Bayesian optimization (BO) has been leveraged for guiding autonomous and high-throughput experiments in materials science. However, few have evaluated the efficiency of BO across a broad range of experimental materials domains. In this work, we quantify the performance of BO with a collection of surrogate model and acquisition function pairs across five diverse experimental materials systems. By defining acceleration and enhancement metrics for materials optimization objectives, we find that surrogate models such as Gaussian Process (GP) with anisotropic kernels and Random Forest (RF) have comparable performance in BO, and both outperform the commonly used GP with isotropic kernels. GP with anisotropic kernels has demonstrated the most robustness, yet RF is a close alternative and warrants more consideration because it is free from distribution assumptions, has smaller time complexity, and requires less effort in initial hyperparameter selection. We also raise awareness about the benefits of using GP with anisotropic kernels in future materials optimization campaigns.

Shape-reprogramming in a polymer is demonstrated, where prescribed 3D geometric information can be encoded, decoded, erased, and re-encoded. In essence, the shape-reprogrammable polymer (SRP) acts as computer hardware that can be reformatted and reprogrammed repeatedly. Such SRPs have the potential to be repurposed directly without going through material disposal and recycling.

a relatively new technology, but it has quickly become a highly impressive contender in this exciting field, underpinned by several unprecedented characteristics including high certified device efficiencies, low temperature solution processability, and mechanical flexibility. [2,3] Additionally, they are light weight, comprised of earth-abundant materials, and are chemically tunable. To date, most demonstrations of this technology are limited to lab scale and based on an extremely labor intensive manual spin-coating process in an inert atmosphere. [4] In order to drive the technology beyond the academic environment and to facilitate further development for commercialization, significant research efforts focused on the "lab-to-fab" translation of the fabrication methods are needed. [5,6] Unfortunately, the transfer of device results derived from spin-coated photoactive layers in a standard laboratory setup to fabrication level devices is nontrivial due to the enormous complexity of the thin-film perovskite growth dynamics [7] and the lack of a generic protocol for fabricating high quality, high performing films, which would ultimately lead to efficient solar cell devices. Indeed, the active layer's morphology critically influences its optoelectronic properties and, in turn, the overall device PV performance. [8,9] Furthermore, the optimized perovskite solar cell performances for lab scale, spin-coated devices tend to be highly technique sensitive as a result of the different mechanisms that drive the active layer film formation.In this work, a method for shifting perovskite solar cell fabrication away from the benchtop toward a more automated, reproducible, and potentially scalable fabrication approach is investigated. While there has been some work reported in fabricating perovskite solar cells using doctor-blading, [10,11] spraycoating, [12][13][14] inkjet-printing, [15] and slot-die coating, [5,16,17] few have focused on aerosol-jet deposition technology. It is shown here that aerosol-jet deposition technology is well suited to deal with the challenges associated with precisely controlling the film morphology, composition, and yield in a fully automated and reproducible way. Furthermore, aerosol-jet deposition shows a great deal of promise toward the manufacture of uniform, large-grained, high-quality, defect-free perovskite films that possess the fidelity necessary for commercial relevance. [18] A high level of automation is desirable to facilitate the lab-to-fab process transfer of the emerging perovskite-based solar technology. Here, an automated aerosol-jet printing technique is introduced for precisely controlling the thin-film perovskite growth in a planar heterojunction p-i-n solar cell device structure. The roles of some of the user defined parameters from a computer-aided design file are studied for the reproducible fabrication of pure CH 3 NH 3 PbI 3 thin films under near ambient conditions. Preliminary power conversion efficiencies up to 15.4% are achieved when such films are incorporated in a poly(3...

A critical component in the development of highly efficient dye‐sensitized solar cells is the interface between the ruthenium bipyridyl complex dye and the surface of the mesoporous titanium dioxide film. In spite of many studies aimed at examining the detailed anchoring mechanism of the dye on the titania surface, there is as yet no commonly accepted understanding. Furthermore, it is generally believed that a single monolayer of strongly attached molecules is required in order to maximize the efficiency of electron injection into the semiconductor. In this study, the amount of adsorbed dye on the mesoporous film is maximised, which in turn increases the light absorption and decreases carrier recombination, resulting in improved device performance. A process that increases the surface concentration of the dye molecules adsorbed on the TiO2 surface by up to 20% is developed. This process is based on partial desorption of the dye after the initial adsorption, followed by readsorption. This desorption/adsorption cycling process can be repeated multiple times and yields a continual increase in dye uptake, up to a saturation limit. The effect on device performance is directly related and a 23% increase in power conversion efficiency is observed. Surface enhanced Raman spectroscopy, infrared spectroscopy, and electrochemical impedance analysis were used to elucidate the fundamental mechanisms behind this observation.

