jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shampa Raghunathan
Source: ChemRxiv (preprint, 2021-06-29)
Field: Materials Science — Materials Chemistry

Intramolecular proton transfer reaction dynamics using machine learned ab initio potential energy surfaces Hydrogen bonding interactions central to various physicochemical processes are investigated in the present study using ab initio-based machine learning potential energy surfaces. Abnormally strong intramolecular O-H...O hydrogen bonds occurring in beta-diketone enols of malonaldehyde, and its derivatives with substituents ranging from various electron-withdrawing to electron-donating functional groups are studied. Machine learning force fields were constructed by using a kernel-based force learning model employing ab initio molecular dynamics reference data. These models were used for molecular dynamics simulations at finite temperature, and dynamical properties were determined by computing their proton transfer free energy surfaces. Chemical systems studied here show progression towards forming barrierless proton transfer events at an accuracy of highly correlated electronic structure methods. Markov state models of the conformational states indicate shorter intramolecular hydrogen bonds exhibiting higher proton transfer rates. We demonstrate how functional group substitution can modulate the strength of intramolecular hydrogen bonds by studying their thermodynamic and kinetic properties. Intramolecular force ; Ab initio ; Hydrogen bond ; Proton ; Chemistry ; Molecular dynamics ; Computational chemistry ; Electron transfer ; Ab initio quantum chemistry methods ; Density functional theory ; Proton-coupled electron transfer ; Chemical physics ; Physical chemistry ; Molecule ; Physics ; Stereochemistry ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies
Authors: Shampa Raghunathan
Source: ChemRxiv (preprint, 2021-07-04)
Field: Materials Science — Materials Chemistry

Intramolecular proton transfer reaction dynamics using machine learned ab initio potential energy surfaces Hydrogen bonding interactions central to various physicochemical processes are investigated in the present study using ab initio-based machine learning potential energy surfaces. Abnormally strong intramolecular O-H...O hydrogen bonds occurring in beta-diketone enols of malonaldehyde, and its derivatives with substituents ranging from various electron-withdrawing to electron-donating functional groups are studied. Machine learning force fields were constructed by using a kernel-based force learning model employing ab initio molecular dynamics reference data. These models were used for molecular dynamics simulations at finite temperature, and dynamical properties were determined by computing their proton transfer free energy surfaces. Chemical systems studied here show progression towards forming barrierless proton transfer events at an accuracy of highly correlated electronic structure methods. Markov state models of the conformational states indicate shorter intramolecular hydrogen bonds exhibiting higher proton transfer rates. We demonstrate how functional group substitution can modulate the strength of intramolecular hydrogen bonds by studying their thermodynamic and kinetic properties. Intramolecular force ; Ab initio ; Hydrogen bond ; Chemistry ; Proton ; Molecular dynamics ; Computational chemistry ; Electron transfer ; Ab initio quantum chemistry methods ; Density functional theory ; Chemical physics ; Proton-coupled electron transfer ; Physical chemistry ; Molecule ; Physics ; Stereochemistry ; Quantum mechanics ; Organic chemistry Machine Learning in Materials Science ; Crystallography and molecular interactions ; Advanced Chemical Physics Studies
Authors: Myneni Sukesh Babu | R. Sarathi | Takahiro Imai
Source:  (article, 2021-06-07)
Field: Materials Science — Materials Chemistry

Understanding Ageing Condition of Epoxy Micro Nanocomposites Using Space Charge Characteristics and by Adopting PCA and ANN Analysis Epoxy micro-nanocomposites are subjected to gamma irradiation for a dosage of 8 kGy and water ageing at <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{90^{\circ}\mathrm{C}}$</tex> for a period of 240 hours respectively, to study the characteristic variation in their space charge behavior after ageing. Exposing to gamma irradiation causes structural changes in the material through radiation induced degradation reactions. Water ageing at high temperatures helps in understanding the effect of long-term water intake phenomenon. Homo-charge accumulation near the electrode-specimen interface of all the test specimens, is noticed. A significant increment in the magnitude of space charge density, is noticed in aged specimens compared to unaged specimen. The gamma irradiated and water aged specimens are classified from unaged specimens by adopting principal component analysis (PCA) and artificial neural network (ANN) analysis to space charge characteristics. The number of hidden layer neurons in the neural network architecture are optimized based on classification accuracy and number of epochs required to converge. Artificial neural network analysis as well as principal component analysis reflected good classification accuracy in classifying the unaged, gamma irradiated and water aged epoxy micro-nanocomposites. Principal component analysis ; Epoxy ; Ageing ; Irradiation ; Materials science ; Space charge ; Composite material ; Artificial neural network ; Analytical Chemistry (journal) ; Artificial intelligence ; Computer science ; Chemistry ; Physics ; Chromatography ; Electron ; Nuclear physics ; Biology High voltage insulation and dielectric phenomena ; Polymer Nanocomposite Synthesis and Irradiation ; Smart Materials for Construction
Authors: Shiyi Qin | Tianyi Jin | Reid C. Van Lehn | Ví­ctor M. Zavala
Source: ChemRxiv (preprint, 2021-04-08)
Field: Materials Science — Materials Chemistry

