Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A self-learning algorithm for biased molecular dynamics   A new self-learning algorithm for accelerated dynamics, reconnaissance
metadynamics, is proposed that is able to work with a very large number of
collective coordinates. Acceleration of the dynamics is achieved by
constructing a bias potential in terms of a patchwork of one-dimensional,
locally valid collective coordinates. These collective coordinates are obtained
from trajectory analyses so that they adapt to any new features encountered
during the simulation. We show how this methodology can be used to enhance
sampling in real chemical systems citing examples both from the physics of
clusters and from the biological sciences.

8
The categories of this paper are: hep-ex
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
27
The categories of this paper are: comp-gas
34
The categories of this paper are: chem-ph
18
The categories of this paper are: econ
28
The categories of this paper are: alg-geom
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph