We report a method to convert discrete representations of molecules to and from a multidimensional continuous representation. This model allows us to generate new molecules for efficient exploration and optimization through open-ended spaces of chemical compounds. A deep neural network was trained on hundreds of thousands of existing chemical structures to construct three coupled functions: an encoder, a decoder, and a predictor. The encoder converts the discrete representation of a molecule into a real-valued continuous vector, and the decoder converts these continuous vectors back to discrete molecular representations. The predictor estimates chemical properties from the latent continuous vector representation of the molecule. Continuous representations of molecules allow us to automatically generate novel chemical structures by performing simple operations in the latent space, such as decoding random vectors, perturbing known chemical structures, or interpolating between molecules. Continuous representations also allow the use of powerful gradient-based optimization to efficiently guide the search for optimized functional compounds. We demonstrate our method in the domain of drug-like molecules and also in a set of molecules with fewer that nine heavy atoms.

Owing to their high power density and superior cyclability relative to batteries, electrochemical double layer capacitors (EDLCs) have emerged as an important electrical energy storage technology that will play a critical role in the large-scale deployment of intermittent renewable energy sources, smart power grids, and electrical vehicles. Because the capacitance and charge-discharge rates of EDLCs scale with surface area and electrical conductivity, respectively, porous carbons such as activated carbon, carbon nanotubes and crosslinked or holey graphenes are used exclusively as the active electrode materials in EDLCs. One class of materials whose surface area far exceeds that of activated carbons, potentially allowing them to challenge the dominance of carbon electrodes in EDLCs, is metal-organic frameworks (MOFs). The high porosity of MOFs, however, is conventionally coupled to very poor electrical conductivity, which has thus far prevented the use of these materials as active electrodes in EDLCs. Here, we show that Ni(2,3,6,7,10,11-hexaiminotriphenylene) (Ni(HITP)), a MOF with high electrical conductivity, can serve as the sole electrode material in an EDLC. This is the first example of a supercapacitor made entirely from neat MOFs as active materials, without conductive additives or other binders. The MOF-based device shows an areal capacitance that exceeds those of most carbon-based materials and capacity retention greater than 90% over 10,000 cycles, in line with commercial devices. Given the established structural and compositional tunability of MOFs, these results herald the advent of a new generation of supercapacitors whose active electrode materials can be tuned rationally, at the molecular level.

Reaction of 2,3,6,7,10, in aqueous NH 3 solution under aerobic conditions produces Ni 3 (HITP) 2 (HITP = 2,3,6,7,10,, a new two-dimensional metal−organic framework (MOF). The new material can be isolated as a highly conductive black powder or dark blue-violet films. Two-probe and van der Pauw electrical measurements reveal bulk (pellet) and surface (film) conductivity values of 2 and 40 S•cm −1 , respectively, both records for MOFs and among the best for any coordination polymer.

The utility of metal-organic frameworks (MOFs) as functional materials in electronic devices has been limited to date by a lack of MOFs that display high electrical conductivity. Here, we report the synthesis of a new electrically conductive 2D MOF, Cu3(HITP)2 (HITP=2,3,6,7,10,11-hexaiminotriphenylene), which displays a bulk conductivity of 0.2 S cm(-1) (pellet, two-point-probe). Devices synthesized by simple drop casting of Cu3(HITP)2 dispersions function as reversible chemiresistive sensors, capable of detecting sub-ppm levels of ammonia vapor. Comparison with the isostructural 2D MOF Ni3(HITP)2 shows that the copper sites are critical for ammonia sensing, indicating that rational design/synthesis can be used to tune the functional properties of conductive MOFs.

Control over the architectural and electronic properties of heterogeneous catalysts poses a major obstacle in the targeted design of active and stable non-platinum group metal electrocatalysts for the oxygen reduction reaction. Here we introduce Ni3(HITP)2 (HITP=2, 3, 6, 7, 10, 11-hexaiminotriphenylene) as an intrinsically conductive metal-organic framework which functions as a well-defined, tunable oxygen reduction electrocatalyst in alkaline solution. Ni3(HITP)2 exhibits oxygen reduction activity competitive with the most active non-platinum group metal electrocatalysts and stability during extended polarization. The square planar Ni-N4 sites are structurally reminiscent of the highly active and widely studied non-platinum group metal electrocatalysts containing M-N4 units. Ni3(HITP)2 and analogues thereof combine the high crystallinity of metal-organic frameworks, the physical durability and electrical conductivity of graphitic materials, and the diverse yet well-controlled synthetic accessibility of molecular species. Such properties may enable the targeted synthesis and systematic optimization of oxygen reduction electrocatalysts as components of fuel cells and electrolysers for renewable energy applications.

| The discovery and development of novel materials in the field of energy are essential to accelerate the transition to a low-carbon economy. Bringing recent technological innovations in automation, robotics and computer science together with current approaches in chemistry , materials synthesis and characterization will act as a catalyst for revolutionizing traditional research and development in both industry and academia. This Perspective provides a vision for an integrated artificial intelligence approach towards autonomous materials discovery , which, in our opinion, will emerge within the next 5 to 10 years. The approach we discuss requires the integration of the following tools, which have already seen substantial development to date: high-throughput virtual screening, automated synthesis planning, automated laboratories and machine learning algorithms. In addition to reducing the time to deployment of new materials by an order of magnitude, this integrated approach is expected to lower the cost associated with the initial discovery. Thus, the price of the final products (for example, solar panels, batteries and electric vehicles) will also decrease. This in turn will enable industries and governments to meet more ambitious targets in terms of reducing greenhouse gas emissions at a faster pace. volume 3 | mAY 2018 | 5 PERSPECTIVES

Electrically conductive metal-organic frameworks (MOFs) are emerging as a subclass of porous materials that can have a transformative effect on electronic and renewable energy devices. Systematic advances in these materials depend critically on the accurate and reproducible characterization of their electrical properties. This is made difficult by the numerous techniques available for electrical measurements and the dependence of metrics on device architecture and numerous external variables. These challenges, common to all types of electronic materials and devices, are especially acute for porous materials, whose high surface area make them even more susceptible to interactions with contaminants in the environment. Here, we use the anisotropic semiconducting framework Cd(TTFTB) (TTFTB = tetrathiafulvalene tetrabenzoate) to benchmark several common methods available for measuring electrical properties in MOFs. We show that factors such as temperature, chemical environment (atmosphere), and illumination conditions affect the quality of the data obtained from these techniques. Consistent results emerge only when these factors are strictly controlled and the morphology and anisotropy of the Cd(TTFTB) single-crystal devices are taken into account. Most importantly, we show that depending on the technique, device construction, and/or the environment, a variance of 1 or even 2 orders of magnitude is not uncommon for even just one material if external factors are not controlled consistently. Differences in conductivity values of even 2 orders of magnitude should therefore be interpreted with caution, especially between different research groups comparing different compounds. These results allow us to propose a reliable protocol for collecting and reporting electrical properties of MOFs, which should help improve the consistency and comparability of reported electrical properties for this important new class of crystalline porous conductors.

Thermoelectric devices utilize waste heat to generate electricity or consume electricity to transfer heat. Sun et al. describe high electrical conductivity and ultralow thermal conductivity in the nanoporous material Ni 3 (HITP) 2 , improve the record of thermoelectric figure of merit in metal-organic frameworks (MOFs), and demonstrate that MOFs are promising candidates for thermoelectrics.