jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Adil Kabylda | J. Thorben Frank | Sergio Suarez Dou | Almaz Khabibrakhmanov | Leonardo Medrano Sandonas | Oliver T. Unke | Stefan Chmiela | Klaus‐Robert Müller | Alexandre Tkatchenko
Source: ChemRxiv (preprint, 2024-10-08)
Field: Materials Science — Materials Chemistry

Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields Machine Learning Force Fields (MLFFs) promise to enable general molecular simulations that can simultaneously achieve efficiency, accuracy, transferability, and scalability for diverse molecules, materials, and hybrid interfaces. A key step toward this goal has been made with the GEMS approach to biomolecular dynamics [Sci. Adv. 10, eadn4397 (2024)]. This work introduces the SO3LR method that integrates the fast and stable SO3krates neural network for semi-local interactions with universal pairwise force fields designed for short-range repulsion, long-range electrostatics, and dispersion interactions. SO3LR is trained on a diverse set of 4 million neutral and charged molecular complexes computed at the PBE0+MBD level of quantum mechanics, ensuring a comprehensive coverage of covalent and non-covalent interactions. Our approach is characterized by computational and data efficiency, scalability to 200 thousand atoms on a single GPU, and reasonable to high accuracy across the chemical space of organic (bio)molecules. SO3LR is applied to study units of four major biomolecule types, polypeptide folding, and nanosecond dynamics of larger systems such as a protein, a glycoprotein, and a lipid bilayer, all in explicit solvent. Finally, we discuss the future challenges toward truly general molecular simulations by combining MLFFs with traditional atomistic models. Molecular dynamics ; Electrostatics ; Scalability ; Computer science ; Force field (fiction) ; Pairwise comparison ; Biomolecule ; Artificial neural network ; Chemical physics ; Statistical physics ; Biological system ; Physics ; Nanotechnology ; Chemistry ; Computational chemistry ; Artificial intelligence ; Materials science ; Quantum mechanics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Force Microscopy Techniques and Applications
Authors: Xinhua Liu | Kaiyi Yang | Lisheng Zhang | Wentao Wang | Sida Zhou | Billy Wu | Mengyu Xiong | Shichun Yang | Rui Tan
Source: Energy Material Advances (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

A Fast Forward Prediction Framework for Energy Materials Design Based on Machine Learning Methods Energy materials play an important role in renewable and green energy technologies. The exploration of new materials, including nanomaterials, is important for breaking through the current bottlenecks of energy density and charging rates. However, traditional theoretical computational methods face the dilemma of long research cycles. Machine learning methods have in recent years shown considerable potential for accelerating research efforts. However, most approaches are limited to specific properties of particular devices. In this paper, we propose a forward prediction and screening framework for functional materials, which includes database selection, attributes, descriptors, machine learning models, and prediction and screening. Based on the Materials Project database, auto-encoding methods are employed to generate Coulomb matrices as the input to train the convolutional neural networks, which finally screen 12 lithium-ion, 6 zinc-ion, and 8 aluminum-ion battery cathode materials satisfying the criteria from 4,300 materials. The results show that the proposed framework can predict material performance well toward rapid initial screening. The proposed framework can provide a specific and complete working process reference for energy materials design work, contributing to the theoretical foundation for the design of core industrial software for materials engineering. Computer science ; Energy (signal processing) ; Machine learning ; Artificial intelligence ; Mathematics Machine Learning in Materials Science
Authors: Yidan Wu | Dongxing Song | Meng An | Cheng Chi | Chunyu Zhao | Bing Yao | Weigang Ma | Xing Zhang
Source: National Science Review (article, 2024-11-23)
Field: Materials Science — Materials Chemistry

