scholarly journals Bringing Chemical Structures to Life with Augmented Reality, Machine Learning and Quantum Chemistry

2022 ◽  
Author(s):  
Sukolsak Sakshuwong ◽  
Hayley Weir ◽  
Umberto Raucci ◽  
Todd Martinez
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Augmented Reality ◽  
Quantum Chemistry ◽  
Mobile Application ◽  
Scientific Community ◽  
Chemical Properties ◽  
Molecular Structures ◽  
Chemical Behavior ◽  
Chemical Structures

Abstract Visualizing 3D molecular structures is crucial to understanding and predicting their chemical behavior. However, static 2D hand-drawn skeletal structures remain the preferred method of chemical communication. Here, we combine cutting-edge technologies in augmented reality (AR), machine learning, and computational chemistry to develop MolAR, a mobile application for visualizing molecules in AR directly from their hand-drawn chemical structures. Users can also visualize any molecule or protein directly from its name or PDB ID, and compute chemical properties in real time via quantum chemistry cloud computing. MolAR provides an easily accessible platform for the scientific community to visualize and interact with 3D molecular structures in an immersive and engaging way.

scholarly journals MolAR: Bringing Chemical Structures to Life with Augmented Reality and Machine Learning

2021 ◽  
Author(s):  
Sukolsak Sakshuwong ◽  
Hayley Weir ◽  
Umberto Raucci ◽  
Todd J. Martínez
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
3D Structure ◽  
Three Dimensional ◽  
Real Life ◽  
Data Bank ◽  
Molecular Structures ◽  
Chemical Structures ◽  
Food And Drink ◽  
Research And Education

Visualizing three-dimensional molecular structures is crucial to understanding and predicting their chemical behavior. Existing visualization software, however, can be cumbersome to use, and, for many, hand-drawn skeletal structures remain the preferred method of chemical communication. Although convenient, the static, two-dimensional nature of these drawings can be misleading in conveying the molecule’s 3D structure, not to mention that dynamic movement is completely disregarded. Here, we combine machine learning and augmented reality (AR) to develop MolAR, an immersive mobile application for visualizing molecules in real-world scenes. The application uses deep learning to recognize hand-drawn hydrocarbons structures which it converts into interactive 3D molecules in AR. Users can also “hunt” for chemicals in food and drink to uncover molecules in their real-life environment. A variety of interesting molecules are pre-loaded into the application, and users can visualize molecules in PubChem by providing their name or SMILES string and proteins in the Protein Data Bank by providing their PDB ID. MolAR was designed to be used in both research and education settings, providing an almost barrierless platform to visualize and interact with 3D molecular structures in a uniquely immersive way.

scholarly journals Unifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
K. T. Schütt ◽  
M. Gastegger ◽  
A. Tkatchenko ◽  
K.-R. Müller ◽  
R. J. Maurer
Keyword(s):  
Machine Learning ◽  
Quantum Chemistry ◽  
Degrees Of Freedom ◽  
Large Scale ◽  
Materials Science ◽  
Chemical Space ◽  
Chemical Properties ◽  
Molecular Structures ◽  
Learning Framework ◽  
Molecular Wavefunctions

AbstractMachine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry.

scholarly journals Editorial: Topological investigations of chemical networks

2021 ◽  
Vol 44 (1) ◽  
pp. 267-269
Author(s):  
Muhammad Javaid ◽  
Muhammad Imran
Keyword(s):  
Information Science ◽  
Biological Activities ◽  
Chemical Properties ◽  
Vital Role ◽  
Topological Indices ◽  
Molecular Structures ◽  
Quantitative Structure ◽  
Structure Property ◽  
Chemical Structures ◽  
Interdisciplinary Field

Abstract The topic of computing the topological indices (TIs) being a graph-theoretic modeling of the networks or discrete structures has become an important area of research nowadays because of its immense applications in various branches of the applied sciences. TIs have played a vital role in mathematical chemistry since the pioneering work of famous chemist Harry Wiener in 1947. However, in recent years, their capability and popularity has increased significantly because of the findings of the different physical and chemical investigations in the various chemical networks and the structures arising from the drug designs. In additions, TIs are also frequently used to study the quantitative structure property relationships (QSPRs) and quantitative structure activity relationships (QSARs) models which correlate the chemical structures with their physio-chemical properties and biological activities in a dataset of chemicals. These models are very important and useful for the research community working in the wider area of cheminformatics which is an interdisciplinary field combining mathematics, chemistry, and information science. The aim of this editorial is to arrange new methods, techniques, models, and algorithms to study the various theoretical and computational aspects of the different types of these topological indices for the various molecular structures.

