jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ji Hoon Park | Ang‐Yu Lu | Mohammad Mahdi Tavakoli | Na Yeon Kim | Ming‐Hui Chiu | Hongwei Liu | Tianyi Zhang | Zhien Wang | Jiangtao Wang | Luiz G. P. Martins | Zhengtang Luo | Miaofang Chi | Jianwei Miao | Jing Kong
Source: Nano Letters (article, 2023-05-17)
Field: Materials Science — Materials Chemistry

Revealing Variable Dependences in Hexagonal Boron Nitride Synthesis via Machine Learning Wafer-scale monolayer two-dimensional (2D) materials have been realized by epitaxial chemical vapor deposition (CVD) in recent years. To scale up the synthesis of 2D materials, a systematic analysis of how the growth dynamics depend on the growth parameters is essential to unravel its mechanisms. However, the studies of CVD-grown 2D materials mostly adopted the control variate method and considered each parameter as an independent variable, which is not comprehensive for 2D materials growth optimization. Herein, we synthesized a representative 2D material, monolayer hexagonal boron nitride (hBN), on single-crystalline Cu (111) by epitaxial chemical vapor deposition and varied the growth parameters to regulate the hBN domain sizes. Furthermore, we explored the correlation between two growth parameters and provided the growth windows for large flake sizes by the Gaussian process. This new analysis approach based on machine learning provides a more comprehensive understanding of the growth mechanism for 2D materials. Chemical vapor deposition ; Epitaxy ; Materials science ; Monolayer ; Wafer ; Characterization (materials science) ; Nanotechnology ; Hexagonal boron nitride ; Graphene ; Layer (electronics) Graphene research and applications ; 2D Materials and Applications ; Ferroelectric and Negative Capacitance Devices
Authors: Chengcheng Liu | Xuandong Wang | Weidong Cai | Hang Su
Source: Journal of Non-Crystalline Solids (article, 2023-12-05)
Field: Materials Science — Materials Chemistry

Prediction of magnetocaloric properties of Fe-based amorphous alloys based on interpretable machine learning Machine learning ; Artificial intelligence ; Amorphous solid ; Test set ; Mean squared error ; Amorphous metal ; Mean absolute percentage error ; Isothermal process ; Magnetic refrigeration ; Feature selection ; Computer science ; Materials science ; Algorithm ; Mathematics ; Alloy ; Artificial neural network ; Magnetic field ; Thermodynamics ; Statistics ; Metallurgy ; Physics ; Chemistry Machine Learning in Materials Science ; Magnetic and transport properties of perovskites and related materials ; High Entropy Alloys Studies
Authors: Boyi Zhou | Yanzi Zhou | Daiqian Xie
Source: Journal of Chemical Theory and Computation (article, 2023-02-01)
Field: Materials Science — Materials Chemistry

Accelerated Quantum Mechanics/Molecular Mechanics Simulations via Neural Networks Incorporated with Mechanical Embedding Scheme A powerful tool to study the mechanism of reactions in solutions or enzymes is to perform the <i>ab initio</i> quantum mechanical/molecular mechanical (QM/MM) molecular dynamics (MD) simulations. However, the computational cost is too high due to the explicit electronic structure calculations at every time step of the simulation. A neural network (NN) method can accelerate the QM/MM-MD simulations, but it has long been a problem to accurately describe the QM/MM electrostatic coupling by NN in the electrostatic embedding (EE) scheme. In this work, we developed a new method to accelerate QM/MM calculations in the mechanic embedding (ME) scheme. The potentials and partial point charges of QM atoms are first learned <i>in vacuo</i> by the embedded atom neural networks (EANN) approach. MD simulations are then performed on this EANN/MM potential energy surface (PES) to obtain free energy (FE) profiles for reactions, in which the QM/MM electrostatic coupling is treated in the mechanic embedding (ME) scheme. Finally, a weighted thermodynamic perturbation (wTP) corrects the FE profiles in the ME scheme to the EE scheme. For two reactions in water and one in methanol, our simulations reproduced the B3LYP/MM free energy profiles within 0.5 kcal/mol with a speed-up of 30-60-fold. The results show that the strategy of combining EANN potential in the ME scheme with the wTP correction is efficient and reliable for chemical reaction simulations in liquid. Another advantage of our method is that the QM PES is independent of the MM subsystem, so it can be applied to various MM environments as demonstrated by an S<sub>N</sub>2 reaction studied in water and methanol individually, which used the same EANN PES. The free energy profiles are in excellent accordance with the results obtained from B3LYP/MM-MD simulations. In future, this method will be applied to the reactions of enzymes and their variants. Embedding ; Molecular dynamics ; Artificial neural network ; Quantum ; Coupling (piping) ; QM/MM ; Work (physics) ; Electrostatics ; Molecular mechanics ; Statistical physics ; Scheme (mathematics) ; Computer science ; Physics ; Chemistry ; Materials science ; Quantum mechanics ; Mathematics ; Artificial intelligence ; Mathematical analysis Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Kong Meng | Chenyu Huang | Yaxin Wang | Yunjiang Zhang | Shuyuan Li | Zhaolin Fang | Huimin Wang | Shihao Wei | Shaorui Sun
Source: Journal of Chemical Information and Modeling (article, 2023-09-18)
Field: Materials Science — Materials Chemistry

