jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Yosuke Harashima | Tomohiro Mano | Keith Slevin | Tomi Ohtsuki
Source: Journal of the Physical Society of Japan (preprint, 2021-08-18)
Field: Materials Science — Materials Chemistry

Analysis of Kohn–Sham Eigenfunctions Using a Convolutional Neural Network in Simulations of the Metal–Insulator Transition in Doped Semiconductors Machine learning has recently been applied to many problems in condensed matter physics. A common point of many proposals is to save computational cost by training the machine with data from a simple example and then using the machine to make predictions for a more complicated example. Convolutional neural networks (CNN), which are one of the tools of machine learning, have proved to work well for assessing eigenfunctions in disordered systems. Here we apply a CNN to assess Kohn-Sham eigenfunctions obtained in density functional theory (DFT) simulations of the metal-insulator transition of a doped semiconductor. We demonstrate that a CNN that has been trained using eigenfunctions from a simulation of a doped semiconductor that neglects electron spin successfully predicts the critical concentration when presented with eigenfunctions from simulations that include spin. Eigenfunction ; Convolutional neural network ; Doping ; Semiconductor ; Artificial neural network ; Computer science ; Density functional theory ; Statistical physics ; Artificial intelligence ; Condensed matter physics ; Physics ; Quantum mechanics ; Eigenvalues and eigenvectors Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides
Authors: Sanket Kadulkar | Michael Howard | Thomas M. Truskett | Venkat Ganesan
Source: ChemRxiv (preprint, 2021-04-15)
Field: Materials Science — Materials Chemistry

Prediction and Optimization of Ion Transport Characteristics in Nanoparticle-Based Electrolytes Using Convolutional Neural Networks &lt;pre&gt;We develop a convolutional neural network (CNN) model to predict the diffusivity of cations in nanoparticle-based electrolytes, and use it to identify the characteristics of morphologies which exhibit optimal transport properties. The ground truth data is obtained from kinetic Monte Carlo (kMC) simulations of cation transport parameterized using a multiscale modeling strategy. We implement deep learning approaches to quantitatively link the diffusivity of cations to the spatial arrangement of the nanoparticles. We then integrate the trained CNN model with a topology optimization algorithm for accelerated discovery of nanoparticle morphologies that exhibit optimal cation diffusivities at a specified nanoparticle loading, and we investigate the ability of the CNN model to quantitatively account for the influence of interparticle spatial correlations on cation diffusivity. Finally, using data-driven approaches, we explore how simple descriptors of nanoparticle morphology correlate with cation diffusivity, thus providing a physical rationale for the observed optimal microstructures. The results of this study highlight the capability of CNNs to serve as surrogate models for structure--property relationships in composites with monodisperse spherical particles, which can in turn be used with inverse methods to discover morphologies that produce optimal target properties.&lt;/pre&gt; Convolutional neural network ; Thermal diffusivity ; Nanoparticle ; Multiscale modeling ; Materials science ; Electrolyte ; Computer science ; Kinetic Monte Carlo ; Biological system ; Artificial neural network ; Inverse ; Monte Carlo method ; Nanotechnology ; Artificial intelligence ; Chemistry ; Physics ; Thermodynamics ; Mathematics ; Computational chemistry Machine Learning in Materials Science ; Electrostatics and Colloid Interactions
Authors: Qiangqiang Gu | Linfeng Zhang | Ji Feng
Source: Science Bulletin (preprint, 2021-09-17)
Field: Materials Science — Materials Chemistry

Neural network representation of electronic structure from ab initio molecular dynamics Statistical physics ; Electronic structure ; Ab initio ; Artificial neural network ; Molecular dynamics ; Representation (politics) ; Physics ; Computer science ; Renormalization ; Quantum mechanics ; Artificial intelligence Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Chemical Physics Studies
Authors: Daniel Gleaves | Edirisuriya M. Dilanga Siriwardane | Yong Zhao | Nihang Fu | Jianjun Hu
Source: arXiv (Cornell University) (preprint, 2021-12-12)
Field: Materials Science — Materials Chemistry

Semi-supervised teacher-student deep neural network for materials discovery Data driven generative machine learning models have recently emerged as one of the most promising approaches for new materials discovery. While the generator models can generate millions of candidates, it is critical to train fast and accurate machine learning models to filter out stable, synthesizable materials with desired properties. However, such efforts to build supervised regression or classification screening models have been severely hindered by the lack of unstable or unsynthesizable samples, which usually are not collected and deposited in materials databases such as ICSD and Materials Project (MP). At the same time, there are a significant amount of unlabelled data available in these databases. Here we propose a semi-supervised deep neural network (TSDNN) model for high-performance formation energy and synthesizability prediction, which is achieved via its unique teacher-student dual network architecture and its effective exploitation of the large amount of unlabeled data. For formation energy based stability screening, our semi-supervised classifier achieves an absolute 10.3\% accuracy improvement compared to the baseline CGCNN regression model. For synthesizability prediction, our model significantly increases the baseline PU learning's true positive rate from 87.9\% to 97.9\% using 1/49 model parameters. To further prove the effectiveness of our models, we combined our TSDNN-energy and TSDNN-synthesizability models with our CubicGAN generator to discover novel stable cubic structures. Out of 1000 recommended candidate samples by our models, 512 of them have negative formation energies as validated by our DFT formation energy calculations. Our experimental results show that our semi-supervised deep neural networks can significantly improve the screening accuracy in large-scale generative materials design. Artificial intelligence ; Computer science ; Artificial neural network ; Classifier (UML) ; Machine learning ; Generator (circuit theory) ; Deep learning ; Baseline (sea) ; Stability (learning theory) ; Supervised learning ; Regression ; Generative grammar ; Pattern recognition (psychology) ; Mathematics ; Power (physics) ; Statistics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Electron and X-Ray Spectroscopy Techniques
Authors: Jiaoyu Wang | Daxiu Zhang
Source: Journal of Physics Conference Series (article, 2021-04-01)
Field: Materials Science — Materials Chemistry

Research on Application of Machine Learning Technology in New Material System Abstract Materials are not only the foundation of the national economy, but also the carrier of high-tech. Machine learning combined with computer science, database theory, statistics, computational mathematics and engineering cannot only show faster calculation speed and reliable predictive ability, significantly improve the efficiency of material calculations, and it can also effectively deal with some systems and problems that are difficult to use traditional simulation computing methods. This article will briefly outline the basic principles of machine learning, introduce several typical algorithms in machine learning models and how machine learning is the application progress in the research of new materials, and the prospects for the future development of machine learning in the field of materials science. Computer science ; Field (mathematics) ; Machine learning ; Computational learning theory ; Artificial intelligence ; Foundation (evidence) ; Science and engineering ; Industrial engineering ; Active learning (machine learning) ; Engineering ; Engineering ethics ; Mathematics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; X-ray Diffraction in Crystallography
Authors: Yingquan Hao | Ying‐Chieh Hung | Yusuke Shimoyama
Source: Acta Crystallographica Section A Foundations and Advances (article, 2021-08-14)
Field: Chemistry — Physical and Theoretical Chemistry

A comparative research between atomic-based and chemical calculation-based descriptors for cocrystal screening machine learning model Introduction:In the past decades, the solubility of the pharmaceuticals in human body has been become lower.To solve this problem, the cocrystal have been considerate.By forming a new crystal structure with an additive which is called as coformer (CF), the dissolution property of the active pharmaceutical ingredient (API) can be modified.But by now, the screening of the cocrystal is mostly carried by experiments.Even there are some approaches attempt to screening the API and CF pair for cocrystal formation by machine learning, but most of them use semi-empirical descriptor or atomic-based molecular structure descriptor to make the chemistry understandable to the computer.However, this make it is very hard to get a machine learning model which can be generalized to the cases which is not learned before.So, in this research, 3D convolution neural network (3D-CNN) is employed with 3D charge distribution calculated by Universal ForceField (UFF) and Gasteiger partial charge method (GPC) to achieve a general model.Also, the performance of new build model is compared with the atomic-based methodology used before such as Graph Convolutional Network (GCN), neural network with extensive connectivity fingerprint (ECFP-NN). Method & Result:The experimental datasets from the literature is used for this research.UFF and GPC are applied to get the initial position and charge of the API and CF, then charge is mapped to a new 3D coordinate system which set the longest edge of the Oriented Bounding Box (OBB) of the molecular as the x-axis, the middle edge as y-axis, the shortest one as z-axis, and the centre of the OBB as the (0,0,0).Finally, the new mapped charge of APIs and CFs is transformed to 3D arrays for inputs to 3D-CNN.The hyperparameter of 3D-CNN is determined by training datasets with Bayesian-optimization with 5-flod cross-validation.3D-CNN shows a training accuracy 80% and accuracy 71% with the test datasets that contain none of the molecular in training datasets, while the ECFP-NN and GCN only give test accuracy lower than 65%.Because the 3D charge information is directly link to the cocrystal formation between API and CF. Cocrystal ; Convolutional neural network ; Computer science ; Artificial neural network ; Artificial intelligence ; Machine learning ; Algorithm ; Chemistry ; Hydrogen bond ; Molecule Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Computational Drug Discovery Methods
Authors: James Dean | Matthias Scheffler | Thomas A. R. Purcell | S. V. Barabash | Rahul Bhowmik | Timur Bazhirov
Source: arXiv (Cornell University) (preprint, 2021-12-01)
Field: Materials Science — Materials Chemistry

Interpretable Machine Learning for Materials Design Fueled by the widespread adoption of Machine Learning (ML) and the high-throughput screening of materials, the data-centric approach to materials design has asserted itself as a robust and powerful tool for the in-silico prediction of materials properties. When training models to predict material properties, researchers often face a difficult choice between a model's interpretability or its performance. We study this trade-off by leveraging four different state-of-the-art ML techniques: XGBoost, SISSO, Roost, and TPOT for the prediction of structural and electronic properties of perovskites and 2D materials. We then assess the future outlook of the continued integration of ML into materials discovery and identify key problems that will continue to challenge researchers as the size of the literature's datasets and complexity of models increases. Finally, we offer several possible solutions to these challenges with a focus on retaining interpretability and share our thoughts on magnifying the impact of ML on materials design. Interpretability ; Computer science ; Machine learning ; Key (lock) ; Artificial intelligence ; Throughput ; Data science ; Focus (optics) Machine Learning in Materials Science
Authors: Robert Hussein | Jonathan Schmidt | Tomás Barros | Miguel A. L. Marques | Silvana Botti
Source: arXiv (Cornell University) (preprint, 2021-11-03)
Field: Materials Science — Materials Chemistry

Machine-learning correction to density-functional crystal structure optimization Density functional theory is routinely applied to predict crystal structures. The most common exchange-correlation functionals used to this end are the Perdew-Burke-Ernzerhof (PBE) approximation and its variant PBEsol. We investigate the performance of these functionals for the prediction of lattice parameters and show how to enhance their accuracy using machine learning. Our dataset is constituted by experimental crystal structures of the Inorganic Crystal Structure Database matched with PBE-optmized structures stored in the materials project database. We complement these data with PBEsol calculations. We demonstrate that the accuracy and precision of PBE/PBEsol volume predictions can be noticeably improved a posteriori by employing simple, explainable machine learning models. These models can improve PBE unit cell volumes to match the accuracy of PBEsol calculations, and reduce the error of the latter with respect to experiment by 35%. Further, the error of PBE lattice constants is reduced by a factor of 3--5. A further benefit of our approach is the implicit correction of finite temperature effects without performing phonon calculations. Crystal structure prediction ; Crystal structure ; Density functional theory ; Lattice (music) ; Lattice constant ; Phonon ; Algorithm ; Crystal (programming language) ; Complement (music) ; Computer science ; Artificial intelligence ; Materials science ; Chemistry ; Crystallography ; Condensed matter physics ; Physics ; Optics ; Computational chemistry ; Diffraction Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Computational Drug Discovery Methods
Authors: Eric Qu | Andrew M. Jimenez | Sanat K. Kumar | Kai Zhang
Source: ChemRxiv (preprint, 2021-01-05)
Field: Materials Science — Materials Chemistry

