jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Xiaoliang Zhang | Yanjun Xie | Tao Feng | Chenxi Sun | Dawei Tang
Source: International Journal of Heat and Mass Transfer (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Thermal transport in MoSi2N4 monolayer: A molecular dynamics study based on machine learning Monolayer ; Dynamics (music) ; Molecular dynamics ; Computer science ; Materials science ; Thermal ; Chemical physics ; Thermodynamics ; Nanotechnology ; Physics ; Computational chemistry ; Chemistry Machine Learning in Materials Science ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Peng Peng | Fuguo Liu | Shuai Long | Cheng Zhang | Xin Wan | Ai Tao Tang | Z. Yu | Fusheng Pan
Source: International Journal of Hydrogen Energy (article, 2025-09-22)
Field: Materials Science — Materials Chemistry

Interpretable regression-based machine learning accelerates the discovery of hydrolysis-active Mg-based alloys with tunable thermodynamic and electronic properties Hybrid functional ; Hydrogen ; Scalability ; Valence (chemistry) ; Energy (signal processing) ; Electronic structure ; Alloy ; Characterization (materials science) Hydrogen Storage and Materials ; Machine Learning in Materials Science ; Hybrid Renewable Energy Systems
Authors: Muhamad Akrom | Harun Al Azies | Wise Herowati | T. Sutojo | Supriadi Rustad | Hermawan Kresno Dipojono | Hideaki Kasai
Source: Chemometrics and Intelligent Laboratory Systems (article, 2025-05-25)
Field: Materials Science — Materials Chemistry

SMILES-driven machine learning for high-throughput investigation of anti-corrosion materials Throughput ; Corrosion ; Computer science ; Artificial intelligence ; Machine learning ; Materials science ; Metallurgy ; Operating system Machine Learning in Materials Science ; Corrosion Behavior and Inhibition ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Junfan Xia | Bin Jiang
Source: ArXiv.org (preprint, 2025-05-10)
Field: Materials Science — Materials Chemistry

Efficient Parallelization of Message Passing Neural Network Potentials for Large-scale Molecular Dynamics Machine learning potentials have achieved great success in accelerating atomistic simulations. Many of them relying on atom-centered local descriptors are natural for parallelization. More recent message passing neural network (MPNN) models have demonstrated their superior accuracy and become increasingly popular. However, efficiently parallelizing MPNN models across multiple nodes remains challenging, limiting their practical applications in large-scale simulations. Here, we propose an efficient parallel algorithm for MPNN models, in which additional data communication is minimized among local atoms only in each MP layer without redundant computation, thus scaling linearly with the layer number. Integrated with our recursively embedded atom neural network model, this algorithm demonstrates excellent strong scaling and weak scaling behaviors in several benchmark systems. This approach enables massive molecular dynamics simulations on MPNN models as fast as on strictly local models for over 100 million atoms, vastly extending the applicability of the MPNN potential to an unprecedented scale. This general parallelization framework can empower various MPNN models to efficiently simulate very large and complex systems. Scaling ; Message passing ; Benchmark (surveying) ; Artificial neural network ; Limiting ; Molecular dynamics ; Layer (electronics) Machine Learning in Materials Science ; Quantum many-body systems ; Block Copolymer Self-Assembly
Authors: Yue Zhou | Haoran Zhu | Yijie Yuan | Ziyu Song | Brendan C. Mort
Source: Journal of Chemical Information and Modeling (article, 2025-05-01)
Field: Chemistry — Spectroscopy

Machine Learning Classification of Chirality and Optical Rotation Using a Simple One-Hot Encoded Cartesian Coordinate Molecular Representation Absolute stereochemical configurations and optical rotations were computed for 121,416 molecular structures from the QM9 quantum chemistry data set using density functional theory. A representation for the molecules was developed using Cartesian coordinate geometries and encoded atom types to serve as input for various machine learning algorithms. Classifiers were developed and trained to predict the chirality and signs of optical rotations using a variety of machine learning methods. These methods are compared, and the results demonstrate that machine learning is a viable tool for making predictions of the stereochemical properties of molecules. Cartesian coordinate system ; Representation (politics) ; Rotation (mathematics) ; Simple (philosophy) ; Chirality (physics) ; Optical rotation ; Artificial intelligence ; Coordinate system ; Computer science ; Physics ; Pattern recognition (psychology) ; Mathematics ; Geometry ; Optics ; Quantum mechanics ; Philosophy Molecular spectroscopy and chirality ; Various Chemistry Research Topics ; Analytical Chemistry and Chromatography
Authors: Waseem Akhtar | Nan Qu | Ishfaq Ahmed | Wei Fan | Ao Chen | Wei Zhang | Danni Yang | Shanza Mubashir | Yong Liu | Jingchuan Zhu
Source: Materials Today Communications (article, 2025-04-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Discovery and design of Equiatomic Heusler alloys magnetic properties using machine learning with DFT approach Materials science ; Condensed matter physics ; Heusler compound ; Engineering physics ; Metallurgy Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science ; Magnetic Properties and Applications
Authors: Mostafa Sadeghian | Arvydas Palevičius | Giedrius Janušas
Source: Crystals (article, 2025-10-28)
Field: Materials Science — Materials Chemistry

A Comprehensive Review of Machine-Learning Approaches for Crystal Structure/Property Prediction Crystal Property Prediction (CPP) and Crystal Structure Prediction (CSP) play an important role in accelerating the design and discovery of advanced materials across various scientific disciplines. Traditional computational approaches to CSP/CPP often face challenges such as high computational costs, limited scalability, and difficulties in exploring complex energy surfaces. In recent years, the combination of machine learning (ML) has emerged as a powerful approach to overcome these limitations, offering data-driven methods that enhance prediction accuracy while significantly reducing computational expenses. This review provides a comprehensive overview of the evolution of CSP and CPP methodologies, with particular emphasis on the transition from classical optimization algorithms to modern ML-based methods. Various supervised and unsupervised ML algorithms applied in this field are discussed in detail. Beyond structure and property prediction, recent advancements in ML-based modeling of crystal defects are also reviewed. Moreover, several recent case studies on CSP/CPP are presented to demonstrate the practical effectiveness of ML approaches. Finally, the review discusses current challenges and provides recommendations for future research in ML-based investigations of CSP/CPP. Property (philosophy) ; Field (mathematics) ; Crystal structure prediction ; Face (sociological concept) ; Computational model ; Crystal (programming language) Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Electron Microscopy Techniques and Applications
Authors: Shahram Yalameha
Source: Computational Materials Science (article, 2025-03-10)
Field: Materials Science — Materials Chemistry

A Machine learning approach for predicting orientation-dependent elastic properties of 2D materials Orientation (vector space) ; Materials science ; Artificial intelligence ; Computer science ; Composite material ; Biological system ; Machine learning ; Mathematics ; Geometry Machine Learning in Materials Science ; Thermal properties of materials ; Fuel Cells and Related Materials
Authors: Haoyu Wan | Yue Wu | Guanhua Qin | Deng Pan
Source: The Journal of Physical Chemistry Letters (article, 2025-02-17)
Field: Materials Science — Materials Chemistry

Investigation of Surface Passivation Mechanisms in CrCoNi Alloys via Interpretable Machine Learning This study investigates the formation and stability of passivation films in medium-entropy CrCoNi alloy using an integrated approach combining first-principles calculations and Gaussian Approximation Potential (GAP). By performing extensive structural relaxations coupled with random occupancy of the alloy and multiple potential adsorption sites, the results based on surface reconstruction are analyzed. Our results demonstrate that disordered atomic structures in medium entropy CrCoNi alloy exhibit improved oxide film stability compared to ordered structures, with lower system energies correlating with more robust passivation layers. Furthermore, by constructing physically meaningful descriptors and employing symbolic regression in complex surface environments, simple yet highly correlated rules are obtained, providing insights into the passivation strengthening of CrCoNi alloy from both global and local perspectives. Our work paves the way for exploring surface regularities in complex medium-entropy alloys with the combination of quantum mechanical accuracy and machine learning efficiency. Passivation ; Materials science ; Surface (topology) ; Computer science ; Artificial intelligence ; Metallurgy ; Nanotechnology ; Geometry ; Mathematics ; Layer (electronics) Machine Learning in Materials Science ; Nuclear Materials and Properties ; Advanced Materials Characterization Techniques
Authors: Yuxuan Chen | Ruoyuan Li | Xuan Sun | Xiangyang Gao | Jiao Teng | Yugang Tian | Fanze Meng | Jiaxin Shi | Shuqian Wang | L. Geng | Junsong Zhang
Source: Solid State Communications (article, 2025-01-07)
Field: Materials Science — Materials Chemistry

