jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Tuğbey Kocabaş | Murat Keçeli | Álvaro Vázquez‐Mayagoitia | Cem Sevik
Source: Nanoscale (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Gaussian approximation potentials for accurate thermal properties of two-dimensional materials Two-dimensional materials (2DMs) continue to attract a lot of attention, particularly for their extreme flexibility and superior thermal properties. Molecular dynamics simulations are among the most powerful methods for computing these properties, but their reliability depends on the accuracy of interatomic interactions. While first principles approaches provide the most accurate description of interatomic forces, they are computationally expensive. In contrast, classical force fields are computationally efficient, but have limited accuracy in interatomic force description. Machine learning interatomic potentials, such as Gaussian Approximation Potentials, trained on density functional theory (DFT) calculations offer a compromise by providing both accurate estimation and computational efficiency. In this work, we present a systematic procedure to develop Gaussian approximation potentials for selected 2DMs, graphene, buckled silicene, and <i>h</i>-XN (X = B, Al, and Ga, as binary compounds) structures. We validate our approach through calculations that require various levels of accuracy in interatomic interactions. The calculated phonon dispersion curves and lattice thermal conductivity, obtained through harmonic and anharmonic force constants (including fourth order) are in excellent agreement with DFT results. HIPHIVE calculations, in which the generated GAP potentials were used to compute higher-order force constants instead of DFT, demonstrated the first-principles level accuracy of the potentials for interatomic force description. Molecular dynamics simulations based on phonon density of states calculations, which agree closely with DFT-based calculations, also show the success of the generated potentials in high-temperature simulations. Interatomic potential ; Molecular dynamics ; Anharmonicity ; Density functional theory ; Gaussian ; Phonon ; Statistical physics ; Work (physics) ; Materials science ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Thermal properties of materials ; Graphene research and applications
Authors: João Morado | Paul N. Mortenson | J. Willem M. Nissink | Jonathan W. Essex | Chris‐Kriton Skylaris
Source: Journal of Chemical Information and Modeling (article, 2023-04-18)
Field: Materials Science — Materials Chemistry

Does a Machine-Learned Potential Perform Better Than an Optimally Tuned Traditional Force Field? A Case Study on Fluorohydrins We present a comparative study that evaluates the performance of a machine learning potential (ANI-2x), a conventional force field (GAFF), and an optimally tuned GAFF-like force field in the modeling of a set of 10 γ-fluorohydrins that exhibit a complex interplay between intra- and intermolecular interactions in determining conformer stability. To benchmark the performance of each molecular model, we evaluated their energetic, geometric, and sampling accuracies relative to quantum-mechanical data. This benchmark involved conformational analysis both in the gas phase and chloroform solution. We also assessed the performance of the aforementioned molecular models in estimating nuclear spin-spin coupling constants by comparing their predictions to experimental data available in chloroform. The results and discussion presented in this study demonstrate that ANI-2x tends to predict stronger-than-expected hydrogen bonding and overstabilize global minima and shows problems related to inadequate description of dispersion interactions. Furthermore, while ANI-2x is a viable model for modeling in the gas phase, conventional force fields still play an important role, especially for condensed-phase simulations. Overall, this study highlights the strengths and weaknesses of each model, providing guidelines for the use and future development of force fields and machine learning potentials. Force field (fiction) ; Benchmark (surveying) ; Maxima and minima ; Intermolecular force ; Computer science ; Field (mathematics) ; Stability (learning theory) ; Conformational isomerism ; Molecular dynamics ; Chloroform ; Statistical physics ; Artificial intelligence ; Machine learning ; Physics ; Computational chemistry ; Chemistry ; Mathematics ; Molecule Machine Learning in Materials Science ; Protein Structure and Dynamics ; Advanced Chemical Physics Studies
Authors: Hao Yang | Qing Li | Wu Yang | Yuanyuan Zhao | Ning Hao | Wei He | Yu Li
Source: Journal of Cleaner Production (article, 2023-09-17)
Field: Materials Science — Materials Chemistry

