The confluence of pervasiveness, bioaccumulation, and toxicity in freshwater contaminants presents an environmental threat second to none. Exemplifying this trifecta, per‐ and polyfluoroalkyl substances (PFAS) present an alarming hazard among the emerging contaminants. State‐of‐the‐art PFAS adsorbents used in drinking water treatment, namely, activated carbons and ion‐exchange resins, are handicapped by low adsorption capacity, competitive adsorption, and/or slow kinetics. To overcome these shortcomings, metal–organic frameworks (MOFs) with tailored pore size, surface, and pore chemistry are promising alternatives. Thanks to the compositional modularity of MOFs and polymer–MOF composites, herein we report on a series of water‐stable zirconium carboxylate MOFs and their low‐cost polymer‐grafted composites as C ₈ –PFAS adsorbents with benchmark kinetics and “parts per billion” removal efficiencies. Bespoke insights into the structure–function relationships of PFAS adsorbents are obtained by leveraging interfacial design principles on solid sorbents, creating a synergy between the extrinsic particle surfaces and intrinsic molecular building blocks.

Compositional engineering of perovskites has enabled the precise control of material properties required for their envisioned applications in photovoltaics. However, challenges remain to address efficiency, stability, and toxicity simultaneously. Mixed lead‐free and inorganic perovskites have recently demonstrated potential for resolving such issues but their composition space is gigantic, making it difficult to discover promising candidates even using high‐throughput methods. A machine learning approach employing a generalized element‐agnostic fingerprint is shown to rapidly and accurately predict key properties using a new database of 344 perovskites generated with density functional theory. Bandgap, formation energy, and convex hull distance are predicted using validation subsets to within 146 meV, 15 meV per atom, and 11 meV per atom, respectively. The resulting model is used to predict trends in entirely different chemical spaces, and perform rapid composition and configuration space sampling without the need for expensive ab initio simulations.

With the demand for renewable energy and efficient devices rapidly increasing, a need arises to find and optimize novel (nano)materials. With sheer limitless possibilities for material combinations and synthetic procedures, obtaining novel, highly functional materials has been a tedious trial and error process. Recently, machine learning has emerged as a powerful tool to help optimize syntheses; however, most approaches require a substantial amount of input data, limiting their pertinence. Here, three well‐known machine‐learning models are merged with Bayesian optimization into one to optimize the synthesis of CsPbBr₃ nanoplatelets with limited data demand. The algorithm can accurately predict the photoluminescence emission maxima of nanoplatelet dispersions using only the three precursor ratios as input parameters. This allows us to fabricate previously unobtainable seven and eight monolayer‐thick nanoplatelets. Moreover, the algorithm dramatically improves the homogeneity of 2–6‐monolayer‐thick nanoplatelet dispersions, as evidenced by narrower and more symmetric photoluminescence spectra. Decisively, only 200 total syntheses are required to achieve this vast improvement, highlighting how rapidly material properties can be optimized. The algorithm is highly versatile and can incorporate additional synthetic parameters. Accordingly, it is readily applicable to other less‐explored nanocrystal syntheses and can help rapidly identify and improve exciting compositions’ quality.

Insight into the relation between morphology and transport properties of organic semiconductors can be gained using multiscale simulations. Since computing electronic properties, such as the intermolecular transfer integral, using quantum chemical (QC) methods requires a high computational cost, existing models assume several approximations. A machine learning (ML)–based multiscale approach is presented that allows to simulate charge transport in organic semiconductors considering the static disorder within disordered crystals. By mapping fingerprints of dimers to their respective transfer integral, a kernel ridge regression ML algorithm for the prediction of charge transfer integrals is trained and evaluated. Since QC calculations of the electronic structure must be performed only once, the use of ML reduces the computation time radically, while maintaining the prediction error small. Transfer integrals predicted by ML are utilized for the computation of charge carrier mobilities using off‐lattice kinetic Monte Carlo (kMC) simulations. Benefiting from the rapid performance of ML, microscopic processes can be described accurately without the need for phenomenological approximations. The multiscale system is tested with the well‐known molecular semiconductor pentacene. The presented methodology allows reproducing the experimentally observed anisotropy of the mobility and enables a fast estimation of the impact of disorder.