Materials Discovery with Extreme Properties via AI-Driven Combinatorial Chemistry

The goal of most materials discovery is to discover materials that are superior to those currently known. Fundamentally, this is close to extrapolation, which is a weak point for most machine learning models that learn the probability distribution of data. Herein, we develop AI-driven combinatorial chemistry, which is a rule-based inverse molecular designer that does not rely on data. Since our model has the potential to generate all possible molecular structures that can be obtained from combinations of molecular fragments, unknown materials with superior properties can be discovered. We theoretically and empirically demonstrate that our model is more suitable for discovering better materials than probability distribution-learning models. In an experiment aimed at discovering molecules that hit seven target properties, our model discovered 1,315 of all target-hitting molecules and 7,629 of five target-hitting molecules out of 100,000 trials, whereas the probability distribution-learning models failed. To illustrate the performance in actual problems, we also demonstrate that our models work well on two practical applications: discovering protein docking materials and HIV inhibitors.

Optimal Planning of Hybrid Energy Storage Systems using Curtailed Renewable Energy through Deep Reinforcement Learning

Energy management systems (EMS) are becoming increasingly important in order to utilize the continuously growing curtailed renewable energy. Promising energy storage systems (ESS), such as batteries and green hydrogen should be employed to maximize the efficiency of energy stakeholders. However, optimal decision-making, i.e., planning the leveraging between different strategies, is confronted with the complexity and uncertainties of large-scale problems. Here, we propose a sophisticated deep reinforcement learning (DRL) methodology with a policy-based algorithm to realize the real-time optimal ESS planning under the curtailed renewable energy uncertainty. A quantitative performance comparison proved that the DRL agent outperforms the scenario-based stochastic optimization (SO) algorithm, even with a wide action and observation space. Owing to the uncertainty rejection capability of the DRL, we could confirm a robust performance, under a large uncertainty of the curtailed renewable energy, with a maximizing net profit and stable system. Action-mapping was performed for visually assessing the action taken by the DRL agent according to the state. The corresponding results confirmed that the DRL agent learns the way like what a human expert would do, suggesting reliable application of the proposed methodology.

A2I Transformer: Permutation-equivariant attention network for pairwise and many-body interactions with minimal featurization

The combination of neural network potential (NNP) with molecular simulations plays an important role in an efficient and thorough understanding of a molecular system's potential energy surface (PES). However, grasping the interplay between input features and their local contribution to NNP is growingly evasive due to heavy featurization. In this work, we suggest an end-to-end model which directly predicts per-atom energy from the coordinates of particles, avoiding expert-guided featurization of the network input. Employing self-attention as the main workhorse, our model is intrinsically equivariant under the permutation operation, resulting in the invariance of the total potential energy. We tested our model against several challenges in molecular simulation problems, including periodic boundary condition (PBC), $n$-body interaction, and binary composition. Our model yielded stable predictions in all tested systems with errors significantly smaller than the potential energy fluctuation acquired from molecular dynamics simulations. Thus, our work provides a minimal baseline model that encodes complex interactions in a condensed phase system to facilitate the data-driven analysis of physicochemical systems.

Generative chemical transformer: attention makes neural machine learn molecular geometric structures via text

Chemical formula is an artificial language that expresses molecules as text. Neural machines that have learned chemical language can be used as a tool for inverse molecular design. Here, we propose a neural machine that creates molecules that meet some desired conditions based on a deep understanding of chemical language (generative chemical Transformer, GCT). Attention-mechanism in GCT allows a deeper understanding of molecular structures, beyond the limitations of chemical language itself that cause semantic discontinuity, by paying attention to characters sparsely. We investigate the significance of language models to inverse molecular design problems by quantitatively evaluating the quality of generated molecules. GCT generates highly realistic chemical strings that satisfy both a chemical rule and grammars of a language. Molecules parsed from generated strings simultaneously satisfy the multiple target properties and are various for a single condition set. GCT generates de novo molecules, and this is done in a short time that human experts cannot. These advances will contribute to improving the quality of human life by accelerating the process of desired material discovery.