Environmentally friendly PAEs alternatives with desired synthesizability by in silico methods Environmentally friendly ; Phthalate ; Biochemical engineering ; In silico ; Computer science ; Quantitative structure–activity relationship ; Chemistry ; Engineering ; Machine learning ; Organic chemistry ; Ecology ; Biology Machine Learning in Materials Science ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Ángel Díaz Carral | Xiang Xu | Simon Gravelle | Azade YazdanYar | Siegfried Schmauder | Maria Fyta
Source: Materials Chemistry and Physics (article, 2023-06-15)
Field: Materials Science — Materials Chemistry

Stability of binary precipitates in Cu-Ni-Si-Cr alloys investigated through active learning Materials science ; Copper ; Binary number ; Ternary operation ; Stability (learning theory) ; Relaxation (psychology) ; Precipitation ; Thermodynamics ; Metallurgy ; Computer science ; Physics ; Machine learning ; Mathematics Machine Learning in Materials Science ; Surface and Thin Film Phenomena ; X-ray Diffraction in Crystallography
Authors: An Chen | Simin Ye | Zhilong Wang | Yanqiang Han | Junfei Cai | Jinjin Li
Source: Patterns (article, 2023-04-01)
Field: Materials Science — Materials Chemistry

Machine-learning-assisted rational design of 2D doped tellurene for fin field-effect transistor devices Fin field-effect transistors (FinFETs) have been widely used in electronic devices on account of their excellent performance, but this new type of device is facing many challenges because of size constraints. Two-dimensional (2D) materials with a layer structure can meet the required thickness of FinFETs and provide ideal carrier transport performance. In this work, we used 2D tellurene as the parent material and modified it with doping techniques to improve electronic device performance. High-performance FinFET devices were prepared with 23 systems screened from 385 doping systems by a combination of first-principle calculations and a machine-learning (ML) model. Moreover, theoretical calculations demonstrated that 1S1@Te and 2S2@Te have high carrier mobility and stability with an electron mobility and a hole mobility of 6.211 × 10<sup>4</sup> cm<sup>2</sup> V<sup>-1</sup> S<sup>-1</sup> and 1.349 × 10<sup>4</sup> cm<sup>2</sup> V<sup>-1</sup> S<sup>-1</sup>, respectively. This work can provide a reference for subsequent experiments and advance the development of functional materials by using an ML-assisted design paradigm. Fin ; Field-effect transistor ; Transistor ; Doping ; Field (mathematics) ; Materials science ; Optoelectronics ; Computer science ; Electrical engineering ; Engineering ; Mathematics ; Composite material 2D Materials and Applications ; Graphene research and applications ; Perovskite Materials and Applications
Authors: Xiaoqing Tan | Yongtao Tang | Tingting Yang | Guoliang Dai | Changqing Ye | Jianxin Meng | Fengyu Li
Source: Analytical Chemistry (article, 2023-01-30)
Field: Materials Science — Materials Chemistry

Explainable Deep Learning-Assisted Photochromic Sensor for β-Lactam Antibiotic Identification Photochromic sensors have the advantages of diverse isomers for multi-analysis, providing more sensing information and possessing more recognition units and more sensitivity to external stimulations, but they present enormous complexity with various stimulations as well. Deep learning (DL) algorithms contribute a huge advantage at analyzing nonlinear and multidimensional data, but they suffer from nontransparent inner networks, “black-boxes”. In this work, we employed the explainable DL approach to process and explicate photochromic sensing. Spirooxazine metallic complexes were adopted to prepare a multi-state analysis array for β-Lactams identification and quantitation. A dataset of 2520 unduplicated fluorescence intensity images was collected for convolutional neural network (CNN) operation. The method clearly discriminated six β-Lactams with 97.98% prediction accuracy and allowed rapid quantification with a concentration range from 1 to 100 mg/L. The photochromic sensing mechanism was verified via molecular simulation and class activation mapping, which explicated how the CNN model assesses the importance of photochromic sensor states and makes a discrimination decision. The explainable DL-assisted analysis method establishes an end-to-end strategy to ascertain and verify the complicated sensing mechanism for device optimization and even new scientific discovery. Photochromism ; Convolutional neural network ; Chemistry ; Identification (biology) ; Deep learning ; Artificial intelligence ; Artificial neural network ; Pattern recognition (psychology) ; Computer science ; Biological system ; Photochemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Photochromic and Fluorescence Chemistry
Authors: Roman Remme | Tobias Kaczun | Maximilian Scheurer | Andreas Dreuw | Fred A. Hamprecht
Source: The Journal of Chemical Physics (article, 2023-10-13)
Field: Materials Science — Materials Chemistry

