The rational design of novel hydrogen-bonded motifs for constructing robust molecular hydrogen-bonded organic frameworks (HOFs) is challenging, particularly for mesoporous solids. Here, we demonstrate that the integration of chemical insights and crystal structure prediction (CSP) enables the rational design of novel HOFs based on trigonal formamide-terminated linkers. The well-defined spikes of low-energy, isostructural HOFs on the predicted lattice-energy landscapes reveal a family of crystal packings with diverse hydrogen-bonded motifs that nonetheless all result in similar predicted pore topologies. These CSP results enabled the experimental discovery of isostructural HOFs with predetermined pore channel sizes and functionalities to be identified. For a linker with extended arms (TTBF), a hydrogen-bonded dimeric structure with cis -configured formamide groups was identified as a higher-energy ‘spike’ in a CSP landscape, corresponding to a mesoporous HOF with unprecedentedly large pores. This material was successfully synthesized and shown to be permanently mesoporous with the largest pore size exhibited by a HOF to date (4.2 nm). Our methodology illustrates the potential of a simple and scalable formylation reaction between formic acid and abundant amine precursors to generate porous HOFs, redefining the limit of pore sizes that can be achieved in molecular solids.

Side-arm steric tuning for topology programming and property modulation.

Ideal symmetry is known to break down under almost any noise. One measure of asymmetry in a periodic crystal is the relative multiplicity Z ′ of geometrically non-equivalent units. However, Z ′ discontinuously changes under almost any displacement of atoms, which can arbitrarily scale up a primitive cell. This discontinuity was recently resolved by a hierarchy of invariant descriptors that continuously change under all small perturbations. We introduce a Continuous Invariant-based Asymmetry (CIA) to quantify (in physically meaningful ångstroms) the deviation of a periodic crystal from a higher-symmetry form. Our experiments on several crystal structure prediction datasets show that about a half of simulated crystals have high values of CIA, while all experimental structures in these datasets have CIA = 0. On another hand, many crystals with high values of Z ′ in the Cambridge Structural Database (CSD) turned out to be close to more symmetric forms with Z ′ ≤ 1 due to low values of CIAs.

The solid-state assembly of crystalline porous materials is dictated by a balance between primary node-linker interactions and other weaker intermolecular forces. Metal-organic frameworks (MOFs) exploit highly directional and rigid coordination bonds that outweigh competing packing effects. This allows high-connectivity secondary building units (SBUs) to be assembled into a diverse range of architectures. By contrast, molecular crystals comprise weaker intermolecular interactions, making it challenging to realize porous packings with the highly connected reticular topologies achieved in MOFs. Here we introduce Coulombic aggregation as a means to generate primary SBU motifs. By suppressing intermolecular dispersion interactions, ammonium halide ion pairs aggregate into discrete multinuclear clusters that function as non-metal SBUs. Coupled with multitopic three-dimensional linkers, these ionic clusters create extended non-metal organic frameworks with diverse topologies, large voids and polar channels. This study extends reticular chemistry from directional chemical bonds to 'soft' ionic interactions, opening pathways to new porous materials and principles of solid-state assembly.

While steric control of linker conformation has proven effective for accessing new Zr-MOF structures, existing strategies have largely relied on modification of the linker core-an approach that intrinsically couples steric effects to framework connectivity, limits available functionalization sites, and often requires complex synthesis, particularly for high-connectivity linkers. Here, we introduce a conceptually distinct and adaptable strategy for topological control based on steric modulation through side-arm functionalization, which enables independent steric tuning without altering linker connectivity while remaining synthetically simple. Six amide or cyano groups positioned on the ligand side arms act as unconventional steric units to induce isolable conformational variability. This design enables the linker to flex and twist, guiding the formation of two isostructural Zr-MOFs, AM-Zr-1 and CN-Zr-1, adopting the rare underlying net 6,8-c nuh1 (or 3,8-c nuh2) with highly distorted, topologically complex porous architectures. Despite their identical connectivity, AM-Zr-1 generates a geometrically unique amide pocket that enhances CO2 binding and affords higher CO2/N2 and CO2/CH4 selectivity, whereas the less bulky cyano substituents confer a more extended conformation to CN-Zr-1, resulting in higher surface area and H2 uptake. These findings highlight steric side-arm functionalization as a simple yet versatile strategy for tuning Zr-MOF topology and function.

