jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Anup Pandey | Reeju Pokharel
Source: arXiv (Cornell University) (preprint, 2020-05-03)
Field: Materials Science — Materials Chemistry

Machine learning enabled surrogate crystal plasticity model for spatially resolved 3D orientation evolution under uniaxial tension We present a novel machine learning based surrogate modeling method for predicting spatially resolved 3D microstructure evolution of polycrystalline materials under uniaxial tensile loading. Our approach is orders of magnitude faster than the existing crystal plasticity methods enabling the simulation of large volumes that would be otherwise computationally prohibitive. This work is a major step beyond existing ML-based modeling results, which have been limited to either 2D structures or only providing average, rather than local, predictions. We demonstrate the speed and accuracy of our surrogate model approach on experimentally measured microstructure from high-energy X-ray diffraction microscopy of a face-centered cubic copper sample, undergoing tensile deformation. Tension (geology) ; Uniaxial tension ; Materials science ; Crystal plasticity ; Orientation (vector space) ; Surrogate model ; Plasticity ; Artificial intelligence ; Computer science ; Biological system ; Composite material ; Machine learning ; Geometry ; Mathematics ; Biology ; Compression (physics) Machine Learning in Materials Science ; Microstructure and mechanical properties ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Joshua A. Schiller | Ricardo J. Toro | Aagam Shah | Mitisha Surana | Kaihao Zhang | Matthew Robertson | Kristina Miller | Kevin Cruse | Kevin Liu | Bomsaerah Seong | Seol Chae | Ian Foster | Ben Blaiszik | Ben Galewsky | Darren Adams | Daniel S. Katz | Placid Ferreira | Elif Ertekin | Sameh Tawfick
Source: ACS Applied Nano Materials (article, 2020-08-27)
Field: Materials Science — Materials Chemistry

Crowd-Sourced Data and Analysis Tools for Advancing the Chemical Vapor Deposition of Graphene: Implications for Manufacturing Industrial production of graphene by chemical vapor deposition (CVD) requires more than the ability to synthesize large domain, high-quality graphene in a lab reactor. The integration of graphene in the fabrication process of electronic devices requires the cost-effective and environmentally friendly production of graphene on dielectric substrates, but current approaches can only produce graphene on metal catalysts. Sustainable manufacturing of graphene should also conserve the catalyst and reaction gases, but today the metal catalysts are typically dissolved after synthesis. Progress toward these objectives is hindered by the hundreds of coupled synthesis parameters that can strongly affect CVD of low-dimensional materials and poor communication in the published literature of the rich experimental data that exists in individual laboratories. We report here on a platform, “graphene recipes for synthesis of high quality material” (Gr-ResQ: pronounced graphene rescue), which includes powerful new tools for data-driven graphene synthesis. At the core of Gr-ResQ is a crowd-sourced database of CVD synthesis recipes and associated experimental results. The database captures ∼300 parameters ranging from synthesis conditions such as a catalyst material and preparation steps, to ambient lab temperature and reactor details, as well as resulting Raman spectra and microscopy images. These parameters are carefully selected to unlock the potential of machine-learning models to advance synthesis. A suite of associated tools enable fast, automated, and standardized processing of Raman spectra and scanning electron microscopy images. To facilitate community-based efforts, Gr-ResQ provides tools for cyber-physical collaborations among research groups, allowing experiments to be designed, executed, and analyzed by different teams. Gr-ResQ also allows publication and discovery of recipes via the Materials Data Facility, which assigns each recipe a unique identifier when published and collects parameters in a search index. We envision that this holistic approach to data-driven synthesis can accelerate CVD recipe discovery and production control and open opportunities for advancing not only graphene but also many other 1D and 2D materials. Graphene ; Chemical vapor deposition ; Nanotechnology ; Materials science ; Data science ; Computer science Graphene research and applications ; Recycling and Waste Management Techniques ; Nanomaterials and Printing Technologies
Authors: Luca Bellucci | Francesco Delfino | Valentina Tozzini
Source: Nanotechnology (article, 2020-10-05)
Field: Materials Science — Materials Chemistry

In silico design, building and gas adsorption of nano-porous graphene scaffolds Graphene-based nano-porous materials (GNM) are potentially useful for all those applications needing a large specific surface area (SSA), typical of the bidimensional graphene, yet realized in the bulk dimensionality. Such applications include for instance gas storage and sorting, catalysis and electrochemical energy storage. While a reasonable control of the structure is achieved in micro-porous materials by using nano-micro particles as templates, the controlled production or even characterization of GNMs with porosity strictly at the nano-scale still raises issues. These are usually produced using dispersion of nano-flakes as precursors resulting in little control on the final structure, which in turn reflects in problems in the structural model building for computer simulations. In this work, we describe a strategy to build models for these materials with predetermined structural properties (SSA, density, porosity), which exploits molecular dynamics simulations, Monte Carlo methods and machine learning algorithms. Our strategy is inspired by the real synthesis process: starting from randomly distributed flakes, we include defects, perforation, structure deformation and edge saturation on the fly, and, after structural refinement, we obtain realistic models, with given structural features. We find relationships between the structural characteristics and size distributions of the starting flake suspension and the final structure, which can give indications for more efficient synthesis routes. We subsequently give a full characterization of the models versus H<sub>2</sub> adsorption, from which we extract quantitative relationship between the structural parameters and the gravimetric density. Our results quantitatively clarify the role of surfaces and edges relative amount in determining the H<sub>2</sub> adsorption, and suggest strategies to overcome the inherent physical limitations of these materials as adsorbers. We implemented the model building and analysis procedures in software tools, freely available upon request. Materials science ; Porosity ; Graphene ; Nanotechnology ; Porous medium ; Nano- ; Adsorption ; Characterization (materials science) ; Composite material Graphene research and applications ; Supercapacitor Materials and Fabrication ; Parallel Computing and Optimization Techniques
Authors: Yuanqing Wang | Josh Fass | John D. Chodera | Herr, John E. | Rufa, Dominic | Zhang, Ivy | Pulido, Iván | Henry, Mike | Chodera, John D.
Source: arXiv (Cornell University) (preprint, 2020-10-02)
Field: Materials Science — Materials Chemistry

End-to-End Differentiable Molecular Mechanics Force Field Construction Molecular mechanics (MM) potentials have long been a workhorse of computational chemistry. Leveraging accuracy and speed, these functional forms find use in a wide variety of applications in biomolecular modeling and drug discovery, from rapid virtual screening to detailed free energy calculations. Traditionally, MM potentials have relied on human-curated, inflexible, and poorly extensible discrete chemical perception rules or applying parameters to small molecules or biopolymers, making it difficult to optimize both types and parameters to fit quantum chemical or physical property data. Here, we propose an alternative approach that uses graph neural networks to perceive chemical environments, producing continuous atom embeddings from which valence and nonbonded parameters can be predicted using invariance-preserving layers. Since all stages are built from smooth neural functions, the entire process is modular and end-to-end differentiable with respect to model parameters, allowing new force fields to be easily constructed, extended, and applied to arbitrary molecules. We show that this approach is not only sufficiently expressive to reproduce legacy atom types, but that it can learn to accurately reproduce and extend existing molecular mechanics force fields. Trained with arbitrary loss functions, it can construct entirely new force fields self-consistently applicable to both biopolymers and small molecules directly from quantum chemical calculations, with superior fidelity than traditional atom or parameter typing schemes. When trained on the same quantum chemical small molecule dataset used to parameterize the openff-1.2.0 small molecule force field augmented with a peptide dataset, the resulting espaloma model shows superior accuracy vis-à-vis experiments in computing relative alchemical free energy calculations for a popular benchmark set. Differentiable function ; Force field (fiction) ; Modular design ; Computer science ; Statistical physics ; Quantum ; Atom (system on chip) ; Field (mathematics) ; Molecular graph ; Theoretical computer science ; Graph ; Biological system ; Physics ; Quantum mechanics ; Artificial intelligence ; Mathematics ; Pure mathematics Machine Learning in Materials Science ; Protein Structure and Dynamics ; Computational Drug Discovery Methods
Authors: Qin Hu | Mouyi Weng | Xin Chen | Shucheng Li | Feng Pan | Lin‐Wang Wang
Source: The Journal of Physical Chemistry Letters (article, 2020-01-30)
Field: Materials Science — Materials Chemistry

Neural Network Force Fields for Metal Growth Based on Energy Decompositions A method using machine learning (ML) is proposed to describe metal growth for simulations, which retains the accuracy of ab initio density functional theory (DFT) and results in a thousands-fold reduction in the computational time. This method is based on atomic energy decomposition from DFT calculations. Compared with other ML methods, our energy decomposition approach can yield much more information with the same DFT calculations. This approach is employed for the amorphous sodium system, where only 1000 DFT molecular dynamics images are enough for training an accurate model. The DFT and neural network potential (NNP) are compared for the dynamics to show that similar structural properties are generated. Finally, metal growth experiments from liquid to solid in a small and larger system are carried out to demonstrate the ability of using NNP to simulate the real growth process. Density functional theory ; Molecular dynamics ; Artificial neural network ; Decomposition ; Ab initio ; Yield (engineering) ; Energy (signal processing) ; Process (computing) ; Reduction (mathematics) ; Materials science ; Computer science ; Computational chemistry ; Biological system ; Chemistry ; Thermodynamics ; Physics ; Artificial intelligence ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Surface and Thin Film Phenomena
Authors: Wesley F. Vaz | Bruno J. Neves | Jean M. F. Custódio | Lucas Lemos da Silva | Giulio D. C. D’Oliveira | Josiel Araújo Lemes | Bruno F.C. Lacerda | Solange Xavier-Santos | Caridad N. Pérez | Hamilton B. Napolitano
Source: Journal of Molecular Structure (article, 2020-09-22)
Field: Chemistry — Organic Chemistry

In silico-driven identification and structural analysis of nitrodihydroquinolinone pesticide candidates with antifungal activity In silico ; Chemistry ; Antifungal ; Combinatorial chemistry ; Computational biology ; Chemical space ; Aspergillus niger ; Identification (biology) ; Rational design ; Biochemical engineering ; Antifungal drug ; Stereochemistry ; Drug discovery ; Nanotechnology ; Biochemistry ; Microbiology ; Biology Synthesis and biological activity ; Synthesis and Biological Evaluation ; Multicomponent Synthesis of Heterocycles
Authors: Clifford Yang | Songchao Chen | Feng Ouyang | Ilseung Yang | CF Tsai
Source: Journal of Food and Drug Analysis (article, 2020-07-14)
Field: Chemistry — Analytical Chemistry

A robust identification model for herbal medicine using near infrared spectroscopy and artificial neural network A robust identification model for herbal medicine was developed by combining near-infrared spectroscopy (NIR) and artificial neural network (ANN) to discriminate raw materials of herbal medicine, which are often similar in appearance and practically impossible to identify by visual inspection alone. The identification by chemical methods is usually higher in cost and lower in efficiency. Compared with other modern inspection methods, NIR is an alternative, which is non-destructive, rapid, and easy to operate. In this study, we employed ANN to analyze the absorption spectra of herbal medicines and successfully built an identification model, which is able to identify 30 different herbal medicines. The best identification model can reach a correct identification rate (CIR) of 99.67% when applied to a training set of 600 samples, and 100% CIR when applied to a test set of 300 samples. Identification (biology) ; Artificial neural network ; Near-infrared spectroscopy ; Artificial intelligence ; Herbal supplement ; Computer science ; Pattern recognition (psychology) ; Traditional medicine ; Machine learning ; Medicine ; Physics ; Optics ; Pathology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Gerardo Raggi | Ignacio Fdez. Galván | Christian L. Ritterhoff | Morgane Vacher | Roland Lindh
Source: ChemRxiv (preprint, 2020-03-09)
Field: Materials Science — Materials Chemistry

Restricted-Variance Molecular Geometry Optimization Based on Gradient-Enhanced Kriging Machine learning techniques, specifically gradient-enhanced Kriging (GEK), have been implemented for molecular geometry optimization. GEK-based optimization has many advantages compared to conventional - step-restricted second-order truncated expansion - molecular optimization methods. In particular, the surrogate model given by GEK can have multiple stationary points, will smoothly converge to the exact model as the number of sample points increases, and contains an explicit expression for the expected error of the model function at an arbitrary point. Machine learning is, however, associated with abundance of data, contrary to the situation desired for efficient geometry optimizations. In the paper we demonstrate how the GEK procedure can be utilized in a fashion such that in the presence of few data points, the surrogate surface will in a robust way guide the optimization to a minimum of a potential energy surface. In this respect the GEK procedure will be used to mimic the behavior of a conventional second-order scheme, but retaining the flexibility of the superior machine learning approach. Moreover, the expected error will be used in the optimization to facilitate restricted-variance optimizations (RVO). A procedure which relates the eigenvalues of the approximate guessed Hessian with the individual characteristic lengths, used in the GEK model, reduces the number of empirical parameters to optimize to two - the value of the trend function and the maximum allowed variance. These parameters are determined using the extended Baker (e-Baker) and part of the Baker transition-state (Baker-TS) test suites as a training set. The so-created optimization procedure is tested using the e-Baker, the full Baker-TS, and the S22 test suites, at the density-functional-theory and second order Møller-Plesset levels of approximation. The results show that the new method is generally of similar or better performance than a state-of-the-art conventional method, even for cases where no significant improvement was expected. Hessian matrix ; Mathematics ; Variance (accounting) ; Kriging ; Mathematical optimization ; Flexibility (engineering) ; Function (biology) ; Optimization problem ; Data point ; Algorithm ; Surrogate model ; Applied mathematics ; Statistics Machine Learning in Materials Science ; Computational Drug Discovery Methods
Authors: Matthew Ragoza | Tomohide Masuda | David Ryan Koes
Source: arXiv (Cornell University) (preprint, 2020-10-17)
Field: Materials Science — Materials Chemistry

Learning a Continuous Representation of 3D Molecular Structures with Deep Generative Models Machine learning in drug discovery has been focused on virtual screening of molecular libraries using discriminative models. Generative models are an entirely different approach that learn to represent and optimize molecules in a continuous latent space. These methods have been increasingly successful at generating two dimensional molecules as SMILES strings and molecular graphs. In this work, we describe deep generative models of three dimensional molecular structures using atomic density grids and a novel fitting algorithm for converting continuous grids to discrete molecular structures. Our models jointly represent drug-like molecules and their conformations in a latent space that can be explored through interpolation. We are also able to sample diverse sets of molecules based on a given input compound and increase the probability of creating valid, drug-like molecules. Discriminative model ; Generative model ; Virtual screening ; Computer science ; Chemical space ; Generative grammar ; Representation (politics) ; Interpolation (computer graphics) ; Artificial intelligence ; Space (punctuation) ; Molecular descriptor ; Machine learning ; Drug discovery ; Theoretical computer science ; Chemistry ; Quantitative structure–activity relationship Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Ruimin Ma | Yamil J. Colón | Tengfei Luo
Source: ChemRxiv (preprint, 2020-04-16)
Field: Chemistry — Inorganic Chemistry