KineticNet: Deep learning a transferable kinetic energy functional for orbital-free density functional theory Orbital-free density functional theory (OF-DFT) holds promise to compute ground state molecular properties at minimal cost. However, it has been held back by our inability to compute the kinetic energy as a functional of electron density alone. Here, we set out to learn the kinetic energy functional from ground truth provided by the more expensive Kohn-Sham density functional theory. Such learning is confronted with two key challenges: Giving the model sufficient expressivity and spatial context while limiting the memory footprint to afford computations on a GPU and creating a sufficiently broad distribution of training data to enable iterative density optimization even when starting from a poor initial guess. In response, we introduce KineticNet, an equivariant deep neural network architecture based on point convolutions adapted to the prediction of quantities on molecular quadrature grids. Important contributions include convolution filters with sufficient spatial resolution in the vicinity of nuclear cusp, an atom-centric sparse but expressive architecture that relays information across multiple bond lengths, and a new strategy to generate varied training data by finding ground state densities in the face of perturbations by a random external potential. KineticNet achieves, for the first time, chemical accuracy of the learned functionals across input densities and geometries of tiny molecules. For two-electron systems, we additionally demonstrate OF-DFT density optimization with chemical accuracy. Density functional theory ; Orbital-free density functional theory ; Computer science ; Statistical physics ; Energy functional ; Context (archaeology) ; Artificial intelligence ; Topology (electrical circuits) ; Time-dependent density functional theory ; Algorithm ; Physics ; Quantum mechanics ; Mathematics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Computational Drug Discovery Methods
Authors: Yangpeng Huang | Naixing Feng | Yijun Cai
Source: Journal of Lightwave Technology (article, 2023-10-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial Intelligence-Generated Terahertz Multi-Resonant Metasurfaces via Improved Transformer and CGAN Neural Networks It is well known that the inverse design of terahertz (THz) multi-resonant graphene metasurfaces by using traditional deep neural networks (DNNs) has limited generalization ability. In this article, we propose improved Transformer and conditional generative adversarial neural networks (CGAN) for the inverse design of graphene metasurfaces based upon THz multi-resonant absorption spectra. The improved Transformer can obtain higher accuracy and generalization performance in the StoV (Spectrum to Vector) design compared to traditional multilayer perceptron (MLP) neural networks, while the StoI (Spectrum to Image) design achieved through CGAN can provide more comprehensive information and higher accuracy than the StoV design obtained by MLP. Moreover, the improved CGAN can achieve the inverse design of graphene metasurface images directly from the desired multi-resonant absorption spectra. It is turned out that this work can finish facilitating the design process of artificial intelligence-generated metasurfaces (AIGM), and even provide a useful guide for developing complex THz metasurfaces based on 2D materials using generative neural networks. Terahertz radiation ; Artificial neural network ; Transformer ; Multilayer perceptron ; Materials science ; Computer science ; Inverse ; Artificial intelligence ; Electronic engineering ; Optoelectronics ; Engineering ; Electrical engineering ; Mathematics ; Voltage Metamaterials and Metasurfaces Applications ; Terahertz technology and applications ; Plasmonic and Surface Plasmon Research
Authors: Zhiwen Ge | Sheng Feng | Changchang Ma | Kai Wei | Kan Hu | Weijie Zhang | Xiaojun Dai | Lufang Fan | Jinghao Hua
Source: Microchemical Journal (article, 2023-11-10)
Field: Chemistry — Inorganic Chemistry

