Person:
Kozinsky, Boris

Profile Picture

Email Address

AA Acceptance Date

Birth Date

Research Projects

Organizational Units

Job Title

Last Name

Kozinsky

First Name

Boris

Name

Kozinsky, Boris
Author's Publications: search.filters.isAuthorOfPublication.e4a3accc-6771-4459-ad8e-028176ef1b7a

Search Results

Now showing 1 - 2 of 2
  • No Thumbnail Available
    Publication
    Fundamental Limits to the Electrochemical Impedance Stability of Dielectric Elastomers in Bioelectronics
    (American Chemical Society (ACS), 2019-11-28) Le Floch, Paul; Molinari, Nicola; Nan, Kewang; Zhang, Shuwen; Kozinsky, Boris; Suo, Zhigang; Liu, Jia
    Incorporation of elastomers into bioelectronics that reduces the mechanical mismatch between electronics and biological systems could potentially improve the long-term electronics–tissue interface. However, the chronic stability of elastomers in physiological conditions has not been systematically studied. Here, using electrochemical impedance spectrum we find that the electrochemical impedance of dielectric elastomers degrades over time in physiological environments. Both experimental and computational results reveal that this phenomenon is due to the diffusion of ions from the physiological solution into elastomers over time. Their conductivity increases by 6 orders of magnitude up to 10–8 S/m. When the passivated conductors are also composed of intrinsically stretchable materials, higher leakage currents can be detected. Scaling analyses suggest fundamental limitations to the electrical performances of interconnects made of stretchable materials.
  • No Thumbnail Available
    Publication
    A Fast Neural Network Approach for Direct Covariant Forces Prediction in Complex Multi-Element Extended Systems
    (Springer Science and Business Media LLC, 2019-09-30) Batzner, Simon; Molinari, Nicola; Kozinsky, Boris; Mailoa, Jonathan; Kornbluth, Mordechai; Samsonidze, Georgy; Lam, Stephen; Vandermause, Jonathan; Ablitt, Chris
    Neural network force field (NNFF) is a method for performing regression on atomic structure–force relationships, bypassing the expensive quantum mechanics calculations that prevent the execution of long ab initio quality molecular dynamics (MD) simulations. However, most NNFF methods for complex multi-element atomic systems indirectly predict atomic force vectors by exploiting just atomic structure rotation-invariant features and network-feature spatial derivatives, which are computationally expensive. Here, we show a staggered NNFF architecture that exploits both rotation-invariant and -covariant features to directly predict atomic force vectors without using spatial derivatives, and we demonstrate 2.2× NNFF–MD acceleration over a state-of-the-art C++ engine using a Python engine. This fast architecture enables us to develop NNFF for complex ternary- and quaternary-element extended systems composed of long polymer chains, amorphous oxide and surface chemical reactions. The rotation-invariant–covariant architecture described here can also directly predict complex covariant vector outputs from local environments, in other domains beyond computational material science.