BNM-CDGNN: Batch Normalization Multilayer Perceptron Crystal Distance Graph Neural Network for Excellent-Performance Crystal Property Prediction Recently, in the field of crystal property prediction, the graph neural network (GNN) model has made rapid progress. The GNN model can effectively capture high-dimensional crystal features from the crystal structure, thereby achieving optimal performance in property prediction. However, the existing GNN model faces limitations in handling the hidden layer after the pooling layer, which restricts the training performance of the model. In the present research, we propose a novel GNN model called the batch normalization multilayer perceptron crystal distance graph neural network (BNM-CDGNN). BNM-CDGNN encodes the crystal's geometry structure only based on the distance vector between atoms. The graph convolutional layer utilizes the radial basis function as the attention mask, ensuring the crystal's rotation invariance and adding the geometric information on the crystal. Subsequently, the average pooling layer is connected after the convolutional layer to enhance the model's ability to learn precise information. BNM-CDGNN connects multiple hidden layers after the average pooling layers, and these layers are processed by the batch normalization layer. Finally, the fully connected layer maps the results to the target property. BNM-CDGNN significantly enhances the accuracy of crystal property prediction compared with previous baseline models such as SchNet, MPNN, CGCNN, MEGNet, and GATGNN. Normalization (sociology) ; Pooling ; Artificial intelligence ; Computer science ; Crystal (programming language) ; Perceptron ; Graph ; Convolutional neural network ; Artificial neural network ; Pattern recognition (psychology) ; Algorithm ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Yu Mao | Lei Wang | Chenduan Chen | Zhan Yang | Jun Wang
Source: Laser & Photonics Review (article, 2023-02-01)
Field: Materials Science — Materials Chemistry

Thickness Determination of Ultrathin 2D Materials Empowered by Machine Learning Algorithms Abstract The number of layers of 2D materials is of great significance for regulating the performance of nanoelectronic devices and optoelectronic devices, where the optimal thickness of the target sample should be determined before further physical research or device manufacturing steps. At present, a variety of different optical technologies have been proposed to determine the thickness of samples by using the relationship between the number of layers and optical properties, including optical contrast, optical imaging, Raman spectra, photoluminescence spectra, nonlinear spectra, near‐field optical imaging, ellipsometry spectra, and hyperspectral imaging. In the past decade, the rapidly growing number of 2D materials and their heterostructures has exceeded the capacity of traditional experimental and computational methods. In recent years, machine learning (ML) is emerging as a powerful tool to support such traditional methods, which brings new opportunities to tap the potential of optical technology in a more perspicacious way. The application of optical technology has greatly facilitated the acquisition of optical information of materials, while ML algorithms provide a fast, high‐throughput, and intelligent way to complete back‐end data processing and inference. A profound integration of conventional optical technologies and ML algorithms significantly helps 2D materials progress in basic studies and practical applications and further promotes industrial manufacturing. Hyperspectral imaging ; Computer science ; Algorithm ; Heterojunction ; Materials science ; Photoluminescence ; Nonlinear optical ; Raman spectroscopy ; Throughput ; Artificial intelligence ; Nanotechnology ; Machine learning ; Optoelectronics ; Nonlinear system ; Optics ; Telecommunications ; Physics 2D Materials and Applications ; Nanowire Synthesis and Applications ; Quantum Dots Synthesis And Properties
Authors: Hisham Alghamdi | Chika Maduabuchi | Divine Mbachu | Abdullah Albaker | Ibrahim Alatawi | Theyab R. Alsenani | Ahmed S. Alsafran | Mohammed A. AlAqil
Source: Applied Thermal Engineering (article, 2023-04-06)
Field: Materials Science — Materials Chemistry