Unlocking new possibilities in ionic thermoelectric materials: a machine learning perspective The high thermopower of ionic thermoelectric (<i>i</i>-TE) materials holds promise for miniaturized waste-heat recovery devices and thermal sensors. However, progress is hampered by laborious trial-and-error experimentations, which lack theoretical underpinning. Herein, by introducing the simplified molecular-input line-entry system, we have addressed the challenge posed by the inconsistency of <i>i</i>-TE material types, and present a machine learning model that evaluates the Seebeck coefficient with an <i>R</i> <sup>2</sup> of 0.98 on the test dataset. Using this tool, we experimentally identify a waterborne polyurethane/potassium iodide ionogel with a Seebeck coefficient of 41.39 mV/K. Furthermore, interpretable analysis reveals that the number of rotatable bonds and the octanol-water partition coefficient of ions negatively affect Seebeck coefficients, which is corroborated by molecular dynamics simulations. This machine learning-assisted framework represents a pioneering effort in the <i>i</i>-TE field, offering significant promise for accelerating the discovery and development of high-performance <i>i</i>-TE materials. Seebeck coefficient ; Thermoelectric effect ; Ionic bonding ; Thermoelectric materials ; Materials science ; Perspective (graphical) ; Computer science ; Nanotechnology ; Engineering physics ; Ion ; Thermodynamics ; Artificial intelligence ; Chemistry ; Physics Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Electrochemical Analysis and Applications
Authors: Zi-Long Wan | Siyuan Chen | Xueyang Feng | Zhao‐Yan Sun
Source: Composites Communications (article, 2024-09-11)
Field: Materials Science — Materials Chemistry

From processing to properties: Enhancing machine learning models with microstructural information in polymer nanocomposites Nanocomposite ; Materials science ; Polymer nanocomposite ; Polymer ; Nanotechnology ; Composite material Machine Learning in Materials Science
Authors: Soundous Touati | Ali Benghia | Zoulikha Hebboul | Ibn Khaldoun Lefkaier | Mohammed Benali Kanoun | Souraya Goumri‐Said
Source: Neural Computing and Applications (article, 2024-05-25)
Field: Materials Science — Materials Chemistry

Predictive machine learning approaches for perovskites properties using their chemical formula: towards the discovery of stable solar cells materials Materials science ; Machine learning ; Nanotechnology ; Artificial intelligence ; Computer science Machine Learning in Materials Science ; Perovskite Materials and Applications ; Chalcogenide Semiconductor Thin Films
Authors: X. Nguyen | Y.A. Liu
Source: Computers & Chemical Engineering (article, 2024-11-14)
Field: Materials Science — Materials Chemistry

Methodology for hyperparameter tuning of deep neural networks for efficient and accurate molecular property prediction Hyperparameter ; Artificial neural network ; Property (philosophy) ; Artificial intelligence ; Computer science ; Machine learning ; Deep neural networks Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Analytical Chemistry and Chromatography
Authors: Lukas Morand | Tarek Iraki | Johannes Dornheim | Stefan Sandfeld | Norbert Link | Dirk Helm
Source: Materials & Design (article, 2024-11-19)
Field: Materials Science — Materials Chemistry

Machine learning for structure-guided materials and process design In recent years, there has been a growing interest in accelerated materials innovation in the context of the process-structure-property chain. In this regard, it is essential to take into account manufacturing processes and tailor materials design approaches to support downstream process design approaches. As a major step into this direction, we present a holistic and generic optimization approach that covers the entire process-structure-property chain in materials engineering. Our approach specifically employs machine learning to address two critical identification problems: a materials design problem, which involves identifying near-optimal material microstructures that exhibit desired properties, and a process design problem that is to find an optimal processing path to manufacture these microstructures. Both identification problems are typically ill-posed, which presents a significant challenge for solution approaches. However, the non-unique nature of these problems offers an important advantage for processing: By having several target microstructures that perform similarly well, processes can be efficiently guided towards manufacturing the best reachable microstructure. The functionality of the approach is demonstrated at manufacturing crystallographic textures with desired properties in a simulated metal forming process. • A holistic optimization approach that covers the entire process-structure-property chain is presented. • Machine learning is employed to sequentially solve two ill-posed identification problems for materials and process design. • The non-unique nature of the design problems is leveraged to guide manufacturing processes efficiently. Materials science ; Process (computing) ; Systems engineering ; Engineering drawing ; Computer science ; Engineering ; Programming language Machine Learning in Materials Science ; Manufacturing Process and Optimization ; Additive Manufacturing and 3D Printing Technologies
Authors: Yunsheng Liu | Yifei Mo
Source: Acta Materialia (article, 2024-02-07)
Field: Materials Science — Materials Chemistry

Assessing the accuracy of machine learning interatomic potentials in predicting the elemental orderings: A case study of Li-Al alloys Alloy ; Transferability ; Materials science ; Interatomic potential ; Elemental analysis ; Set (abstract data type) ; Lattice (music) ; Process (computing) ; Computer science ; Machine learning ; Statistical physics ; Molecular dynamics ; Computational chemistry ; Metallurgy ; Physics ; Chemistry Machine Learning in Materials Science ; Nuclear Materials and Properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Bing Yu | Lai‐Chang Zhang | Xiaoxia Ye | JunQi Wu | Huang Ying | Wei Zhu | ZhongYi Yu | Xiaoming Wu
Source: Chemical Engineering Science (article, 2024-12-24)
Field: Materials Science — Materials Chemistry

State-of-the-art review on various applications of machine learning techniques in materials science and engineering State (computer science) ; Science and engineering ; Engineering ; State of art ; Manufacturing engineering ; Engineering ethics ; Computer science ; Biochemical engineering ; Programming language Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Wenhui Guo | You Wu | Lingxiang Shi | Jili Jia | Ranbin Wang | Hengtong Bu | Ze Zhu | Yang Shao | Kefu Yao
Source: Acta Materialia (article, 2024-12-09)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Integrating ultra-high saturation magnetization intensity and extreme-low coercivity in FeCoBSiCu alloy assisted by machine learning Coercivity ; Materials science ; Alloy ; Intensity (physics) ; Saturation (graph theory) ; Metallurgy ; Condensed matter physics ; Optics Magnetic Properties of Alloys ; Magnetic Properties and Applications ; Magnetic properties of thin films
Authors: Soundous Touati | Ali Benghia | Zoulikha Hebboul | Ibn Khaldoun Lefkaier | Mohammed Benali Kanoun | Souraya Goumri‐Said
Source: ACS Omega (article, 2024-11-18)
Field: Materials Science — Materials Chemistry

Machine Learning Models for Efficient Property Prediction of ABX<sub>3</sub> Materials: A High-Throughput Approach Recently, ABX<sub>3</sub> materials have garnered significant attention due to their diverse applications in photovoltaics, catalysis, and optoelectronics as well as their remarkable efficiency in energy conversion. However, progress has been somewhat slow due to the high expenses of the experiment or the time-consuming density functional theory (DFT) calculation. In this study, we utilized the extreme gradient boosting (XGBoost) algorithm to facilitate the discovery and characterization of ABX<sub>3</sub> compounds based on vast data sets generated by DFT calculations. While the XGBoost algorithm provides a powerful tool for accelerating the discovery of ABX<sub>3</sub> compounds, it is crucial to acknowledge that different DFT approximation levels can significantly impact the predicted band gaps, potentially introducing discrepancies when compared with experimental values. In the first step, we predict the space group of 13947 oxides and halides using the Open Quantum Materials Database and elemental features. Our analysis yields classification accuracies ranging from 82.39% to 99.14% across these materials. Following this, XGBoost regression algorithms are employed to interrogate the data set, enabling predictions of volume (achieving an optimal accuracy of 98.41%, with a mean absolute error (MAE) of 2.395 Å<sup>3</sup> and a root-mean-square error (RMSE) of 4.416 Å<sup>3</sup>), formation energy (an optimal accuracy of 97.36%, with an MAE of 0.075 eV/atom and an RMSE of 0.132 eV/atom), and band gap energy (an optimal accuracy of 87.00%, an MAE of 0.391 eV, and an RMSE of 0.574 eV). Finally, these prediction models are employed to identify the possible space groups for each of the 1252 new ABX<sub>3</sub> formulas. Then, we predict the volume, the formation energy, and the band gap energy for each candidate space group. Through these predictive models, machine learning accelerates the exploration of new materials with enhanced performance and functionality. Mean squared error ; Band gap ; Density functional theory ; Computer science ; Gradient boosting ; Ranging ; Boosting (machine learning) ; Photovoltaics ; Support vector machine ; Artificial intelligence ; Machine learning ; Algorithm ; Materials science ; Mathematics ; Photovoltaic system ; Statistics ; Chemistry ; Computational chemistry ; Engineering ; Random forest Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Photocatalysis Techniques
Authors: Zepeng Jia | Sen Lu | Pei Song | Tiren Peng | Zhikai Gao | Zhiguo Wang | Qing Jiang | Xue Bai | Hong Cui | Weizhi Tian | Rong Feng | Qin Kang | Zhiyong Liang | Hongkuan Yuan
Source: Separation and Purification Technology (article, 2024-05-31)
Field: Materials Science — Materials Chemistry