A Transfer Learning Study of Gas Adsorption in Metal-Organic Frameworks &lt;p&gt;Metal-organic frameworks (MOFs) are a class of materials promising for gas adsorption due to their highly tunable nano-porous structures and host-guest interactions. While machine learning (ML) has been leveraged to aid the design or screen of MOFs for different purposes, the needs of big data are not always met, limiting the applicability of ML models trained against small data sets. In this work, we introduce a transfer learning technique to improve the accuracy and applicability of ML models trained with small amount of MOF adsorption data. This technique leverages potentially shareable knowledge from a source task to improve the models on the target tasks. As demonstrations, a deep neural network (DNN) trained on H&lt;sub&gt;2&lt;/sub&gt; adsorption data with 13,506 MOF structures at 100 bar and 243 K is used as the source task. When transferring knowledge from the source task to H&lt;sub&gt;2&lt;/sub&gt; adsorption at 100 bar and 130 K (one target task), the predictive accuracy on target task was improved from 0.960 (direct training) to 0.991 (transfer learning). We also tested transfer learning across different gas species (i.e. from H&lt;sub&gt;2&lt;/sub&gt; to CH&lt;sub&gt;4&lt;/sub&gt;), with predictive accuracy of CH&lt;sub&gt;4&lt;/sub&gt; adsorption being improved from 0.935 (direct training) to 0.980 (transfer learning). Based on further analysis, transfer learning will always work on the target tasks with low generalizability. However, when transferring the knowledge from the source task to Xe/Kr adsorption, the transfer learning does not improve the predictive accuracy, which is attributed to the lack of common descriptors that is key to the underlying knowledge. &lt;b&gt;&lt;/b&gt;&lt;/p&gt; Transfer of learning ; Adsorption ; Task (project management) ; Limiting ; Artificial neural network ; Metal-organic framework ; Bar (unit) ; Transfer (computing) ; Computer science ; Artificial intelligence ; Multi-task learning ; Machine learning ; Chemistry ; Materials science ; Engineering ; Physics ; Parallel computing ; Physical chemistry ; Mechanical engineering ; Systems engineering Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; Catalytic Processes in Materials Science
Authors: Wu Zhouzhi | Xiaomin Zhang | Zhipeng Zhao | Zhang Hengjia | Hongwu Tang | Liang Yuan
Source: Ceramics International (article, 2020-07-04)
Field: Materials Science — Materials Chemistry

Transfer learning aid the prediction of sintering densification Sintering ; Materials science ; Powder metallurgy ; Process (computing) ; Artificial neural network ; Field (mathematics) ; Mode (computer interface) ; Transfer of learning ; Domain (mathematical analysis) ; Metallurgy ; Process engineering ; Computer science ; Artificial intelligence Nuclear Materials and Properties ; Advanced materials and composites ; Powder Metallurgy Techniques and Materials
Authors: Jonathan Willman | Ashley Williams | Kien Nguyen-Cong | Aidan P. Thompson | Mitchell Wood | A. B. Belonoshko | Ivan Oleynik
Source: AIP conference proceedings (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

Quantum accurate SNAP carbon potential for MD shock simulations We present a new quantum accurate Spectral Neighbor Analysis Potential (SNAP) machine-learning potential for simulating carbon under extreme conditions of dynamic compression (pressures up to 1 TPa and temperatures up to 10,000 K). The development of SNAP potential involves (1) the generation of the training database comprised of the consistent and meaningful set of first-principles DFT (Density Functional Theory) data for carbon materials at high pressure and temperature; (2) the robust and physically guided training of the SNAP parameters on first-principles data involving statistical data analysis; and (3) the validation of the SNAP potential in MD simulations of carbon at high PT conditions. The excellent performance of quadratic SNAP potential is demonstrated by simulating the radial distribution functions at high pressure-temperature conditions and melt curve of diamond, which were found in good agreement with DFT. Snap ; Density functional theory ; Carbon fibers ; Quantum ; Computer science ; Materials science ; Diamond ; Quadratic equation ; Statistical physics ; Algorithm ; Physics ; Quantum mechanics ; Mathematics ; Composite number Machine Learning in Materials Science ; High-pressure geophysics and materials ; Force Microscopy Techniques and Applications