Machine learning model for transient exergy performance of a phase change material integrated-concentrated solar thermoelectric generator Exergy ; Thermoelectric generator ; Exergy efficiency ; Thermoelectric effect ; Transient (computer programming) ; Process engineering ; Thermoelectric cooling ; Electricity generation ; Mechanical engineering ; Phase-change material ; Solar irradiance ; Computer science ; Environmental science ; Materials science ; Engineering ; Power (physics) ; Meteorology ; Phase change ; Thermodynamics ; Engineering physics ; Physics Advanced Thermoelectric Materials and Devices ; Phase Change Materials Research ; Solar Thermal and Photovoltaic Systems
Authors: Shijin Xie
Source: Journal of Biomaterials Applications (review, 2023-01-11)
Field: Materials Science — Materials Chemistry

Perspectives on development of biomedical polymer materials in artificial intelligence age Polymer materials are widely used in biomedicine, chemistry and material science, whose traditional preparations are mainly based on experience, intuition and conceptual insight, having been applied to the development of many new materials, but facing great challenges due to the vast design space for biomedical polymers. So far, the best way to solve these problems is to accelerate material design through artificial intelligence, especially machine learning. Herein, this paper will introduce several successful cases, and analyze the latest progress of machine learning in the field of biomedical polymers, then discuss the opportunities of this novel method. In particular, this paper summarizes the material database, open-source determination tools, molecular generation methods and machine learning models that have been used for biopolymer synthesis and property prediction. Overall, machine learning could be more effectively deployed on the material design of biomedical polymers, and it is expected to become an extensive driving force to meet the huge demand for customized designs. Computer science ; Biomedicine ; Intuition ; Artificial intelligence ; Nanotechnology ; Materials science ; Bioinformatics Machine Learning in Materials Science
Authors: Qiuhong Huang | Xueying Yuan | Lifeng Li | Yaling Yan | Xiao Yang | Wei Wang | Yu Chen | Hong Liang | Hanyu Gao | Yufang Wu | Zhiwei Qiao
Source: Chemical Engineering Science (article, 2023-06-25)
Field: Chemistry — Inorganic Chemistry

Machine learning and molecular fingerprint screening of high-performance 2D/3D MOF membranes for Kr/Xe separation Membrane ; Separation (statistics) ; Support vector machine ; Boosting (machine learning) ; Univariate ; Gas separation ; Random forest ; Computation ; Metal-organic framework ; Chemistry ; Decision tree ; Artificial intelligence ; Analytical Chemistry (journal) ; Computer science ; Algorithm ; Materials science ; Machine learning ; Chromatography ; Multivariate statistics ; Physical chemistry ; Adsorption Metal-Organic Frameworks: Synthesis and Applications ; Chemical Synthesis and Characterization ; Membrane Separation and Gas Transport
Authors: Bita Farhadi | Jiaxue You | Dexu Zheng | Lu Liu | Sajian Wu | Jianxun Li | Zhipeng Li | Kai Wang | Shengzhong Liu
Source: Next Materials (article, 2023-06-25)
Field: Materials Science — Materials Chemistry

Machine learning for fast development of advanced energy materials With its unique advantages in artificial intelligence, data analysis, interpolation and numerical extrapolation, etc. ML has recently been quickly developed for the discovery of advanced energy materials. In particular, many algorithms have been developed to predict material properties. Herein, we first introduce the ML algorithms used in material science and the structure of each algorithm. Then we examine the algorithms that have been used recently in functional materials, especially in solar cells, batteries, and phase-change materials. Finally, advantages and disadvantages of each algorithm are compared to aid readers in choosing a suitable algorithm for specific applications. Extrapolation ; Computer science ; Interpolation (computer graphics) ; Algorithm ; Development (topology) ; Energy (signal processing) ; Machine learning ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Machine Learning and ELM
Authors: Kristine Laws | Marcus Tze‐Kiat Ng | Abhishek Sharma | Yibin Jiang | Alexander Hammer | Leroy Cronin
Source: ChemElectroChem (article, 2023-11-16)
Field: Materials Science — Materials Chemistry

