jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Takuya Taniguchi | Mayuko Hosokawa | Toru Asahi
Source: ACS Omega (article, 2023-10-16)
Field: Materials Science — Materials Chemistry

Graph Comparison of Molecular Crystals in Band Gap Prediction Using Neural Networks In material informatics, the representation of the material structure is fundamentally essential to obtaining better prediction results, and graph representation has attracted much attention in recent years. Molecular crystals can be graphically represented in molecular and crystal representations, but a comparison of which representation is more effective has not been examined. In this study, we compared the prediction accuracy between molecular and crystal graphs for band gap prediction. The results showed that the prediction accuracies using crystal graphs were better than those obtained using molecular graphs. While this result is not surprising, error analysis quantitatively evaluated that the error of the crystal graph was 0.4 times that of the molecular graph with moderate correlation. The novelty of this study lies in the comparison of molecular crystal representations and in the quantitative evaluation of the contribution of crystal structures to the band gap. Crystal (programming language) ; Representation (politics) ; Novelty ; Graph ; Molecular graph ; Crystal structure prediction ; Band gap ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Materials science ; Crystal structure ; Theoretical computer science ; Chemistry ; Crystallography ; Optoelectronics ; Psychology Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Various Chemistry Research Topics
Authors: Dana Bishara | Shaofan Li
Source: Computational Mechanics (article, 2023-02-13)
Field: Materials Science — Materials Chemistry

A machine-learning aided multiscale homogenization model for crystal plasticity: application for face-centered cubic single crystals Homogenization (climate) ; Finite element method ; Mesoscale meteorology ; Solver ; Multiscale modeling ; Computer science ; Micromechanics ; Computational Science and Engineering ; Materials science ; Mechanics ; Statistical physics ; Algorithm ; Applied mathematics ; Computational science ; Mathematics ; Structural engineering ; Physics ; Engineering ; Composite number Machine Learning in Materials Science ; Composite Material Mechanics ; Microstructure and mechanical properties
Authors: Meng Zhang | Koki Hibi | Junya Inoue
Source: Computer Physics Communications (article, 2023-01-03)
Field: Materials Science — Materials Chemistry

GPU-accelerated artificial neural network potential for molecular dynamics simulation Computer science ; Graphics processing unit ; CUDA ; Artificial neural network ; Graphics ; Computational science ; Molecular dynamics ; Computation ; Central processing unit ; Parallel computing ; General-purpose computing on graphics processing units ; Algorithm ; Artificial intelligence ; Computer hardware ; Computer graphics (images) Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Ferroelectric and Negative Capacitance Devices
Authors: Ouri Cohen | Ricardo Otazo
Source: NMR in Biomedicine (article, 2023-04-18)
Field: Materials Science — Materials Chemistry

Global deep learning optimization of chemical exchange saturation transfer magnetic resonance fingerprinting acquisition schedule Chemical exchange saturation transfer (CEST) MRI is a promising molecular imaging technique but suffers from long scan times and complicated processing. CEST was recently combined with magnetic resonance fingerprinting (MRF) to address these shortcomings. However, the CEST-MRF signal depends on multiple acquisition and tissue parameters so selecting an optimal acquisition schedule is challenging. In this work, we propose a novel dual-network deep learning framework to optimize the CEST-MRF acquisition schedule. The quality of the optimized schedule was assessed in a digital brain phantom and compared with alternate deep learning optimization approaches. The effect of schedule length on the reconstruction error was also investigated. A healthy subject was scanned with optimized and random schedules and with a conventional CEST sequence for comparison. The optimized schedule was also tested in a subject with metastatic renal cell carcinoma. Reproducibility was assessed via test-retest experiments and the concordance correlation coefficient calculated for white matter (WM) and grey matter (GM). The optimized schedule was 12% shorter but yielded equal or lower normalized root mean square error for all parameters. The proposed optimization also provided a lower error compared with alternate methodologies. Longer schedules generally yielded lower error. In vivo maps obtained with the optimized schedule showed reduced noise and improved delineation of GM and WM. CEST curves synthesized from the optimized parameters were highly correlated (r = 0.99) with measured conventional CEST. The mean concordance correlation coefficient in WM/GM for all tissue parameters was 0.990/0.978 for the optimized schedule but only 0.979/0.975 for the random schedule. The proposed schedule optimization is widely applicable to MRF pulse sequences and provides accurate and reproducible tissue maps with reduced noise at a shorter scan time than a randomly generated schedule. Computer science ; Imaging phantom ; Magnetic resonance imaging ; Artificial intelligence ; Concordance correlation coefficient ; Schedule ; Deep learning ; Mean squared error ; Nuclear magnetic resonance ; Algorithm ; Pattern recognition (psychology) ; Mathematics ; Nuclear medicine ; Statistics ; Physics ; Medicine ; Radiology Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; MRI in cancer diagnosis
Authors: Takeyuki Kato | Federica Lodesani | Shingo Urata
Source: Journal of the American Ceramic Society (article, 2023-12-19)
Field: Materials Science — Ceramics and Composites

