Actively Searching: Inverse Design of Novel Molecules with Simultaneously Optimized Properties.

Combining quantum chemistry characterizations with generative machine learning models has the potential to accelerate molecular discovery. In this paradigm, quantum chemistry acts as a relatively cost-effective oracle for evaluating the properties of particular molecules, while generative models provide a means of sampling chemical space based on learned structure-function relationships. For practical applications, multiple potentially orthogonal properties must be optimized in tandem during a discovery workflow.

Transferable Ring Corrections for Predicting Enthalpy of Formation of Cyclic Compounds.

Computational predictions of the thermodynamic properties of molecules and materials play a central role in contemporary reaction prediction and kinetic modeling. Due to the lack of experimental data and computational cost of high-level quantum chemistry methods, approximate methods based on additivity schemes and more recently machine learning are currently the only approaches capable of supplying the chemical coverage and throughput necessary for such applications. For both approaches, ring-containing molecules pose a challenge to transferability due to the nonlocal interactions associated with conjugation and strain that significantly impact thermodynamic properties.

Simpler is Better: How Linear Prediction Tasks Improve Transfer Learning in Chemical Autoencoders.

Transfer learning is a subfield of machine learning that leverages proficiency in one or more prediction tasks to improve proficiency in a related task. For chemical property prediction, transfer learning models represent a promising approach for addressing the data scarcity limitations of many properties by utilizing potentially abundant data from one or more adjacent applications. Transfer learning models typically utilize a latent variable that is common to several prediction tasks and provides a mechanism for information exchange between tasks.

Improved Chemical Prediction from Scarce Data Sets via Latent Space Enrichment.

Modern machine learning provides promising methods for accelerating the discovery and characterization of novel chemical species. However, in many areas experimental data remain costly and scarce, and computational models are unavailable for targeted figures of merit. Here we report a promising pathway to address this challenge by using chemical latent space enrichment, whereby disparate data sources are combined in joint prediction tasks to enable improved prediction in data-scarce applications.

Sodium Alginate Toughening of Gelatin Hydrogels.

Gelatin is a popular material for the creation of tissue phantoms due to its ease-of-use, safety, low relative cost, and its amenability to tuning physical properties through the use of additives. One difficulty that arises when using gelatin, especially in low concentrations, is the brittleness of the material. In this paper, we show that small additions of another common biological polymer, sodium alginate, significantly increase the toughness of gelatin without changing the Young's modulus or other low-strain stress relaxation properties of the material.