Investigation of thermal transformation hysteresis of NiTiHf shape memory alloys via machine learning Shape-memory alloy ; Hysteresis ; Thermal hysteresis ; Transformation (genetics) ; Materials science ; Thermal ; Condensed matter physics ; Metallurgy ; Thermodynamics ; Physics ; Chemistry ; Phase transition Shape Memory Alloy Transformations ; Magnetic and transport properties of perovskites and related materials ; Creativity in Education and Neuroscience
Authors: B. O. Mukhamedov | Ferenc Tasnádi | Igor A. Abrikosov
Source: Materials & Design (article, 2025-03-21)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability • ML potentials can be a reliably tool for simulating materials properties near dynamical instability • Elastic moduli of β-Ti 94-x Nb x Zr 6 alloys show strong non-linearity near the point of instability • The alloys exhibit the elinvar effect over a broad temperature range • Predicted moduli are comparable to those of human bone, supporting potential biomedical applications • Directional anisotropy of elastic moduli enhances near the point of dynamical instability Machine learning-augmented first-principles simulations facilitate the exploration of alloying and thermal treatments for tailoring material properties in industrial applications. However, addressing challenges near dynamical instabilities requires rigorous validation of machine-learned interatomic potentials (MLIP) to ensure their reliable applicability. In this study we have trained MLIP using moment tensor potentials to simulate finite temperature elastic properties of multicomponent β-Ti 94-x Nb x Zr 6 alloys. Our simulations predict the presence of the elinvar effect for the wide range of temperatures. Importantly, we predict that in a vicinity of dynamical and mechanical instability, the β-Ti 94-x Nb x Zr 6 alloys demonstrate strongly non-linear concentration-dependence of elastic moduli, which leads to low values of moduli comparable to that of human bone. Moreover, these alloys demonstrate a strong anisotropy of directional Young’s modulus which can be helpful for microstructure tailoring and design of materials with desired elastic properties. Materials science ; Instability ; Interatomic potential ; Modulus ; Metallurgy ; Titanium alloy ; Condensed matter physics ; Crystallography ; Composite material ; Molecular dynamics ; Computational chemistry ; Alloy ; Mechanics Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; Metal and Thin Film Mechanics
Authors: Henrik Christiansen | Takashi Maruyama | Federico Errica | Viktor Zaverkin | Makoto Takamoto | Francesco Alesiani
Source: The Journal of Chemical Physics (article, 2025-11-11)
Field: Materials Science — Materials Chemistry

