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Full-Text Articles in Physical Sciences and Mathematics
Applying Machine Learning To Computational Chemistry: Can We Predict Molecular Properties Faster Without Compromising Accuracy?, Hanjing Xu, Pradeep Gurunathan, Lyudmila Slipchenko
Applying Machine Learning To Computational Chemistry: Can We Predict Molecular Properties Faster Without Compromising Accuracy?, Hanjing Xu, Pradeep Gurunathan, Lyudmila Slipchenko
The Summer Undergraduate Research Fellowship (SURF) Symposium
Non-covalent interactions are crucial in analyzing protein folding and structure, function of DNA and RNA, structures of molecular crystals and aggregates, and many other processes in the fields of biology and chemistry. However, it is time and resource consuming to calculate such interactions using quantum-mechanical formulations. Our group has proposed previously that the effective fragment potential (EFP) method could serve as an efficient alternative to solve this problem. However, one of the computational bottlenecks of the EFP method is obtaining parameters for each molecule/fragment in the system, before the actual EFP simulations can be carried out. Here we present a …
Machine Learning In Xenon1t Analysis, Dillon A. Davis, Rafael F. Lang, Darryl P. Masson
Machine Learning In Xenon1t Analysis, Dillon A. Davis, Rafael F. Lang, Darryl P. Masson
The Summer Undergraduate Research Fellowship (SURF) Symposium
In process of analyzing large amounts of quantitative data, it can be quite time consuming and challenging to uncover populations of interest contained amongst the background data. Therefore, the ability to partially automate the process while gaining additional insight into the interdependencies of key parameters via machine learning seems quite appealing. As of now, the primary means of reviewing the data is by manually plotting data in different parameter spaces to recognize key features, which is slow and error prone. In this experiment, many well-known machine learning algorithms were applied to a dataset to attempt to semi-automatically identify known populations, …
Predicting Locations Of Pollution Sources Using Convolutional Neural Networks, Yiheng Chi, Nickolas D. Winovich, Guang Lin
Predicting Locations Of Pollution Sources Using Convolutional Neural Networks, Yiheng Chi, Nickolas D. Winovich, Guang Lin
The Summer Undergraduate Research Fellowship (SURF) Symposium
Pollution is a severe problem today, and the main challenge in water and air pollution controls and eliminations is detecting and locating pollution sources. This research project aims to predict the locations of pollution sources given diffusion information of pollution in the form of array or image data. These predictions are done using machine learning. The relations between time, location, and pollution concentration are first formulated as pollution diffusion equations, which are partial differential equations (PDEs), and then deep convolutional neural networks are built and trained to solve these PDEs. The convolutional neural networks consist of convolutional layers, reLU layers …