For applications in gas sensing, purification, and capture, we often wish to search a large set of metal–organic frameworks (MOFs) for the top-K in terms of their Henry coefficients for an adsorbate. A molecular simulation to predict the Henry coefficient of a MOF constitutes a Monte Carlo integration where each sample consists of inserting an adsorbate in the MOF at a random position, orientation, and configuration, then calculating the MOF–adsorbate interaction energy. Our idea is to leverage top-K arm identification algorithms, developed for the multi-armed bandit problem in reinforcement learning, to sequentially and adaptively allocate adsorbate insertions among the MOFs, in a data-driven manner, to obtain the most accurate top-K subset under a fixed insertion budget. By analogy, each MOF is a slot machine in a casino that, upon pulling its arm (inserting an adsorbate), offers a stochastic reward (a noisy estimate of its Henry coefficient) sampled from a static, unknown probability distribution. Each adaptive allocation algorithm (1) proceeds in a feedback loop of (i) allocate adsorbate insertions to MOF(s), (ii) update the running estimates of the Henry coefficients of the MOF(s), then (iii) judiciously allocate adsorbate insertions to the next MOF(s); (2) sequentially dials-up the fidelities of ongoing molecular simulations in the MOFs, giving a multifidelity computational screening; and (3) circumvents the need to hand-craft structural or chemical features of the MOFs for decision making. As a case study, we implement, benchmark, and analyze the sequential halving, successive accepts and rejects, and narrowing exploration (our proposed heuristic) algorithms to adaptively allocate xenon insertions to screen a set of ca. 300 MOFs for the top-K Xe Henry coefficient subset over differing insertion budgets. Provided with a sufficient budget, we find that these adaptive insertion algorithms can significantly reduce (by a factor of 2–3) the simple regret (sum of true minus empirical top-K true Henry coefficients) and error in the top-K subset of MOFs output by a computational screening. By another metric, adaptive insertion allocation provided a ca. 60% discount on the computational cost to identify the top-K MOFs with less than 5% error. We thereby demonstrate that top-K arm identification algorithms may generally be useful for more efficiently screening materials for various properties via Monte Carlo molecular simulations. This efficiency improvement is especially important when adopting more computationally expensive, sophisticated force fields or even ab initio calculations for the potential energy of configurations to lend higher-fidelity screenings.

This paper describes a chemiresistive sensor array using four structurally analogous, but chemically distinct, conductive covalent organic frameworks (COFs) (M-COF-DC-8, M = Fe, Co, Ni, and Cu) capable of detecting and differentiating four important gaseous analytes: nitric oxide (NO), carbon monoxide (CO), hydrogen sulfide (H2S), and ammonia (NH3). The COFs were synthesized from the condensation of 2,3,9,10,16,17,23,24-octaamino-metallophthalocyanine precursors with pyrenetetraone linkers resulting in chemically robust and electrically conductive materials. Chemiresistive sensing experiments, together with machine learning to parse the response pattern of the sensor array, show that the M-COF-DC-8 (M = Fe, Co, Ni, Cu) materials can detect and differentiate this suite of oxidizing and reducing gases at parts-per-million concentrations, with theoretical limits of detection (LOD) in the parts-per-billion range in dry N2. Importantly, the COF array containing M-COF-DC-8 (M = Co, Ni, Cu) retains its ability to detect and differentiate these analytes in air and humidity under low power consumption. Spectroscopic investigations reveal that the synthetic control over the identity of the metallophthalocyanine core efficiently tunes material–analyte interactions and, therefore, emergent device performance. The use of highly tunable COFs as the active material in sensor arrays enables low-power, sensitive, and real-time gas detection with future applications in healthcare and personal protection.

Atmospheric water harvesting (AWH) is a method to obtain clean water in remote or underdeveloped regions including, but not limited to, those with an arid or desert climate. For passive (i.e., relying...

A story of bowling pins, patterns and medical miracles.

We aim to reconstruct the horizontal cross-sectional area profile of an exogenous, heavy, unseen solid contained in a tank from measurements of the li…

The Diphelleia grayi or “Skeleton Flower” is finally a reason to look forward to rainy days. This rare flower transforms into a translucent beauty [its skeletal form] when exposed to water, its white ...