jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wissam A. Saidi
Source:  (article, 2024-09-10)
Field: Materials Science — Materials Chemistry

Unifying Quantum Materials Modeling and Experiments: The Role of Machine Learning Interatomic Potentials Computational experiments have emerged as a powerful complement to traditional experiments in the design of new materials. The development of machine learning (ML) and deep learning techniques, combined with database construction and data mining, has significantly enhanced traditional quantum mechanical methods. This synergy enables the rapid development of structure-property relationships. In this talk, I will discuss our recent efforts in applying Machine Learning Interatomic Potentials (MLIAPs) to accelerate materials modeling across various material classes and challenging applications where traditional methods fall short. First, I will highlight the success of MLIAPs in accurately modeling the melting behavior of complex materials. Our results demonstrate high fidelity with experimental observations and also with calculated reference melting temperatures. In the second application, I will discuss how MLIAPs are trained and applied to elucidate the interplay between segregation tendencies and surface reconstructions in CuNi alloys under oxidizing conditions. A key factor in the success of these MLIAP applications is the design of minimalistic yet flexible datasets along with a computational framework for training MLIAPs. Computer science ; Interatomic potential ; Quantum ; Quantum mechanics ; Physics ; Molecular dynamics Machine Learning in Materials Science
Authors: Д. Р. Байгутлин | V. V. Sokolovskiy | V. D. Buchelnikov | Sergey Taskaev
Source: arXiv (Cornell University) (preprint, 2024-09-22)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning algorithms for optimization of magnetocaloric effect in all-d-metal Heusler alloys This study examines the application of machine learning algorithms, specifically the Random Forest regression model, to optimize the magnetocaloric effect in all-d-metal Heusler alloys. The model was trained using descriptors related to the mean properties of individual atoms, the properties of simple compounds in their ground state, and measures of chemical disorder. It demonstrated high accuracy in predicting structural properties, while exhibiting moderate accuracy in predicting magnetic properties. To identify optimal alloy compositions, a genetic algorithm was used to find those with the greatest differences in magnetization during martensitic transitions. Using this combined approach, the Ni-Co-Mn-Ti alloy system was thoroughly explored, resulting in the discovery of an alloy with a maximum magnetization difference. These results are consistent with previous research based on Density Functional Theory (DFT) and highlight the effectiveness of integrating machine learning with genetic algorithms for the discovery of new materials with outstanding magnetocaloric properties. The study emphasizes the need for further refinement of models capable of accurately predicting complex magnetic interactions, which is essential for fully leveraging the potential of all-d-metal Heusler alloys in practical applications. Magnetic refrigeration ; Optimization algorithm ; Algorithm ; Materials science ; Computer science ; Physics ; Mathematics ; Mathematical optimization ; Quantum mechanics ; Magnetization ; Magnetic field Heusler alloys: electronic and magnetic properties ; Machine Learning in Materials Science
Authors: Shahriyar Keshavarz | Yuwei Mao | Andrew Reid | Ankit Agrawal
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Advancing Material Simulations: Physics-Informed Neural Networks and Object-Oriented Crystal Plasticity Finite Element Methods Crystal plasticity ; Finite element method ; Object (grammar) ; Element (criminal law) ; Plasticity ; Artificial neural network ; Computer science ; Object-oriented programming ; Physics ; Theoretical physics ; Statistical physics ; Cognitive science ; Artificial intelligence ; Psychology ; Political science ; Programming language Machine Learning in Materials Science ; Metallurgy and Material Forming
Authors: B. O. Mukhamedov | Ferenc Tasnádi | Igor A. Abrikosov
Source: arXiv (Cornell University) (preprint, 2024-12-09)
Field: Materials Science — Materials Chemistry