Quantifying Nanoparticle Assembly States in Polymer Matrix Through Deep Learning There is great interest in controlling the spatial dispersion of inorganic nanoparticles (NPs) in an organic polymer matrix, because this centrally underpins the property enhancements obtained from these hybrid materials. Currently, qualitative information on NP spatial distribution is obtained by visual inspection of transmission electron microscopy (TEM) images. Quantitative information is only indirectly obtained through the use of scattering probes such as small angle X-ray/neutron scattering. While the main challenge, that scattering probes operate in reciprocal space, can be remedied by Fourier inverting the data into real space, a much harder issue is deconvolving the contribution of the particle form factor (which is affected by the details of the NP size and shape) from the structure factor which contains information on the NP spatial distribution. These problems become acute when we deal with the popular topic of NPs grafted with polymer chains, because the polymeric corona, and hence the particle form factor, becomes context dependent and hard to quantify. To make progress, we develop and apply a deep-learning based image analysis method to quantify the distribution of spherical NPs in a polymer matrix directly from their real-space TEM images. A dataset of NP detection (DOPAD) is built by manually labeling particle positions on experimental TEM images of diverse polymer composite systems. A convolutional neural network (CNN) object detection model is then trained on DOPAD. Together with sliding-window and merging algorithms, an automated pipeline is established, which takes a large TEM image as input and extracts NP locations and sizes. We validate the structural information resulting from this method against SAXS derived structural information for NPs ordered by polymer crystallization, and then use it to distinguish between different states of the assembly of polymer grafted NPs in a polymer matrix achieved by using their surfactancy. We show that this data-rich protocol allows us to draw critical facets of experimental behavior which have previously not been accessible. The DOPAD dataset, Python source code and trained model are shared on GitHub. Scattering ; Computer science ; Deep learning ; Convolutional neural network ; Context (archaeology) ; Matrix (chemical analysis) ; Materials science ; Pipeline (software) ; Polymer ; Artificial intelligence ; Biological system ; Nanotechnology ; Optics ; Physics ; Composite material Machine Learning in Materials Science
Authors: Jun Chen | Tan Jin | Tonghao Shen | Mingjun Yang | Zhe‐Ning Chen
Source:  (preprint, 2021-10-01)
Field: Materials Science — Materials Chemistry

Efficient Dynamic Computational Strategy for Heterogeneous Catalysis Based on Neural Network Potential Energy Surface: A Case Study of Temperature-Dependent Thermodynamics and Kinetics for the Chemisorbed on-surface CO As a favorable alternative and complement of experimental techniques, computational tools on top of ab initio calculations have played an indispensable role in revealing the molecular details, thermodynamics and kinetics in catalytic reactions. The static computational strategy, which recovers the reaction thermodynamics and kinetics based on the calculations of a few stationary geometries at zero temperature and some ideal statistic mechanics models, is the most popular approach in theoretical catalysis due to its simplicity. In comparison, the ab initio molecular dynamics (AIMD) is a well-tested approach to provide more precise descriptions of catalytic processes, however, experiencing a significantly expensive computational cost in the direct ab initio calculation of potential energy and gradients. Here we proposed a highly efficient dynamic computational strategy for the calculation of thermodynamic and kinetic properties in heterogeneous catalysis on the basis of neural network potential energy surface (NN PES) and MD simulations. Taking CO adsorbate on Ru(0001) surface as the illustrative model catalytic system, we demonstrated that our NN-PES-based MD simulations can efficiently generate the reliable smooth two-dimensional potential-of-mean-force (2-D PMF) surfaces in a wide range of temperatures (from 300 to 900 K), and thus temperature-dependent thermodynamic properties can be obtained in a comprehensive investigation on the whole PMF surface rather than a rough estimation using ideal models based on a few optimized geometries. Moreover, MD simulations offer an effective way to describe the surface kinetics such as the CO adsorbate on-surface movement, which goes beyond the most popular static estimation based on calculated free energy barrier and transition state theory (TST). By comparing the results obtained in the dynamic and static approaches, we further revealed that the dynamic strategy significantly improves the predictions of both thermodynamic and kinetic properties as compared to the popular ideal statistic mechanics approaches such as harmonic analysis and TST. It is expected that this accurate yet efficient dynamic strategy can be a powerful tool in understanding reaction mechanisms and reactivity of a catalytic surface system, and further guides the rational design of heterogeneous catalysts. Thermodynamics ; Ab initio ; Molecular dynamics ; Chemistry ; Potential energy surface ; Kinetics ; Statistical physics ; Catalysis ; Kinetic energy ; Computational chemistry ; Physics ; Classical mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; nanoparticles nucleation surface interactions
Authors: Aditi S. Krishnapriyan | Joseph Montoya | Maciej Harańczyk | Jens S. Hummelshøj | Dmitriy Morozov
Source: Scientific Reports (preprint, 2021-04-26)
Field: Materials Science — Materials Chemistry

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material's structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal-organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25-30% decrease in root-mean-squared-deviation and an average increase of 40-50% in R<sup>2</sup> scores. A key advantage of our approach is interpretability: Our model identifies the pores that correlate best to adsorption at different pressures, which contributes to understanding atomic-level structure-property relationships for materials design. Interpretability ; Computer science ; Nanoporous ; Artificial intelligence ; Machine learning ; Transferability ; Representation (politics) ; Natural language processing ; Data mining ; Theoretical computer science ; Materials science ; Nanotechnology Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Computational Drug Discovery Methods