Predicting Critical Micelle Concentrations for Surfactants using Graph Convolutional Neural Networks Surfactants are amphiphilic molecules that are widely used in consumer products, industrial processes, and biological applications. A critical property of a surfactant is the critical micelle concentration (CMC), which is the concentration at which surfactant molecules undergo cooperative self-assembly in solution. Notably, the primary method to obtain CMCs experimentally—tensiometry—is laborious and expensive. In this work, we show that graph convolutional neural networks (GCNs) can predict CMCs directly from the surfactant molecular structure. Specifically, we developed a GCN architecture that encodes the surfactant structure in the form of a molecular graph and trained it using experimental CMC data. We found that the GCN can predict CMCs with higher accuracy than previously proposed methods and that it can generalize to anionic, cationic, zwitterionic, and nonionic surfactants. Molecular saliency maps revealed how atom types and surfactant molecular substructures contribute to CMCs and found this to be in agreement with physical rules that correlate constitutional and topological information to CMCs. Following such rules, we proposed a small set of new surfactants for which experimental CMCs are not available; for these molecules, CMCs predicted with our GCN exhibited similar trends to those obtained from molecular simulations. These results provide evidence that GCNs can enable high-throughput screening of surfactants with desired self-assembly characteristics. Pulmonary surfactant ; Critical micelle concentration ; Micelle ; Cationic polymerization ; Molecule ; Graph ; Amphiphile ; Chemistry ; Molecular dynamics ; Computer science ; Biological system ; Materials science ; Combinatorial chemistry ; Computational chemistry ; Organic chemistry ; Polymer ; Theoretical computer science ; Copolymer ; Aqueous solution Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Advanced Polymer Synthesis and Characterization
Authors: Akash Singh | Yumeng Li
Source: AIAA Scitech 2021 Forum (article, 2021-01-04)
Field: Materials Science — Materials Chemistry

Uncertainty Management and Reduction of Machine Learning Potential View Video Presentation: https://doi.org/10.2514/6.2021-1962.vid Machine learning potential has drawn more and more attention in the recent years due to its ability in enabling automatic construction of high-fidelity force field potentials. They allow the successful application of reliable high throughput atomistic simulations in accelerating material discovery and design with novel functions and properties. Those methods circumvent the parameterization problem as faced by most empirical potentials and decrease model bias by using machine learning techniques to interpolate the highly nonlinear potential energy surface based on first principle calculations. Despite the advantages such as a well automated process and high flexibility of model functions, systematic uncertainty quantifications are still missing to develop uncertainty management techniques for the quantification and reduction of uncertainty involved in the developed machine learning interpolated atomic potential energy surface. In this paper, the uncertainty resources associated with the development of machine learning potential will be identified. Corresponding uncertainty quantification will be conducted through a systematic sensitivity analysis to investigate the reliability of the developed machine learning potential for graphene, an emergent promising 2D material. Machine learning ; Computer science ; Artificial intelligence ; Flexibility (engineering) ; Uncertainty quantification ; Field (mathematics) ; Reduction (mathematics) ; Process (computing) ; Reliability (semiconductor) ; Sensitivity (control systems) ; Engineering ; Mathematics Machine Learning in Materials Science ; Fuel Cells and Related Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Krishnendu Mukherjee | Alexander W. Dowling | Yamil J. Colón
Source: arXiv (Cornell University) (preprint, 2021-09-30)
Field: Materials Science — Materials Chemistry

Sequential Design of Adsorption Simulations in Metal-Organic Frameworks The large number of possible structures of metal-organic frameworks (MOFs) and their limitless potential applications has motivated molecular modelers and researchers to develop methods and models to efficiently assess MOF performance. Some of the techniques include large-scale high-throughput molecular simulations and machine learning models. Despite those advances, the number of possible materials and the potential conditions that could be used still pose a formidable challenge for model development requiring large data sets. Therefore, there is a clear need for algorithms that can efficiently explore the spaces while balancing the number of simulations with prediction accuracy. Here, we present how active learning can sequentially select simulation conditions for gas adsorption, ultimately resulting in accurate adsorption predictions with an order of magnitude less number of simulations. We model adsorption of pure components methane and carbon dioxide in Cu-BTC. We employ Gaussian process regression (GPR) and use the resulting uncertainties in the predictions to guide the next sampling point for molecular simulation. We outline the procedure and demonstrate how this model can emulate adsorption isotherms at 300 K from $10^{-6}$ to 300 bar (methane)/100 bar (carbon dioxide). We also show how this procedure can be used for predicting adsorption on a temperature-pressure phase space for a temperature range of 100 to 300 K, and pressure range of $10^{-6}$ to 300 bar (methane)/100 bar (carbon dioxide) Methane ; Adsorption ; Bar (unit) ; Computer science ; Range (aeronautics) ; Materials science ; Process engineering ; Chemistry ; Physics ; Physical chemistry ; Engineering Machine Learning in Materials Science ; Metal-Organic Frameworks: Synthesis and Applications ; Phase Equilibria and Thermodynamics
Authors: Fumitatsu Iwase
Source: Journal of the Physical Society of Japan (article, 2021-08-10)
Field: Materials Science — Materials Chemistry

Machine Learning Approach for the Prediction of Nuclear Quadrupole Resonance Frequencies Nuclear quadrupole resonance (NQR) is an indispensable experimental technique, which can elucidate microscopic electronic state of materials. NQR frequency, νQ, of material is important information which reflects the properties of the material. In the present study, a supervised machine learning (ML) technique is applied to predict νQ of 35Cl NQR in organic molecules. The input data of the ML is feature vectors encoded by molecular SMILES. To find the optimum labeling of experimental νQ to Cl in SMILES, an Metropolis–Hastings-like algorithm combined with ML is developed. νQ is also estimated by calculating electric-field gradient at Cl site based on the density functional theory (DFT) considering all electrons (full-potential local-orbital method). The accuracy of the prediction with only ML using SMILES is comparable to that of DFT calculation. The ML prediction scheme has great advantage of not requiring atomic coordinates and many computer resources and is potentially applicable to the prediction of the other spectroscopic data. Nuclear quadrupole resonance ; Quadrupole ; Electric field gradient ; Computer science ; Field (mathematics) ; Density functional theory ; Resonance (particle physics) ; Physics ; Statistical physics ; Feature (linguistics) ; Algorithm ; Artificial intelligence ; Atomic physics ; Computational physics ; Materials science ; Nuclear magnetic resonance ; Quantum mechanics ; Mathematics Solid-state spectroscopy and crystallography ; Advanced NMR Techniques and Applications ; Crystallography and molecular interactions
Authors: Kristopher Nolte | Yunyun Gao | Sabrina Stäb | Philip Kollmansberger | Andrea Thorn
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-10-29)
Field: Materials Science — Materials Chemistry

Detecting ice artefacts in processed macromolecular diffraction data with machine learning Abstract Contamination with diffraction from ice crystals can negatively affect, or even impede macromolecular structure determination and therefore, detecting the resulting artefacts in diffraction data is crucial. However, once the data have been processed, it can be very difficult to automatically recognize this problem. To address this, a set of convolutional neural networks named Helcaraxe has been developed which can detect ice diffraction artefacts in processed diffraction data from macromolecular crystals. The networks outperform previous algorithms and will be available as part of the AUSPEX webserver and CCP4-distributed software. Synopsis A program utilizing artificial learning and convolutional neural networks, named Helcaraxe, has been developed which can detect ice crystal artefacts in processed macromolecular diffraction data with unprecedented accuracy. Convolutional neural network ; Diffraction ; Computer science ; Macromolecule ; Artificial neural network ; Set (abstract data type) ; Software ; Data set ; Artificial intelligence ; Chemistry ; Optics ; Physics Enzyme Structure and Function ; Machine Learning in Materials Science ; Protein Structure and Dynamics
Authors: Atharva Kelkar | Bradley C. Dallin | Reid C. Van Lehn
Source: ChemRxiv (preprint, 2021-08-03)
Field: Materials Science — Materials Chemistry

