Comparison of the Performance of Machine Learning Models in Representing High-Dimensional Free Energy Surfaces and Generating Observables

2020 ◽  
Vol 124 (18) ◽  
pp. 3647-3660 ◽  
Author(s):  
Joseph R. Cendagorta ◽  
Jocelyn Tolpin ◽  
Elia Schneider ◽  
Robert Q. Topper ◽  
Mark E. Tuckerman
Keyword(s):  
Machine Learning ◽  
Free Energy ◽  
High Dimensional ◽  
Learning Models ◽  
Energy Surfaces ◽  
Machine Learning Models

scholarly journals Description of Potential Energy Surfaces of Molecules Using FFLUX Machine Learning Models

2018 ◽  
Vol 15 (1) ◽  
pp. 116-126 ◽  
Author(s):  
Zak E. Hughes ◽  
Joseph C. R. Thacker ◽  
Alex L. Wilson ◽  
Paul L. A. Popelier
Keyword(s):  
Machine Learning ◽  
Potential Energy ◽  
Potential Energy Surfaces ◽  
Learning Models ◽  
Energy Surfaces ◽  
Machine Learning Models

Accurate classification of depression through optimized machine learning models on high-dimensional noisy data

2022 ◽  
Vol 71 ◽  
pp. 103237
Author(s):  
Xingang Fang ◽  
Julia Klawohn ◽  
Alexander De Sabatino ◽  
Harsh Kundnani ◽  
Jonathan Ryan ◽  
...  
Keyword(s):  
Machine Learning ◽  
Noisy Data ◽  
High Dimensional ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Quantum Optical Experiments Modeled by Long Short-Term Memory

Photonics ◽  
2021 ◽  
Vol 8 (12) ◽  
pp. 535
Author(s):  
Thomas Adler ◽  
Manuel Erhard ◽  
Mario Krenn ◽  
Johannes Brandstetter ◽  
Johannes Kofler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Short Term Memory ◽  
Random Search ◽  
High Dimensional ◽  
Learning Models ◽  
Short Term ◽  
Term Memory ◽  
Output State ◽  
Long Short Term Memory ◽  
Machine Learning Models

We demonstrate how machine learning is able to model experiments in quantum physics. Quantum entanglement is a cornerstone for upcoming quantum technologies, such as quantum computation and quantum cryptography. Of particular interest are complex quantum states with more than two particles and a large number of entangled quantum levels. Given such a multiparticle high-dimensional quantum state, it is usually impossible to reconstruct an experimental setup that produces it. To search for interesting experiments, one thus has to randomly create millions of setups on a computer and calculate the respective output states. In this work, we show that machine learning models can provide significant improvement over random search. We demonstrate that a long short-term memory (LSTM) neural network can successfully learn to model quantum experiments by correctly predicting output state characteristics for given setups without the necessity of computing the states themselves. This approach not only allows for faster search, but is also an essential step towards the automated design of multiparticle high-dimensional quantum experiments using generative machine learning models.

Improving XGBoost with Imagination Sampling

2020 ◽  
Vol 2 (1) ◽  
pp. 3-6
Author(s):  
Eric Holloway
Keyword(s):  
Machine Learning ◽  
General System ◽  
Learning Models ◽  
Starting Point ◽  
Machine Learning Models

Imagination Sampling is the usage of a person as an oracle for generating or improving machine learning models. Previous work demonstrated a general system for using Imagination Sampling for obtaining multibox models. Here, the possibility of importing such models as the starting point for further automatic enhancement is explored.

Epigenetic Target Prediction with Accurate Machine Learning Models

2021 ◽  
Author(s):  
Norberto Sánchez-Cruz ◽  
Jose L. Medina-Franco
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Predictive Models ◽  
Large Scale ◽  
Target Prediction ◽  
Quantitative Measure ◽  
Learning Models ◽  
Discovery Research ◽  
Drug Discovery Research ◽  
Machine Learning Models

<p>Epigenetic targets are a significant focus for drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represents a large amount of structure-activity relationships that has not been exploited thus far for the development of predictive models to support medicinal chemistry efforts. Herein, we report the first large-scale study of 26318 compounds with a quantitative measure of biological activity for 55 protein targets with epigenetic activity. Through a systematic comparison of machine learning models trained on molecular fingerprints of different design, we built predictive models with high accuracy for the epigenetic target profiling of small molecules. The models were thoroughly validated showing mean precisions up to 0.952 for the epigenetic target prediction task. Our results indicate that the herein reported models have considerable potential to identify small molecules with epigenetic activity. Therefore, our results were implemented as freely accessible and easy-to-use web application.</p>

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