Machine learning accelerates design of bilayer-modified graphene hydrogen storage materials Hydrogen storage ; Graphene ; Density functional theory ; Materials science ; Hydrogen ; Generalizability theory ; Adsorption ; Energy storage ; Thermodynamics ; Nanotechnology ; Chemistry ; Computational chemistry ; Physical chemistry ; Mathematics ; Physics ; Organic chemistry Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Advancements in Battery Materials
Authors: M. Coffigniez | P.-P. De Breuck | Laurine Choisez | Matthieu Marteleur | Michiel J. van Setten | Guido Petretto | Gian‐Marco Rignanese | Pascal Jacques
Source: Materials & Design (article, 2024-02-25)
Field: Materials Science — Materials Chemistry

Combination of ab initio descriptors and machine learning approach for the prediction of the plasticity mechanisms in β-metastable Ti alloys This study proposes a machine-learning model combining ab initio calculations and an experimental dataset of 201 alloys (in addition to pure Ti) to predict the activated plasticity mechanisms in β-Ti alloys. This methodology is shown to be more efficient than the so-called Bo¯-Md¯ approach, achieving 82% prediction accuracy while the Bo-Md approach leads to 52% correct predictions on the same dataset. In addition, four new alloy compositions were produced to verify the model validity. Specific cases where the present model disagreed with the Bo-Md predictions were chosen to increase the benefits of the produced results. The plasticity mechanisms of the four alloys experimentally confirmed the validity of the ML model. This approach particularly helps the design of specific Ti alloys exhibiting a high work hardening rate owing to the simultaneous activations of the Transformation-Induced Plasticity (TRIP) and mechanical twinning (TWIP) effects. Indeed, the class corresponding to the combination of TRIP and TWIP effects reach a prediction accuracy of 88%. Materials science ; Metastability ; Plasticity ; Ab initio ; Artificial intelligence ; Computer science ; Composite material ; Physics Titanium Alloys Microstructure and Properties ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Marco Rossi | Lei Zhang | Xiao Qing Chen | Che Liu | Giuseppe Castaldi | Tie Jun Cui | Vincenzo Galdi
Source: Advanced Functional Materials (article, 2024-06-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine‐Learning‐Enabled Multi‐Frequency Synthesis of Space‐Time‐Coding Digital Metasurfaces Abstract Digital metasurfaces based on space‐time coding have established themselves as a powerful and versatile platform for joint spatial/spectral control of electromagnetic waves. However, their advanced design remains a largely open problem with significant computational challenges. This study introduces a novel approach, based on deep neural networks, to address this challenge. The proposed technique enables the simultaneous and independent multi‐frequency synthesis of scattering patterns, allowing precise tailoring of the harmonic equivalent currents (both in magnitude and phase), and enhancing spectral efficiency. These results, experimentally validated at X‐band microwave frequencies, substantially broaden the capabilities of space‐time coding digital metasurfaces, paving the way for advanced applications in wireless communications, sensing, and imaging. Coding (social sciences) ; Computer science ; Microwave ; Wireless ; Electronic engineering ; Materials science ; Telecommunications ; Engineering Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Wireless Communication Technologies
Authors: Zhikai Yang | Dinggang Shen | Kannie W. Y. Chan | Jianpan Huang
Source: IEEE Journal of Biomedical and Health Informatics (article, 2024-05-22)
Field: Materials Science — Materials Chemistry

