  The discovery of novel materials and functional molecules can help to solve
some of society's most urgent challenges, ranging from efficient energy
harvesting and storage to uncovering novel pharmaceutical drug candidates.
Traditionally matter engineering -- generally denoted as inverse design -- was
based massively on human intuition and high-throughput virtual screening. The
last few years have seen the emergence of significant interest in
computer-inspired designs based on evolutionary or deep learning methods. The
major challenge here is that the standard strings molecular representation
SMILES shows substantial weaknesses in that task because large fractions of
strings do not correspond to valid molecules. Here, we solve this problem at a
fundamental level and introduce SELFIES (SELF-referencIng Embedded Strings), a
string-based representation of molecules which is 100\% robust. Every SELFIES
string corresponds to a valid molecule, and SELFIES can represent every
molecule. SELFIES can be directly applied in arbitrary machine learning models
without the adaptation of the models; each of the generated molecule candidates
is valid. In our experiments, the model's internal memory stores two orders of
magnitude more diverse molecules than a similar test with SMILES. Furthermore,
as all molecules are valid, it allows for explanation and interpretation of the
internal working of the generative models.
