jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Cheol Woo Park | Chris Wolverton
Source: Physical Review Materials (article, 2020-06-01)
Field: Materials Science — Materials Chemistry

Developing an improved crystal graph convolutional neural network framework for accelerated materials discovery The recently proposed crystal graph convolutional neural network (CGCNN) offers a highly versatile and accurate machine learning (ML) framework by learning material properties directly from graphlike representations of crystal structures (``crystal graphs''). Here, we develop an improved variant of the CGCNN model (iCGCNN) that outperforms the original by incorporating information of the Voronoi tessellated crystal structure, explicit three-body correlations of neighboring constituent atoms, and an optimized chemical representation of interatomic bonds in the crystal graphs. We demonstrate the accuracy of the improved framework in two distinct illustrations: First, when trained/validated on 180 000/20 000 density functional theory (DFT) calculated thermodynamic stability entries taken from the Open Quantum Materials Database (OQMD) and evaluated on a separate test set of 230 000 entries, iCGCNN achieves a predictive accuracy that is significantly improved, i.e., 20% higher than that of the original CGCNN. Second, when used to assist a high-throughput search for materials in the $\mathrm{ThC}{\mathrm{r}}_{2}\mathrm{S}{\mathrm{i}}_{2}$ structure-type, iCGCNN exhibited a success rate of $31%$ which is 155 times higher than an undirected high-throughput search and 2.4 times higher than that of the original CGCNN. Using both CGCNN and iCGCNN, we screened 132 600 compounds with elemental decorations of the $\mathrm{ThC}{\mathrm{r}}_{2}\mathrm{S}{\mathrm{i}}_{2}$ prototype crystal structure and identified a total of 97 unique stable compounds by performing 757 DFT calculations, accelerating the computational time of the high-throughput search by a factor of 65. Our results suggest that the iCGCNN can be used to accelerate high-throughput discoveries of new materials by quickly and accurately identifying crystalline compounds with properties of interest. Convolutional neural network ; Voronoi diagram ; Crystal (programming language) ; Representation (politics) ; Graph ; Crystal structure ; Set (abstract data type) ; Stability (learning theory) ; Artificial neural network Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; X-ray Diffraction in Crystallography
Authors: Marcos F. Calegari Andrade | Hsin-Yu Ko | Linfeng Zhang | Roberto Car | Annabella Selloni
Source: Chemical Science (article, 2020-01-01)
Field: Chemistry — Electrochemistry

Free energy of proton transfer at the water–TiO<sub>2</sub> interface from <i>ab initio</i> deep potential molecular dynamics TiO<sub>2</sub> is a widely used photocatalyst in science and technology and its interface with water is important in fields ranging from geochemistry to biomedicine. Yet, it is still unclear whether water adsorbs in molecular or dissociated form on TiO<sub>2</sub> even for the case of well-defined crystalline surfaces. To address this issue, we simulated the TiO<sub>2</sub>-water interface using molecular dynamics with an <i>ab initio</i>-based deep neural network potential. Our simulations show a dynamical equilibrium of molecular and dissociative adsorption of water on TiO<sub>2</sub>. Water dissociates through a solvent-assisted concerted proton transfer to form a pair of short-lived hydroxyl groups on the TiO<sub>2</sub> surface. Molecular adsorption of water is Δ<i>F</i> = 8.0 ± 0.9 kJ mol<sup>-1</sup> lower in free energy than the dissociative adsorption, giving rise to a 5.6 ± 0.5% equilibrium water dissociation fraction at room temperature. Due to the relevance of surface hydroxyl groups to the surface chemistry of TiO<sub>2</sub>, our model might be key to understanding phenomena ranging from surface functionalization to photocatalytic mechanisms. Ab initio ; Interface (matter) ; Photocatalysis ; Proton ; Chemical physics ; Molecular dynamics ; Ab initio quantum chemistry methods ; Materials science ; Biomedicine ; Nanotechnology ; Computational chemistry ; Chemistry ; Physical chemistry ; Physics ; Molecule ; Catalysis ; Nuclear physics ; Bioinformatics ; Organic chemistry Electrochemical Analysis and Applications ; Spectroscopy and Quantum Chemical Studies ; Electronic and Structural Properties of Oxides
Authors: Sungwon Kim | Juhwan Noh | Geun Ho Gu | Alán Aspuru‐Guzik | Yousung Jung
Source: ACS Central Science (article, 2020-07-10)
Field: Materials Science — Materials Chemistry