Attention-Based MultiOffset Deep Learning Reconstruction of Chemical Exchange Saturation Transfer (AMO-CEST) MRI One challenge of chemical exchange saturation transfer (CEST) magnetic resonance imaging (MRI) is the long scan time due to multiple acquisitions of images at different saturation frequency offsets. k-space under-sampling strategy is commonly used to accelerate MRI acquisition, while this could introduce artifacts and reduce signal-to-noise ratio (SNR). To accelerate CEST-MRI acquisition while maintaining suitable image quality, we proposed an attention-based multioffset deep learning reconstruction network (AMO-CEST) with a multiple radial k-space sampling strategy for CEST-MRI. The AMO-CEST also contains dilated convolution to enlarge the receptive field and data consistency module to preserve the sampled k-space data. We evaluated the proposed method on a mouse brain dataset containing 5760 CEST images acquired at a pre-clinical 3 T MRI scanner. Quantitative results demonstrated that AMO-CEST showed obvious improvement over zero-filling method with a PSNR enhancement of 11 dB, a SSIM enhancement of 0.15, and a NMSE decrease of [Formula: see text] in three acquisition orientations. Compared with other deep learning-based models, AMO-CEST showed visual and quantitative improvements in images from three different orientations. We also extracted molecular contrast maps, including the amide proton transfer (APT) and the relayed nuclear Overhauser enhancement (rNOE). The results demonstrated that the CEST contrast maps derived from the CEST images of AMO-CEST were comparable to those derived from the original high-resolution CEST images. The proposed AMO-CEST can efficiently reconstruct high-quality CEST images from under-sampled k-space data and thus has the potential to accelerate CEST-MRI acquisition. Artificial intelligence ; Magnetic resonance imaging ; Nuclear magnetic resonance ; Undersampling ; k-space ; Nuclear medicine ; Computer science ; Deep learning ; Iterative reconstruction ; Image quality ; Pattern recognition (psychology) ; Physics ; Image (mathematics) ; Radiology ; Medicine Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: Yuzhou Hao | Yuting Zuo | Jiongzhi Zheng | Wenjie Hou | Hong Gu | Xiaoying Wang | Xuejie Li | Jun Sun | Xiangdong Ding | Zhibin Gao
Source: ACS Applied Materials & Interfaces (article, 2024-09-02)
Field: Materials Science — Materials Chemistry

Machine Learning for Predicting Ultralow Thermal Conductivity and High <i>ZT</i> in Complex Thermoelectric Materials Efficient and precise calculations of thermal transport properties and figures of merit, alongside a deep comprehension of thermal transport mechanisms, are essential for the practical utilization of advanced thermoelectric materials. In this study, we explore the microscopic processes governing thermal transport in the distinguished crystalline material Tl<sub>9</sub>SbTe<sub>6</sub> by integrating a unified thermal transport theory with machine learning-assisted self-consistent phonon calculations. Leveraging machine learning potentials, we expedite the analysis of phonon energy shifts, higher-order scattering mechanisms, and thermal conductivity arising from various contributing factors, such as population and coherence channels. Our finding unveils an exceptionally low thermal conductivity of 0.31 W m<sup>-1</sup> K<sup>-1</sup> at room temperature, a result that closely correlates with experimental observations. Notably, we observe that the off-diagonal terms of heat flux operators play a significant role in shaping the overall lattice thermal conductivity of Tl<sub>9</sub>SbTe<sub>6</sub>, where the ultralow thermal conductivity resembles that of glass due to limited group velocities. Furthermore, we achieve a maximum <i>ZT</i> value of 3.17 in the <i>c</i>-axis orientation for <i>p</i>-type Tl<sub>9</sub>SbTe<sub>6</sub> at 600 K and an optimal <i>ZT</i> value of 2.26 in the <i>a</i>-axis and <i>b</i>-axis direction for <i>n</i>-type Tl<sub>9</sub>SbTe<sub>6</sub> at 500 K. The crystalline Tl<sub>9</sub>SbTe<sub>6</sub> not only showcases remarkable thermal insulation but also demonstrates impressive electrical properties owing to the dual-degeneracy phenomenon within its valence band. These results not only elucidate the underlying reasons for the exceptional thermoelectric performance of Tl<sub>9</sub>SbTe<sub>6</sub> but also suggest potential avenues for further experimental exploration. Materials science ; Thermal conductivity ; Thermoelectric effect ; Thermoelectric materials ; Engineering physics ; Thermal ; Nanotechnology ; Composite material ; Thermodynamics Advanced Thermoelectric Materials and Devices ; Thermal properties of materials ; Machine Learning in Materials Science