Materials exploration and development for three-dimensional (3D) printing technologies is slow and labor-intensive. Each 3D printing material developed requires unique print parameters be learned for successful part fabrication, and sub-optimal settings often result in defects or fabrication failure. To address this, we developed the Additive Manufacturing Autonomous Research System (AM ARES). As a preliminary test, we tasked AM ARES with autonomously modulating four print parameters to direct-write single-layer print features that matched target specifications. AM ARES employed automated image analysis as closed-loop feedback to an online Bayesian optimizer and learned to print target features in fewer than 100 experiments. In due course, this first-of-its-kind research robot will be tasked with autonomous multi-dimensional optimization of print parameters to accelerate materials discovery and development in the field of AM. The combining of open-source ARES OS software with low-cost hardware makes autonomous AM highly accessible, promoting mainstream adoption and rapid technological advancement. Impact statement The discovery and development of new materials and processes for three-dimensional (3D) printing is hindered by slow and labor-intensive trial-and-error optimization processes. Coupled with a pervasive lack of feedback mechanisms in 3D printers, this has inhibited the advancement and adoption of additive manufacturing (AM) technologies as a mainstream manufacturing approach. To accelerate new materials development and streamline the print optimization process for AM, we have developed a low-cost and accessible research robot that employs online machine learning planners, together with our ARES OS software, which we will release to the community as open-source, to rapidly and effectively optimize the complex, high-dimensional parameter sets associated with 3D printing. In preliminary trials, the first-of-its-kind research robot, the Additive Manufacturing Autonomous Research System (AM ARES), learned to print single-layer material extrusion specimens that closely matched targeted feature specifications in under 100 iterations. Delegating repetitive and high-dimensional cognitive labor to research robots such as AM ARES frees researchers to focus on more creative, insightful, and fundamental scientific work and reduces the cost and time required to develop new AM materials and processes. The teaming of human and robot researchers begets a synergy that will exponentially propel technological progress in AM.

High-aspect-ratio transition metal oxide nanotube arrays with a high density of well-aligned pore channels and high surface areas can be attractive structures for use in a number of chemical, electrical, electrochemical, optical, photochemical, and biochemical devices, such as high throughput (photo)catalysts or adsorbants, aligned electrodes for solar cells or batteries, sensitive and rapid gas detectors, precise fl uid fl ow control devices, or functionalized membranes for selective (bio)molecular separation. [ 1 ] A common strategy used to fabricate aligned oxide nanotube arrays is to apply an oxide coating to a template possessing well-aligned nanopore channels (e.g., a porous alumina template prepared by anodization of an aluminum fi lm, [ 2 ] or a track-etch polymer membrane [ 3 ] ), followed by selective removal of the underlying template. [ 4 ] Gas-phase atomic layer deposition [ 5 ] and liquid-phase sol-gel [ 6 ] or surface sol-gel deposition [ 7 ] processes have been used by a number of authors to apply continuous and conformal coatings to such aligned-pore templates. For compact and complete coatings, partial removal of the coating may then be used to expose the underlying template so as to allow for template removal (e.g., by selective dissolution or pyrolysis) to yield freestanding nanotubes or nanotube arrays. [ 4d , 5a ] The purpose of the present paper is to demonstrate, for the fi rst time, the use of an aqueous, protein-enabled layer-by-layer (LbL) oxide deposition process to buildup conformal protein/oxide composite coatings on transient aligned-nanochannel templates so that, upon organic pyrolysis, a coating composed of a co-continuous network of pores and oxide nanoparticles is formed. Subsequent selective dissolution of the template through the interconnected pore network then yields freestanding, high-aspect-ratio, porous-wall nanotube arrays; that is, partial removal of the conformal coating is not needed to allow for dissolution of the underlying template. [ 4d , 5a ] Unlike gas-phase atomic layer deposition, this protein-based process does not require the use of vapor precursors, controlled atmospheres, or vapor-generating equipment. Furthermore, this biomimetic mineralization process does not utilize moisturesensitive precursors (e.g., alkoxides used in sol-gel processing) and does not require hydroxyl-rich templates or multistep surface functionalization treatments (for enriching templates with hydroxyl groups) needed for the surface sol-gel-based deposition of continuous and conformal coatings. [ 7 , 8 ] Prior work by Dickerson, et al. [ 9a ] involving the use of bacteriophage display biopanning indicated that 12-mer peptides enriched in basic residues (arginine, lysine, histidine) were particularly effective at binding to titania and at inducing the formation An aqueous, protein-enabled (biomimetic), layer-by-layer titania deposition process is developed, for the fi rst time, to convert aligned-nanochannel templates into high-aspect-ratio, aligned nanotube arrays with thin (34...

A novel two-component system consisting of a hyperbranched polycarbosilane (HBPCS) and a dihydrosilane cross-linker is presented as a synthetic route for the generation of silicon oxycarbide (SiOC) and silicon carbide (SiC) ceramic materials. Upon addition of the reactive silane cross-linker (33 wt %), rapid gelation of the HBPCS occurs indicating network cross-linking and increased molecular weight. After thermal oxidative curing, the gel is converted to a rigid solid state material that has an 80 wt % ceramic yield of SiOC when pyrolyzed to 1000 °C. Continued heating of the ceramic to 1800 °C induces reorganization and crystallization, providing crystalline β-SiC. This new system affords the opportunity to modulate the hyperbranched polymer’s chemical and rheological properties, to boost ceramic yield, and is amenable to the aerosol jet printing of polymer-derived ceramics.

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