483 research outputs found
Machine learning with systematic density-functional theory calculations: Application to melting temperatures of single and binary component solids
A combination of systematic density functional theory (DFT) calculations and
machine learning techniques has a wide range of potential applications. This
study presents an application of the combination of systematic DFT calculations
and regression techniques to the prediction of the melting temperature for
single and binary compounds. Here we adopt the ordinary least-squares
regression (OLSR), partial least-squares regression (PLSR), support vector
regression (SVR) and Gaussian process regression (GPR). Among the four kinds of
regression techniques, the SVR provides the best prediction. In addition, the
inclusion of physical properties computed by the DFT calculation to a set of
predictor variables makes the prediction better. Finally, a simulation to find
the highest melting temperature toward the efficient materials design using
kriging is demonstrated. The kriging design finds the compound with the highest
melting temperature much faster than random designs. This result may stimulate
the application of kriging to efficient materials design for a broad range of
applications
ChemTS: An Efficient Python Library for de novo Molecular Generation
Automatic design of organic materials requires black-box optimization in a
vast chemical space. In conventional molecular design algorithms, a molecule is
built as a combination of predetermined fragments. Recently, deep neural
network models such as variational auto encoders (VAEs) and recurrent neural
networks (RNNs) are shown to be effective in de novo design of molecules
without any predetermined fragments. This paper presents a novel python library
ChemTS that explores the chemical space by combining Monte Carlo tree search
(MCTS) and an RNN. In a benchmarking problem of optimizing the octanol-water
partition coefficient and synthesizability, our algorithm showed superior
efficiency in finding high-scoring molecules. ChemTS is available at
https://github.com/tsudalab/ChemTS
A generative model for molecule generation based on chemical reaction trees
Deep generative models have been shown powerful in generating novel molecules
with desired chemical properties via their representations such as strings,
trees or graphs. However, these models are limited in recommending synthetic
routes for the generated molecules in practice. We propose a generative model
to generate molecules via multi-step chemical reaction trees. Specifically, our
model first propose a chemical reaction tree with predicted reaction templates
and commercially available molecules (starting molecules), and then perform
forward synthetic steps to obtain product molecules. Experiments show that our
model can generate chemical reactions whose product molecules are with desired
chemical properties. Also, the complete synthetic routes for these product
molecules are provided
Discovery of low thermal conductivity compounds with first-principles anharmonic lattice dynamics calculations and Bayesian optimization
Compounds of low lattice thermal conductivity (LTC) are essential for seeking
thermoelectric materials with high conversion efficiency. Some strategies have
been used to decrease LTC. However, such trials have yielded successes only
within a limited exploration space. Here we report the virtual screening of a
library containing 54,779 compounds. Our strategy is to search the library
through Bayesian optimization using for the initial data the LTC obtained from
first-principles anharmonic lattice dynamics calculations for a set of 101
compounds. We discovered 221 materials with very low LTC. Two of them have even
an electronic band gap < 1 eV, what makes them exceptional candidates for
thermoelectric applications. In addition to those newly discovered
thermoelectric materials, the present strategy is believed to be powerful for
many other applications in which chemistry of materials are required to be
optimized.Comment: 6 pages, 4 figure
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