Machine learning interatomic potential for the low-modulus Ti-Nb-Zr alloys in the vicinity of dynamical instability Traditionally, alloying and thermal treatment are considered as the main tools for design of new materials. Application of first-principles simulations can significantly accelerate the process of materials design, however, to account for both, multicomponent chemical disorder and finite temperature effects in theoretical simulations is a challenging task. In this work we have trained machine learning interatomic potential to effectively simulate finite temperature elastic properties of multicomponent β-Ti94-xNbxZr6 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-Ti94-xNbxZr6 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. Instability ; Interatomic potential ; Materials science ; Modulus ; Condensed matter physics ; Physics ; Composite material ; Molecular dynamics ; Mechanics ; Quantum mechanics Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Titanium Alloys Microstructure and Properties
Authors: Keisuke Kameda | Takaaki Ariga | Kazuma Ito | Manabu Ihara | Sergei Manzhos
Source: arXiv (Cornell University) (preprint, 2024-06-25)
Field: Materials Science — Materials Chemistry

Machine learning the screening factor in the soft bond valence approach for rapid crystal structure estimation Development of new functional ceramics is important for several applications, including electrochemical batteries and fuel cells. Computational prescreening and selection of such materials can help discover novel materials but is challenging due to the high cost of electronic structure calculations which would be needed to compute the structures and properties of interest such as the material's stability and ion diffusion properties. The soft bond valence (SoftBV) approach is attractive for rapid prescreening among multiple compositions and structures, but the simplicity of the approximation can make the results inaccurate. We explore the possibility of enhancing the accuracy of the SoftBV approach when estimating crystal structures by adapting the parameters of the approximation to the chemical composition. Specifically, on the examples of perovskite- and spinel-type oxides that have been proposed as promising solid-state ionic conductors, the screening factor, an independent parameter of the SoftBV approximation, is modeled using linear and non-linear methods as a function of descriptors of chemical composition. We find that making the screening factor a function of composition can noticeably improve the ability of SoftBV to correctly model structures, in particular new, putative crystal structures whose structural parameters are yet unknown. We also analyze the relative importance of nonlinearity and coupling in improving the model and find that while the quality of the model is improved by including nonlinearity, coupling is relatively unimportant. While using a neural network showed no improvement over linear regression, the recently proposed GPR-NN method that is a hybrid between a single hidden layer neural network and kernel regression showed substantial improvement, enabling the prediction of structural parameters of new ceramics with accuracy on the order of 1%. Valence (chemistry) ; Crystal structure ; Bond ; Computer science ; Artificial intelligence ; Materials science ; Machine learning ; Crystallography ; Chemistry ; Business ; Organic chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Kenji Konashi | Nobuhiko Kato | Kazuki Mori | Ken Kurosaki
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Neural Network Potential for Molecular Dynamics Calculation of Uo2 Dynamics (music) ; Molecular dynamics ; Artificial neural network ; Computer science ; Statistical physics ; Neuroscience ; Artificial intelligence ; Physics ; Chemistry ; Computational chemistry ; Psychology Nuclear Materials and Properties ; Nuclear Physics and Applications ; Radioactive element chemistry and processing
Authors: Bowen Deng | Peichen Zhong | KyuJung Jun | Janosh Riebesell | Kevin Han | Christopher J. Bartel | Gerbrand Ceder
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Foundational Machine Learning Interatomic Potential to Study Li-Ion Battery Cathode Phase Transformation with Charge Transfer Large-scale simulations with complex electron interactions remain one of the greatest challenges for atomistic modeling of electrochemical systems. Our recent work, Crystal Hamiltonian Graph Neural Network (CHGNet), presents foundational graph-neural-network-based machine-learning interatomic potential (MLIP). The inclusion of charge information by magnetic moment prediction enables CHGNet to better describe both atomic and electronic degrees of freedom, which opens the possibility for a better understanding of electron-redox coupled behaviors that are essential for applications such as battery cathode materials for energy storage. We demonstrate the use of CHGNet MLIP in electrochemistry by modeling in detail the process by which orthorhombic LixMnO2 transforms to a spinel-like structure. This transformation is facilitated by the valence-dependent Mn migration and charge disproportionation which can only be modeled accurately with an MLIP that captures the very different chemical behavior of transition metals’ different valence states. Moreover, we show the Mn-rich disordered rocksalts (DRX) exhibit similar phase transformation to a spinel-like phase, leading to improved capacity and Li-ion transport kinetics. Our study demonstrates critical atomic-level insights into Li-ion cathode electrochemistry, which became possible with charge-informed long-time simulations at scale enabled by modern AI methods. Transformation (genetics) ; Battery (electricity) ; Charge (physics) ; Cathode ; Ion ; Phase (matter) ; Atomic physics ; Materials science ; Chemical physics ; Electrical engineering ; Physics ; Chemistry ; Thermodynamics ; Engineering ; Quantum mechanics Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Electron and X-Ray Spectroscopy Techniques
Authors: Gavin Winter | Nathan Walemba | Rafael Gómez‐Bombarelli
Source: ChemRxiv (preprint, 2024-07-15)
Field: Materials Science — Materials Chemistry