Organic crystalline materials are potential candidates for photocatalytic overall water splitting (OWS). Although organic crystals have been heavily investigated for application in organic electronics, such as organic light-emitting diodes (OLEDs) and solar cells, there have been comparatively fewer studies into OWS in these materials. A major challenge is the large number of electronic and structural criteria that must be met for a material to make a viable OWS photocatalyst. Optical absorption, reduction and oxidation potentials and charge-transport properties are among the key considerations, and these are influenced both by molecular structure and the solid-state packing arrangement, making computational modelling challenging. Here, we investigate a series of known organic electronic materials using periodic density functional theory (DFT) and compare their calculated electronic properties of optical absorption and reduction and oxidation potentials with literature experimental data. We also perform a series of gas-phase molecular calculations, which show a good agreement with both the literature data and periodic DFT calculations for the optoelectronic properties of the systems studied, showing that gas-phase molecular calculations can be used to screen organic crystals for OWS at a reduced computational cost.

Autonomous manipulation of powders remains a significant challenge for robotic automation in scientific laboratories. The inherent variability and complex physical interactions of powders in flow, coupled with variability in laboratory conditions necessitates adaptive automation. This work introduces FLIP, a flowability-informed powder weighing framework designed to enhance robotic policy learning for granular material handling. Our key contribution lies in using material flowability, quantified by the angle of repose, to optimise physics-based simulations through Bayesian inference. This yields material-specific simulation environments capable of generating accurate training data, which reflects diverse powder behaviours, for training "robot chemists". Building on this, FLIP integrates quantified flowability into a curriculum learning strategy, fostering efficient acquisition of robust robotic policies by gradually introducing more challenging, less flowable powders. We validate the efficacy of our method on a robotic powder weighing task under real-world laboratory conditions. Experimental results show that FLIP with a curriculum strategy achieves a low dispensing error of 2.12 +/- 1.53 mg, outperforming methods that do not leverage flowability data, such as domain randomisation (6.11 +/- 3.92 mg). These results demonstrate FLIP's improved ability to generalise to previously unseen, more cohesive powders and to new target masses.

The use of robotics and automation in self-driving laboratories (SDLs) can introduce additional safety complexities, beyond those already present in conventional research laboratories. Personal protective equipment (PPE) is an essential requirement for ensuring the safety and well-being of workers in all laboratories, self-driving or otherwise. Fires are another important risk factor in chemical laboratories. In SDLs, fires that occur close to mobile robots, which use flammable lithium batteries, could have increased severity. Here, we present Chemist Eye, a distributed safety monitoring system designed to enhance situational awareness in SDLs. The system integrates multiple stations equipped with RGB, depth, and infrared cameras, designed to monitor incidents in SDLs. Chemist Eye is also designed to spot workers who have suffered a potential accident or medical emergency, PPE compliance and fire hazards. To do this, Chemist Eye uses decision-making driven by a vision-language model (VLM). Chemist Eye is designed for seamless integration, enabling real-time communication with robots. Based on the VLM recommendations, the system attempts to drive mobile robots away from potential fire locations, exits, or individuals not wearing PPE, and issues audible warnings where necessary. It also integrates with third-party messaging platforms to provide instant notifications to lab personnel. We tested Chemist Eye with real-world data from an SDL equipped with three mobile robots and found that the spotting of possible safety hazards and decision-making performances reached 88% and 95%, respectively.

Process chemistry creates scalable routes for new lead molecules and is a crucial but laborious stage in pharmaceutical and agrochemical development cycles. We have built an automated process chemistry platform that tackles late-stage process development. The modular workflow integrates both industry-standard tools and bespoke devices to enable process scale synthesis, work-up, and analysis. A multitasking mobile robot works between an automated synthesis reactor and an ultra-high-performance liquid chromatography-mass spectrometer (UHPLC-MS) for product analysis, cleaning the reactor between runs. The robot's anthropomorphic manipulation capabilities allow it to interface with minimally redesigned equipment that can be shared with human researchers. Reaction yields and purity match human chemist performance. Timings for round-the-clock, back-to-back experiments suggest that the weekly reaction output of the robot operating multiple reactors could exceed that of a human process chemist by a factor of 12 in an industrial setting.