An Autonomous Electrochemical Discovery Robot that Utilises Probabilistic Algorithms: Probing the Redox Behaviour of Inorganic Materials Abstract The discovery of new electroactive materials is slow due to the large combinatorial chemical space of possible experiments. Efficient exploration of redox‐active chemical space requires a machine learning assisted robotic platform with real‐time feedback. Here, we developed a closed‐loop robotic platform which is capable of synthesis and electrochemical characterisation controlled using a probabilistic algorithm. This was used to probe the redox behaviour of different polyoxometalates (POMs) precursors and explore the formation of redox‐active coordination complexes. The system can run accurate analytical electrochemical measurements whilst maintaining the performance and accuracy of both the working and reference electrodes. The platform successfully ran and analysed 336 coordination chemistry reactions by performing ca . 2500 cyclic voltammetry (CV) scans for analysis and electrode cleaning. Overall, the platform carried out over 9900 operations in 350 hours at a rate of 28 operations per hour, and we identified 24 complex solutions which showed significantly different redox activity. Experiments were performed using a universal chemical synthesis language (χDL) with variable inputs. The platform was used autonomously to investigate a range of POM precursor materials demonstrating 45 % increase in capacitance. The experiments ran for 36 hours with more than 6400 operations during which we analysed 200 POM precursor solutions. Redox ; Chemical space ; Electrochemistry ; Cyclic voltammetry ; Electrode ; Computer science ; Probabilistic logic ; Horizontal scan rate ; Algorithm ; Materials science ; Chemistry ; Biological system ; Nanotechnology ; Artificial intelligence ; Inorganic chemistry Polyoxometalates: Synthesis and Applications ; Advanced Nanomaterials in Catalysis ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Emi Minamitani | Ippei Obayashi | Kōji Shimizu | Satoshi Watanabe
Source: The Journal of Chemical Physics (article, 2023-08-22)
Field: Materials Science — Materials Chemistry

Persistent homology-based descriptor for machine-learning potential of amorphous structures High-accuracy prediction of the physical properties of amorphous materials is challenging in condensed-matter physics. A promising method to achieve this is machine-learning potentials, which is an alternative to computationally demanding ab initio calculations. When applying machine-learning potentials, the construction of descriptors to represent atomic configurations is crucial. These descriptors should be invariant to symmetry operations. Handcrafted representations using a smooth overlap of atomic positions and graph neural networks (GNN) are examples of methods used for constructing symmetry-invariant descriptors. In this study, we propose a novel descriptor based on a persistence diagram (PD), a two-dimensional representation of persistent homology (PH). First, we demonstrated that the normalized two-dimensional histogram obtained from PD could predict the average energy per atom of amorphous carbon at various densities, even when using a simple model. Second, an analysis of the dimensional reduction results of the descriptor spaces revealed that PH can be used to construct descriptors with characteristics similar to those of a latent space in a GNN. These results indicate that PH is a promising method for constructing descriptors suitable for machine-learning potentials without hyperparameter tuning and deep-learning techniques. Artificial intelligence ; Hyperparameter ; Invariant (physics) ; Computer science ; Histogram ; Graph ; Machine learning ; Pattern recognition (psychology) ; Algorithm ; Theoretical computer science ; Physics ; Quantum mechanics ; Image (mathematics) Machine Learning in Materials Science ; Topological and Geometric Data Analysis ; Computational Drug Discovery Methods
Authors: Rui Zhou | Rui Ma | Luyao Bao | Meirong Cai | Feng Zhou | Weimin Li | Xiaobo Wang
Source: Tribology International (article, 2023-03-02)
Field: Materials Science — Materials Chemistry

