the discovery of ultrahigh Young’s modulus crystals. The proposed method significantly enhances the accuracy and efficiency of maximum Young’s modulus identification, providing a valuable strategy for accelerating the development of next-generation advanced materials for structural and engineering applications. Modulus ; Materials science ; Composite material Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electronic and Structural Properties of Oxides
Authors: Liyang Xu | Yi Yang | Bo Yin
Source: Chemical Physics Letters (article, 2025-06-16)
Field: Materials Science — Materials Chemistry

Predicting possible polymer sequences based on given adhesive free energies of polymer-surface interactions using deep learning Polymer ; Adhesive ; Surface (topology) ; Materials science ; Polymer science ; Polymer chemistry ; Chemical physics ; Chemistry ; Nanotechnology ; Composite material ; Mathematics ; Geometry Machine Learning in Materials Science ; Injection Molding Process and Properties ; Polymer crystallization and properties
Authors: Erkan Tur | Joseph Betts | Laurent Perge | Alborz Shokrani
Source: Procedia CIRP (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Machine learning-driven multi-objective alloy selection framework for mechanical property criteria This study presents a new machine learning framework for multi-objective alloy selection, focusing on key mechanical properties such as tensile strength, elongation, hardness, and Charpy energy. Traditional tools are often limited in their ability to manage large datasets and the complex, non-linear relationships between alloy composition, process parameters, and mechanical properties. In contrast, machine learning models such as XGBoost, Fine-Tuned Stacking, and Ensemble methods provide a scalable solution, allowing for the simultaneous consideration of multiple mechanical property objectives. The models were trained on a comprehensive dataset of stainless steel alloys, filtering materials that meet predefined performance criteria. Among the models, the Ensemble approach achieved the best results, with a precision of 0.98 and recall of 0.93. The findings show that integrating machine learning into the alloy selection process has the potential to improve decision-making accuracy for practical engineering applications. Property (philosophy) ; Selection (genetic algorithm) ; Alloy ; Computer science ; Mechanical engineering ; Materials science ; Artificial intelligence ; Engineering ; Structural engineering ; Metallurgy Machine Learning in Materials Science ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming