Applications of computational chemistry, artificial intelligence, and machine learning in aquatic chemistry research

2021 ◽  
pp. 131810
Author(s):  
Lei He ◽  
Lu Bai ◽  
Dionysios D. Dionysiou ◽  
Zongsu Wei ◽  
Richard Spinney ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Chemistry ◽  
Aquatic Chemistry ◽  
Chemistry Research

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

Nanoinformatics, and the big challenges for the science of small things

Nanoscale ◽  
2019 ◽  
Vol 11 (41) ◽  
pp. 19190-19201 ◽  
Author(s):  
A. S. Barnard ◽  
B. Motevalli ◽  
A. J. Parker ◽  
J. M. Fischer ◽  
C. A. Feigl ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computational Chemistry ◽  
Functional Properties ◽  
Materials Science ◽  
Structural Features ◽  
Computational Materials Science ◽  
Computational Materials ◽  
Small Things

The combination of computational chemistry and computational materials science with machine learning and artificial intelligence provides a powerful way of relating structural features of nanomaterials with functional properties.

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

Machine Learning in Agriculture

2018 ◽  
Vol 8 (6) ◽  
pp. 26 ◽  
Author(s):  
Matthew N. O. Sadiku ◽  
Chandra M. M Kotteti ◽  
Sarhan M. Musa
Keyword(s):  
Climate Change ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Disease Diagnosis ◽  
Automated Detection ◽  
Disease Prediction ◽  
Paper Briefly ◽  
Agriculture Sector ◽  
Modern Agriculture ◽  
Crop Disease

Machine learning is an emerging field of artificial intelligence which can be applied to the agriculture sector. It refers to the automated detection of meaningful patterns in a given data.  Modern agriculture seeks ways to conserve water, use nutrients and energy more efficiently, and adapt to climate change.  Machine learning in agriculture allows for more accurate disease diagnosis and crop disease prediction. This paper briefly introduces what machine learning can do in the agriculture sector.

Development of innovative hygienic technologies for preservation of the workers’ reproductive health

2020 ◽  
pp. 792-792
Author(s):  
M. A. Fesenko ◽  
G. V. Golovaneva ◽  
A. V. Miskevich
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Health ◽  
Reproductive System ◽  
Preventive Measures ◽  
Learning Algorithms ◽  
Reproductive Function ◽  
Machine Learning Algorithms ◽  
New Model

The new model «Prognosis of men’ reproductive function disorders» was developed. The machine learning algorithms (artificial intelligence) was used for this purpose, the model has high prognosis accuracy. The aim of the model applying is prioritize diagnostic and preventive measures to minimize reproductive system diseases complications and preserve workers’ health and efficiency.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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