Boron coordination and three‐membered ring formation in sodium borate glasses: a machine‐learning molecular dynamics study Abstract Classical molecular dynamics (CMD) simulations that employ analytical force fields have been commonly utilized to investigate mechanical, chemical, and thermal properties of oxide glasses owing to their superior computational efficiency. Conversely, simple functional forms limit the accuracy in modeling complicated glass structures, specifically, in alkaline borate glasses, which exhibit boron coordination numbers that vary nonlinearly with changes in glass composition and temperature. Machine‐learning potentials (MLPs), which are trained using datasets on energy and force evaluated via the density functional theory (DFT), are garnering significant attention as a novel simulation technology for enhancing the accuracy in modeling materials. Therefore, this study applied a universal MLP, PreFerred Potential (PFP) (trade‐name: Matlantis), to model sodium borate glasses, and its accuracy was verified in reproducing boron coordination and ring structures by comparing its results to available experimental data. We found that PFP can quantitatively reproduce the boron coordination change with glass composition without any empirical correction, while the boron coordination in the melts at high temperatures is overestimated, even though the qualitative variation was better estimated than CMD simulations. Furthermore, the MLP could generate many 3‐rings, unlike the analytical force‐field. Accordingly, we demonstrated superior accuracy of the MLP in modeling alkaline borate glasses, while discussing the challenges faced in reproducing the elaborated microstructures in borate glasses. Boron ; Coordination number ; Molecular dynamics ; Boron oxide ; Glass transition ; Density functional theory ; Force field (fiction) ; Materials science ; Microstructure ; Borate glass ; Thermal ; Chemistry ; Thermodynamics ; Computational chemistry ; Computer science ; Physics ; Artificial intelligence ; Metallurgy ; Composite material ; Organic chemistry Glass properties and applications ; Phase-change materials and chalcogenides ; Material Dynamics and Properties
Authors: Bowen Deng | Peichen Zhong | KyuJung Jun | Riebesell, Janosh | Kevin Han | Christopher J. Bartel | Gerbrand Ceder
Source: arXiv (Cornell University) (preprint, 2023-02-28)
Field: Materials Science — Materials Chemistry

CHGNet: Pretrained universal neural network potential for charge-informed atomistic modeling The simulation of large-scale systems with complex electron interactions remains one of the greatest challenges for the atomistic modeling of materials. Although classical force fields often fail to describe the coupling between electronic states and ionic rearrangements, the more accurate \textit{ab-initio} molecular dynamics suffers from computational complexity that prevents long-time and large-scale simulations, which are essential to study many technologically relevant phenomena, such as reactions, ion migrations, phase transformations, and degradation. In this work, we present the Crystal Hamiltonian Graph neural Network (CHGNet) as a novel machine-learning interatomic potential (MLIP), using a graph-neural-network-based force field to model a universal potential energy surface. CHGNet is pretrained on the energies, forces, stresses, and magnetic moments from the Materials Project Trajectory Dataset, which consists of over 10 years of density functional theory static and relaxation trajectories of $\sim 1.5$ million inorganic structures. The explicit inclusion of magnetic moments enables CHGNet to learn and accurately represent the orbital occupancy of electrons, enhancing its capability to describe both atomic and electronic degrees of freedom. We demonstrate several applications of CHGNet in solid-state materials, including charge-informed molecular dynamics in Li$_x$MnO$_2$, the finite temperature phase diagram for Li$_x$FePO$_4$ and Li diffusion in garnet conductors. We critically analyze the significance of including charge information for capturing appropriate chemistry, and we provide new insights into ionic systems with additional electronic degrees of freedom that can not be observed by previous MLIPs. Degrees of freedom (physics and chemistry) ; Statistical physics ; Hamiltonian (control theory) ; Ionic bonding ; Molecular dynamics ; Ab initio ; Electron ; Artificial neural network ; Phase diagram ; Computer science ; Electronic structure ; Atomic units ; Physics ; Ion ; Condensed matter physics ; Phase (matter) ; Quantum mechanics ; Artificial intelligence ; Mathematics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Topic Modeling
Authors: Chang‐An Wang | Guoqing An | Jing Lin | Xiaowei Zhang | Zhiyuan Liu | Yibin Luo | Siqi Liu | Zhixiang Cheng | Tingting Guo | Hongyi Gao | Ge Wang | Xingtian Shu
Source: Aggregate (article, 2023-01-28)
Field: Chemistry — Inorganic Chemistry