Quantifying and comparing the effects of key chemical descriptors on metal–organic frameworks water stability with CatBoost and SHAP Stability (learning theory) ; Metal-organic framework ; Computer science ; Categorical variable ; Adsorption ; Artificial intelligence ; Machine learning ; Biochemical engineering ; Chemistry ; Engineering ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Andreas E. Robertson | Conlain Kelly | Michael Buzzy | Surya R. Kalidindi
Source: Acta Materialia (article, 2023-04-28)
Field: Materials Science — Materials Chemistry

Local–Global Decompositions for Conditional Microstructure Generation Microstructure ; Computer science ; Artificial intelligence ; Conditional random field ; Machine learning ; Statistics ; Materials science ; Mathematics Machine Learning in Materials Science ; Generative Adversarial Networks and Image Synthesis ; Model Reduction and Neural Networks
Authors: Rongzhi Dong | Yuqi Song | Edirisuriya M. Dilanga Siriwardane | Jianjun Hu
Source: Advanced Intelligent Systems (article, 2023-10-10)
Field: Materials Science — Materials Chemistry

Discovery of 2D Materials using Transformer Network‐Based Generative Design Two‐dimensional (2D) materials offer great potential in various fields like superconductivity, quantum systems, and topological materials. However, designing them systematically remains challenging due to the limited pool of fewer than 100 experimentally synthesized 2D materials. Recent advancements in deep learning, data mining, and density functional theory (DFT) calculations have paved the way for exploring new 2D material candidates. Herein, a generative material design pipeline known as the material transformer generator (MTG) is proposed. MTG leverages two distinct 2D material composition generators, both trained using self‐learning neural language models rooted in transformers, with and without transfer learning. These models generate numerous potential 2D compositions, which are plugged into established templates for known 2D materials to predict their crystal structures. To ensure stability, DFT computations assess their thermodynamic stability based on energy‐above‐hull and formation energy metrics. MTG has found four new DFT‐validated stable 2D materials: NiCl 4 , IrSBr, CuBr 3 , and CoBrCl, all with zero energy‐above‐hull values that indicate thermodynamic stability. Additionally, GaBrO and NbBrCl 3 are found with energy‐above‐hull values below 0.05 eV. CuBr 3 and GaBrO exhibit dynamic stability, confirmed by phonon dispersion analysis. In summary, the MTG pipeline shows significant potential for discovering new 2D and functional materials. Transformer ; Density functional theory ; Crystal structure prediction ; Computer science ; Generative grammar ; Computation ; Materials science ; Hull ; Stability (learning theory) ; Template ; Artificial neural network ; Topology (electrical circuits) ; Artificial intelligence ; Nanotechnology ; Physics ; Machine learning ; Crystal structure ; Chemistry ; Electrical engineering ; Algorithm ; Quantum mechanics ; Engineering Machine Learning in Materials Science ; 2D Materials and Applications ; MXene and MAX Phase Materials
Authors: Ya-Ting Gu | Yuming Gu | Qiantu Tao | Xinzhu Wang | Qin Zhu | Jing Ma
Source: Journal of Materials Informatics (article, 2023-09-06)
Field: Chemistry — Inorganic Chemistry

