jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Omar Al-Saban | Sameh O. Abdellatif
Source:  (article, 2021-07-13)
Field: Materials Science — Materials Chemistry

Optoelectronic Materials Informatics: Utilizing Random-forest Machine Learning in Optimizing the Harvesting Capabilities of Mesostructured-based Solar Cells Materials informatics is a study field that focuses on investigating and applying the techniques of informatics to materials science and engineering. Machine Learning (ML) refers to data analysis techniques and algorithms take fall under the concept of artificial intelligence (AI). This paper targets applying both concepts to the renewable energy field study. More specifically, to analyze, enhance and optimize the performance of Mesostructured-based solar cells, as a booming light harvester in low-power applications. Herein, we investigate both dye-sensitized solar cells (DSSCs) and perovskite solar cells (PSCs). The power conversion efficiency of both cells is optimized in terms of the active mesoporous layer thickness as well as compact layer. A Random-forest algorithm is utilized for model regression, prediction, and optimization. Random forest ; Computer science ; Informatics ; Renewable energy ; Materials informatics ; Artificial intelligence ; Field (mathematics) ; Perovskite (structure) ; Machine learning ; Materials science ; Health informatics ; Electrical engineering ; Engineering ; Engineering informatics Machine Learning in Materials Science ; Nanowire Synthesis and Applications ; Quantum Dots Synthesis And Properties
Authors: Didier Mathieu
Source: Molecular Informatics (article, 2021-12-10)
Field: Chemistry — Organic Chemistry

Molecular Energies Derived from Deep Learning: Application to the Prediction of Formation Enthalpies Up to High Energy Compounds Total electronic energies and frequencies predicted using the deep learning models ANI-1x and ANI-1ccx are converted to gas-phase formation enthalpies Δ<sub>f</sub> H<sup>0</sup> using an atom equivalent (AE) scheme for a database of CHNO compounds. As expected from the accuracy of those models in predicting reference DFT frequencies and DLPNO-CCSD(T)/CBS energies, this procedure usually outperforms DFT-based AE schemes. However, for some compounds, including energetic molecules, significant deviations from experiment are observed, larger than obtained using DFT procedures. A close examination of the GDB-11 database from which the training data was drawn reveals that many structures of interest in the energetic materials community are excluded from this extensive compilation primarily focused on drug discovery and designed with stability constraints in mind. This points to the urgent need to set up a comparable database including energetic species of interest for the design of energetic materials such as propellants or explosives. Explosive material ; Propellant ; Stability (learning theory) ; Density functional theory ; Chemistry ; Standard enthalpy of formation ; Database ; Computational chemistry ; Computer science ; Physical chemistry ; Machine learning Chemical Thermodynamics and Molecular Structure ; Computational Drug Discovery Methods ; Energetic Materials and Combustion
Authors: Tobias Gensch | Gabriel dos Passos Gomes | Pascal Friederich | Ellyn Peters | Théophile Gaudin | Robert Pollice | Kjell Jorner | AkshatKumar Nigam | Michael Lindner D'Addario | Matthew S. Sigman | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2021-04-27)
Field: Materials Science — Materials Chemistry

A Comprehensive Discovery Platform for Organophosphorus Ligands for Catalysis The design of molecular catalysts typically involves reconciling multiple conflicting property requirements, largely relying on human intuition and local structural searches. However, the vast number of potential catalysts requires pruning of the candidate space by efficient property prediction with quantitative structure-property relationships. Data-driven workflows embedded in a library of potential catalysts can be used to build predictive models for catalyst performance and serve as a blueprint for novel catalyst designs. Herein we introduce kraken, a discovery platform covering monodentate organophosphorus(III) ligands providing comprehensive physicochemical descriptors based on representative conformer ensembles. Using quantum-mechanical methods, we calculated descriptors for 1,558 ligands, including commercially available examples, and trained machine learning models to predict properties of over 300,000 new ligands. We demonstrate the application of kraken to systematically explore the property space of organophosphorus ligands and how existing datasets in catalysis can be used to accelerate ligand selection during reaction optimization. Blueprint ; Computer science ; Intuition ; Workflow ; Biochemical engineering ; Engineering ; Database Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Asymmetric Hydrogenation and Catalysis
Authors: Mufei Li | Jinjing Zhou | Jiajing Hu | Wenxuan Fan | Yangkang Zhang | Yaxin Gu | George Karypis
Source: ACS Omega (preprint, 2021-10-05)
Field: Materials Science — Materials Chemistry

