Quantum chemistry–machine learning approach for predicting and elucidating molecular hyperpolarizability: Application to [2.2]paracyclophane-containing push–pull polymers

2021 ◽  
Vol 154 (12) ◽  
pp. 124107
Author(s):  
Mariia V. Ivonina ◽  
Yuuichi Orimoto ◽  
Yuriko Aoki
Keyword(s):  
Machine Learning ◽  
Quantum Chemistry ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Molecular Hyperpolarizability

scholarly journals Big Data Meets Quantum Chemistry Approximations: The Δ-Machine Learning Approach

2015 ◽  
Vol 11 (5) ◽  
pp. 2087-2096 ◽  
Author(s):  
Raghunathan Ramakrishnan ◽  
Pavlo O. Dral ◽  
Matthias Rupp ◽  
O. Anatole von Lilienfeld
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Quantum Chemistry ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals A Mixed Quantum Chemistry/Machine Learning Approach for the Fast and Accurate Prediction of Biochemical Redox Potentials and Its Large-Scale Application to 315 000 Redox Reactions

2019 ◽  
Vol 5 (7) ◽  
pp. 1199-1210 ◽  
Author(s):  
Adrian Jinich ◽  
Benjamin Sanchez-Lengeling ◽  
Haniu Ren ◽  
Rebecca Harman ◽  
Alán Aspuru-Guzik
Keyword(s):  
Machine Learning ◽  
Quantum Chemistry ◽  
Redox Reactions ◽  
Large Scale ◽  
Accurate Prediction ◽  
Learning Approach ◽  
Redox Potentials ◽  
Machine Learning Approach

The impact of economic plans on the Chinese education system: a machine learning approach

CADMO ◽  
2018 ◽  
pp. 37-49
Author(s):  
Wenjun Lin ◽  
Xuefu Xu ◽  
Francesco Dell’Anna
Keyword(s):  
Machine Learning ◽  
Education System ◽  
Learning Approach ◽  
Chinese Education ◽  
System A ◽  
Machine Learning Approach ◽  
The Impact

Improving Bandwidth Utilization and Fairness between TCP Flows based on a Machine-learning Approach

2013 ◽  
Vol 133 (6) ◽  
pp. 1259-1268
Author(s):  
Akihiro Shiozu ◽  
Syunji Yazaki ◽  
K^|^ocirc;ki Abe
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Bandwidth Utilization ◽  
Machine Learning Approach

A Machine Learning Approach to Anaphora Resolution in Arabic

2014 ◽  
Vol 9 (12) ◽  
pp. 1956
Author(s):  
Abdullatif Abolohom ◽  
Nazlia Omar
Keyword(s):  
Machine Learning ◽  
Learning Approach ◽  
Anaphora Resolution ◽  
Machine Learning Approach

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use

A Machine Learning Approach to Jet-Surface Interaction Noise Modeling

2020 ◽  
Author(s):  
Clifford A. Brown ◽  
Jonny Dowdall ◽  
Brian Whiteaker ◽  
Lauren McIntyre
Keyword(s):  
Machine Learning ◽  
Surface Interaction ◽  
Learning Approach ◽  
Noise Modeling ◽  
Machine Learning Approach

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.

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