The interplay among molecular structures, crystal symmetries and lattice energy landscapes revealed using unsupervised machine learning: a closer look at pyrrole azaphenacenes

CrystEngComm ◽  
2019 ◽  
Vol 21 (41) ◽  
pp. 6173-6185 ◽  
Author(s):  
Jack Yang ◽  
Nathan Li ◽  
Sean Li
Keyword(s):  
Machine Learning ◽  
Lattice Energy ◽  
Molecular Structures ◽  
Energy Landscapes ◽  
Unsupervised Machine Learning

Using unsupervised machine learning and CSPs to help crystallographers better understand how crystallizations are affected by molecular structures.

The Interplay among Molecular Structures, Crystal Symmetries and Lattice Energy Landscapes Revealed by Unsupervised Machine Learning: A Closer Look at Pyrrole Azaphenacenes

2019 ◽  
Author(s):  
Jack Yang ◽  
Nathan Li ◽  
Sean Li
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Crystal Engineering ◽  
Crystal Packing ◽  
Similarity Index ◽  
Lattice Energy ◽  
Molecular Structures ◽  
Machine Learning Techniques ◽  
Specific Reference ◽  
Energy Landscapes

The ability to perform large-scale crystal structure predictions (CSP) have significantly advanced the synthesis of functional molecular solids by designs. In our recent work [Chem. Mater., 30, 4361 (2018)], we demonstrated our latest developments in organic CSPs by screening a set of 28 pyrrole azaphenacene isomers which led to one new molecule with higher thermodynamic stability and carrier mobilities in its crystalline form, compared to the one reported experimentally. Hereby, using the lattice energy landscapes for pyrrole azaphenacenes as examples, we applied machine-learning techniques to statistically reveal in more details, on how molecular symmetry and Z' values translate to the crystal packing landscapes, which in terms affect the coverage of landscape through quasi-random crystal structure samplings. A recurring theme in crystal engineering is to identify the probabilities of targeting isostructures to a specific reference crystal upon chemical functionalisations. For this, we propose here a global similarity index in conjunction with the Energy-Density Isostructurality (EDI) map to analyse the lattice energy landscapes for halogen substituted pyrrole azaphenacenes. A continue effort in the field is to accelerate CSPs for sampling a much wider chemical space for high-throughput material screenings, we propose a potential solution to this challenge drawn upon this study. Our work will hopefully stimulate the crystal engineering community in adapting a more statistically-oriented approach in understanding crystal packing of organic molecules in the age of digitisation.

The Interplay among Molecular Structures, Crystal Symmetries and Lattice Energy Landscapes Revealed by Unsupervised Machine Learning: A Closer Look at Pyrrole Azaphenacenes

2019 ◽  
Author(s):  
Jack Yang ◽  
Nathan Li ◽  
Sean Li
Keyword(s):  
Crystal Structure ◽  
Machine Learning ◽  
Crystal Engineering ◽  
Crystal Packing ◽  
Similarity Index ◽  
Lattice Energy ◽  
Molecular Structures ◽  
Machine Learning Techniques ◽  
Specific Reference ◽  
Energy Landscapes

The ability to perform large-scale crystal structure predictions (CSP) have significantly advanced the synthesis of functional molecular solids by designs. In our recent work [Chem. Mater., 30, 4361 (2018)], we demonstrated our latest developments in organic CSPs by screening a set of 28 pyrrole azaphenacene isomers which led to one new molecule with higher thermodynamic stability and carrier mobilities in its crystalline form, compared to the one reported experimentally. Hereby, using the lattice energy landscapes for pyrrole azaphenacenes as examples, we applied machine-learning techniques to statistically reveal in more details, on how molecular symmetry and Z' values translate to the crystal packing landscapes, which in terms affect the coverage of landscape through quasi-random crystal structure samplings. A recurring theme in crystal engineering is to identify the probabilities of targeting isostructures to a specific reference crystal upon chemical functionalisations. For this, we propose here a global similarity index in conjunction with the Energy-Density Isostructurality (EDI) map to analyse the lattice energy landscapes for halogen substituted pyrrole azaphenacenes. A continue effort in the field is to accelerate CSPs for sampling a much wider chemical space for high-throughput material screenings, we propose a potential solution to this challenge drawn upon this study. Our work will hopefully stimulate the crystal engineering community in adapting a more statistically-oriented approach in understanding crystal packing of organic molecules in the age of digitisation.