Fast, modular, and differentiable framework for machine learning-enhanced molecular simulations We present an end-to-end differentiable molecular simulation framework (DIMOS) for molecular dynamics and Monte Carlo simulations. DIMOS easily integrates machine-learning-based interatomic potentials and implements classical force fields including an efficient implementation of particle-mesh Ewald. Thanks to its modularity, both classical and machine-learning-based approaches can be easily combined into a hybrid description of the system (machine learning/mechanics modeling). By supporting key molecular dynamics features, such as efficient neighborlists and constraint algorithms for larger time steps, the framework makes steps in bridging the gap between hand-optimized simulation engines and the flexibility of a PyTorch implementation. We show that due to improved linear scaling instead of quadratic scaling as a function of system size, DIMOS is able to obtain speed-up factors of up to 170× for classical force field simulations against another fully differentiable simulation framework. The advantage of differentiability is demonstrated by an end-to-end optimization of the proposal distribution in a Markov Chain Monte Carlo simulation based on Hamiltonian Monte Carlo. Using these optimized simulation parameters, a 3× acceleration is observed in comparison with ad-hoc chosen simulation parameters. The code is available at https://github.com/nec-research/DIMOS. Differentiable function ; Molecular dynamics ; Monte Carlo method ; Hybrid Monte Carlo ; Scaling ; Monte Carlo molecular modeling ; Force field (fiction) ; Quadratic equation ; Acceleration Machine Learning in Materials Science ; Block Copolymer Self-Assembly ; Protein Structure and Dynamics
Authors: Jiachen Wang | Zhiwei Cui | Xin Zhang | Jikai Zhao | Fan Li | Zhongbin Zhou | Nathan Saye Teah | Yunfei Gao | Gaochao Zhao | Yang Yang
Source: Science Progress (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Prediction of dielectric properties of ferroelectric materials based on deep neural networks Ferroelectric materials have emerged as significant research hotspots within the field of materials science and engineering, primarily due to their unique electrical properties. However, the electrical characteristics of these materials are influenced by various factors, including material composition, microstructure, and preparation processes, which introduce considerable randomness and uncertainty. Traditional experimental and simulation methods are often insufficient for capturing these complex interactions, thereby hindering the prediction and optimization of material performance. This paper presents a novel approach for predicting the electrical properties of ferroelectric materials by utilizing deep neural networks (DNNs). The DNNs are trained using experimental data and serve as a proxy model to predict critical electrical properties, such as the dielectric constant and dielectric peak. The (1-<i>x</i>)Na<sub>0.5</sub>Bi<sub>0.5</sub>TiO<sub>3</sub>-<i>x</i>SrZrO<sub>3</sub> ceramics were synthesized via the solid-state reaction method, and both the phase structure and electrical properties of NBT-<i>x</i>SZ were measured. The experimental results indicate that the DNN model effectively captures the intricate influences of factors such as material composition, preparation processes, and microstructure on electrical properties. The discrepancy between predicted values and experimental results remains within an acceptable range. By comparing the absolute error (<5) between measured and predicted data, alongside evaluation metrics such as MAPE, SMAPE, and <i>R</i>², the practicality and reliability of the DNN model are substantiated. The strong performance of this model not only accelerates the development of new materials but also enhances the optimization of the performance of existing materials. Dielectric ; Materials science ; Ferroelectricity ; Microstructure ; Randomness ; Artificial neural network ; Ceramic ; Material properties ; Reliability (semiconductor) ; Mean absolute percentage error ; Computer science ; Biological system ; Machine learning ; Thermodynamics ; Composite material ; Statistics ; Optoelectronics ; Mathematics ; Physics Ferroelectric and Piezoelectric Materials ; Multiferroics and related materials ; Ferroelectric and Negative Capacitance Devices
Authors: Pınar Özdemir | Ramazan Yıldırım
Source: ChemBioEng Reviews (article, 2025-06-02)
Field: Materials Science — Materials Chemistry

ML@ChemE: Past, Present, and Future of Machine Learning in Chemical Engineering Abstract This paper aims to review the machine learning (ML) applications in chemical engineering (ChemE) and provide perspectives for the future. First, the evolution of ML, data structures, and ML applications in ChemE were reviewed; then, the current state of the art in ML and its ChemE applications were summarized. Finally, a perspective for the future developments, including recently popularized tools like generative artificial intelligence (AI) and large language models (LLMs), as well as major challenges and limitations, was provided. Although the initial applications were mainly on fault detection, signal processing, and process modeling, the focus had been extended to other fields involving material development, property estimation, and performance analysis in later years with the use of more complex models and datasets. In future, new developments like LLMs will likely spread more; the other new applications like automated ML, physics‐informed ML, and transfer learning, as well as field‐specific databases, will also get more attention. ML applications in ChemE‐related fields, like new energy technologies, environmental issues, and new material discovery, are expected to grow further. Computer science ; Artificial intelligence Machine Learning in Materials Science ; Scientific Computing and Data Management ; Advanced Data Processing Techniques
Authors: Neha Rajput | Himanshu Bhusan Sandhibigraha | Neeraj Agrawal | Vishnu Mahadeva Iyer
Source: IEEE Transactions on Power Electronics (article, 2025-04-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

