We present here the results from an ongoing determination of the critical quark mass in simulations of (2+1)-flavor QCD with an imaginary chemical potential. Studies with unimproved actions found the existence of a critical quark mass value at which the crossover transition ends on a second order phase transition and becomes first order for smaller values of the quark mass for the case of both vanishing and imaginary chemical potential. We use the Highly Improved Staggered Quark (HISQ) action and perform calculations in the Roberge-Weiss (RW) plane, where the value of the critical mass is expected to be largest. The lowest quark mass value used in our simulation corresponds to the pion mass m π , down to 40 MeV. Contrary to calculations performed with unimproved actions we find no evidence for the occurrence of first order transitions at the smallest quark mass values explored so far. Moreover we also show that the chiral observables are sensitive to the RW transition. Our results also indicate that the RW transition and chiral transition could coincide in the chiral limit.

Optimizing reactions in the chemical industry is one of the major challenges in the pursuit of economic and ecological sustainability. With ongoing research in this field, the amount of available data has greatly increased, which makes it suitable for machine learning approaches. In this paper, the application of reinforcement learning for finding optimal reaction conditions of the partial oxidation of methane (POX) is tested. Q-learning (QL) agents and deep deterministic policy gradient (DDPG) agents are trained to maximize H2 production by partial oxidation of methane in a simulated plug flow reactor. Although the QL agent showed promising results in a simplified environment, it was not able to achieve improvements in the simulation environment. A clear superiority of the DDPG agent was observed, as it was able to maximize H2 production by adjusting temperature, pressure, flow velocity, and substrate composition. This proves that reinforcement learning is applicable for reaction optimization and a promising concept to improve efficiency in chemical processes.

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