scholarly journals VDB Entropy Measures and Irregularity-Based Indices for the Rectangular Kekulene System

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Weidong Zhao ◽  
K. Julietraja ◽  
P. Venugopal ◽  
Xiujun Zhang
Keyword(s):  
Coal Gasification ◽  
Molecular Graph ◽  
Chemical Properties ◽  
Mathematical Expression ◽  
Cost Effective ◽  
Molecular Structures ◽  
Specific Chemical ◽  
Chemical Structures ◽  
Broad Array ◽  
Polycyclic Aromatic

Theoretical chemists are fascinated by polycyclic aromatic hydrocarbons (PAHs) because of their unique electromagnetic and other significant properties, such as superaromaticity. The study of PAHs has been steadily increasing because of their wide-ranging applications in several fields, like steel manufacturing, shale oil extraction, coal gasification, production of coke, tar distillation, and nanosciences. Topological indices (TIs) are numerical quantities that give a mathematical expression for the chemical structures. They are useful and cost-effective tools for predicting the properties of chemical compounds theoretically. Entropic network measures are a type of TIs with a broad array of applications, involving quantitative characterization of molecular structures and the investigation of some specific chemical properties of molecular graphs. Irregularity indices are numerical parameters that quantify the irregularity of a molecular graph and are used to predict some of the chemical properties, including boiling points, resistance, enthalpy of vaporization, entropy, melting points, and toxicity. This study aims to determine analytical expressions for the VDB entropy and irregularity-based indices in the rectangular Kekulene system.

scholarly journals A Diversified Machine Learning Strategy for Predicting and Understanding Molecular Melting Points

2019 ◽  
Author(s):  
ganesh sivaraman ◽  
Nicholas Jackson ◽  
Benjamin Sanchez-Lengeling ◽  
Alvaro Vazquez-Mayagoitia ◽  
Alan Aspuru-Guzik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Molecule ◽  
Learning Strategy ◽  
Chemical Properties ◽  
Industrial Applications ◽  
Molecular Structures ◽  
Grand Challenge ◽  
Clustering Methods ◽  
Melting Points ◽  
Data Set

<p>The ability to predict multi-molecule processes, using only knowledge of single molecule structure, stands as a grand challenge for molecular modeling. Methods capable of predicting melting points (MP) solely from chemical structure represent a canonical example, and are highly desirable in many crucial industrial applications. In this work, we explore a data-driven approach utilizing machine learning (ML) techniques to predict and understand the MP of molecules. Several experimental databases are aggregated from the literature to design a low-bias dataset that includes 3D structural and quantum-chemical properties. Using experimental and polymorph-induced uncertainties, we derive a tenable lower limit for MP prediction accuracy, and apply graph neural networks and Gaussian processes to predict MP competitive with these error bounds. To further understand how MP correlates with molecular structure, we employ several semi-supervised and unsupervised ML techniques. First, we use unsupervised clustering methods to identify classes of molecules, their common fragments, and expected errors for each data set. We then build molecular geometric spaces shaped by MP with a semi-supervised variational autoencoder and graph embedding spaces, and apply graph attribution methods to highlight atom-level contributions to MP within the datasets. Overall, this work serves as a case study of how to employ a diversified ML toolkit to predict and understand correlations between molecular structures and thermophysical properties of interest.</p>

scholarly journals ChemVox: Voice-Controlled Quantum Chemistry

2020 ◽  
Author(s):  
Umberto Raucci ◽  
alessio Valentini ◽  
Elisa Pieri ◽  
Hayley Weir ◽  
Stefan Seritan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Quantum Chemistry ◽  
Computational Chemistry ◽  
Natural Sciences ◽  
Machine Learning Algorithms ◽  
Quantum Chemistry Calculations ◽  
Voice Control ◽  
Opening Up

Over the last decade, artificial intelligence has been propelled forward by advances in machine learning algorithms and computational hardware, opening up myriad new avenues for scientific research. Nevertheless, virtual assistants and voice control have yet to be widely utilized in the natural sciences. Here, we present ChemVox, an interactive Amazon Alexa skill that uses speech recognition to perform quantum chemistry calculations. This new application interfaces Alexa with cloud computing and returns the results through a capable device. ChemVox paves the way to making computational chemistry routinely accessible to the wider community

scholarly journals Study of Vanadium Carbide Structures Based on Ve and Ev-Degree Topological Indices

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Abdul Rauf ◽  
Saba Maqbool ◽  
Muhammad Naeem ◽  
Adnan Aslam ◽  
Hamideh Aram ◽  
...  
Keyword(s):  
Fossil Fuels ◽  
Vanadium Carbide ◽  
Chemical Properties ◽  
Molecular Structures ◽  
Biologically Active ◽  
Topological Descriptors ◽  
Biological Applications ◽  
Chemical Behavior ◽  
Physical Chemical ◽  
Atom Bond

