Computational material discovery is under intense study owing to its ability to explore the vast space of chemical systems. Neural network potentials (NNPs) have been shown to be particularly effective in conducting atomistic simulations for such purposes. However, existing NNPs are generally designed for narrow target materials, making them unsuitable for broader applications in material discovery. Here we report a development of universal NNP called PreFerred Potential (PFP), which is able to handle any combination of 45 elements. Particular emphasis is placed on the datasets, which include a diverse set of virtual structures used to attain the universality. We demonstrated the applicability of PFP in selected domains: lithium diffusion in LiFeSO4F, molecular adsorption in metal-organic frameworks, an order–disorder transition of Cu-Au alloys, and material discovery for a Fischer–Tropsch catalyst. They showcase the power of PFP, and this technology provides a highly useful tool for material discovery.
Automatic design of molecules with specific chemical
and biochemical
properties is an important process in material informatics and computational
drug discovery. In this study, we designed a novel coarse-grained
tree representation of molecules (Reversible Junction Tree; “RJT”)
for the aforementioned purposes, which is reversely convertible to
the original molecule without external information. By leveraging
this representation, we further formulated the molecular design and
optimization problem as a tree-structure construction using deep reinforcement
learning (“RJT-RL”). In this method, all of the intermediate
and final states of reinforcement learning are convertible to valid
molecules, which could efficiently guide the optimization process
in simple benchmark tasks. We further examined the multiobjective
optimization and fine-tuning of the reinforcement learning models
using RJT-RL, demonstrating the applicability of our method to more
realistic tasks in drug discovery.
In this paper, we introduce ChainerRL, an open-source Deep Reinforcement Learning (DRL) library built using Python and the Chainer deep learning framework. ChainerRL implements a comprehensive set of DRL algorithms and techniques drawn from the state-of-the-art research in the field. To foster reproducible research, and for instructional purposes, ChainerRL provides scripts that closely replicate the original papers' experimental settings and reproduce published benchmark results for several algorithms. Lastly, ChainerRL offers a visualization tool that enables the qualitative inspection of trained agents. The ChainerRL source code can be found on GitHub: https://github.com/chainer/chainerrl.
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