Generative Adversarial Networks for Crystal Structure Prediction [Image: see text] The constant demand for novel functional materials calls for efficient strategies to accelerate the materials discovery, and crystal structure prediction is one of the most fundamental tasks along that direction. In addressing this challenge, generative models can offer new opportunities since they allow for the continuous navigation of chemical space via latent spaces. In this work, we employ a crystal representation that is inversion-free based on unit cell and fractional atomic coordinates and build a generative adversarial network for crystal structures. The proposed model is applied to generate the Mg–Mn–O ternary materials with the theoretical evaluation of their photoanode properties for high-throughput virtual screening (HTVS). The proposed generative HTVS framework predicts 23 new crystal structures with reasonable calculated stability and band gap. These findings suggest that the generative model can be an effective way to explore hidden portions of the chemical space, an area that is usually unreachable when conventional substitution-based discovery is employed. Generative grammar ; Computer science ; Chemical space ; Representation (politics) ; Crystal structure prediction ; Crystal (programming language) ; Ternary operation ; Adversarial system ; Generative model ; Stability (learning theory) ; Generative adversarial network ; Space (punctuation) ; Artificial intelligence ; Machine learning ; Deep learning ; Crystal structure ; Drug discovery ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Qi Yang | Yao Li | Jin‐Dong Yang | Yidi Liu | Long Zhang | Sanzhong Luo | Jin‐Pei Cheng
Source: Angewandte Chemie International Edition (article, 2020-07-16)
Field: Chemistry — Organic Chemistry

Holistic Prediction of the p<i>K</i><sub>a</sub> in Diverse Solvents Based on a Machine‐Learning Approach While many approaches to predict aqueous pK<sub>a</sub> values exist, the fast and accurate prediction of non-aqueous pK<sub>a</sub> values is still challenging. Based on the iBonD experimental pK<sub>a</sub> database (39 solvents), a holistic pK<sub>a</sub> prediction model was established using machine learning. Structural and physical-organic-parameter-based descriptors (SPOC) were introduced to represent the electronic and structural features of the molecules. The models trained with a neural network or the XGBoost algorithm showed the best prediction performance with a low MAE value of 0.87 pK<sub>a</sub> units. The approach allows a comprehensive mapping of all possible pK<sub>a</sub> correlations between different solvents and it was validated by predicting the aqueous pK<sub>a</sub> and micro-pK<sub>a</sub> of pharmaceutical molecules and pK<sub>a</sub> values of organocatalysts in DMSO and MeCN with high accuracy. An online prediction platform was constructed based on the current model, which can provide pK<sub>a</sub> prediction for different types of X-H acidity in the most commonly used solvents. Artificial intelligence ; Machine learning ; Computer science ; Chemistry ; Psychology Free Radicals and Antioxidants ; Chemistry and Chemical Engineering ; Computational Drug Discovery Methods
Authors: Steph-Yves Louis | Yong Zhao | Alireza Nasiri | Xiran Wang | Yuqi Song | Fei Liu | Jianjun Hu
Source: Physical Chemistry Chemical Physics (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Graph convolutional neural networks with global attention for improved materials property prediction The development of an efficient and powerful machine learning (ML) model for materials property prediction (MPP) remains an important challenge in materials science. While various techniques have been proposed to extract physicochemical features in MPP, graph neural networks (GNN) have also shown very strong capability in capturing effective features for high-performance MPP. Nevertheless, current GNN models do not effectively differentiate the contributions from different atoms. In this paper we develop a novel graph neural network model called GATGNN for predicting properties of inorganic materials. GATGNN is characterized by its composition of augmented graph-attention layers (AGAT) and a global attention layer. The application of AGAT layers and global attention layers respectively learn the local relationship among neighboring atoms and overall contribution of the atoms to the material's property; together making our framework achieve considerably better prediction performance on various tested properties. Through extensive experiments, we show that our method is able to outperform existing state-of-the-art GNN models while it can also provide a measurable insight into the correlation between the atoms and their material property. Our code can found on - https://github.com/superlouis/GATGNN. Computer science ; Graph ; Convolutional neural network ; Artificial neural network ; Popularity ; Artificial intelligence ; Property (philosophy) ; Theoretical computer science ; Machine learning Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Catalysis and Oxidation Reactions
Authors: P.N. Rowe | Volker L. Deringer | Piero Gasparotto | Gábor Cśanyi | Angelos Michaelides
Source: UCL Discovery (University College London) (article, 2020-07-21)
Field: Materials Science — Materials Chemistry

