29 research outputs found
Microscopic origin of tunable assembly forces in chiral active environments
The fluctuations of a nonequilibrium bath enable dynamics inaccessible to any
equilibrium system. Exploiting the driven dynamics of active matter in order to
do useful work has become a topic of significant experimental and theoretical
interest. Due to the unique modalities controlling self-assembly, the interplay
between passive solutes and the particles in an active bath has been studied as
a potential driving force to guide assembly of otherwise non-interacting
objects. Here, we investigate and characterize the microscopic origins of the
attractive and repulsive interactions between passive solutes in an active
bath. We show that, while assembly does not occur dynamically for achiral
active baths, chiral active particles can produce stable and robust assembly
forces. We both explain the observed oscillatory force profile for active
Brownian particles and demonstrate that chiral active motion leads to fluxes
consistent with an odd diffusion tensor that, when appropriately tuned,
produces long-ranged assembly forces
Efficiency and Large Deviations in Time-Asymmetric Stochastic Heat Engines
In a stochastic heat engine driven by a cyclic non-equilibrium protocol,
fluctuations in work and heat give rise to a fluctuating efficiency. Using
computer simulations and tools from large deviation theory, we have examined
these fluctuations in detail for a model two-state engine. We find in general
that the form of efficiency probability distributions is similar to those
described by Verley et al. [2014 Nat Comm, 5 4721], in particular featuring a
local minimum in the long-time limit. In contrast to the time-symmetric engine
protocols studied previously, however, this minimum need not occur at the value
characteristic of a reversible Carnot engine. Furthermore, while the local
minimum may reside at the global minimum of a large deviation rate function, it
does not generally correspond to the least likely efficiency measured over
finite time. We introduce a general approximation for the finite-time
efficiency distribution, , based on large deviation statistics of work
and heat, that remains very accurate even when deviates significantly
from its large deviation form.Comment: 10 pages, 3 figure
Near-optimal protocols in complex nonequilibrium transformations
The development of sophisticated experimental means to control nanoscale
systems has motivated efforts to design driving protocols which minimize the
energy dissipated to the environment. Computational models are a crucial tool
in this practical challenge. We describe a general method for sampling an
ensemble of finite-time, nonequilibrium protocols biased towards a low average
dissipation. We show that this scheme can be carried out very efficiently in
several limiting cases. As an application, we sample the ensemble of
low-dissipation protocols that invert the magnetization of a 2D Ising model and
explore how the diversity of the protocols varies in response to constraints on
the average dissipation. In this example, we find that there is a large set of
protocols with average dissipation close to the optimal value, which we argue
is a general phenomenon.Comment: 6 pages and 3 figures plus 4 pages and 5 figures of supplemental
materia
Ensuring thermodynamic consistency with invertible coarse-graining
Coarse-grained models are a core computational tool in theoretical chemistry
and biophysics. A judicious choice of a coarse-grained model can yield physical
insight by isolating the essential degrees of freedom that dictate the
thermodynamic properties of a complex, condensed-phase system. The reduced
complexity of the model typically leads to lower computational costs and more
efficient sampling compared to atomistic models. Designing ``good''
coarse-grained models is an art. Generally, the mapping from fine-grained
configurations to coarse-grained configurations itself is not optimized in any
way; instead, the energy function associated with the mapped configurations is.
In this work, we explore the consequences of optimizing the coarse-grained
representation alongside its potential energy function. We use a graph machine
learning framework to embed atomic configurations into a low dimensional space
to produce efficient representations of the original molecular system. Because
the representation we obtain is no longer directly interpretable as a real
space representation of the atomic coordinates, we also introduce an inversion
process and an associated thermodynamic consistency relation that allows us to
rigorously sample fine-grained configurations conditioned on the coarse-grained
sampling. We show that this technique is robust, recovering the first two
moments of the distribution of several observables in proteins such as
chignolin and alanine dipeptide