Kenta Hongo

Genki I. Prayogo, Andrea Tirelli, Keishu Utimula et al.

A common approach for studying a solid solution or disordered system within a periodic ab-initio framework is to create a supercell in which a certain amount of target elements is substituted with other ones. The key to generating supercells is determining how to eliminate symmetry-equivalent structures from the large number of substitution patterns. Although the total number of substitutions is on the order of trillions, only symmetry-inequivalent atomic substitution patterns need to be identified, and their number is far smaller than the total. A straightforward solution would be to classify them after determining all possible patterns, but it is redundant and practically unfeasible. Therefore, to alleviate this drawback, we developed a new formalism based on the {\it canonical augmentation}, and successfully applied it to the atomic substitution problem. Our developed \verb|python| software package, which is called \textsc{SHRY} (\underline{S}uite for \underline{H}igh-th\underline{r}oughput generation of models with atomic substitutions implemented by p\underline{y}thon), enables us to pick up only symmetry-inequivalent structures from the vast number of candidates very efficiently. We demonstrate that the computational time required by our algorithm to find $N$ symmetry-inequivalent structures scales {\it linearly} with $N$ up to $\sim 10^9$. This is the best scaling for such problems.

Keishu Utimula, Ken-taro Hayaschi, Trevor J. Bihl et al.

When agents swarm to execute a mission, some of them frequently exhibit sudden failure, as observed from the command base. It is generally difficult to determine whether a failure is caused by actuators (hypothesis, $h_a$) or sensors (hypothesis, $h_s$) by solely relying on the communication between the command base and concerning agent. However, by instigating collusion between the agents, the cause of failure can be identified; in other words, we expect to detect corresponding displacements for $h_a$ but not for $h_s$. In this study, we considered the question as to whether artificial intelligence can autonomously generate an action plan $\boldsymbol{g}$ to pinpoint the cause as aforedescribed. Because the expected response to $\boldsymbol{g}$ generally depends upon the adopted hypothesis [let the difference be denoted by $D(\boldsymbol{g})$], a formulation that uses $D\left(\boldsymbol{g}\right)$ to pinpoint the cause can be made. Although a $\boldsymbol{g}^*$ that maximizes $D(\boldsymbol{g})$ would be a suitable action plan for this task, such an optimization is difficult to achieve using the conventional gradient method, as $D(\boldsymbol{g})$ becomes nonzero in rare events such as collisions with other agents, and most swarm actions $\boldsymbol{g}$ give $D(\boldsymbol{g})=0$. In other words, throughout almost the entire space of $\boldsymbol{g}$, $D(\boldsymbol{g})$ has zero gradient, and the gradient method is not applicable. To overcome this problem, we formulated an action plan using Q-table reinforcement learning. Surprisingly, the optimal action plan generated via reinforcement learning presented a human-like solution to pinpoint the problem by colliding other agents with the failed agent. Using this simple prototype, we demonstrated the potential of applying Q-table reinforcement learning methods to plan autonomous actions to pinpoint the causes of failure.