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Narrowing the gap between machine learning scoring functions and free energy perturbation using augmented data
Machine learning offers great promise for fast and accurate binding affinity predictions. However, current models lack robust evaluation and fail on tasks encountered in (hit-to-) lead optimisation, such as ranking the binding affinity of a congeneric series of ligands, thereby limiting their application in drug discovery. Here, we address these issues by first introducing a novel attention-based graph neural network model called AEV-PLIG (atomic environment vector–protein ligand interaction graph). Second, we introduce a new and more realistic out-of-distribution test set called the OOD Test. We benchmark our model on this set, CASF-2016, and a test set used for free energy perturbation (FEP) calculations, that not only highlights the competitive performance of AEV-PLIG, but provides a realistic assessment of machine learning models with rigorous physics-based approaches. Moreover, we demonstrate how leveraging augmented data (generated using template-based modelling or molecular docking) can significantly improve binding affinity prediction correlation and ranking on the FEP benchmark (weighted mean PCC and Kendall’s τ increases from 0.41 and 0.26 to 0.59 and 0.42). These strategies together are closing the performance gap with FEP calculations (FEP+ achieves weighted mean PCC and Kendall’s τ of 0.68 and 0.49 on the FEP benchmark) while being ~400,000 times faster.
Suppressed ballistic transport of dislocations at strain rates up to 109 s–1 in a stable nanocrystalline alloy
Dislocations are crucial to plastic deformation in crystals. At extreme strain rates, their motion shifts from thermally activated glide to ballistic transport, causing significant drag due to interactions with phonons, which can lead to embrittlement and failure in metals. The concept of dislons, quantized dislocations, has emerged to better understand these types of interactions. Similar to quantum treatment of dislocation-electron interactions, confining dislocations to nanometer scales, especially in nanocrystalline metals, could also yield unique mechanical behaviors different from bulk materials. Here, we present evidence showing that in Cu-3Ta, a thermo-mechanically stable nanocrystalline alloy, the phonon drag effect is entirely suppressed even at ultra-high strain rates (109 s−1). This is due to the stable confinement of dislocations within several-nanometer range, limiting their velocity and interaction with phonons. Our study indicates that in confined environments, the dislocation-phonon drag effect is minimal, potentially improving material performance under extreme conditions.
ToF-SIMS sputter depth profiling of interphases and coatings on lithium metal surfaces
Lithium metal as a negative electrode material offers ten times the specific capacity of graphitic electrodes, but its rechargeable operation poses challenges like excessive and continuous interphase formation, high surface area lithium deposits and safety issues. Improving the lithium | electrolyte interface and interphase requires powerful surface analysis techniques, such as ToF-SIMS sputter depth profiling.This study investigates lithium metal sections with an SEI layer by ToF-SIMS using different sputter ions. An optimal sputter ion is chosen based on the measured ToF-SIMS sputter depth profiles and SEM analysis of the surface damage. Further, this method is adapted to lithium metal foil with an intermetallic coating. ToF-SIMS sputter depth profiles in both polarities provide comprehensive insights into the coating structure. Both investigations highlight the value of ToF-SIMS sputter depth profiling in lithium metal battery research and offer guidance for future studies.
Flash Joule heating for synthesis, upcycling and remediation
Electric heating methods are being developed and used to electrify industrial applications and lower their carbon emissions. Direct Joule resistive heating is an energy-efficient electric heating technique that has been widely tested at the bench scale and could replace some energy-intensive and carbon-intensive processes. In this Review, we discuss the use of flash Joule heating (FJH) in processes that are traditionally energy-intensive or carbon-intensive. FJH uses pulse current discharge to rapidly heat materials directly to a desired temperature; it has high-temperature capabilities (>3,000 °C), fast heating and cooling rates (>102 °C s−1), short duration (milliseconds to seconds) and high energy efficiency (~100%). Carbon materials and metastable inorganic materials can be synthesized using FJH from virgin materials and waste feedstocks. FJH is also applied in resource recovery (such as from e-waste) and waste upcycling. An emerging application is in environmental remediation, where FJH can be used to rapidly degrade perfluoroalkyl and polyfluoroalkyl substances and to remove or immobilize heavy metals in soil and solid wastes. Life-cycle and technoeconomic analyses suggest that FJH can reduce energy consumption and carbon emissions and be cost-efficient compared with existing methods. Bringing FJH to industrially relevant scales requires further equipment and engineering development.
Modeling the impact of structure and coverage on the reactivity of realistic heterogeneous catalysts
Adsorbates often cover the surfaces of catalysts densely as they carry out reactions, dynamically altering their structure and reactivity. Understanding adsorbate-induced phenomena and harnessing them in our broader quest for improved catalysts is a substantial challenge that is only beginning to be addressed. Here we chart a path toward a deeper understanding of such phenomena by focusing on emerging in silico modeling methodologies, which will increasingly incorporate machine learning techniques. We first examine how adsorption on catalyst surfaces can lead to local and even global structural changes spanning entire nanoparticles, and how this affects their reactivity. We then evaluate current efforts and the remaining challenges in developing robust and predictive simulations for modeling such behavior. Last, we provide our perspectives in four critical areas—integration of artificial intelligence, building robust catalysis informatics infrastructure, synergism with experimental characterization, and adaptive modeling frameworks—that we believe can help surmount the remaining challenges in rationally designing catalysts in light of these complex phenomena.
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