Aromatic compounds‐mediated synthesis of anatase‐free hierarchical TS‐1 zeolite: Exploring design strategies via machine learning and enhanced catalytic performance Abstract Simultaneous achievement of constructing mesopores and eliminating anatase is a long‐term pursuit for enhancing the catalytic performance of TS‐1. Here, we developed an aromatic compounds‐mediated synthesis method to prepare anatase‐free and hierarchical TS‐1 for olefin epoxidation. A series of hierarchical TS‐1 zeolites were prepared by introducing aromatic compounds containing different functional groups via the crystallization process. The formation of intercrystalline mesopores and insertion of titanium into framework were facilitated at different extent. The synergistic coordination of carboxyl and hydroxyl in aromatic compounds with Ti(OH) 4 realizes the uniform distribution of titanium species and eliminates the generation of anatase. Noteworthily, eight machine learning models were trained to reveal the mechanism of additive functional groups and preparation conditions on anatase formation and microstructure optimization. The prediction accuracy of most models can reach more than 80%. Benefiting from the larger mesopore volumes (0.37 cm 3 ·g −1 ) and higher content of framework Ti species, TS‐DHBDC‐48h samples exhibit a higher catalytic performance than other zeolites, giving 1‐hexene conversion of 49.3% and 1,2‐epoxyhenane selectivity of 99.9%. The paper provides a facile aromatic compounds‐mediated synthesis strategy and promotes the application of machine learning toward the design and optimization of new zeolites. Anatase ; Catalysis ; Mesoporous material ; Crystallization ; Zeolite ; Titanium ; Selectivity ; Materials science ; Chemical engineering ; Combinatorial chemistry ; Chemistry ; Organic chemistry ; Photocatalysis Zeolite Catalysis and Synthesis ; Polyoxometalates: Synthesis and Applications ; Metal-Organic Frameworks: Synthesis and Applications
Authors: Ryoichi Furushima | Yuki Nakashima | You Zhou | Kiyoshi Hirao | Tatsuki Ohji | Manabu Fukushima
Source: Ceramics International (article, 2023-12-19)
Field: Materials Science — Ceramics and Composites

Thermal conductivity prediction of sintered reaction bonded silicon nitride ceramics using a machine learning approach based on process conditions Materials science ; Silicon nitride ; Ceramic ; Thermal conductivity ; Process (computing) ; Nitride ; Composite material ; Thermal ; Silicon ; Process engineering ; Metallurgy ; Computer science ; Thermodynamics Advanced ceramic materials synthesis ; Thermal properties of materials ; Ferroelectric and Piezoelectric Materials
Authors: Lisha Sheng | Yi Wang | Xinzhu Mou | Bo Xu | Zhenqian Chen
Source: ACS Applied Materials & Interfaces (article, 2023-11-21)
Field: Chemistry — Inorganic Chemistry

