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Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO 2 nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.

Abstract Rapid discovery and synthesis of future materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data collection strategies (SwitchBAX, InfoBAX, and MeanBAX), bypassing the time-consuming and difficult process of task-specific acquisition function design. Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We demonstrate this approach on datasets for TiO 2 nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches. Overall, our framework provides a practical solution for navigating the complexities of materials design, and helps lay groundwork for the accelerated development of advanced materials.

Twisted layered van-der-Waals materials often exhibit unique electronic and optical properties absent in their non-twisted counterparts. Unfortunately, predicting such properties is hindered by the difficulty in determining the atomic structure in materials displaying large moiré domains. Here, we introduce a split machine-learned interatomic potential and dataset curation approach that separates intralayer and interlayer interactions and significantly improves model accuracy -- with a tenfold increase in energy and force prediction accuracy relative to conventional models. We further demonstrate that traditional MLIP validation metrics -- force and energy errors -- are inadequate for moiré structures and develop a more holistic, physically-motivated metric based on the distribution of stacking configurations. This metric effectively compares the entirety of large-scale moiré domains between two structures instead of relying on conventional measures evaluated on smaller commensurate cells. Finally, we establish that one-dimensional instead of two-dimensional moiré structures can serve as efficient surrogate systems for validating MLIPs, allowing for a practical model validation protocol against explicit DFT calculations. Applying our framework to HfS2/GaS bilayers reveals that accurate structural predictions directly translate into reliable electronic properties. Our model-agnostic approach integrates seamlessly with various intralayer and interlayer interaction models, enabling computationally tractable relaxation of moiré materials, from bilayer to complex multilayers, with rigorously validated accuracy.

We introduce an adhesion parameter that enables rapid screening for materials interfaces with high adhesion. This parameter is obtained by density functional theory calculations of individual single-material slabs rather than slabs consisting of combinations of two materials, eliminating the need to calculate all configurations of a prohibitively vast space of possible interface configurations. Cleavage energy calculations are used as an upper bound for electrolyte and coating energies and implemented in an adapted contact angle equation to derive the adhesion parameter. In addition to good adhesion, we impose further constraints in electrochemical stability window, abundance, bulk reactivity, and stability to screen for coating materials for next-generation solid-state batteries. Good adhesion is critical in combating delamination and resistance to Lithium diffusivity in solid-state batteries. Here, we identify several promising coating candidates for the Li7La3Zr2O12 and sulfide electrolyte systems including the previously investigated electrode coating materials LiAlSiO4 and Li5AlO8, making them especially attractive for experimental optimization and commercialization.

The electrical resistivity of conventional metals, such as copper, is known to increase in thin films due to electron-surface scattering, limiting the performance of metals in nanoscale electronics. Here, we find an unusual reduction of resistivity with decreasing film thickness in niobium phosphide (NbP) semimetal deposited at relatively low temperatures of 400 \\deg C. In films thinner than 5 nm, the room temperature resistivity (~34 microohm*cm for 1.5-nm-thick NbP) was up to six times lower than the bulk NbP resistivity, and lower than conventional metals at similar thickness (typically ~100 microohm*cm). Remarkably, the NbP films are not crystalline, but display local nanocrystalline, short-range order within an amorphous matrix. Our analysis suggests that the lower effective resistivity is due to conduction via surface channels, together with high surface carrier density and sufficiently good mobility as the film thickness is reduced. These results and the fundamental insights obtained here could enable ultrathin, low-resistivity wires for nanoelectronics, beyond the limitations of conventional metals.

