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The discovery of novel materials and functional molecules can help to solvesome of society's most urgent challenges, ranging from efficient energyharvesting and storage to uncovering novel pharmaceutical drug candidates.Traditionally matter engineering -- generally denoted as inverse design -- wasbased massively on human intuition and high-throughput virtual screening. Thelast few years have seen the emergence of significant interest incomputer-inspired designs based on evolutionary or deep learning methods. Themajor challenge here is that the standard strings molecular representationSMILES shows substantial weaknesses in that task because large fractions ofstrings do not correspond to valid molecules. Here, we solve this problem at afundamental level and introduce SELFIES (SELF-referencIng Embedded Strings), astring-based representation of molecules which is 100\% robust. Every SELFIESstring corresponds to a valid molecule, and SELFIES can represent everymolecule. SELFIES can be directly applied in arbitrary machine learning modelswithout the adaptation of the models; each of the generated molecule candidatesis valid. In our experiments, the model's internal memory stores two orders ofmagnitude more diverse molecules than a similar test with SMILES. Furthermore,as all molecules are valid, it allows for explanation and interpretation of theinternal working of the generative models.

Self-Referencing Embedded Strings (SELFIES): A 100% robust molecular string representation