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Abstract Coarse graining techniques play an essential role in accelerating molecular simulations of systems with large length and time scales. Theoretically grounded bottom-up models are appealing due to their thermodynamic consistency with the underlying all-atom models. In this direction, machine learning approaches hold great promise to fitting complex many-body data. However, training models may require collection of large amounts of expensive data. Moreover, quantifying trained model accuracy is challenging, especially in cases of non-trivial free energy configurations, where training data may be sparse. We demonstrate a path towards uncertainty-aware models of coarse grained free energy surfaces. Specifically, we show that principled Bayesian model uncertainty allows for efficient data collection through an on-the-fly active learning framework and opens the possibility of adaptive transfer of models across different chemical systems. Uncertainties also characterize models’ accuracy of free energy predictions, even when training is performed only on forces. This work helps pave the way towards efficient autonomous training of reliable and uncertainty aware many-body machine learned coarse grain models. 

The next-generation semiconductors and devices, such as halide perovskites and flexible electronics, are extremely sensitive to water, thus demanding highly effective protection that not only seals out water in all forms (vapor, droplet, and ice), but simultaneously provides mechanical flexibility, durability, transparency, and self-cleaning. Although various solid-state encapsulation methods have been developed, no strategy is available that can fully meet all the above requirements. Here, we report a bioinspired liquid-based encapsulation strategy that offers protection from water without sacrificing the operational properties of the encapsulated materials. Using halide perovskite as a model system, we show that damage to the perovskite from exposure to water is drastically reduced when it is coated by a polymer matrix with infused hydrophobic oil. With a combination of experimental and simulation studies, we elucidated the fundamental transport mechanisms of ultralow water transmission rate that stem from the ability of the infused liquid to fill-in and reduce defects in the coating layer, thus eliminating the low-energy diffusion pathways, and to cause water molecules to diffuse as clusters, which act together as an excellent water permeation barrier. Importantly, the presence of the liquid, as the central component in this encapsulation method provides a unique possibility of reversing the water transport direction; therefore, the lifetime of enclosed water-sensitive materials could be significantly extended via replenishing the hydrophobic oils regularly. We show that the liquid encapsulation platform presented here has high potential in providing not only water protection of the functional device but also flexibility, optical transparency, and self-healing of the coating layer, which are critical for a variety of applications, such as in perovskite solar cells and bioelectronics. 

Electronic devicesforrecording neuralactivityinthe nervoussyste m needto bescalableacrosslargespatialandte mporalscales whilealso providing millisecondandsingle-cellspatiote mporalresolution. H o w e v e r, e xi s ti n g hi g h- r e s ol u ti o n n e u r al r e c o r di n g d e vi c e s c a n n o t achievesi multaneousscalability on bothspatialandte mporallevels due toatrade-offbetweensensordensityand mechanicalflexibility. Here weintroduceathree-di mensional(3D)stackingi mplantableelectronic platfor m,basedonperfluorinateddielectricelasto mersandtissue-levelsoft multilayerelectrodes,thatenablesspatiote mporallyscalablesingle-cell neuralelectrophysiologyinthenervoussyste m. Ourelasto mersexhibit stable dielectric perfor mancefor overayearin physiologicalsolutions andare10,000ti messofterthanconventional plastic dielectrics. By leveragingthese uniquecharacteristics we developthe packaging of lithographednano metre-thickelectrodearraysina3Dconfiguration with across-sectionaldensityof7.6electrodesper100μ m2.Theresulting3D integrated multilayersoftelectrodearrayretainstissue-levelflexibility, reducingchronici m muneresponsesin mouse neuraltissues,and de monstratestheabilitytoreliablytrackelectricalactivityinthe mouse brain orspinalcord over months without disruptingani mal behaviour.