jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pascal Friederich | Salvador León | José Darío Perea | Loı̈c M. Roch | Alán Aspuru‐Guzik
Source: ChemRxiv (preprint, 2020-03-17)
Field: Materials Science — Polymers and Plastics

The Influence of Sorbitol Doping on Aggregation and Electronic Properties of PEDOT:PSS: A Theoretical Study Many organic electronics applications such as organic solar cells or thermoelectric generators rely on PEDOT:PSS as a conductive polymer that is printable and transparent. It was found that doping PEDOT:PSS with sorbitol enhances the conductivity through morphological changes. However, the microscopic mechanism is not well understood. In this work, we combine computational tools with machine learning to investigate changes in morphological and electronic properties of PEDOT:PSS when doped with sorbitol. We find that sorbitol improves the alignment of PEDOT oligomers, leading to a reduction of energy disorder and an increase in electronic couplings between PEDOT chains. The high accuracy (r2 > 0.9) and speed up of energy level predictions of neural networks compared to density functional theory enables us to analyze HOMO energies of PEDOT oligomers as a function of time. We find a surprisingly low degree of static energy disorder compared to other organic semiconductors. This finding might help to better understand the microscopic origin of the high charge carrier mobility of PEDOT:PSS in general and potentially help to design new conductive polymers. PEDOT:PSS ; Doping ; Materials science ; Conductive polymer ; Thermoelectric effect ; Sorbitol ; Conductivity ; Electrical conductor ; Nanotechnology ; Optoelectronics ; Polymer ; Chemical physics ; Chemistry ; Physical chemistry ; Physics ; Thermodynamics ; Composite material Conducting polymers and applications ; Supercapacitor Materials and Fabrication ; Advanced Thermoelectric Materials and Devices
Authors: Andreas Goetz | Vinícius Wilian D. Cruzeiro | Ronak Roy
Source:  (preprint, 2020-04-30)
Field: Materials Science — Materials Chemistry

Insights into aqueous solvation of atmospheric gases from molecular simulations with machine learning-based many-body potentials Solvation ; Aqueous solution ; Chemical physics ; Chemistry ; Computational chemistry ; Physical chemistry ; Molecule ; Organic chemistry Machine Learning in Materials Science ; Advanced Thermodynamics and Statistical Mechanics
Authors: Howard Yanxon | David Zagaceta | Brandon C. Wood | Qiang Zhu
Source: arXiv (Cornell University) (article, 2020-01-03)
Field: Materials Science — Materials Chemistry

On Transferability of Machine Learning Force Fields: A Case Study on Silicon In this article, we present a systematic study in developing machine learning interatomic potential (MLIAP) for elemental silicon. Previously, many of MLIAP applications involve fitting models to small or localized training sets, which do not allow for transferability. The need for transferability of MLIAP is crucial in sampling the vast phase space in the application of crystal structure prediction. To address for this need, we indicate that two primary factors can have major influences on the transferability of the MLIAP. First, transferability depends strongly on the diversity of the training data set. In particular, the use of a random structures generating algorithm based on crystal symmetry is a suitable methodology for generating a training data set with sufficient diversity. Second, we introduce a framework that allows Gaussian symmetry functions and bispectrum coefficients descriptors to be fitted with generalized linear regression or neural network. According to our substantial benchmark, the best accuracy trained on the diverse data set can be achieved by using a flexible machine learning method coupled with highly orthogonal descriptors. Therefore, we will show that neural network potential fitting with bispectrum coefficients as the descriptor is a feasible method for obtaining high-quality and transferable MLIAP. Bispectrum ; Computer science ; Machine learning ; Artificial intelligence ; Artificial neural network ; Benchmark (surveying) ; Transferability ; Set (abstract data type) ; Data set ; Data mining ; Algorithm ; Spectral density Machine Learning in Materials Science ; Force Microscopy Techniques and Applications ; Electron and X-Ray Spectroscopy Techniques
Authors: Hyun Woo Kim | Sung Woo Lee | Gyoung S. Na | Seung Ju Han | Seok Ki Kim | Jung‐Ho Shin | Hyunju Chang | Yong Tae Kim
Source: ChemRxiv (preprint, 2020-10-20)
Field: Materials Science — Materials Chemistry

