The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material and the design rules change to finding the set of Pareto optimal materials. In this work, we leverage an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. We apply our algorithm to de novo polymer design with a prohibitively large search space. Using molecular simulations, we compute key descriptors for dispersant applications and drastically reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence. This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.

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<div>The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material, and the design rules change to finding the set of Pareto optimal materials. </div><div>In this work, we introduce an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. <br></div><div>We apply our algorithm to de novo polymer design with a prohibitively large search space.</div><div>Using molecular simulations, we compute key descriptors for dispersant applications and reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence by over 98% compared to random search.</div><div>This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.</div>

The ever-increasing concerns over urban air quality, noise pollution, and considerable savings in total cost of ownership encouraged more and more cities to introduce battery electric buses (e-bus). Based on the sensor records of 99 e-buses that included over 250,000 h across 4.7 million kilometers, this paper unveiled the relationship between driving behaviors and e-bus battery energy consumption under various environments. Battery efficiency was evaluated by the distance traveled per unit battery energy (1% SoC, State of Charge). Mix effect regression was applied to quantify the magnitude and correlation between multiple factors; and 13 machine learning methods were adopted for enhanced prediction and optimization. Although regenerative braking could make a positive contribution to e-bus battery energy recovery, unstable driving styles with greater speed variation or acceleration would consume more energy, hence reduce the battery efficiency. The timing window is another significant factor and the result showed higher efficiency at night, over weekends, or during cooler seasons. Assuming a normal driving behavior, this paper investigated the most economical driving speed in order to maximize battery efficiency. An average of 19% improvement could be achieved, and the optimal driving speed is time-dependent, ranging from 11 to 18 km/h.

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