Simulations enabled by machine learning potentials predict sulfide solid electrolyte with covalent network structure and glassy behavior Machine learning (ML) accelerated simulations were used to explore how the isovalent substitution of oxygen by sulfur in LiGa(SeO3)2 alters its ionic conductivity. Molecular dynamics (MD) driven by a custom-trained neural network (NN) potential were scaled to simulate 5,120 atoms for 100 nanoseconds in order to identify and characterize a unique structural prototype for glassy sulfide solid electrolytes with the stoichiometry LiGa(SeS3)2, featuring GaS4 tetrahedra interconnected by bridging Se-S covalent chains. A phase transition from a crystalline phase to a structurally-disordered phase of LiGa(SeS3)2 was identified in the simulations between 325 and 350 K, whereby the molar volume and ionic conductivity significantly increase, while the vibrational density of states spectra for Li+ becomes characteristically broadened, similar to superionic conductors. The ionic conductivity is calculated to be around 10^-2 S/cm at 350 K. Furthermore, this glassy sulfide solid electrolyte exhibits an anomalously low bulk modulus of 0.7 GPa, which may be beneficial for accommodating plasticity in the solid electrolyte of an all solid-state inorganic battery. The electrochemical stability window is predicted to be relatively narrow since the band gap is conservatively 1.45 eV, but the stability window could potentially be tuned by doping to form an oxysulfide. Electrolyte ; Sulfide ; Covalent bond ; Network structure ; Materials science ; Chemical physics ; Chemistry ; Computer science ; Physical chemistry ; Machine learning ; Metallurgy ; Organic chemistry Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Zijing Lin | Chuan-Nan Li | Han-Pu Liang | Siyuan Xu | Xie Zhang | Su‐Huai Wei
Source: Research Square (preprint, 2024-03-01)
Field: Materials Science — Materials Chemistry

Minimizing the training dataset for efficient crystal structure prediction by graph neural network Artificial neural network ; Training (meteorology) ; Computer science ; Artificial intelligence ; Graph ; Crystal structure prediction ; Machine learning ; Pattern recognition (psychology) ; Data mining ; Theoretical computer science ; Crystal structure ; Geography ; Crystallography ; Chemistry Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallization and Solubility Studies
Authors: Rina Ibragimova | Mikhail S. Kuklin | Tigany Zarrouk | Miguel A. Caro
Source: arXiv (Cornell University) (preprint, 2024-09-12)
Field: Materials Science — Materials Chemistry

Unifying the description of hydrocarbons and hydrogenated carbon materials with a chemically reactive machine learning interatomic potential We present a general-purpose machine learning (ML) interatomic potential for carbon and hydrogen which is capable of simulating various materials and molecules composed of these elements. This ML interatomic potential is trained using the Gaussian approximation potential (GAP) framework and an extensive dataset of C-H configurations obtained from density functional theory. The dataset is constructed through iterative training and structure-search techniques that generate a broad range of configurations to comprehensively sample the potential energy surface. Furthermore, the dataset is supplemented with relevant bulk, molecular, and high-pressure structures. Finally, long-range van der Waals interactions are added as a locally parametrized model. The accuracy and generality of the potential are validated through the analysis of different simulations under a wide range of conditions, including weak interactions, high temperature, and high pressure. We show that our CH GAP model describes different problems such as the formation of simple and complex alkanes, aromatic hydrocarbons, hydrogenated amorphous carbon (a-C:H), and CH systems at extreme conditions, while retaining good accuracy for pure carbon materials. We use this model to generate hydrocarbons of different sizes and complexity without prior knowledge of organic chemistry rules, and to highlight intrinsic limitations to the simultaneous description on intra and intermolecular interactions within a single computational framework. Our general-purpose ML interatomic potential has the capability to significantly advance research in the field of H-containing carbon materials and compounds, particularly in the areas where longer dynamics, reactivity and large-scale effects may be important. Carbon fibers ; Materials science ; Chemistry ; Chemical engineering ; Nanotechnology ; Chemical physics ; Engineering ; Composite material Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Zhichong Wang
Source: SVOA materials science & technology. (article, 2024-09-23)
Field: Materials Science — Materials Chemistry

Advances in Machine Learning Applications in Material Science: From Non-2D to 2D Materials This mini-review presents recent advancements in the application of machine learning to both non-2D and 2D materials. For non-2D materials, the main focus is on construction materials, energy and environmental materials, and advanced functional materials. In the case of 2D materials, the mini-review emphasizes the role of machine learning in predicting electronic and structural properties, as well as in identifying defects and phase preferences. The mini-review underscores the crucial role that machine learning plays in advancing materials science by enabling rapid screening of materials, predicting their properties, and significantly reducing the time required for such processes. Nanotechnology ; Computer science ; Materials science Machine Learning in Materials Science
Authors: Yoshiki Hasukawa | Micke Kuwahara | Fernando Garcia‐Escobar | Lauren Takahashi | Toshiaki Taniike | Keisuke Takahashi
Source: Science and Technology of Advanced Materials Methods (article, 2024-12-16)
Field: Materials Science — Materials Chemistry

Web-based graphical user interface for automated materials feature engineering for machine learning Designing descriptors for materials and catalysts within machine learning is a complex process. In particular, material compositions can be represented using information from the periodic table. Additionally, feature engineering and feature selection add further complexity, requiring significant programming skills and knowledge of machine learning. To address this challenge, a web-based graphical user interface (GUI) for feature assignment, feature engineering, and feature selection has been developed. This GUI is designed to facilitate materials and catalysts design using machine learning, without the need for advanced programming or machine learning expertise. Computer science ; Graphical user interface ; Feature engineering ; Feature (linguistics) ; Graphical user interface testing ; Human–computer interaction ; Web application ; User interface ; Artificial intelligence ; World Wide Web ; User interface design ; Operating system ; Deep learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Zirui Zhao | Xiaoke Wang | Si Wu | Pengfei Zhou | Qian Zhao | Guangping Xu | Kaitong Sun | Haifeng Li
Source: arXiv (Cornell University) (preprint, 2024-09-02)
Field: Materials Science — Materials Chemistry