Exchange Spin Coupling from Gaussian Process Regression

2020 ◽  
Author(s):  
Marc Philipp Bahlke ◽  
Natnael Mogos ◽  
Jonny Proppe ◽  
Carmen Herrmann
Keyword(s):  
Machine Learning ◽  
Gaussian Process ◽  
Gaussian Process Regression ◽  
Molecular Magnets ◽  
Molecular Structures ◽  
Spin Coupling ◽  
Structure Property ◽  
Data Set ◽  
Uncertainty Estimates

Heisenberg exchange spin coupling between metal centers is essential for describing and understanding the electronic structure of many molecular catalysts, metalloenzymes, and molecular magnets for potential application in information technology. We explore the machine-learnability of exchange spin coupling, which has not been studied yet. We employ Gaussian process regression since it can potentially deal with small training sets (as likely associated with the rather complex molecular structures required for exploring spin coupling) and since it provides uncertainty estimates (“error bars”) along with predicted values. We compare a range of descriptors and kernels for 257 small dicopper complexes and find that a simple descriptor based on chemical intuition, consisting only of copper-bridge angles and copper-copper distances, clearly outperforms several more sophisticated descriptors when it comes to extrapolating towards larger experimentally relevant complexes. Exchange spin coupling is similarly easy to learn as the polarizability, while learning dipole moments is much harder. The strength of the sophisticated descriptors lies in their ability to linearize structure-property relationships, to the point that a simple linear ridge regression performs just as well as the kernel-based machine-learning model for our small dicopper data set. The superior extrapolation performance of the simple descriptor is unique to exchange spin coupling, reinforcing the crucial role of choosing a suitable descriptor, and highlighting the interesting question of the role of chemical intuition vs. systematic or automated selection of features for machine learning in chemistry and material science.

Mol2vec: Unsupervised Machine Learning Approach with Chemical Intuition

2017 ◽  
Author(s):  
Sabrina Jaeger ◽  
Simone Fulle ◽  
Samo Turk
Keyword(s):  
Machine Learning ◽  
Language Processing ◽  
Supervised Machine Learning ◽  
Learning Approach ◽  
Learning Approaches ◽  
Unsupervised Machine Learning ◽  
Feature Representations ◽  
Machine Learning Approach ◽  
The Individual ◽  
Vector Representations

Inspired by natural language processing techniques we here introduce Mol2vec which is an unsupervised machine learning approach to learn vector representations of molecular substructures. Similarly, to the Word2vec models where vectors of closely related words are in close proximity in the vector space, Mol2vec learns vector representations of molecular substructures that are pointing in similar directions for chemically related substructures. Compounds can finally be encoded as vectors by summing up vectors of the individual substructures and, for instance, feed into supervised machine learning approaches to predict compound properties. The underlying substructure vector embeddings are obtained by training an unsupervised machine learning approach on a so-called corpus of compounds that consists of all available chemical matter. The resulting Mol2vec model is pre-trained once, yields dense vector representations and overcomes drawbacks of common compound feature representations such as sparseness and bit collisions. The prediction capabilities are demonstrated on several compound property and bioactivity data sets and compared with results obtained for Morgan fingerprints as reference compound representation. Mol2vec can be easily combined with ProtVec, which employs the same Word2vec concept on protein sequences, resulting in a proteochemometric approach that is alignment independent and can be thus also easily used for proteins with low sequence similarities.