Accelerating Metal–Organic Framework Selection for Type III Porous Liquids by Synergizing Machine Learning and Molecular Simulation MOF-based type III porous liquids, comprising porous MOFs dissolved in a liquid solvent, have attracted increasing attention in carbon capture. However, discovering appropriate MOFs to prepare porous liquids was still limited in experiments, wasting time and energy. In this study, we have used the density functional theory and molecular dynamics simulation methods to identify 4530 MOF candidates as the core database based on the idea of prohibiting the pore occupancy of porous liquids by the solvent, [DBU-PEG][NTf<sub>2</sub>] ionic liquid. Based on high-throughput molecular simulation, random forest machine learning models were first trained to predict the CO<sub>2</sub> sorption and the CO<sub>2</sub>/N<sub>2</sub> sorption selectivity of MOFs to screen the MOFs to prepare porous liquids. The feature importance was inferred based on Shapley Additive Explanations (SHAP) interpretation, and the ranking of the top 5 descriptors for sorption/selectivity trade-off (TSN) was gravimetric surface area (GSA) > porosity > density > metal fraction > pore size distribution (PSD, 3.5-4 Å). RICBEM was predicted to be one candidate for preparing porous liquid with CO<sub>2</sub> sorption capacity of 20.87 mmol/g and CO<sub>2</sub>/N<sub>2</sub> sorption selectivity of 16.75. The experimental results showed that the RICBEM-based porous liquid was successfully synthesized with CO<sub>2</sub> sorption capacity of 2.21 mmol/g and CO<sub>2</sub>/N<sub>2</sub> sorption selectivity of 63.2, the best carbon capture performance known to date. Such a screening method would advance the screening of cores and solvents for preparing type III porous liquids with different applications by addressing corresponding factors. Sorption ; Materials science ; Porosity ; Selectivity ; Ionic liquid ; Gravimetric analysis ; Metal-organic framework ; Porous medium ; Solvent ; Chemical engineering ; Adsorption ; Organic chemistry ; Chemistry ; Composite material ; Catalysis Metal-Organic Frameworks: Synthesis and Applications ; Carbon dioxide utilization in catalysis ; Covalent Organic Framework Applications
Authors: Daniel Gleaves | Nihang Fu | Edirisuriya M. Dilanga Siriwardane | Yong Zhao | Jianjun Hu
Source: Digital Discovery (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Materials synthesizability and stability prediction using a semi-supervised teacher-student dual neural network A semi-supervised deep neural network (TSDNN) model based on teacher-student architecture is developed for high-performance formation energy and synthesizability prediction by exploiting a large number of unlabelled samples. Dual (grammatical number) ; Artificial neural network ; Stability (learning theory) ; Artificial intelligence ; Computer science ; Machine learning ; Psychology ; Mathematics education ; Philosophy ; Linguistics Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Advanced Memory and Neural Computing
Authors: Jerome Workman | Howard Mark
Source: Spectroscopy (article, 2023-06-01)
Field: Chemistry — Analytical Chemistry

Artificial Intelligence in Analytical Spectroscopy, Part II: Examples in Spectroscopy In Part I (February 2023) of this two-part series on artificial intelligence (AI), and its subfield machine learning (ML), we presented the variety of chemometric algorithms used to compare AI, ML, and chemometrics. These algorithms included those used for classification, regression, clustering, ensemble learning, signal processing, and component analysis. Now, in Part II, we discuss the applications of AI to electronic and vibrational spectroscopy. We also touch on some applications of deep learning (DL), which is a subfield of machine learning where more complex artificial neural networks (ANNs) with more hidden layers are used. This column article includes a number of selected references that discuss the application of AI in analytical chemistry and in molecular spectroscopy. We give a few early and late examples of AI and ML as applied to different vibrational spectroscopy methods, such as Raman, infrared (FT-IR), near-infrared (NIR), and ultraviolet–visible (UV-vis) spectroscopic techniques. This article is intended only as a sampling of the numerous research manuscripts addressing this subject. Chemometrics ; Spectroscopy ; Artificial intelligence ; Artificial neural network ; Raman spectroscopy ; Infrared spectroscopy ; Machine learning ; Ultraviolet visible spectroscopy ; Computer science ; Cluster analysis ; Pattern recognition (psychology) ; Chemistry ; Physics ; Optics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Pan Zhang | Mi Qin | Zhenhua Zhang | Dan Jin | Yong Liu | Ziyu Wang | Zhihong Lu | Jing Shi | Rui Xiong
Source: Physical Chemistry Chemical Physics (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Accessing the thermal conductivities of Sb<sub>2</sub>Te<sub>3</sub> and Bi<sub>2</sub>Te<sub>3</sub>/Sb<sub>2</sub>Te<sub>3</sub> superlattices by molecular dynamics simulations with a deep neural network potential Phonon thermal transport is a key feature for the operation of thermoelectric materials, but it is challenging to accurately calculate the thermal conductivity of materials with strong anharmonicity or large cells. In this work, a deep neural network potential (NNP) is developed using a dataset based on density functional theory (DFT) and applied to describe the lattice dynamics of Sb<sub>2</sub>Te<sub>3</sub> and Bi<sub>2</sub>Te<sub>3</sub>/Sb<sub>2</sub>Te<sub>3</sub> superlattices. The lattice thermal conductivities of Sb<sub>2</sub>Te<sub>3</sub> are first predicted using equilibrium molecular dynamics (EMD) simulations combined with an NNP and the results match well with experimental values. Then, through further exploration of weighted phase spaces and the Grüneisen parameter, we find that there is a stronger anharmonicity in the out-of-plane direction in Sb<sub>2</sub>Te<sub>3</sub>, which is the reason why the thermal conductivities are overestimated more in the out-of-plane direction than in the in-plane direction by solving the phonon Boltzmann transport equation (BTE) with only three-phonon scattering processes being considered. More importantly, the lattice thermal conductivities of Bi<sub>2</sub>Te<sub>3</sub>/Sb<sub>2</sub>Te<sub>3</sub> superlattices with different periods are accurately predicted using non-equilibrium molecular dynamics (NEMD) simulations together with an NNP, which serves as a good example to explore the thermal transport physics of superlattices using a deep neural network potential. Anharmonicity ; Phonon ; Superlattice ; Boltzmann equation ; Thermal conductivity ; Condensed matter physics ; Thermoelectric materials ; Thermoelectric effect ; Molecular dynamics ; Materials science ; Phonon scattering ; Lattice (music) ; Scattering ; Thermal ; Physics ; Thermodynamics ; Optics ; Quantum mechanics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Machine Learning in Materials Science
Authors: Atreyee Banerjee | Aysenur Iscen | Kurt Kremer | Oleksandra Kukharenko
Source: The Journal of Chemical Physics (article, 2023-08-21)
Field: Materials Science — Materials Chemistry