Reaction Condition Optimization for Non-Oxidative Conversion of Methane Using Artificial Intelligence Chemical reactions typically have numerous controllable factors that need to be optimized to yield the desired products. Although traditional experimental methods are limited to explore possible combinations of these factors, artificial intelligence (AI) can provide the optimal solution based on chemical reaction data. In this study, we optimize the non-oxidative conversion of methane to C&lt;sub&gt;2&lt;/sub&gt; compounds using AI, such as machine learning (ML) to predict experimental results and metaheuristics to optimize reaction conditions. A decision tree-based machine learning method can reasonably predict the reaction outcomes (CH&lt;sub&gt;4&lt;/sub&gt; conversion, C&lt;sub&gt;2&lt;/sub&gt; yield, and selectivities for C&lt;sub&gt;2&lt;/sub&gt; and coke) with an error of &lt; 5%. Trained ML models are applied to maximize the C&lt;sub&gt;2&lt;/sub&gt; yield by optimizing the reaction parameters with metaheuristics. We can simultaneously enhance the C&lt;sub&gt;2&lt;/sub&gt; yield and suppress the coke formation by improving the multi-objective function for the optimization. We believe that our method will be helpful to optimize the chemical reaction conditions with multiple targets.&lt;br&gt; Yield (engineering) ; Chemistry ; Reaction conditions ; Computer science ; Biological system ; Materials science ; Catalysis ; Organic chemistry Machine Learning in Materials Science ; Catalysis and Oxidation Reactions
Authors: Avadhut Sardeshmukh | Sreedhar Reddy | B. P. Gautham | Pushpak Bhattacharyya
Source: MRS Communications (article, 2020-09-18)
Field: Materials Science — Materials Chemistry

Microstructure representation learning using Siamese networks Representation (politics) ; Artificial intelligence ; Metadata ; Machine learning ; Computer science ; Metric (unit) ; Bridge (graph theory) ; Process (computing) ; Similarity (geometry) ; Feature learning ; Microstructure ; Property (philosophy) ; Space (punctuation) ; Materials science ; Engineering Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Hidetoshi Miyazaki | Tomoyuki Tamura | Masashi Mikami | Kosuke Watanabe | Ide Naoki | Osman Murat Özkendir | Yoichi Nishino
Source: arXiv (Cornell University) (preprint, 2020-10-23)
Field: Materials Science — Materials Chemistry

Machine-Learning-based Prediction of Lattice Thermal Conductivity for Half-Heusler Compounds using Atomic Information The half-Heusler compound has drawn attention in a variety of fields as a candidate material for thermoelectric energy conversion and spintronics technology. This is because it has various electronic structures, such as semi-metals, semiconductors, and a topological insulator. When the half-Heusler compound is incorporated into the device, the control of high lattice thermal conductivity owing to high crystal symmetry is a challenge for the thermal manager of the device. The calculation for the prediction of lattice thermal conductivity, which is an important physical parameter for controlling the thermal management of the device, requires a calculation cost of several 100 times as much as the usual density functional theory calculation. Therefore, we examined whether lattice thermal conductivity prediction by machine learning was possible on the basis of only the atomic information of constituent elements for thermal conductivity calculated by the density functional theory calculation in various half-Heusler compounds. Consequently, we constructed a machine learning model, which can predict the lattice thermal conductivity with high accuracy from the information of only atomic radius and atomic mass of each site in the half-Heusler type crystal structure. Applying our results, the lattice thermal conductivity for an unknown half-Heusler compound can be immediately predicted. In the future, low-cost and short-time development of new functional materials can be realized, leading to breakthroughs in the search of novel functional materials. Thermal conductivity ; Materials science ; Thermoelectric effect ; Density functional theory ; Lattice (music) ; Thermoelectric materials ; Condensed matter physics ; Crystal structure ; Semiconductor ; Thermal conduction ; Atomic radius ; Thermodynamics ; Optoelectronics ; Computational chemistry ; Physics ; Chemistry ; Crystallography ; Quantum mechanics ; Composite material Machine Learning in Materials Science ; Advanced Thermoelectric Materials and Devices ; Thermal properties of materials
Authors: Conor D. Rankine | Marwah Madkhali | Thomas J. Penfold
Source: ChemRxiv (preprint, 2020-04-28)
Field: Materials Science — Materials Chemistry

