Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Machine learning plasma-surface interface for coupling sputtering and
  gas-phase transport simulations   Thin film processing by means of sputter deposition inherently depends on the
interaction of energetic particles with a target surface and the subsequent
particle transport. The length and time scales of the underlying physical
phenomena span orders of magnitudes. A theoretical description which bridges
all time and length scales is not practically possible. Advantage can be taken
particularly from the well-separated time scales of the fundamental surface and
plasma processes. Initially, surface properties may be calculated from a
surface model and stored for a number of representative cases. Subsequently,
the surface data may be provided to gas-phase transport simulations via
appropriate model interfaces (e.g., analytic expressions or look-up tables) and
utilized to define insertion boundary conditions. During run-time evaluation,
however, the maintained surface data may prove to be not sufficient. In this
case, missing data may be obtained by interpolation (common), extrapolation
(inaccurate), or be supplied on-demand by the surface model (computationally
inefficient). In this work, a potential alternative is established based on
machine learning techniques using artificial neural networks. As a proof of
concept, a multilayer perceptron network is trained and verified with sputtered
particle distributions obtained from transport of ions in matter based
simulations for Ar projectiles bombarding a Ti-Al composite. It is demonstrated
that the trained network is able to predict the sputtered particle
distributions for unknown, arbitrarily shaped incident ion energy
distributions. It is consequently argued that the trained network may be
readily used as a machine learning based model interface (e.g., by
quasi-continuously sampling the desired sputtered particle distributions from
the network), which is sufficiently accurate also in scenarios which have not
been previously trained.

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The categories of this paper are: astro-ph
32
The categories of this paper are: atom-ph
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th
1
The categories of this paper are: cs
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The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: cond-mat
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The categories of this paper are: acc-phys
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The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
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The categories of this paper are: q-bio
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The categories of this paper are: solv-int
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The categories of this paper are: comp-gas