A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks.

Gas separation is crucial for industrial production and environmental protection, with metal-organic frameworks (MOFs) offering a promising solution due to their tunable structural properties and chemical compositions. Traditional simulation approaches, such as molecular dynamics, are complex and computationally demanding. Although feature engineering-based machine learning methods perform better, they are susceptible to overfitting because of limited labeled data. Furthermore, these methods are typically designed for single tasks, such as predicting gas adsorption capacity under specific conditions, which restricts the utilization of comprehensive datasets including all adsorption capacities. To address these challenges, we propose Uni-MOF, an innovative framework for large-scale, three-dimensional MOF representation learning, designed for multi-purpose gas prediction. Specifically, Uni-MOF serves as a versatile gas adsorption estimator for MOF materials, employing pure three-dimensional representations learned from over 631,000 collected MOF and COF structures. Our experimental results show that Uni-MOF can automatically extract structural representations and predict adsorption capacities under various operating conditions using a single model. For simulated data, Uni-MOF exhibits remarkably high predictive accuracy across all datasets. Additionally, the values predicted by Uni-MOF correspond with the outcomes of adsorption experiments. Furthermore, Uni-MOF demonstrates considerable potential for broad applicability in predicting a wide array of other properties.

Superstructural ordering in self-sorting coacervate-based protocell networks.

Bottom-up assembly of higher-order cytomimetic systems capable of coordinated physical behaviours, collective chemical signalling and spatially integrated processing is a key challenge in the study of artificial multicellularity. Here we develop an interactive binary population of coacervate microdroplets that spontaneously self-sort into chain-like protocell networks with an alternating sequence of structurally and compositionally dissimilar microdomains with hemispherical contact points. The protocell superstructures exhibit macromolecular self-sorting, spatially localized enzyme/ribozyme biocatalysis and interdroplet molecular translocation. They are capable of topographical reconfiguration using chemical or light-mediated stimuli and can be used as a micro-extraction system for macroscale biomolecular sorting. Our methodology opens a pathway towards the self-assembly of multicomponent protocell networks based on selective processes of coacervate droplet-droplet adhesion and fusion, and provides a step towards the spontaneous orchestration of protocell models into artificial tissues and colonies with ordered architectures and collective functions.

Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors.

Porous carbons are the active materials of choice for supercapacitor applications because of their power capability, long-term cycle stability, and wide operating temperatures. However, the development of carbon active materials with improved physicochemical and electrochemical properties is generally carried out via time-consuming and cost-ineffective experimental processes. In this regard, machine-learning technology provides a data-driven approach to examine previously reported research works to find the critical features for developing ideal carbon materials for supercapacitors. Here, we report the design of a machine-learning-derived activation strategy that uses sodium amide and cross-linked polymer precursors to synthesize highly porous carbons (i.e., with specific surface areas > 4000 m2/g). Tuning the pore size and oxygen content of the carbonaceous materials, we report a highly porous carbon-base electrode with 0.7 mg/cm2 of electrode mass loading that exhibits a high specific capacitance of 610 F/g in 1 M H2SO4. This result approaches the specific capacitance of a porous carbon electrode predicted by the machine learning approach. We also investigate the charge storage mechanism and electrolyte transport properties via step potential electrochemical spectroscopy and quasielastic neutron scattering measurements.

Efficient force field and energy emulation through partition of permutationally equivalent atoms.

Gaussian process (GP) emulator has been used as a surrogate model for predicting force field and molecular potential, to overcome the computational bottleneck of ab initio molecular dynamics simulation. Integrating both atomic force and energy in predictions was found to be more accurate than using energy alone, yet it requires O((NM)3) computational operations for computing the likelihood function and making predictions, where N is the number of atoms and M is the number of simulated configurations in the training sample due to the inversion of a large covariance matrix. The high computational cost limits its applications to the simulation of small molecules. The computational challenge of using both gradient information and function values in GPs was recently noticed in machine learning communities, whereas conventional approximation methods may not work well. Here, we introduce a new approach, the atomized force field model, that integrates both force and energy in the emulator with many fewer computational operations. The drastic reduction in computation is achieved by utilizing the naturally sparse covariance structure that satisfies the constraints of the energy conservation and permutation symmetry of atoms. The efficient machine learning algorithm extends the limits of its applications on larger molecules under the same computational budget, with nearly no loss of predictive accuracy. Furthermore, our approach contains an uncertainty assessment of predictions of atomic forces and energies, useful for developing a sequential design over the chemical input space.

Membrane-confined liquid-liquid phase separation toward artificial organelles.

As the basic unit of life, cells are compartmentalized microreactors with molecularly crowded microenvironments. The quest to understand the cell origin inspires the design of synthetic analogs to mimic their functionality and structural complexity. In this work, we integrate membraneless coacervate microdroplets, a prototype of artificial organelles, into a proteinosome to build hierarchical protocells that may serve as a more realistic model of cellular organization. The protocell subcompartments can sense extracellular signals, take actions in response to these stimuli, and adapt their physicochemical behaviors. The tiered protocells are also capable of enriching biomolecular reactants within the confined organelles, thereby accelerating enzymatic reactions. The ability of signal processing inside protocells allows us to design the Boolean logic gates (NOR and NAND) using biochemical inputs. Our results highlight possible exploration of protocell-community signaling and render a flexible synthetic platform to study complex metabolic reaction networks and embodied chemical computation.

A GPU implementation of classical density functional theory for rapid prediction of gas adsorption in nanoporous materials.

Nanoporous materials are promising as the next generation of absorbents for gas storage and separation with ultrahigh capacity and selectivity. The recent advent of data-driven approaches in materials modeling provides alternative routes to tailor nanoporous materials for customized applications. Typically, a data-driven model requires a large amount of training data that cannot be generated solely by experimental methods or molecular simulations. In this work, we propose an efficient implementation of classical density functional theory with a graphic processing unit (GPU) for the fast yet accurate prediction of gas adsorption isotherms in nanoporous materials. In comparison to serial computing with the central processing unit, the massively parallelized GPU implementation reduces the computational cost by more than two orders of magnitude. The proposed algorithm renders new opportunities not only for the efficient screening of a large materials database for gas adsorption but it may also serve as an important stepping stone toward the inverse design of nanoporous materials tailored to desired applications.