An Empirical Model Informed Neural Network Core Loss Predictor for Soft Magnetic Materials Advancements in materials, improved modeling, and optimized design of power magnetic components are imperative to push the efficiency and power density bounds of power electronic converters. The MagNet Challenge was launched in 2023 as the first IEEE Power Electronics Society International Challenge on Design Methods in Power Electronics, to leverage data-driven approaches for modeling losses in power magnetics with a goal to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">‘Upgrade the Steinmetz Equation.’</i> The nonlinear magnetic characteristics, material properties, tolerances introduced by material processing and manufacturing of ferrites, and circuit and component level non-idealities that impact core-loss measurements cannot be easily modeled using traditional methods. However, some of these effects can be captured using a neural network since they are good at deciphering the complex relationships between the input and output data. We presented an EMPirical model Informed Neural Network (EMPINN) approach for core loss modeling of soft ferrites for the 2023 MagNet Challenge. In this work, we have radically improved our approach and reduced the size of our model by a factor of 20 to present an EMPINN core loss predictor. The paper also delves deep into data preparation, choice of empirical models, custom training loss function, and how the neural network can find the localized fit Steinmetz parameters across a wide range of operating frequencies and peak flux-density ranges. The proposed model is compared with empirical and neural network-based approaches to showcase the benefits of combining these approaches. The proposed EMPINN core loss predictor outperforms state-of-the-art core loss models in terms of accuracy and size. Artificial neural network ; Core (optical fiber) ; Materials science ; Magnetic core ; Computer science ; Electronic engineering ; Electrical engineering ; Engineering ; Artificial intelligence ; Composite material Magnetic Properties and Applications ; Magnetic Field Sensors Techniques ; Non-Destructive Testing Techniques
Authors: Aparna P. A. Subramanyam | Danny Pérez
Source: npj Computational Materials (article, 2025-07-07)
Field: Materials Science — Materials Chemistry

Information-entropy-driven generation of material-agnostic datasets for machine-learning interatomic potentials In contrast to their empirical counterparts, machine-learning interatomic potentials (MLIAPs) promise to deliver near-quantum accuracy over broad regions of configuration space. However, due to their generic functional forms and extreme flexibility, they can catastrophically fail to capture the properties of novel, out-of-sample configurations, making the quality of the training set a determining factor, especially when investigating materials under extreme conditions. We propose a novel automated dataset generation method based on the maximization of the information entropy of the feature distribution, aiming at an extremely broad coverage of the configuration space in a way that is agnostic to the properties of specific target materials. The ability of the dataset to capture unique material properties is demonstrated on a range of unary materials, including elements with the FCC (Al), BCC (W), HCP (Be, Re and Os), graphite (C), and trigonal (Sb, Te) ground states. MLIAPs trained to this dataset are shown to be accurate over a range of application-relevant metrics, as well as extremely robust over very broad swaths of configurations space, even without dataset fine-tuning or hyper-parameter optimization, making the approach extremely attractive to rapidly and autonomously develop general-purpose MLIAPs suitable for simulations in extreme conditions. Computer science ; Interatomic potential ; Entropy (arrow of time) ; Artificial intelligence ; Statistical physics ; Machine learning ; Physics ; Molecular dynamics ; Thermodynamics ; Quantum mechanics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Pushpa Bhakuni Negi | Mayank Pathak | Kundan Singh Rawat | Nirvik Sahoo | Anita Rana | Chetna Tewari | Shiva Biswas | Yong Chae Jung | Nanda Gopal Sahoo
Source: Materials Chemistry and Physics (article, 2025-09-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Green synthesis and machine learning driven analysis of SiO2 mixed carbon nanomaterial from agriwaste (Rice Husk) for supercapacitor applications Supercapacitor ; Capacitance ; Nanomaterials ; Current density ; Power density ; Carbon fibers ; Horizontal scan rate ; Energy storage ; Specific surface area Supercapacitor Materials and Fabrication ; Graphene and Nanomaterials Applications ; Aerogels and thermal insulation