Deep learning-driven evaluation and prediction of ion-doped NASICON materials for enhanced solid-state battery performance We developed a convolutional neural network (CNN) model capable of predicting the performance of various ion-doped NASICON compounds by leveraging extensive datasets from prior experimental investigation.The model demonstrated high accuracy and efficiency in predicting ionic conductivity and electrochemical properties. Key findings include the successful synthesis and validation of three NASICON materials predicted by the model, with experimental results closely matching the model predictions. This research not only enhances the understanding of ion-doping effects in NASICON materials but also establishes a robust framework for material design and practical applications. It bridges the gap between theoretical predictions and experimental validations. Fast ion conductor ; Doping ; Materials science ; Battery (electricity) ; Ion ; Solid-state ; Engineering physics ; Nanotechnology ; Optoelectronics ; Chemical engineering ; Computer science ; Electrolyte ; Chemistry ; Thermodynamics ; Engineering ; Electrode ; Physical chemistry ; Physics Machine Learning in Materials Science ; Advanced Battery Technologies Research ; Advancements in Battery Materials
Authors: Keita Kobayashi | Hiroki Nakamura | Masahiko Okumura | Mitsuhiro Itakura | Masahiko Machida
Source: arXiv (Cornell University) (preprint, 2024-12-16)
Field: Materials Science — Materials Chemistry

Specific Heat Anomalies and Local Symmetry Breaking in (Anti-)Fluorite Materials: A Machine Learning Molecular Dynamics Study Understanding the high-temperature properties of materials with (anti-)fluorite structures is crucial for their application in nuclear reactors. In this study, we employ machine learning molecular dynamics (MLMD) simulations to investigate the high-temperature thermal properties of thorium dioxide, which has a fluorite structure, and lithium oxide, which has an anti-fluorite structure. Our results show that MLMD simulations effectively reproduce the reported thermal properties of these materials. A central focus of this work is the analysis of specific heat anomalies in these materials at high temperatures, commonly referred to as Bredig, pre-melting, or $λ$-transitions. We demonstrate that a local order parameter, analogous to those used to describe liquid-liquid transitions in supercooled water and liquid silica, can effectively characterize these specific heat anomalies. The local order parameter identifies two distinct types of defective structures: lattice defect-like and liquid-like local structures. Above the transition temperature, liquid-like local structures predominate, and the sub-lattice character of mobile atoms disappears. Fluorite ; Symmetry breaking ; Dynamics (music) ; Symmetry (geometry) ; Molecular dynamics ; Local symmetry ; Physics ; Chemical physics ; Materials science ; Mathematics ; Geometry ; Quantum mechanics ; Metallurgy Machine Learning in Materials Science
Authors: Mostafa Elaskalany | Kamran Behdinan
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Interpretable Machine Learning for Optimizing Piezoresistivity of Carbon Nanotube Polymer Nanocomposites for Strain Sensing Carbon nanotube ; Nanocomposite ; Materials science ; Composite material ; Strain (injury) ; Polymer ; Nanotube ; Polymer nanocomposite ; Nanotechnology Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials
Authors: Chunbo Lin | Han Zheng
Source: arXiv (Cornell University) (preprint, 2024-03-29)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning Strategies for Accurate Prediction of Thermal Conductivity in Polymeric Systems Polymers, integral to advancements in high-tech fields, necessitate the study of their thermal conductivity (TC) to enhance material attributes and energy efficiency. The TC of polymers obtained by molecular dynamics (MD) calculations and experimental measurements is slow, and it is difficult to screen polymers with specific TC in a wide range. Existing machine learning (ML) techniques for determining polymer TC suffer from the problems of too large feature space and cannot guarantee very high accuracy. In this work, we leverage TCs from accessible datasets to decode the Simplified Molecular Input Line Entry System (SMILES) of polymers into ten features of distinct physical significance. A novel evaluation model for polymer TC is formulated, employing four ML strategies. The Gradient Boosting Decision Tree (GBDT)-based model, a focal point of our design, achieved a prediction accuracy of R$^2$=0.88 on a dataset containing 400 polymers. Furthermore, we used an interpretable ML approach to discover the significant contribution of quantitative estimate of drug-likeness and number of rotatable bonds features to TC, and analyzed the physical mechanisms involved. The ML method we developed provides a new idea for physical modeling of polymers, which is expected to be generalized and applied widely in constructing polymers with specific TCs and predicting all other properties of polymers. Thermal conductivity ; Artificial intelligence ; Machine learning ; Computer science ; Materials science ; Biological system ; Composite material Machine Learning in Materials Science