Autonomous, high-accuracy powder dispensing of heterogeneous solid materials remains an open challenge in automated chemistry laboratories. Existing systems often fail with diverse powder morphologies because they neglect the critical initial material acquisition step (i.e., scooping). We present an end-to-end, vision-guided powder dispensing system that integrates an adaptive scooping mechanism with a deep reinforcement learning-based policy for dispensing. Our system utilises a parametrised scooping motion and assesses visually the acquired material volume after each scooping attempt. This visual feedback drives an iterative correction loop, allowing the robot to adjust its motion parameters to reject failures and obtain a suitable quantity for subsequent dispensing. We evaluated our system in real laboratory conditions using a set of 7 powders with varying physical properties. Our experiments demonstrate that the fully adaptive system outperformed fixed scooping baselines, achieving the lowest average absolute weighing error of 1.93 mg +/- 2.04 mg across all materials.

A common strategy to improve the efficiency of organic photocatalysts for hydrogen production from aqueous mixtures is to create bulk heterojunction nanoparticles comprised of intermixed donor and acceptor phases that allow for efficient charge separation after photoexcitation. However, many of these systems possess poor stability due to aggregation of these nanoparticles under operating conditions. Moreover, the use of surfactants, that inhibit aggregation and promote donor-acceptor phase intermixing, can form an insulating barrier that reduces the photocatalytic efficiency of these nanoparticles. Here, these issues are bypassed by preparing a single-component organic heterojunction-type polymer, P40, in which a molecular donor, pyrene, is tethered to poly(fluorene-co-dibenzo[b,d]thiophene sulfone), a conjugated polymer acceptor. By tethering the donor and acceptor together, phase intermixing is guaranteed without the need for costly post-synthesis processing or insulating surfactants. Moreover, the influence of pyrene in P40 is determined to be multifaceted, as it influences the dynamics of the excited state, the aggregate microstructure, and the local solvent environment. P40 is found to have an exceptional external quantum efficiency of 38% at 420 nm in the presence of triethylamine as a hole scavenger, the highest value reported for any linear conjugated polymer to date for sacrificial hydrogen production.

A high-throughput (HTE) robotic colourimetric titration workstation was developed using a commercial liquid handling robot (Opentrons OT-2) and computer vision-based analysis. While designed for multiple titration applications, hydrogen peroxide (H2O2) determination serves as the most elaborate and well-characterized demonstration of its capabilities. Specifically, potassium permanganate (KMnO4) redox titration was employed to quantify the hydrogen peroxide (H2O2) concentration, leveraging the distinct colourimetric transition from colourless to pale pink at the titration endpoint. To monitor this colour change, a webcam was installed on the OT-2 pipette mount, capturing real-time titration progress. Image analysis was enhanced through VGG-augmented UNet for segmentation and the CIELab colour model, ensuring robust and reproducible detection of subtle colour changes. The sensitivity test of the computer vision-aided colour analysis was strongly correlated to UV-vis spectroscopy (R2 = 0.9996), with a good linear dynamic range at low concentrations. The analytical accuracy of this workstation was +/- 11.9% in a 95% confidence interval and its corresponding absolute concentration difference was only 0.50 mM. To validate its real-world applicability, this workstation was first deployed to monitor the photoproduction of H2O2 over a conjugated polymer photocatalyst, DE7. In addition to performing redox titrations, we demonstrated that this workstation can also be used for acid-base titration and complexometric titration, capturing a diverse range of colour changes.