Machine learning for prediction of CO<sub>2</sub>/N<sub>2</sub>/H<sub>2</sub>O selective adsorption and separation in metal-zeolites Carbon dioxide (CO2) capture, utilization, and storage technologies are crucial in reducing global warming and producing various high-value chemicals and fuels. It is challenging to effectively separate the molecules of CO2, N2, and H2O, whose kinetic diameters are close to each other. Although zeolites have garnered considerable attention in gas separation, the huge chemical space of metal-doped zeolites (metal-zeolites) coming from the combination of different metal active sites, topology, and Si/Al ratios poses a difficulty in finding an optimal material for selectively trapping CO2. In this study, we build machine learning (ML) models to predict the selective adsorption of CO2/N2/H2O on metal-zeolites through the regulation of electrostatic polarization interaction. The stability of 208 metal-zeolites encompassing five distinct topological structures is estimated through the formation energy (E f), indicating the potential accessibility in the experiment for most of the studied systems, especially for Sc-, Y-, and Zr-zeolites. Adsorption of CO2 on metal sites has two possible configurations: linear vs. bent CO2, depending on different embedded metals. The concerted binding of CO2 with both carbon and oxygen atoms on the metal center leads to the bent geometry and larger binding energies on metal-zeolites (Zr-, Nb-, Mo-zeolites). Accessible descriptors associated with the zeolites, adsorbates, and metals are selected to train the adsorption strength index (I ), showing good performance [mean absolute error (MAE) = 0.04, R2 = 0.88]. The predicted adsorption selectivity is in agreement with the experimental systems (Co-, Zn-, Cu-, Fe-SSZ-13). It is found that medium-pore-sized zeolites [pore limiting diameter (PLD) = ~7 Å] anchored with Zr, Nb, or Mo are promising materials for the CO2 adsorption and separation. The proposed ML scheme may also be applicable to give a fast prediction of CO2 adsorption and separation ability in other porous metal-organic frameworks or amorphous materials. Adsorption ; Metal ; Zeolite ; Molecule ; Selectivity ; Transition metal ; Binding energy ; Chemistry ; Materials science ; Topology (electrical circuits) ; Inorganic chemistry ; Physical chemistry ; Catalysis ; Atomic physics ; Organic chemistry Metal-Organic Frameworks: Synthesis and Applications ; Catalytic Processes in Materials Science ; Zeolite Catalysis and Synthesis
Authors: Bulat N. Galimzyanov | Maria A. Doronina | А. В. Мокшин
Source: Materials (article, 2023-01-28)
Field: Materials Science — Materials Chemistry

Arrhenius Crossover Temperature of Glass-Forming Liquids Predicted by an Artificial Neural Network The Arrhenius crossover temperature, TA, corresponds to a thermodynamic state wherein the atomistic dynamics of a liquid becomes heterogeneous and cooperative; and the activation barrier of diffusion dynamics becomes temperature-dependent at temperatures below TA. The theoretical estimation of this temperature is difficult for some types of materials, especially silicates and borates. In these materials, self-diffusion as a function of the temperature <i>T</i> is reproduced by the Arrhenius law, where the activation barrier practically independent on the temperature <i>T</i>. The purpose of the present work was to establish the relationship between the Arrhenius crossover temperature TA and the physical properties of liquids directly related to their glass-forming ability. Using a machine learning model, the crossover temperature TA was calculated for silicates, borates, organic compounds and metal melts of various compositions. The empirical values of the glass transition temperature Tg, the melting temperature Tm, the ratio of these temperatures Tg/Tm and the fragility index <i>m</i> were applied as input parameters. It has been established that the temperatures Tg and Tm are significant parameters, whereas their ratio Tg/Tm and the fragility index <i>m</i> do not correlate much with the temperature TA. An important result of the present work is the analytical equation relating the temperatures Tg, Tm and TA, and that, from the algebraic point of view, is the equation for a second-order curved surface. It was shown that this equation allows one to correctly estimate the temperature TA for a large class of materials, regardless of their compositions and glass-forming abilities. Fragility ; Arrhenius equation ; Thermodynamics ; Glass transition ; Materials science ; Melting point ; Diffusion ; Activation energy ; Melting temperature ; Work (physics) ; Chemistry ; Physical chemistry ; Polymer ; Composite material ; Physics Material Dynamics and Properties ; Glass properties and applications ; Geological and Geochemical Analysis