58 research outputs found
Constant Size Molecular Descriptors For Use With Machine Learning
A set of molecular descriptors whose length is independent of molecular size
is developed for machine learning models that target thermodynamic and
electronic properties of molecules. These features are evaluated by monitoring
performance of kernel ridge regression models on well-studied data sets of
small organic molecules. The features include connectivity counts, which
require only the bonding pattern of the molecule, and encoded distances, which
summarize distances between both bonded and non-bonded atoms and so require the
full molecular geometry. In addition to having constant size, these features
summarize information regarding the local environment of atoms and bonds, such
that models can take advantage of similarities resulting from the presence of
similar chemical fragments across molecules. Combining these two types of
features leads to models whose performance is comparable to or better than the
current state of the art. The features introduced here have the advantage of
leading to models that may be trained on smaller molecules and then used
successfully on larger molecules.Comment: 18 pages, 5 figure
Autonomous discovery in the chemical sciences part II: Outlook
This two-part review examines how automation has contributed to different
aspects of discovery in the chemical sciences. In this second part, we reflect
on a selection of exemplary studies. It is increasingly important to articulate
what the role of automation and computation has been in the scientific process
and how that has or has not accelerated discovery. One can argue that even the
best automated systems have yet to ``discover'' despite being incredibly useful
as laboratory assistants. We must carefully consider how they have been and can
be applied to future problems of chemical discovery in order to effectively
design and interact with future autonomous platforms.
The majority of this article defines a large set of open research directions,
including improving our ability to work with complex data, build empirical
models, automate both physical and computational experiments for validation,
select experiments, and evaluate whether we are making progress toward the
ultimate goal of autonomous discovery. Addressing these practical and
methodological challenges will greatly advance the extent to which autonomous
systems can make meaningful discoveries.Comment: Revised version available at 10.1002/anie.20190998
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