A Deep Neural Network for the Rapid Prediction of X-ray Absorption Spectra X-ray spectroscopy delivers strong impact across the physical and biological sciences by providing end-users with highly-detailed information about the electronic and geometric structure of matter. To decode this information in challenging cases, e.g. in operando catalysts, batteries, and temporally-evolving systems, advanced theoretical calculations are necessary. The complexity and resource requirements often render these out of reach for end-users, and therefore data are often not interpreted exhaustively, leaving a wealth of valuable information unexploited. In this paper, we introduce supervised machine learning of X-ray absorption spectra, by developing a deep neural network (DNN) that is able to estimate Fe K-edge X-ray absorption near-edge structure spectra in less than a second with no input beyond geometric information about the local environment of the absorption site. We predict peak positions with sub-eV accuracy and peak intensities with errors over an order of magnitude smaller than the spectral variations that the model is engineered to capture. The performance of the DNN is promising, as illustrated by its application to the structural refinement of iron(II)tris(bipyridine) and nitrosylmyoglobin, but also highlights areas for which future developments should focus. Computer science ; Artificial neural network ; Focus (optics) ; Absorption (acoustics) ; Enhanced Data Rates for GSM Evolution ; Spectral line ; Absorption spectroscopy ; X-ray absorption spectroscopy ; Artificial intelligence ; Physics ; Optics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Yefei Li | Zhi‐Pan Liu
Source: Chinese Science Bulletin (Chinese Version) (article, 2020-07-01)
Field: Materials Science — Materials Chemistry

Machine learning leads to the discovery of Cu-Al alloy for efficient CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; reduction Ampere ; Alloy ; Reduction (mathematics) ; Chemistry ; Materials science ; Metallurgy ; Electrical engineering ; Mathematics ; Engineering ; Voltage Nanoporous metals and alloys ; Catalytic Processes in Materials Science ; Advanced materials and composites
Authors: Arun Mannodi‐Kanakkithodi | Michael Y. Toriyama | Fatih G. Sen | Michael J. Davis | Robert F. Klie | Maria K. Y. Chan
Source: npj Computational Materials (preprint, 2020-04-23)
Field: Materials Science — Materials Chemistry

Machine-learned impurity level prediction for semiconductors: the example of Cd-based chalcogenides Abstract The ability to predict the likelihood of impurity incorporation and their electronic energy levels in semiconductors is crucial for controlling its conductivity, and thus the semiconductor’s performance in solar cells, photodiodes, and optoelectronics. The difficulty and expense of experimental and computational determination of impurity levels makes a data-driven machine learning approach appropriate. In this work, we show that a density functional theory-generated dataset of impurities in Cd-based chalcogenides CdTe, CdSe, and CdS can lead to accurate and generalizable predictive models of defect properties. By converting any semiconductor + impurity system into a set of numerical descriptors, regression models are developed for the impurity formation enthalpy and charge transition levels. These regression models can subsequently predict impurity properties in mixed anion CdX compounds (where X is a combination of Te, Se and S) fairly accurately, proving that although trained only on the end points, they are applicable to intermediate compositions. We make machine-learned predictions of the Fermi-level-dependent formation energies of hundreds of possible impurities in 5 chalcogenide compounds, and we suggest a list of impurities which can shift the equilibrium Fermi level in the semiconductor as determined by the dominant intrinsic defects. Machine learning predictions for the dominating impurities compare well with DFT predictions, revealing the power of machine-learned models in the quick screening of impurities likely to affect the optoelectronic behavior of semiconductors. Impurity ; Semiconductor ; Chalcogenide ; Materials science ; Fermi level ; Machine learning ; Condensed matter physics ; Optoelectronics ; Chemistry ; Computer science ; Physics ; Electron ; Quantum mechanics Machine Learning in Materials Science ; Chalcogenide Semiconductor Thin Films ; Advanced Semiconductor Detectors and Materials
Authors: Tanvi P. Gujarati | Tyler Y. Takeshita | Andreas Hintennach | Eunseok Lee
Source: arXiv (Cornell University) (preprint, 2020-04-02)
Field: Materials Science — Materials Chemistry

A Heuristic Quantum-Classical Algorithm for Modeling Substitutionally Disordered Binary Crystalline Materials Improving the efficiency and accuracy of energy calculations has been of significant and continued interest in the area of materials informatics, a field that applies machine learning techniques to computational materials data. Here, we present a heuristic quantum-classical algorithm to efficiently model and predict the energies of substitutionally disordered binary crystalline materials. Specifically, a quantum circuit that scales linearly in the number of lattice sites is designed and trained to predict the energies of quantum chemical simulations in an exponentially-scaling feature space. This circuit is trained by classical supervised-learning using data obtained from classically-computed quantum chemical simulations. As a part of the training process, we introduce a sub-routine that is able to detect and rectify anomalies in the input data. The algorithm is demonstrated on the complex layer-structured of Li-cobaltate system, a widely-used Li-ion battery cathode material component. Our results shows that the proposed quantum circuit model presents a suitable choice for modelling the energies obtained from such quantum mechanical systems. Furthermore, analysis of the anomalous data provides important insights into the thermodynamic properties of the systems studied. Quantum ; Scaling ; Algorithm ; Heuristic ; Binary number ; Statistical physics ; Computer science ; Quantum machine learning ; Lattice (music) ; Quantum algorithm ; Physics ; Quantum mechanics ; Artificial intelligence ; Mathematics ; Geometry Machine Learning in Materials Science ; Chemical and Physical Properties of Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Kevin Maik Jablonka | Giriprasad Melpatti Jothiappan | Shefang Wang | Berend Smit | Brian Yoo
Source: ChemRxiv (preprint, 2020-11-09)
Field: Materials Science — Materials Chemistry

Bias Free Multiobjective Active Learning for Materials Design and Discovery The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material, and the design rules change to finding the set of Pareto optimal materials. In this work, we introduce an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. We apply our algorithm to de novo polymer design with a prohibitively large search space.Using molecular simulations, we compute key descriptors for dispersant applications and reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence by over 98% compared to random search.This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches. Computer science ; Pareto principle ; Set (abstract data type) ; Multi-objective optimization ; Key (lock) ; Relation (database) ; Mathematical optimization ; Space (punctuation) ; Pareto optimal ; Machine learning ; Data mining ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Multi-Objective Optimization Algorithms
Authors: Ling Tang | Zhenqing Yang | Tongqi Wen | K. M. Ho | M. J. Kramer | Cai‐Zhuang Wang
Source: arXiv (Cornell University) (preprint, 2020-08-27)
Field: Materials Science — Ceramics and Composites

Short- and medium-range orders in Al90Tb10 glass and their relation to the structures of competing crystalline phases Molecular dynamics simulations using an interatomic potential developed by artificial neural network deep machine learning are performed to study the local structural order in Al90Tb10 metallic glass. We show that more than 80% of the Tb-centered clusters in Al90Tb10 glass have short-range order (SRO) with their 17 first coordination shell atoms stacked in a '3661' or '15551' sequence. Medium-range order (MRO) in Bergman-type packing extended out to the second and third coordination shells is also clearly observed. Analysis of the network formed by the '3661' and '15551' clusters show that ~82% of such SRO units share their faces or vertexes, while only ~6% of neighboring SRO pairs are interpenetrating. Such a network topology is consistent with the Bergman-type MRO around the Tb-centers. Moreover, crystal structure searches using genetic algorithm and the neural network interatomic potential reveal several low-energy metastable crystalline structures in the composition range close to Al90Tb10. Some of these crystalline structures have the '3661' SRO while others have the '15551' SRO. While the crystalline structures with the '3661' SRO also exhibit the MRO very similar to that observed in the glass, the ones with the '15551' SRO have very different atomic packing in the second and third shells around the Tb centers from that of the Bergman-type MRO observed in the glassy phase. Metastability ; Materials science ; Coordination number ; Phase (matter) ; Chemical physics ; Range (aeronautics) ; Crystal structure ; Crystallography ; Molecular physics ; Topology (electrical circuits) ; Condensed matter physics ; Physics ; Ion ; Chemistry ; Composite material ; Combinatorics ; Mathematics Glass properties and applications ; Phase-change materials and chalcogenides ; Chemical and Physical Properties of Materials
Authors: Xiaowei Xie | Kristin A. Persson | David W. Small
Source: arXiv (Cornell University) (preprint, 2020-03-04)
Field: Materials Science — Materials Chemistry

Incorporating electronic information into Machine Learning potential energy surfaces via approaching the ground-state electronic energy as a function of atom-based electronic populations Machine Learning (ML) approximations to Density Functional Theory (DFT) potential energy surfaces (PESs) are showing great promise for reducing the computational cost of accurate molecular simulations, but at present they are not applicable to varying electronic states, and in particular, they are not well suited for molecular systems in which the local electronic structure is sensitive to the medium to long-range electronic environment. With this issue as the focal point, we present a new Machine Learning approach called bpopNN for obtaining efficient approximations to DFT PESs. The methodology is based on approaching the true DFT energy as a function of electron populations on atoms, which may be realized in practice with constrained DFT (CDFT). The new approach creates approximations to this function with deep neural networks. These approximations thereby incorporate electronic information naturally into a ML approach, and optimizing the model energy with respect to populations allows the electronic terms to self-consistently adapt to the environment, as in DFT. We confirm the effectiveness of this approach with a variety of calculations on Li$_n$H$_n$ clusters. Electronic structure ; Density functional theory ; Atom (system on chip) ; Range (aeronautics) ; Function (biology) ; Computer science ; Potential energy ; Artificial neural network ; Ground state ; Energy (signal processing) ; Statistical physics ; Artificial intelligence ; Physics ; Atomic physics ; Quantum mechanics ; Aerospace engineering ; Engineering Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; X-ray Diffraction in Crystallography
Authors: Nan Hu | Xiao-Jing Ma | Haibo Zhao | Shaojie Tang | Weihua Liu | Dawei Wang | Chun‐Lin Jia
Source: Acta Physica Sinica (article, 2020-12-04)
Field: Materials Science — Materials Chemistry

Detection of intensity peaks in high-resolution transmission electron microscopy image based on YOLOv3 The detection of intensity peaks, which correspond to atom positions, in high-resolution (scanning) transmission electron microscopy images is of great practical significance. By quantitatively determining the locations of these peaks, it is possible to obtain important information such as the structural deformation and the electric dipole distribution inside a material on the nanoscale. The detection of the peak positions in image processing can be regarded as a target detection problem, for which breakthroughs have been made with deep-learning neural networks. Comparing to the traditional target detection algorithms, which are based on specifically designed feature extractor and classifier, the deep-learning approach can obtain the features at multiple levels of abstraction automatically, thus improving the robustness of the detection process. In this paper, we realize the automatic detection of the intensity peaks in high-resolution electron microscopy images by building a high-quality atomic image sample set and using the YOLOv3 target detection framework. With its accuracy and speed, which are superior over other target detection neural networks, the YOLOv3 is suitable for image processing as the number of images increases explosively. The YOLOv3 network converges well in the training process using our atomic image sample set, with the loss function reaching a minimum after 500 epoches; the trained neural network can detect almost all the major atoms in the images, showing its excellent ability. With the aid of YOLOv3, we also develop a program to organize the detected atoms, enabling the detection of all the other atoms within each unit cell. It is found that, combining YOLOv3 with the newly developed algorithm, almost all the atoms can be successfully determined, showing its advantages over previous algorithms based on bravis lattice construction, especially for high-resolution transmission electron microscopy images with lattice defects. Our results show that this image processing technique has the potential in overcoming the bottleneck in the fast processing of high resolution electron microscopy images. Artificial intelligence ; Computer science ; Artificial neural network ; Robustness (evolution) ; Pattern recognition (psychology) ; Image processing ; Computer vision ; Image (mathematics) Electronic and Structural Properties of Oxides ; Advanced X-ray and CT Imaging ; Advanced Electron Microscopy Techniques and Applications