Identifying Nonadditive Contributions to the Hydrophobicity of Chemically Heterogeneous Surfaces via Dual-Loop Active Learning Hydrophobic interactions drive numerous phenomena involving surfaces that are chemically heterogeneous at the nanoscale. Nonadditive contributions to the hydrophobicity of such surfaces depend on the chemical identities and spatial patterns of polar and nonpolar groups in ways that remain poorly understood. Here, we develop an active learning framework that utilizes molecular dynamics (MD) simulations, enhanced sampling, and a convolutional neural network to predict the hydration free energy (a thermodynamic descriptor of hydrophobicity) for nearly 200,000 chemically heterogeneous self-assembled monolayers (SAMs). Analysis of this data set reveals that SAMs with distinct polar groups exhibit substantial variations in hydrophobicity as a function of their composition and patterning, but the clustering of nonpolar groups is a common signature of highly hydrophobic patterns. Further MD analysis relates such clustering to the perturbation of interfacial water structure. These results provide new insight into the influence of chemical heterogeneity on hydrophobicity via quantitative analysis of a large set of surfaces, enabled by the active learning approach. Chemistry ; Polar ; Chemical physics ; Molecular dynamics ; Cluster analysis ; Monolayer ; Polar surface area ; Biological system ; Molecule ; Nanotechnology ; Computational chemistry ; Materials science ; Computer science ; Physics ; Organic chemistry ; Artificial intelligence Machine Learning in Materials Science ; Cold Atom Physics and Bose-Einstein Condensates ; Electrostatics and Colloid Interactions
Authors: Atharva Kelkar | Bradley C. Dallin | Reid C. Van Lehn
Source:  (preprint, 2021-08-03)
Field: Materials Science — Materials Chemistry

Identifying Nonadditive Contributions to the Hydrophobicity of Chemically Heterogeneous Surfaces via Dual-Loop Active Learning Hydrophobic interactions drive numerous phenomena involving surfaces that are chemically heterogeneous at the nanoscale. Nonadditive contributions to the hydrophobicity of such surfaces depend on the chemical identities and spatial patterns of polar and nonpolar groups in ways that remain poorly understood. Here, we develop an active learning framework that utilizes molecular dynamics (MD) simulations, enhanced sampling, and a convolutional neural network to predict the hydration free energy (a thermodynamic descriptor of hydrophobicity) for nearly 200,000 chemically heterogeneous self-assembled monolayers (SAMs). Analysis of this data set reveals that SAMs with distinct polar groups exhibit substantial variations in hydrophobicity as a function of their composition and patterning, but the clustering of nonpolar groups is a common signature of highly hydrophobic patterns. Further MD analysis relates such clustering to the perturbation of interfacial water structure. These results provide new insight into the influence of chemical heterogeneity on hydrophobicity via quantitative analysis of a large set of surfaces, enabled by the active learning approach. Polar ; Chemistry ; Chemical physics ; Cluster analysis ; Molecular dynamics ; Biological system ; Nanotechnology ; Computational chemistry ; Materials science ; Computer science ; Physics ; Artificial intelligence Machine Learning in Materials Science ; Cold Atom Physics and Bose-Einstein Condensates ; Electrostatics and Colloid Interactions
Authors: Patrick Trampert | Dmitri Rubinstein | Faysal Boughorbel | Christian Schlinkmann | Maria Luschkova | Philipp Slusallek | Tim Dahmen | Stefan Sandfeld
Source:  (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Deep Neural Networks for Analysis of Microscopy Images—Synthetic Data Generation and Adaptive Sampling The analysis of microscopy images has always been an important yet time consuming process in materials science. Convolutional Neural Networks (CNNs) have been very successfully used for a number of tasks, such as image segmentation. However, training a CNN requires a large amount of hand annotated data, which can be a problem for material science data. We present a procedure to generate synthetic data based on ad hoc parametric data modelling for enhancing generalization of trained neural network models. Especially for situations where it is not possible to gather a lot of data, such an approach is beneficial and may enable to train a neural network reasonably. Furthermore, we show that targeted data generation by adaptively sampling the parameter space of the generative models gives superior results compared to generating random data points. Computer science ; Artificial intelligence ; Convolutional neural network ; Artificial neural network ; Parametric statistics ; Segmentation ; Synthetic data ; Adaptive sampling ; Deep learning ; Pattern recognition (psychology) ; Machine learning ; Parametric model ; Process (computing) ; Generalization ; Sampling (signal processing) ; Computer vision ; Mathematics ; Monte Carlo method Machine Learning in Materials Science ; Cell Image Analysis Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Chinedu E. Ekuma | Z Liu | Srihari Kastuar
Source: Research Square (preprint, 2021-09-16)
Field: Materials Science — Materials Chemistry

Efficient Prediction of Temperature-dependent Elastic and Mechanical Properties of 2D Materials Abstract An efficient automated toolkit for predicting the mechanical properties of materials can accelerate new materials design and discovery; this process often involves screening large configurational space in high-throughput calculations. Herein, we present the ElasTool toolkit for these applications. In particular, we use the ElasTool to study diversity of 2D materials and heterostructures, including their temperature-dependent mechanical properties and developed a machine learning algorithm for exploring predicted properties. Process (computing) ; Computer science ; Throughput ; Space (punctuation) ; Materials science ; Machine learning ; Artificial intelligence Machine Learning in Materials Science ; 2D Materials and Applications ; Electronic and Structural Properties of Oxides