Vanadium is a biologically active product with significant industrial and biological applications. Vanadium is found in a variety of minerals and fossil fuels, the most common of which are sandstones, crude oil, and coal. Topological descriptors are numerical numbers assigned to the molecular structures and have the ability to predict certain of their physical/chemical properties. In this paper, we have studied topological descriptors of vanadium carbide structure based on ev and ve degrees. In particular, we have computed the closed forms of Zagreb, Randic, geometric-arithmetic, and atom-bond connectivity (ABC) indices of vanadium carbide structure based on ev and ve degrees. This kind of study may be useful for understanding the biological and chemical behavior of the structure.

scholarly journals ChemVox: Voice-Controlled Quantum Chemistry

2020 ◽  
Author(s):  
Umberto Raucci ◽  
alessio Valentini ◽  
Elisa Pieri ◽  
Hayley Weir ◽  
Stefan Seritan ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cloud Computing ◽  
Quantum Chemistry ◽  
Computational Chemistry ◽  
Natural Sciences ◽  
Machine Learning Algorithms ◽  
Quantum Chemistry Calculations ◽  
Voice Control ◽  
Opening Up

Over the last decade, artificial intelligence has been propelled forward by advances in machine learning algorithms and computational hardware, opening up myriad new avenues for scientific research. Nevertheless, virtual assistants and voice control have yet to be widely utilized in the natural sciences. Here, we present ChemVox, an interactive Amazon Alexa skill that uses speech recognition to perform quantum chemistry calculations. This new application interfaces Alexa with cloud computing and returns the results through a capable device. ChemVox paves the way to making computational chemistry routinely accessible to the wider community

scholarly journals A Diversified Machine Learning Strategy for Predicting and Understanding Molecular Melting Points

2019 ◽  
Author(s):  
ganesh sivaraman ◽  
Nicholas Jackson ◽  
Benjamin Sanchez-Lengeling ◽  
Alvaro Vazquez-Mayagoitia ◽  
Alan Aspuru-Guzik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Molecule ◽  
Learning Strategy ◽  
Chemical Properties ◽  
Industrial Applications ◽  
Molecular Structures ◽  
Grand Challenge ◽  
Clustering Methods ◽  
Melting Points ◽  
Data Set

<p>The ability to predict multi-molecule processes, using only knowledge of single molecule structure, stands as a grand challenge for molecular modeling. Methods capable of predicting melting points (MP) solely from chemical structure represent a canonical example, and are highly desirable in many crucial industrial applications. In this work, we explore a data-driven approach utilizing machine learning (ML) techniques to predict and understand the MP of molecules. Several experimental databases are aggregated from the literature to design a low-bias dataset that includes 3D structural and quantum-chemical properties. Using experimental and polymorph-induced uncertainties, we derive a tenable lower limit for MP prediction accuracy, and apply graph neural networks and Gaussian processes to predict MP competitive with these error bounds. To further understand how MP correlates with molecular structure, we employ several semi-supervised and unsupervised ML techniques. First, we use unsupervised clustering methods to identify classes of molecules, their common fragments, and expected errors for each data set. We then build molecular geometric spaces shaped by MP with a semi-supervised variational autoencoder and graph embedding spaces, and apply graph attribution methods to highlight atom-level contributions to MP within the datasets. Overall, this work serves as a case study of how to employ a diversified ML toolkit to predict and understand correlations between molecular structures and thermophysical properties of interest.</p>

Cloud Computing in Library

2014 ◽  
Vol 3 ◽  
pp. 94-112
Author(s):  
Angelė Pečeliūnaitė
Keyword(s):  
Cloud Computing ◽  
Scientific Community ◽  
Digital Age ◽  
International Experience ◽  
The Internet ◽  
Library Services ◽  
General Activity ◽  
Computing Services ◽  
Cloud Computing Services

The article analyses the possibility of how Cloud Computing can be used by libraries to organise activities online. In order to achieve a uniform understanding of the essence of technology SaaS, IaaS, and PaaS, the article discusses the Cloud Computing services, which can be used for the relocation of libraries to the Internet. The improvement of the general activity of libraries in the digital age, the analysis of the international experience in the libraries are examples. Also the article discusses the results of a survey of the Lithuanian scientific community that confirms that 90% of the scientific community is in the interest of getting full access to e-publications online. It is concluded that the decrease in funding for libraries, Cloud Computing can be an economically beneficial step, expanding the library services and improving their quality.

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