Porous materials, such as metal-organic frameworks (MOFs) and porous organic salts, are promising materials for proton conduction. Recently, we developed a new subclass of porous materials, isoreticular nonmetal organic frameworks (N-MOFs), which can be designed using crystal structure prediction (CSP). Here, two porous, isostructural, and water-stable halide N-MOFs were prepared and found to show good proton conductivity of up to 1.1 × 10-1 S cm-1 at 70 °C and 90% relative humidity. Changing the halides in these N-MOF materials affects the resulting proton conductivity, as observed in previous studies involving MOFs and lead halides. Although this is the first study of proton conductivity in N-MOFs, the bromide salt, TTBT.Br, shows a higher conductivity than most polycrystalline MOFs and porous organic salts, approaching that of Nafion.

Recently, we reported the reconstruction of two-dimensional (2D) to three-dimensional (3D) covalent organic frameworks (COFs) via base-catalyzed boronate ester to spiroborate linkage conversion. In that work, we tentatively attributed the interlayer close-packing in the 2D BPDA-COF as the main cause for the long reaction time-40 days-required to complete the structure reconstruction in N,N-diethylformamide (DEF). Here, we address this hypothesis by designing methyl-substituted 4,4'-biphenyldiboronic acid (BPDA) with large molecular twist to weaken the packing between boronate esters. Experiments show that the spiroborate COF formation is accelerated by increased molecular twist in three linear diboronic acids linkers, with the pure 3D spiroborate phase obtained in 3 days via reaction of Co(ii) 2,3,9,10,16,17,23,24-octahydroxyphthalocyaninato ((OH)8PcCo) in N,N-diethylformamide (DEF). Mechanistic studies reveal that methyl-substituted linear diboronic acids are more liable to protodeboronation, which also contributes to the accelerated spiroborate structure formation.

For the May Focus issue of Nature Chemical Engineering, we asked seven leading researchers working across automation, control and robotics to share their perspectives on a facet of their field that they believe will drive transformative progress within these interconnected domains.

Porous materials are important for many technologies, but the measurement of porosity by gas adsorption isotherms is slow, taking around one day per sample using a single-port gas sorption analyzer, even when using a "quick" analysis method with relatively few data points. With the increased use of automated platforms for material generation, porosity analysis is now frequently the bottleneck in the discovery of new porous materials. Here, we present a semiautomated pre-screening strategy that uses dye adsorption to create a colorimetric array that is combined with computer vision analysis for porosity screening. By using a six-dye multichannel array and a defined porosity threshold, our method rapidly screened 50 candidate materials that spanned molecular solids, polymers, and metal-organic frameworks. The method showed a 98-100% classification accuracy compared with gas uptake measurements. While this method is more qualitative than quantitative, it is more than 30 times faster than conventional gas sorption measurements, and it has the scope to be made much faster with greater parallelization and automation. This makes this colorimetric method suitable for pre-screening arrays of materials to choose samples that merit more detailed conventional porosity analysis.

The separation of carbon dioxide from industrial flue gas streams using porous materials is often thwarted by humidity. Most porous sorbents adsorb water more effectively than CO2. Hence, water can out-compete CO2 for adsorption sites, lowering the working CO2 sorption capacity and increasing sorbent regeneration costs. Here, two pyrene-based hydrogen bonded organic frameworks (HOFs) are described that can separate CO2 under humid conditions. The framework building blocks were chosen in a high-throughput density functional theory screen, followed by crystal structure prediction (CSP) to target a hydrophobic two-dimensionally porous framework. Gas sorption experiments showed selective adsorption of CO2 and exceptionally low water adsorption in these HOFs. Dynamic column breakthrough measurements using mixed gas environments showed that the CO2 working capacity was totally unaffected by water under simulated flue gas conditions up to 75% relative humidity. One of the CO2-selective HOFs, diMeTBAP-α, was shown by CSP to be the most thermodynamically stable structure on the crystal energy landscape. This stability prediction was reflected by experiments, where an isostructural, scalable analogue of diMeTBAP-α, MeTBAP-α, retained its porosity and crystallinity after boiling in aqueous acids, which is important for carbon capture from acidic, humid flue gas.

Science laboratory automation enables accelerated discovery in life sciences and materials. However, it requires interdisciplinary collaboration to address challenges such as robust and flexible autonomy, reproducibility, throughput, standardization, the role of human scientists, and ethics. This article highlights these issues, reflecting perspectives from leading experts in laboratory automation across different disciplines of the natural sciences.