Machine learning-accelerated design and synthesis of polyelemental heterostructures.

In materials discovery efforts, synthetic capabilities far outpace the ability to extract meaningful data from them. To bridge this gap, machine learning methods are necessary to reduce the search space for identifying desired materials. Here, we present a machine learning­driven, closed-loop experimental process to guide the synthesis of polyelemental nanomaterials with targeted structural properties. By leveraging data from an eight-dimensional chemical space (Au-Ag-Cu-Co-Ni-Pd-Sn-Pt) as inputs, a Bayesian optimization algorithm is used to suggest previously unidentified nanoparticle compositions that target specific interfacial motifs for synthesis, results of which are iteratively shared back with the algorithm. This feedback loop resulted in successful syntheses of 18 heterojunction nanomaterials that are too complex to discover by chemical intuition alone, including extremely chemically complex biphasic nanoparticles reported to date. Platforms like the one developed here are poised to transform materials discovery across a wide swath of applications and industries.

Chirality-Directed Regioselectivity: An Approach for the Synthesis of Alternating Poly(Lactic-co-Glycolic Acid).

We report the synthesis of alternating poly(lactic-co-glycolic acid) via a regioselective ring-opening polymerization of (S)-methyl glycolide. An enantiopure aluminum salen catalyst with binaphthyl backbone facilitates the regioselective ring-opening of this unsymmetrical cyclic diester exclusively at the glycolide acyl-oxygen bond site. This living, chain-growth polymerization is able to reach low dispersities with tailored molecular weights. Quantitative regioselectivity calculations and sequence error analysis have been established for this sequence-controlled polymer.

Learning from Peptides to Access Functional Precision Polymer Sequences.

Functional precision polymers based on monodisperse oligo(N-substituted acrylamide)s and oligo(2-substituted-α-hydroxy acid)s have been synthesized. The discrete sequences originate from a direct translation of side-chain functionality sequences of a peptide with well-studied properties. The peptide was previously selected to solubilize the photosensitizer meta-tetra(hydroxyphenyl)chlorin. The resulting peptidomimetic formulation additives preserve the drug solubilization and release characteristics of the parent peptide. In some cases, superior properties are obtained, reaching up to 40 % higher payloads and 27-times faster initial drug release.

Sequence-Controlled Polymers Through Entropy-Driven Ring-Opening Metathesis Polymerization: Theory, Molecular Weight Control, and Monomer Design.

The bulk properties of a copolymer are directly affected by monomer sequence, yet efficient, scalable, and controllable syntheses of sequenced copolymers remain a defining challenge in polymer science. We have previously demonstrated, using polymers prepared by a step-growth synthesis, that hydrolytic degradation of poly(lactic- co-glycolic acid)s is dramatically affected by sequence. While much was learned, the step-growth mechanism gave no molecular weight control, unpredictable yields, and meager scalability. Herein, we describe the synthesis of closely related sequenced polyesters prepared by entropy-driven ring-opening metathesis polymerization (ED-ROMP) of strainless macromonomers with imbedded monomer sequences of lactic, glycolic, 6-hydroxy hexanoic, and syringic acids. The incorporation of ethylene glycol and metathesis linkers facilitated synthesis and provided the olefin functionality needed for ED-ROMP. Ring-closing to prepare the cyclic macromonomers was demonstrated using both ring-closing metathesis and macrolactonization reactions. Polymerization produced macromolecules with controlled molecular weights on a multigram scale. To further enhance molecular weight control, the macromonomers were prepared with cis-olefins in the metathesis-active segment. Under these selectivity-enhanced (SEED-ROMP) conditions, first-order kinetics and narrow dispersities were observed and the effect of catalyst initiation rate on the polymerization was investigated. Enhanced living character was further demonstrated through the preparation of block copolymers. Computational analysis suggested that the enhanced polymerization kinetics were due to the cis-macrocyclic olefin being less flexible and having a larger population of metathesis-reactive conformers. Although used for polyesters in this investigation, SEED-ROMP represents a general method for incorporation of sequenced segments into molecular weight-controlled polymers.