10 citations · 17 across the 6 of their papers we have counts for
6 papers
Understanding the Structure of QM7b and QM9 Quantum Mechanical Datasets Using Unsupervised Learning
Julio J. Valdés, Alain B. Tchagang
This paper explores the internal structure of two quantum mechanics datasets (QM7b, QM9), composed of several thousands of organic molecules and described in terms of electronic pr…
Deep Evolutionary Learning for Molecular Design
Yifeng Li, Hsu Kiang Ooi, Alain Tchagang
In this paper, we propose a deep evolutionary learning (DEL) process that integrates fragment-based deep generative model and multi-objective evolutionary computation for molecular…
Molecular Design Using Signal Processing and Machine Learning: Time-Frequency-like Representation and Forward Design
Alain B. Tchagang, Ahmed H. Tewfik, Julio J. Valdés
Accumulation of molecular data obtained from quantum mechanics (QM) theories such as density functional theory (DFTQM) make it possible for machine learning (ML) to accelerate the…
A Classification Scheme for Inverse Design of Molecules: from Targeted Electronic Properties to Atomicity
Alain Tchagang, Julio Valdés
In machine learning and molecular design, there exist two approaches: discriminative and generative. In the discriminative approach dubbed forward design, the goal is to map a set…
Prediction of the Atomization Energy of Molecules Using Coulomb Matrix and Atomic Composition in a Bayesian Regularized Neural Networks
Alain Tchagang, Julio Valdés
Exact calculation of electronic properties of molecules is a fundamental step for intelligent and rational compounds and materials design. The intrinsically graph-like and non-vect…
Discrete Fourier Transform Improves the Prediction of the Electronic Properties of Molecules in Quantum Machine Learning
Alain Tchagang, Julio Valdés
High-throughput approximations of quantum mechanics calculations and combinatorial experiments have been traditionally used to reduce the search space of possible molecules, drugs…