Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Hydration free energies from kernel-based machine learning:
  Compound-database bias   We consider the prediction of a basic thermodynamic property---hydration free
energies---across a large subset of the chemical space of small organic
molecules. Our in silico study is based on computer simulations at the
atomistic level with implicit solvent. We report on a kernel-based machine
learning approach that is inspired by recent work in learning electronic
properties, but differs in key aspects: The representation is averaged over
several conformers to account for the statistical ensemble. We also include an
atomic-decomposition ansatz, which we show offers significant added
transferability compared to molecular learning. Finally, we explore the
existence of severe biases from databases of experimental compounds. By
performing a combination of dimensionality reduction and cross-learning models,
we show that the rate of learning depends significantly on the breadth and
variety of the training dataset. Our study highlights the dangers of fitting
machine-learning models to databases of narrow chemical range.

30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
1
The categories of this paper are: cs
13
The categories of this paper are: quant-ph
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
9
The categories of this paper are: nlin
19
The categories of this paper are: eess