scholarly journals Improving molecular force fields across configurational space by combining supervised and unsupervised machine learning

2021 ◽  
Vol 154 (12) ◽  
pp. 124102
Author(s):  
Gregory Fonseca ◽  
Igor Poltavsky ◽  
Valentin Vassilev-Galindo ◽  
Alexandre Tkatchenko
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Unsupervised Machine Learning ◽  
Configurational Space ◽  
Molecular Force

scholarly journals A transferable active-learning strategy for reactive molecular force fields

2021 ◽  
Author(s):  
Tom Young ◽  
Tristan Johnston-Wood ◽  
Volker L. Deringer ◽  
Fernanda Duarte
Keyword(s):  
Machine Learning ◽  
Active Learning ◽  
Learning Strategy ◽  
Force Fields ◽  
Molecular Simulations ◽  
Quantum Mechanical ◽  
Interatomic Potentials ◽  
Molecular Force ◽  
High Level ◽  
Active Learning Strategy

Predictive molecular simulations require fast, accurate and reactive interatomic potentials. Machine learning offers a promising approach to construct such potentials by fitting energies and forces to high-level quantum-mechanical data, but...

scholarly journals Machine learning of correlated dihedral potentials for atomistic molecular force fields

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Pascal Friederich ◽  
Manuel Konrad ◽  
Timo Strunk ◽  
Wolfgang Wenzel
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Molecular Force

Utilizing Machine Learning for Efficient Parameterization of Coarse Grained Molecular Force Fields

2019 ◽  
Vol 59 (10) ◽  
pp. 4278-4288 ◽  
Author(s):  
James L. McDonagh ◽  
Ardita Shkurti ◽  
David J. Bray ◽  
Richard L. Anderson ◽  
Edward O. Pyzer-Knapp
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Coarse Grained ◽  
Molecular Force

scholarly journals sGDML: Constructing accurate and data efficient molecular force fields using machine learning

2019 ◽  
Vol 240 ◽  
pp. 38-45 ◽  
Author(s):  
Stefan Chmiela ◽  
Huziel E. Sauceda ◽  
Igor Poltavsky ◽  
Klaus-Robert Müller ◽  
Alexandre Tkatchenko
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Molecular Force

scholarly journals Machine learning of accurate energy-conserving molecular force fields

2017 ◽  
Vol 3 (5) ◽  
pp. e1603015 ◽  
Author(s):  
Stefan Chmiela ◽  
Alexandre Tkatchenko ◽  
Huziel E. Sauceda ◽  
Igor Poltavsky ◽  
Kristof T. Schütt ◽  
...  
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Molecular Force ◽  
Energy Conserving

scholarly journals Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields

2020 ◽  
Vol 153 (12) ◽  
pp. 124109
Author(s):  
Huziel E. Sauceda ◽  
Michael Gastegger ◽  
Stefan Chmiela ◽  
Klaus-Robert Müller ◽  
Alexandre Tkatchenko
Keyword(s):  
Machine Learning ◽  
Force Fields ◽  
Molecular Force ◽  
Gradient Domain

scholarly journals Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces

2019 ◽  
Vol 150 (11) ◽  
pp. 114102 ◽  
Author(s):  
Huziel E. Sauceda ◽  
Stefan Chmiela ◽  
Igor Poltavsky ◽  
Klaus-Robert Müller ◽  
Alexandre Tkatchenko
Keyword(s):  
Machine Learning ◽  
Small Molecules ◽  
Force Fields ◽  
Coupled Cluster ◽  
Molecular Force ◽  
Gradient Domain

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.

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