Analysis of the Bath Motion in the MM-SQC Dynamics Using Unsupervised Machine Learning Dimensionality Reduction Approaches: Principal Component Analysis

2020 ◽  
Author(s):  
Jiawei Peng ◽  
Yu Xie ◽  
Deping Hu ◽  
Zhenggang Lan
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Collective Motion ◽  
Principal Component ◽  
Component Analysis ◽  
Nonadiabatic Dynamics ◽  
Trajectory Data ◽  
Unsupervised Machine Learning ◽  
Physical Knowledge ◽  
Vibronic Couplings

The system-plus-bath model is an important tool to understand nonadiabatic dynamics for large molecular systems. The understanding of the collective motion of a huge number of bath modes is essential to reveal their key roles in the overall dynamics. We apply the principal component analysis (PCA) to investigate the bath motion based on the massive data generated from the MM-SQC (symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian) nonadiabatic dynamics of the excited-state energy transfer dynamics of Frenkel-exciton model. The PCA method clearly clarifies that two types of bath modes, which either display the strong vibronic couplings or have the frequencies close to electronic transition, are very important to the nonadiabatic dynamics. These observations are fully consistent with the physical insights. This conclusion is obtained purely based on the PCA understanding of the trajectory data, without the large involvement of pre-defined physical knowledge. The results show that the PCA approach, one of the simplest unsupervised machine learning methods, is very powerful to analyze the complicated nonadiabatic dynamics in condensed phase involving many degrees of freedom.

scholarly journals Eye-blink artifact removal from single channel EEG with k-means and SSA

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ajay Kumar Maddirala ◽  
Kalyana C Veluvolu
Keyword(s):  
Machine Learning ◽  
Single Channel ◽  
Learning Algorithm ◽  
Singular Spectrum Analysis ◽  
Machine Learning Algorithm ◽  
Eeg Signal ◽  
Eeg Signals ◽  
Unsupervised Machine Learning ◽  
Eye Blink ◽  
Blink Artifact

AbstractIn recent years, the usage of portable electroencephalogram (EEG) devices are becoming popular for both clinical and non-clinical applications. In order to provide more comfort to the subject and measure the EEG signals for several hours, these devices usually consists of fewer EEG channels or even with a single EEG channel. However, electrooculogram (EOG) signal, also known as eye-blink artifact, produced by involuntary movement of eyelids, always contaminate the EEG signals. Very few techniques are available to remove these artifacts from single channel EEG and most of these techniques modify the uncontaminated regions of the EEG signal. In this paper, we developed a new framework that combines unsupervised machine learning algorithm (k-means) and singular spectrum analysis (SSA) technique to remove eye blink artifact without modifying actual EEG signal. The novelty of the work lies in the extraction of the eye-blink artifact based on the time-domain features of the EEG signal and the unsupervised machine learning algorithm. The extracted eye-blink artifact is further processed by the SSA method and finally subtracted from the contaminated single channel EEG signal to obtain the corrected EEG signal. Results with synthetic and real EEG signals demonstrate the superiority of the proposed method over the existing methods. Moreover, the frequency based measures [the power spectrum ratio ($$\Gamma $$ Γ ) and the mean absolute error (MAE)] also show that the proposed method does not modify the uncontaminated regions of the EEG signal while removing the eye-blink artifact.

Evaluation of caridean ecological distribution in the Ubatuba region, southeastern Brazilian coast using unsupervised machine learning technique

2021 ◽  
Author(s):  
Alexandre Oliveira Marques ◽  
Aline Nonato Sousa ◽  
Veronica Pereira Bernardes ◽  
Camila Hipolito Bernardo ◽  
Danielle Monique Reis ◽  
...  
Keyword(s):  
Machine Learning ◽  
Brazilian Coast ◽  
Ecological Distribution ◽  
Machine Learning Technique ◽  
Unsupervised Machine Learning ◽  
Learning Technique
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