DGL-LifeSci: An Open-Source Toolkit for Deep Learning on Graphs in Life Science Graph neural networks (GNNs) constitute a class of deep learning methods for graph data. They have wide applications in chemistry and biology, such as molecular property prediction, reaction prediction, and drug-target interaction prediction. Despite the interest, GNN-based modeling is challenging as it requires graph data preprocessing and modeling in addition to programming and deep learning. Here, we present Deep Graph Library (DGL)-LifeSci, an open-source package for deep learning on graphs in life science. Deep Graph Library (DGL)-LifeSci is a python toolkit based on RDKit, PyTorch, and Deep Graph Library (DGL). DGL-LifeSci allows GNN-based modeling on custom datasets for molecular property prediction, reaction prediction, and molecule generation. With its command-line interfaces, users can perform modeling without any background in programming and deep learning. We test the command-line interfaces using standard benchmarks MoleculeNet, USPTO, and ZINC. Compared with previous implementations, DGL-LifeSci achieves a speed up by up to 6×. For modeling flexibility, DGL-LifeSci provides well-optimized modules for various stages of the modeling pipeline. In addition, DGL-LifeSci provides pretrained models for reproducing the test experiment results and applying models without training. The code is distributed under an Apache-2.0 License and is freely accessible at https://github.com/awslabs/dgl-lifesci. Computer science ; Python (programming language) ; Deep learning ; Artificial intelligence ; MIT License ; Implementation ; Machine learning ; Graph ; Computer architecture ; Programming language ; Theoretical computer science ; Software Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Graph Neural Networks
Authors: Julien Parinet
Source: Heliyon (article, 2021-12-01)
Field: Chemistry — Spectroscopy

Predicting reversed-phase liquid chromatographic retention times of pesticides by deep neural networks To be able to predict reversed phase liquid chromatographic (RPLC) retention times of contaminants is an asset in order to solve food contamination issues. The development of quantitative structure-retention relationship models (QSRR) requires selection of the best molecular descriptors and machine-learning algorithms. In the present work, two main approaches have been tested and compared, one based on an extensive literature review to select the best set of molecular descriptors (16), and a second with diverse strategies in order to select among 1545 molecular descriptors (MD), 16 MD. In both cases, a deep neural network (DNN) were optimized through a gridsearch. Molecular descriptor ; Artificial neural network ; Artificial intelligence ; Set (abstract data type) ; Selection (genetic algorithm) ; Chromatography ; Computer science ; Machine learning ; Chemistry ; Quantitative structure–activity relationship Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Pesticide Residue Analysis and Safety
Authors: Ayana Ghosh | Maxim Ziatdinov | Ondrej Dyck | Bobby G. Sumpter | Sergei V. Kalinin
Source: arXiv (Cornell University) (article, 2021-09-09)
Field: Materials Science — Materials Chemistry

Bridging microscopy with molecular dynamics and quantum simulations: An AtomAI based pipeline Recent advances in (scanning) transmission electron microscopy have enabled routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for atomistic simulations. In this fashion, theory will address experimentally emerging structures, as opposed to the full range of theoretically possible atomic configurations. However, this challenge is highly non-trivial due to the extreme disparity between intrinsic time scales accessible to modern simulations and microscopy, as well as latencies of microscopy and simulations per se. Addressing this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment, to enable the selection of regions of interest and exploring them using physical simulations. Here we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical descriptors. The pathways to ensure the structural stability and compensate for the observational biases universally present in the data are identified in the workflow. This approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing effects. However, it is universal and can be used for other material systems. Python (programming language) ; Bridging (networking) ; Workflow ; Computer science ; Molecular dynamics ; Graphene ; Nanotechnology ; Statistical physics ; Artificial intelligence ; Computational science ; Materials science ; Physics ; Chemistry ; Computational chemistry Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Electron and X-Ray Spectroscopy Techniques
Authors: Ferenc Tasnádi | Florian Bock | Johan Tidholm | Alexander V. Shapeev | Igor A. Abrikosov
Source: Thin Solid Films (article, 2021-09-14)
Field: Materials Science — Materials Chemistry

Efficient prediction of elastic properties of Ti0.5Al0.5N at elevated temperature using machine learning interatomic potential High-temperature thermal stability, elastic moduli and anisotropy are among the key properties, which are used in selecting materials for cutting and machining applications. The high computational demand of ab initio molecular dynamics (AIMD) simulations in calculating elastic constants of alloys promotes the development of alternative approaches. Machine learning concept grasped as hybride classical molecular dynamics and static first principles calculations have several orders less computational costs. Here we prove the applicability of the concept considering the recently developed moment tensor potentials (MTP), where moment tensors are used as material’s descriptors which can be trained to predict the elastic constants of the prototypical hard coating alloy, Ti0.5Al0.5N at 900 K. We demonstrate excellent agreement between classical molecular dynamics simulations with MTPs and AIMD simulations. Moreover, we show that using MTPs one overcomes the inaccuracy issues present in approximate AIMD simulations of elastic constants of alloys. Molecular dynamics ; Moment (physics) ; Elastic modulus ; Machining ; Materials science ; Interatomic potential ; Statistical physics ; Anisotropy ; Stability (learning theory) ; Computer science ; Chemistry ; Physics ; Computational chemistry ; Classical mechanics ; Machine learning ; Composite material ; Metallurgy ; Optics Machine Learning in Materials Science ; Boron and Carbon Nanomaterials Research ; Metal and Thin Film Mechanics
Authors: Wei Yu | Chaoyue Ji | Xuhao Wan | Zhaofu Zhang | John Robertson | Sheng Liu | Yuzheng Guo
Source: International journal of mechanical system dynamics (article, 2021-12-01)
Field: Materials Science — Materials Chemistry