“Lubrication Brain” ― A machine learning framework of lubrication oil molecule design Lubrication ; Artificial neural network ; Computer science ; Feed forward ; Materials science ; Generative grammar ; Reinforcement learning ; Molecule ; Artificial intelligence ; Mechanical engineering ; Control engineering ; Engineering ; Chemistry ; Organic chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Injection Molding Process and Properties
Authors: Puhan Zhang | Gia-Wei Chern
Source: npj Computational Materials (article, 2023-03-03)
Field: Materials Science — Materials Chemistry

Machine learning nonequilibrium electron forces for spin dynamics of itinerant magnets Abstract We present a generalized potential theory for conservative as well as nonconservative forces for the Landau-Lifshitz magnetization dynamics. Importantly, this formulation makes possible an elegant generalization of the Behler-Parrinello machine learning (ML) approach, which is a cornerstone of ML-based quantum molecular dynamics methods, to the modeling of force fields in adiabatic spin dynamics of out-of-equilibrium itinerant magnetic systems. We demonstrate our approach by developing a deep-learning neural network that successfully learns the electron-mediated exchange fields in a driven s-d model computed from the nonequilibrium Green’s function method. We show that dynamical simulations with forces predicted from the neural network accurately reproduce the voltage-driven domain-wall propagation. Our work also lays the foundation for ML modeling of spin transfer torques and opens a avenue for ML-based multi-scale modeling of nonequilibrium dynamical phenomena in itinerant magnets and spintronics. Non-equilibrium thermodynamics ; Magnet ; Condensed matter physics ; Dynamics (music) ; Spin (aerodynamics) ; Physics ; Electron ; Quantum mechanics ; Thermodynamics Machine Learning in Materials Science ; Advanced Electron Microscopy Techniques and Applications ; Protein Structure and Dynamics
Authors: Bing Wang | Penghua Ying | Jin Zhang
Source: Nanoscale (article, 2023-11-22)
Field: Materials Science — Materials Chemistry

The thermoelastic properties of monolayer covalent organic frameworks studied by machine-learning molecular dynamics Two-dimensional (2D) covalent organic frameworks (COFs) are emerging as promising 2D polymeric materials with broad applications owing to their unique properties, among which the mechanical properties are quite important for various applications. However, the mechanical properties of 2D COFs have not been systematically studied yet. Herein, a machine-learned neuroevolution potential (NEP) was developed to study the elastic properties of two representative monolayer 2D COFs, namely COF-1 and COF-5. The trained NEP enables one to study the elastic properties of 2D COFs in realistic situations (<i>e.g.</i>, finite size and temperature) and possesses greatly improved computational efficiency when compared with density functional theory calculations. With the aid of the obtained NEP, molecular dynamics (MD) simulations together with a strain-fluctuation method were employed to evaluate the elastic constants of the considered 2D COFs at different temperatures. The elastic constants of COF-1 and COF-5 monolayers were found to decrease with an increase in the temperature, though they were almost isotropic irrespective of the temperature. The thermally induced softening of 2D COFs below a critical temperature was observed, which is mainly attributed to their inherent ripple configurations at finite temperatures, while above the critical temperature, the damping effect of anharmonic vibrations became the dominant factor. Based on the proposed mechanisms, analytical models were developed for capturing the temperature dependence of elastic constants, which were found to agree with the MD simulation results well. This work provides an in-depth insight into the thermoelastic properties of monolayer COFs, which can guide the development of 2D COF materials with tailored mechanical behaviors for enhancing their performance in various applications. Thermoelastic damping ; Covalent bond ; Monolayer ; Materials science ; Molecular dynamics ; Chemical physics ; Dynamics (music) ; Nanotechnology ; Thermal ; Computational chemistry ; Chemistry ; Thermodynamics ; Physics ; Organic chemistry Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Covalent Organic Framework Applications
Authors: Nikhil K. Barua | Andrew Golabek | Anton O. Oliynyk | Holger Kleinke
Source: Journal of Materials Chemistry C (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Experimentally validated machine learning predictions of ultralow thermal conductivity for SnSe materials Machine-learning (ML) models are used to predict optimal thermoelectric properties for efficient thermoelectric devices. Materials science ; Thermal conductivity ; Thermal ; Conductivity ; Composite material ; Engineering physics ; Thermodynamics ; Physical chemistry ; Physics Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science ; Thermal properties of materials