Determining glass transition in all-atom acrylic polymeric melt simulations using machine learning The functionality of many polymeric materials depends on their glass transition temperatures (Tg). In computer simulations, Tg is often calculated from the gradual change in macroscopic properties. Precise determination of this change depends on the fitting protocols. We previously proposed a robust data-driven approach to determine Tg from the molecular dynamics simulation data of a coarse-grained semiflexible polymer model. In contrast to the global macroscopic properties, our method relies on high-resolution microscopic details. Here, we demonstrate the generality of our approach by using various dimensionality reduction and clustering methods and apply it to an atomistic model of acrylic polymers. Our study reveals the explicit contribution of the side chain and backbone residues in influencing the determination of the glass transition temperature. Glass transition ; Polymer ; Materials science ; Molecular dynamics ; Curse of dimensionality ; Statistical physics ; Generality ; Cluster analysis ; Chemical physics ; Biological system ; Computer science ; Thermodynamics ; Physics ; Composite material ; Chemistry ; Artificial intelligence ; Computational chemistry Material Dynamics and Properties ; Theoretical and Computational Physics ; Phase Equilibria and Thermodynamics
Authors: Xiang Zhang | Zichun Zhou | Ming Chen | Yi‐Yang Sun
Source: The Journal of Physical Chemistry Letters (article, 2023-12-08)
Field: Materials Science — Materials Chemistry

GPT-Assisted Learning of Structure–Property Relationships by Graph Neural Networks: Application to Rare-Earth-Doped Phosphors Two challenges facing machine learning tasks in materials science are data set construction and descriptor design. Graph neural networks circumvent the need for empirical descriptors by encoding geometric information in graphs. Large language models have shown promise for database construction via text extraction. Here, we apply OpenAI's Generative Pre-trained Transformer 4 (GPT-4) and the Crystal Graph Convolutional Neural Network (CGCNN) to the problem of discovering rare-earth-doped phosphors for solid-state lighting. We used GPT-4 to datamine the chemical formulas and emission wavelengths of 264 Eu<sup>2+</sup>-doped phosphors from 274 articles. A CGCNN model was trained on the acquired data set, achieving a test <i>R</i><sup>2</sup> of 0.77. Using this model, we predicted the emission wavelengths of over 40 000 inorganic materials. We also used transfer learning to fine-tune a bandgap-predicting CGCNN model for emission wavelength prediction. The workflow requires minimal human supervision and is generalizable to other fields. Phosphor ; Computer science ; Artificial neural network ; Transformer ; Graph ; Convolutional neural network ; Artificial intelligence ; Workflow ; Test set ; Machine learning ; Theoretical computer science ; Optoelectronics ; Materials science ; Database ; Engineering ; Electrical engineering ; Voltage Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography