Advances in materials are an important contributor to our technological progress, and yet the process of materials discovery and development itself is slow. Our current research process is human-centred, where human researchers design, conduct, analyse and interpret experiments, and then decide what to do next. We have built an Autonomous Research System (ARES)-an autonomous research robot capable of first-of-its-kind closed-loop iterative materials experimentation. ARES exploits advances in autonomous robotics, artificial intelligence, data sciences, and high-throughput and in situ techniques, and is able to design, execute and analyse its own experiments orders of magnitude faster than current research methods. We applied ARES to study the synthesis of singlewalled carbon nanotubes, and show that it successfully learned to grow them at targeted growth rates. ARES has broad implications for the future roles of humans and autonomous research robots, and for human-machine partnering. We believe autonomous research robots like ARES constitute a disruptive advance in our ability to understand and develop complex materials at an unprecedented rate.

Microbial cell factories offer an attractive approach for production of biobased products.Unfortunately, designing, building, and optimizing biosynthetic pathways remains a complex challenge, especially for industrially-relevant, non-model organisms. To address this challenge, we describe a platform for in vitro Prototyping and Rapid Optimization of Biosynthetic Enzymes (iPROBE). In iPROBE, cell lysates are enriched with biosynthetic enzymes by cell-free protein synthesis and then metabolic pathways are assembled in a mix-and-match fashion to assess pathway performance. We demonstrate iPROBE with two examples. First, we tested and ranked 54 different pathways for 3-hydroxybutyrate production, improving in vivo production in Clostridium by 20-fold to 14.63 ± 0.48 g/L and identifying a new biosynthetic route to (S)-(+)-1,3butanediol. Second, we used iPROBE and data-driven design to optimize a 6-step n-butanol pathway, increasing titers 4-fold across 205 pathways, and showed strong correlation between cell-free and cellular performance. We expect iPROBE to accelerate design-build-test cycles for industrial biotechnology.

We report the use of the molecular signatures known as "Property-Encoded Shape Distributions" (PESD) together with standard Support Vector Machine (SVM) techniques to produce validated models that can predict the binding affinity of a large number of protein ligand complexes. This "PESD-SVM" method uses PESD signatures that encode molecular shapes and property distributions on protein and ligand surfaces as features to build SVM models that require no subjective feature selection. A simple protocol was employed for tuning the SVM models during their development, and the results were compared to SFCscore -a regression-based method that was previously shown to perform better than 14 other scoring functions. Although the PESD-SVM method is based on only two surface property maps, the overall results were comparable. For most complexes with a dominant enthalpic contribution to binding (ΔH/-TΔS > 3), a good correlation between true and predicted affinities was observed. Entropy and solvent were not considered in the present approach and further improvement in accuracy would require accounting for these components rigorously.

Accelerated insertion of nanocomposites into advanced applications is predicated on the ability to perform a priori property predictions on the resulting materials. In this paper, a paradigm for the virtual design of spherical nanoparticle-filled polymers is demonstrated. A key component of this “Materials Genomics” approach is the development and use of Materials Quantitative Structure-Property Relationship (MQSPR) models trained on atomic-level features of nanofiller and polymer constituents and used to predict the polar and dispersive components of their surface energies. Surface energy differences are then correlated with the nanofiller dispersion morphology and filler/matrix interface properties and integrated into a numerical analysis approach that allows the prediction of thermomechanical properties of the spherical nanofilled polymer composites. Systematic experimental studies of silica nanoparticles modified with three different surface chemistries in polystyrene (PS), poly(methyl methacrylate) (PMMA), poly(ethyl methacrylate) (PEMA) and poly(2-vinyl pyridine) (P2VP) are used to validate the models. While demonstrated here as effective for the prediction of meso-scale morphologies and macro-scale properties under quasi-equilibrium processing conditions, the protocol has far ranging implications for Virtual Design.

Microbial cell factories offer an attractive approach for production of biobased products.Unfortunately, designing, building, and optimizing biosynthetic pathways remains a complex challenge, especially for industrially-relevant, non-model organisms. To address this challenge, we describe a platform for in vitro Prototyping and Rapid Optimization of Biosynthetic Enzymes (iPROBE). In iPROBE, cell lysates are enriched with biosynthetic enzymes by cell-free protein synthesis and then metabolic pathways are assembled in a mix-and-match fashion to assess pathway performance. We demonstrate iPROBE with two examples. First, we tested and ranked 54 different pathways for 3-hydroxybutyrate production, improving in vivo production in Clostridium by 20-fold to 14.63 ± 0.48 g/L and identifying a new biosynthetic route to (S)-(+)-1,3butanediol. Second, we used iPROBE and data-driven design to optimize a 6-step n-butanol pathway, increasing titers 4-fold across 205 pathways, and showed strong correlation between cell-free and cellular performance. We expect iPROBE to accelerate design-build-test cycles for industrial biotechnology.