An accurate and transferable machine learning potential for carbon. We present an accurate machine learning (ML) model for atomistic simulations of carbon, constructed using the Gaussian approximation potential (GAP) methodology. The potential, named GAP-20, describes the properties of the bulk crystalline and amorphous phases, crystal surfaces, and defect structures with an accuracy approaching that of direct ab initio simulation, but at a significantly reduced cost. We combine structural databases for amorphous carbon and graphene, which we extend substantially by adding suitable configurations, for example, for defects in graphene and other nanostructures. The final potential is fitted to reference data computed using the optB88-vdW density functional theory (DFT) functional. Dispersion interactions, which are crucial to describe multilayer carbonaceous materials, are therefore implicitly included. We additionally account for long-range dispersion interactions using a semianalytical two-body term and show that an improved model can be obtained through an optimization of the many-body smooth overlap of atomic positions descriptor. We rigorously test the potential on lattice parameters, bond lengths, formation energies, and phonon dispersions of numerous carbon allotropes. We compare the formation energies of an extensive set of defect structures, surfaces, and surface reconstructions to DFT reference calculations. The present work demonstrates the ability to combine, in the same ML model, the previously attained flexibility required for amorphous carbon [V. L. Deringer and G. Csányi, Phys. Rev. B 95, 094203 (2017)] with the high numerical accuracy necessary for crystalline graphene [Rowe et al., Phys. Rev. B 97, 054303 (2018)], thereby providing an interatomic potential that will be applicable to a wide range of applications concerning diverse forms of bulk and nanostructured carbon. Graphene ; Density functional theory ; Interatomic potential ; Materials science ; Amorphous solid ; Molecular dynamics ; Phonon ; Ab initio ; Amorphous carbon ; Carbon fibers ; Statistical physics ; Computational chemistry ; Nanotechnology ; Physics ; Condensed matter physics ; Chemistry ; Quantum mechanics ; Crystallography Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Hilal Daglar | Seda Keskın
Source: Coordination Chemistry Reviews (article, 2020-07-27)
Field: Chemistry — Inorganic Chemistry

Recent advances, opportunities, and challenges in high-throughput computational screening of MOFs for gas separations In the last two decades, metal organic frameworks (MOFs) have gained significant attention as adsorbent and membrane materials for gas separations. Due to the large number and diversity of existing MOFs, identifying the best MOF materials for a gas separation of interest is very challenging. High-throughput computational screening studies have played an important role in accurately assessing adsorption and membrane-based gas separation performances of MOFs in a time-efficient manner. Computational methods, mainly molecular simulations, are invaluable in narrowing down the number of promising MOFs from thousands to tens and directing the future experimental efforts, resources, and time to the best materials. In this review, we addressed the recent advances in high-throughput computational screening methods used for MOFs and described how to use the results of computer simulations to predict various adsorbent and membrane performance metrics of MOFs. Current large-scale computational studies on using MOFs for different gas separations were then reviewed. Finally, both the opportunities and challenges in the field were discussed to shed light on future MOF studies. Gas separation ; Nanotechnology ; Biochemical engineering ; Throughput ; Chemistry ; Metal-organic framework ; Computational model ; Process engineering ; Adsorption ; Computer science ; Membrane ; Engineering ; Artificial intelligence ; Telecommunications ; Materials science Metal-Organic Frameworks: Synthesis and Applications ; Membrane Separation and Gas Transport ; Catalytic Processes in Materials Science
Authors: Ryo Nagai | Ryosuke Akashi | Osamu Sugino
Source: npj Computational Materials (article, 2020-05-05)
Field: Materials Science — Materials Chemistry

Completing density functional theory by machine learning hidden messages from molecules Abstract Kohn–Sham density functional theory (DFT) is the basis of modern computational approaches to electronic structures. Their accuracy heavily relies on the exchange-correlation energy functional, which encapsulates electron–electron interaction beyond the classical model. As its universal form remains undiscovered, approximated functionals constructed with heuristic approaches are used for practical studies. However, there are problems in their accuracy and transferability, while any systematic approach to improve them is yet obscure. In this study, we demonstrate that the functional can be systematically constructed using accurate density distributions and energies in reference molecules via machine learning. Surprisingly, a trial functional machine learned from only a few molecules is already applicable to hundreds of molecules comprising various first- and second-row elements with the same accuracy as the standard functionals. This is achieved by relating density and energy using a flexible feed-forward neural network, which allows us to take a functional derivative via the back-propagation algorithm. In addition, simply by introducing a nonlocal density descriptor, the nonlocal effect is included to improve accuracy, which has hitherto been impractical. Our approach thus will help enrich the DFT framework by utilizing the rapidly advancing machine-learning technique. Density functional theory ; Computer science ; Heuristic ; Transferability ; Artificial intelligence ; Orbital-free density functional theory ; Basis (linear algebra) ; Artificial neural network ; Hybrid functional ; Energy minimization ; Energy (signal processing) ; Machine learning ; Algorithm ; Theoretical computer science ; Statistical physics ; Computational chemistry ; Mathematics ; Physics ; Quantum mechanics ; Chemistry Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Crystallography and molecular interactions
Authors: Dooraid N. Ahmed | Laith A. Naji | Ayad A.H. Faisal | Nadhir Al‐Ansari | Mu. Naushad
Source: Scientific Reports (article, 2020-02-06)
Field: Materials Science — Materials Chemistry

Waste foundry sand/MgFe-layered double hydroxides composite material for efficient removal of Congo red dye from aqueous solution We aimed to obtain magnesium/iron (Mg/Fe)-layered double hydroxides (LDHs) nanoparticles-immobilized on waste foundry sand-a byproduct of the metal casting industry. XRD and FT-IR tests were applied to characterize the prepared sorbent. The results revealed that a new peak reflected LDHs nanoparticles. In addition, SEM-EDS mapping confirmed that the coating process was appropriate. Sorption tests for the interaction of this sorbent with an aqueous solution contaminated with Congo red dye revealed the efficacy of this material where the maximum adsorption capacity reached approximately 9127.08 mg/g. The pseudo-first-order and pseudo-second-order kinetic models helped to describe the sorption measurements, indicating that the physical and chemical forces governed the removal process. Sorbent ; Layered double hydroxides ; Sorption ; Congo red ; Foundry ; Aqueous solution ; Adsorption ; Chemical engineering ; Materials science ; Langmuir adsorption model ; Composite number ; Nanoparticle ; Metallurgy ; Chemistry ; Composite material ; Nanotechnology ; Organic chemistry Layered Double Hydroxides Synthesis and Applications ; Materials Engineering and Processing ; Nanomaterials for catalytic reactions
Authors: Ashish Avasthi | Carlos Caro | Esther Pozo-Torres | Manuel Pernía Leal | Maria Luisa García‐Martín
Source: Topics in Current Chemistry (review, 2020-05-07)
Field: Materials Science — Biomaterials

Magnetic Nanoparticles as MRI Contrast Agents Iron oxide nanoparticles (IONPs) have emerged as a promising alternative to conventional contrast agents (CAs) for magnetic resonance imaging (MRI). They have been extensively investigated as CAs due to their high biocompatibility and excellent magnetic properties. Furthermore, the ease of functionalization of their surfaces with different types of ligands (antibodies, peptides, sugars, etc.) opens up the possibility of carrying out molecular MRI. Thus, IONPs functionalized with epithelial growth factor receptor antibodies, short peptides, like RGD, or aptamers, among others, have been proposed for the diagnosis of various types of cancer, including breast, stomach, colon, kidney, liver or brain cancer. In addition to cancer diagnosis, different types of IONPs have been developed for other applications, such as the detection of brain inflammation or the early diagnosis of thrombosis. This review addresses key aspects in the development of IONPs for MRI applications, namely, synthesis of the inorganic core, functionalization processes to make IONPs biocompatible and also to target them to specific tissues or cells, and finally in vivo studies in animal models, with special emphasis on tumor models. Chemistry ; Magnetic resonance imaging ; Biocompatibility ; Surface modification ; Nanotechnology ; In vivo ; Aptamer ; Iron oxide nanoparticles ; Nanoparticle ; Molecular imaging ; Cancer research ; Biophysics ; Molecular biology ; Materials science ; Radiology ; Medicine Nanoparticle-Based Drug Delivery ; Characterization and Applications of Magnetic Nanoparticles ; Nanoplatforms for cancer theranostics
Authors: Yi Yang | Xiaohui Liu | Chengpin Shen | Yu Lin | Pengyuan Yang | Liang Qiao
Source: Nature Communications (article, 2020-01-09)
Field: Chemistry — Spectroscopy

In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics Data-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by data-dependent acquisition (DDA) experiments are required prior to DIA analysis, which is time-consuming and limits the identification/quantification by DIA to the peptides identified by DDA. Herein, we propose DeepDIA, a deep learning-based approach to generate in silico spectral libraries for DIA analysis. We demonstrate that the quality of in silico libraries predicted by instrument-specific models using DeepDIA is comparable to that of experimental libraries, and outperforms libraries generated by global models. With peptide detectability prediction, in silico libraries can be built directly from protein sequence databases. We further illustrate that DeepDIA can break through the limitation of DDA on peptide/protein detection, and enhance DIA analysis on human serum samples compared to the state-of-the-art protocol using a DDA library. We expect this work expanding the toolbox for DIA proteomics. In silico ; Computer science ; Toolbox ; Computational biology ; Identification (biology) ; Proteomics ; Quantitative proteomics ; Deep learning ; Artificial intelligence ; Data mining ; Bioinformatics ; Machine learning ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Aleksandrs Leitis | Andreas Heßler | Sophia Wahl | Matthias Wuttig | Thomas Taubner | Andreas Tittl | Hatice Altug
Source: Advanced Functional Materials (article, 2020-03-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

All‐Dielectric Programmable Huygens' Metasurfaces Abstract Low‐loss nanostructured dielectric metasurfaces have emerged as a breakthrough platform for ultrathin optics and cutting‐edge photonic applications, including beam shaping, focusing, and holography. However, the static nature of their constituent materials has traditionally limited them to fixed functionalities. Tunable all‐dielectric infrared Huygens' metasurfaces consisting of multi‐layer Ge disk meta‐units with strategically incorporated non‐volatile phase change material Ge 3 Sb 2 Te 6 are introduced. Switching the phase‐change material between its amorphous and crystalline structural state enables nearly full dynamic light phase control with high transmittance in the mid‐IR spectrum. The metasurface is realized experimentally, showing post‐fabrication tuning of the light phase within a range of 81% of the full 2π phase shift. Additionally, the versatility of the tunable Huygen's metasurfaces is demonstrated by optically programming the spatial light phase distribution of the metasurface with single meta‐unit precision and retrieving high‐resolution phase‐encoded images using hyperspectral measurements. The programmable metasurface concept overcomes the static limitations of previous dielectric metasurfaces, paving the way for “universal” metasurfaces and highly efficient, ultracompact active optical elements like tunable lenses, dynamic holograms, and spatial light modulators. Materials science ; Holography ; Dielectric ; Transmittance ; Optics ; Optoelectronics ; Phase (matter) ; Photonics ; Physics Metamaterials and Metasurfaces Applications ; Advanced Optical Imaging Technologies ; Advanced Antenna and Metasurface Technologies