Machine‐learning‐based interatomic potentials for advanced manufacturing Abstract This paper summarizes the progress of machine‐learning‐based interatomic potentials and their applications in advanced manufacturing. Interatomic potential is essential for classical molecular dynamics. The advancements made in machine learning (ML) have enabled the development of fast interatomic potential with ab initio accuracy. The accelerated atomic simulation can greatly transform the design principle of manufacturing technology. The most widely used supervised and unsupervised ML methods are summarized and compared. Then, the emerging interatomic models based on ML are discussed: Gaussian approximation potential, spectral neighbor analysis potential, deep potential molecular dynamics, SCHNET, hierarchically interacting particle neural network, and fast learning of atomistic rare events. Interatomic potential ; Molecular dynamics ; Computer science ; Artificial intelligence ; Ab initio ; Statistical physics ; Gaussian ; Machine learning ; Chemistry ; Physics ; Computational chemistry ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Ricardo Espinosa | Hiram Pönce | Josué Ortiz‐Medina
Source: Computational Materials Science (article, 2021-10-21)
Field: Materials Science — Materials Chemistry

A 3D orthogonal vision-based band-gap prediction using deep learning: A proof of concept Convolutional neural network ; Bayesian optimization ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Data set ; Transformation (genetics) ; Mean squared error ; Set (abstract data type) ; Deep learning ; Artificial neural network ; Function (biology) ; Mathematics ; Chemistry ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Ziyan Zhang | Jakoah Brgoch
Source: The Journal of Physical Chemistry Letters (article, 2021-07-15)
Field: Materials Science — Materials Chemistry

Determining Temperature-Dependent Vickers Hardness with Machine Learning Assessing the hardness of structural materials at elevated temperatures is experimentally and computationally challenging, yet crucial for their success. In this work, a machine-learning method was developed to determine a material's temperature-dependent hardness based on its chemical composition and crystal structure. A total of 593 Vickers hardness data collected at various temperatures were extracted from the literature and used to train an extreme gradient boosting (XGBoost) machine-learning model. Applying a combination of composition descriptors and smooth overlap of atomic positions (SOAP) structural descriptors to represent these materials resulted in outstanding accuracy (<i>R</i><sup>2</sup> = 0.91; MAE = 2.52 GPa). The model's intrinsic variance was also measured by using a bootstrap aggregating (bagging) method, and the subsequent predictions showed strong agreement with the experimental data. The capability of the trained model was finally verified by demonstrating the model's ability to discriminate polymorphs, separate the properties of similar compositions, and reproduce the high-temperature hardness of several classic structural materials. Vickers hardness test ; Boosting (machine learning) ; Materials science ; Experimental data ; Machine learning ; Variance (accounting) ; Artificial intelligence ; Biological system ; Computer science ; Mathematics ; Composite material ; Microstructure ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Crystallography and molecular interactions
Authors: Varvara Gribova | Анастасія Наваліхіна | Oleksandr Lysenko | Cynthia Calligaro | Eloïse Lebaudy | Lucie Deiber | Bernard Senger | Philippe Lavall�e | Nihal Engin Vrana
Source: Scientific Reports (article, 2021-09-21)
Field: Materials Science — Surfaces, Coatings and Films

Prediction of coating thickness for polyelectrolyte multilayers via machine learning Layer-by-layer (LbL) deposition method of polyelectrolytes is a versatile way of developing functional nanoscale coatings. Even though the mechanisms of LbL film development are well-established, currently there are no predictive models that can link film components with their final properties. The current health crisis has shown the importance of accelerated development of biomedical solutions such as antiviral coatings, and the implementation of machine learning methodologies for coating development can enable achieving this. In this work, using literature data and newly generated experimental results, we first analyzed the relative impact of 23 coating parameters on the coating thickness. Next, a predictive model has been developed using aforementioned parameters and molecular descriptors of polymers from the DeepChem library. Model performance was limited because of insufficient number of data points in the training set, due to the scarce availability of data in the literature. Despite this limitation, we demonstrate, for the first time, utilization of machine learning for prediction of LbL coating properties. It can decrease the time necessary to obtain functional coating with desired properties, as well as decrease experimental costs and enable the fast first response to crisis situations (such as pandemics) where coatings can positively contribute. Besides coating thickness, which was selected as an output value in this study, machine learning approach can be potentially used to predict functional properties of multilayer coatings, e.g. biocompatibility, cell adhesive, antibacterial, antiviral or anti-inflammatory properties. Coating ; Materials science ; Polyelectrolyte ; Biocompatibility ; Machine learning ; Computer science ; Nanotechnology ; Layer by layer ; Layer (electronics) ; Polymer ; Composite material Polymer Surface Interaction Studies ; Advanced Sensor and Energy Harvesting Materials ; Dendrimers and Hyperbranched Polymers
Authors: Xiangguo Li | Ben Blaiszik | Marcus Schwarting | Ryan Jacobs | Aristana Scourtas | K. J. Schmidt | Paul M. Voyles | Dane Morgan
Source: The Journal of Chemical Physics (article, 2021-10-15)
Field: Materials Science — Materials Chemistry

Graph network based deep learning of bandgaps Recent machine learning models for bandgap prediction that explicitly encode the structure information to the model feature set significantly improve the model accuracy compared to both traditional machine learning and non-graph-based deep learning methods. The ongoing rapid growth of open-access bandgap databases can benefit such model construction not only by expanding their domain of applicability but also by requiring constant updating of the model. Here, we build a new state-of-the-art multi-fidelity graph network model for bandgap prediction of crystalline compounds from a large bandgap database of experimental and density functional theory (DFT) computed bandgaps with over 806 600 entries (1500 experimental, 775 700 low-fidelity DFT, and 29 400 high-fidelity DFT). The model predicts bandgaps with a 0.23 eV mean absolute error in cross validation for high-fidelity data, and including the mixed data from all different fidelities improves the prediction of the high-fidelity data. The prediction error is smaller for high-symmetry crystals than for low symmetry crystals. Our data are published through a new cloud-based computing environment, called the "Foundry," which supports easy creation and revision of standardized data structures and will enable cloud accessible containerized models, allowing for continuous model development and data accumulation in the future. Computer science ; Fidelity ; Deep learning ; Cloud computing ; Band gap ; Graph ; Artificial intelligence ; Data set ; High fidelity ; Machine learning ; Algorithm ; Theoretical computer science ; Materials science ; Physics ; Optoelectronics Machine Learning in Materials Science ; CO2 Reduction Techniques and Catalysts ; Catalysis and Oxidation Reactions
Authors: Jun Liu | Jianxing Zhang | Zhenglin Tan | Qin Hou | Ruirui Liu
Source: Spectrochimica Acta Part A Molecular and Biomolecular Spectroscopy (article, 2021-12-16)
Field: Chemistry — Analytical Chemistry

Detecting the content of the bright blue pigment in cream based on deep learning and near-infrared spectroscopy Artificial intelligence ; Preprocessor ; Deep learning ; Convolutional neural network ; Computer science ; Curse of dimensionality ; Artificial neural network ; Near-infrared spectroscopy ; Machine learning ; Pattern recognition (psychology) ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Xiangang Luo | Mingbo Pu | Yinghui Guo | Xiong Li | Xiaoliang Ma
Source: Advanced Photonics Research (article, 2021-05-08)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Electromagnetic Architectures: Structures, Properties, Functions and Their Intrinsic Relationships in Subwavelength Optics and Electromagnetics In recent years, with the development of photonic crystals, metamaterials, metasurfaces, and other related disciplines, the relationship between the structures and functionalities of artificial optical/electromagnetic materials and devices has become a research hotspot. This article reviews the optical/electromagnetic properties and performance of typical artificial micro/nanostructures. Based on the analogy of mechanics and optics/electromagnetism, the concept of “Electromagnetic Architectures” is proposed. In particular, we reviewed several typical methods for the study of optical/electromagnetic structural‐functional relationships, including semi‐classical analytical models, heuristic optimization methods, deep neural networks, topological optimization methods, etc. The viewpoint of this paper may push further researches on the structural‐functional relationship of artificially structured materials and devices, and find broad application prospects in imaging, communication, photonic chips, among many others. Metamaterial ; Electromagnetism ; Electromagnetics ; Photonics ; Nanophotonics ; Computer science ; Analogy ; Photonic metamaterial ; Electromagnetic radiation ; Electromagnetic field ; Physics ; Optics ; Engineering physics ; Quantum mechanics Metamaterials and Metasurfaces Applications ; Photonic Crystals and Applications ; Plasmonic and Surface Plasmon Research