Rapid discovery and synthesis of new materials requires intelligent data acquisition strategies to navigate large design spaces. A popular strategy is Bayesian optimization, which aims to find candidates that maximize material properties; however, materials design often requires finding specific subsets of the design space which meet more complex or specialized goals. We present a framework that captures experimental goals through straightforward user-defined filtering algorithms. These algorithms are automatically translated into one of three intelligent, parameter-free, sequential data acquisition strategies (SwitchBAX, InfoBAX, and MeanBAX). Our framework is tailored for typical discrete search spaces involving multiple measured physical properties and short time-horizon decision making. We evaluate this approach on datasets for TiO\\(_2\\) nanoparticle synthesis and magnetic materials characterization, and show that our methods are significantly more efficient than state-of-the-art approaches.

The electrical resistivity of conventional metals, such as copper, is known to increase in thinner films due to electron-surface scattering, limiting the performance of metals in nanoscale electronics. Here, we uncover a reduction of resistivity with decreasing film thickness in NbP semimetal, deposited at relatively low temperatures of 400 \\deg C. In sub-5 nm thin films, we find a significantly lower room temperature resistivity (~34 microOhm*cm for 1.5 nm thin NbP) than in the bulk form, and lower than conventional metals at similar thickness. Remarkably, the NbP films are not crystalline, but display local nanocrystalline, short-range order within an amorphous matrix. Our analysis suggests that the lower resistivity is due to conduction via surface channels, together with high surface carrier density and sufficiently good mobility as the film thickness is reduced. These results and the fundamental insights obtained here could enable ultrathin, low-resistivity wires for nanoelectronics, beyond the limitations of conventional metals.

Aditya-L1 is the first space-based solar observatory from India, which is studying the Sun and solar wind from the first Lagrangian point (L1) in a halo orbit. Among the seven payloads, four of them are remote sensing and three are in situ ones. The Plasma-Analyzer Package for Aditya (PAPA) is one among the in situ payloads for exploring the composition of the solar wind and its energy distribution (in the range from 0.01 to 3 keV for electrons and 0.01 to 25 keV for ions) continuously throughout the lifetime of the mission. PAPA has two sensors: the Solar-Wind Electron Energy Probe (SWEEP) indented to measure the solar-wind electron flux and the Solar-Wind Ion Composition AnalyzeR (SWICAR) indented to measure the ion flux and composition as a function of direction and energy as well as electrons. Thus, SWEEP measures only electron parameters, whereas SWICAR has two modes of operation – ion mode in which ion parameters are measured and electron mode in which electron parameters are measured. These two modes in SWICAR are mutually exclusive. The payload is unique and the technologies like the high-voltage (± 5 kV DC) programmable power supply and the dual-mode (electrons and ions) detection of particles using a single sensor (SWICAR) are notable first-time developments. Data from PAPA will provide detailed knowledge of the solar-wind conditions with high time resolution. SWICAR will also provide: (1) the elemental composition of solar-wind ions in the mass range of 1 – 60 amu, and (2) the differential energy flux and abundances of dominant ion species. The key parameters such as bulk speed, density, and kinetic temperature of the solar-wind electrons and dominant ion species can be regularly derived. From these, inferences can be made on the coronal temperatures, plasma sources of suprathermal ion populations, and the nature and dynamics of the solar-wind plasma, with the support of models. In this article, the scientific objectives as well as the design aspects of PAPA payload are discussed in detail along with the calibration and first on board observational results.

A/B tests are the gold standard for evaluating digital experiences on the web. However, traditional \"fixed-horizon\" statistical methods are often incompatible with the needs of modern industry practitioners as they do not permit continuous monitoring of experiments. Frequent evaluation of fixed-horizon tests (\"peeking\") leads to inflated type-I error and can result in erroneous conclusions. We have released an experimentation service on the Adobe Experience Platform based on anytime-valid confidence sequences, allowing for continuous monitoring of the A/B test and data-dependent stopping. We demonstrate how we adapted and deployed asymptotic confidence sequences in a full featured A/B testing platform, describe how sample size calculations can be performed, and how alternate test statistics like \"lift\" can be analyzed. On both simulated data and thousands of real experiments, we show the desirable properties of using anytime-valid methods instead of traditional approaches.