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Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision-making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that the performance measure can be exactly and correctly specified in the form of a utility or reward function” [S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach (Pearson, 2020)]. Once the reward function is known, any problem can be formulated as an optimization or search problem—the areas well explored within the AI community. However, while the long-term objectives of a specific activity are often well defined, constructing short-term rewards that remain aligned with those goals and consistent with real-world constraints remains a major unresolved challenge. Such alignment has been achieved in domains such as chess, Go, and supervised machine learning problems, where objectives are well defined and easily simulated. However, no universal solution exists for defining intermediate rewards for complex, evolving scientific goals, which remains an open challenge. Correspondingly, the key to operationalizing automated instruments, integrating multi-instrument self-driving laboratories, and building geographically distributed research facilities is to generate experiment-aligned, probabilistic, domain-specific reward functions. These rewards must be consistent with long-term experimental objectives while remaining actionable on the timescales of decision-making on laboratory tools in microscopy and materials synthesis labs—proper reward definition operationalizes scientific intent. Here, we review the extant reward structures in the physical sciences and summarize opportunities for reward design informed by physical principles, human heuristics, and LLM-based reasoning. 

In the evolving landscape of scientific research, the complexity of global challenges demands innovative approaches to experimental planning and execution. Self-Driving Laboratories (SDLs) automate experimental tasks in chemical and materials sciences and the design and selection of experiments to optimize research processes and reduce material usage. This perspective explores improving access to SDLs via centralized facilities and distributed networks. We discuss the technical and collaborative challenges in realizing SDLs’ potential to enhance human–machine and human–human collaboration, ultimately fostering a more inclusive research community and facilitating previously untenable research projects. 

Abstract The intriguing functionalities of emerging quasi‐2D metal halide perovskites (MHPs) have led to further exploration of this material class for sustainable and scalable optoelectronic applications. However, the chemical complexities in precursors—primarily determined by the 2D:3D compositional ratio—result in uncontrolled phase heterogeneities in these materials, which compromises the optoelectronic performances. Yet, this phenomenon remains poorly understood due to the massive quasi‐2D compositional space. To systematically explore the fundamental principles, herein, a high‐throughput automated synthesis‐characterization workflow is designed and implemented to formamidinium (FA)‐based quasi‐2D MHP system. It is revealed that the stable 3D‐like phases, where the α‐FAPbI₃surface is passivated by 2D spacers, exclusively emerge at the compositional range (35–55% of FAPbI₃), deviating from the stoichiometric considerations. A quantitative crystallographic study via high‐throughput grazing‐incidence wide‐angle X‐ray scattering (GIWAXS) experiments integrated with automated peak analysis function quickly reveals that the 3D‐like phases are vertically aligned, facilitating vertical charge conduction that can be beneficial for optoelectronic applications. Together, this study uncovers the optimal 2D:3D compositional range for complex quasi‐2D MHP systems, realizing promising optoelectronic functionalities. The automated experimental workflow significantly accelerates materials discoveries and processing optimizations that are transferrable to other deposition methods, while providing fundamental insights into complex materials systems. 

Cesium‐based quasi‐2D halide perovskites (HPs) offer promising functionalities and low‐temperature manufacturability, suited to stable tandem photovoltaics. However, the chemical interplays between the molecular spacers and the inorganic building blocks during crystallization cause substantial phase complexities in the resulting matrices. To successfully optimize and implement the quasi‐2D HP functionalities, a systematic understanding of spacer chemistry, along with the seamless navigation of the inherently discrete molecular space, is necessary. Herein, by utilizing high‐throughput automated experimentation, the phase complexities in the molecular space of quasi‐2D HPs are explored, thus identifying the chemical roles of the spacer cations on the synthesis and functionalities of the complex materials. Furthermore, a novel active machine learning algorithm leveraging a two‐stage decision‐making process, called gated Gaussian process Bayesian optimization is introduced, to navigate the discrete ternary chemical space defined with two distinctive spacer molecules. Through simultaneous optimization of photoluminescence intensity and stability that “tailors” the chemistry in the molecular space, a ternary‐compositional quasi‐2D HP film realizing excellent optoelectronic functionalities is demonstrated. This work not only provides a pathway for the rational and bespoke design of complex HP materials but also sets the stage for accelerated materials discovery in other multifunctional systems. 

Abstract Controlled fabrication of nanopores in 2D materials offer the means to create robust membranes needed for ion transport and nanofiltration. Techniques for creating nanopores have relied upon either plasma etching or direct irradiation; however, aberration‐corrected scanning transmission electron microscopy (STEM) offers the advantage of combining a sub‐Å sized electron beam for atomic manipulation along with atomic resolution imaging. Here, a method for automated nanopore fabrication is utilized with real‐time atomic visualization to enhance the mechanistic understanding of beam‐induced transformations. Additionally, an electron beam simulation technique, Electron‐Beam Simulator (E‐BeamSim) is developed to observe the atomic movements and interactions resulting from electron beam irradiation. Using the MXene Ti₃C₂T_x, the influence of temperature on nanopore fabrication is explored by tracking atomic transformations and find that at room temperature the electron beam irradiation induces random displacement and results in titanium pileups at the nanopore edge, which is confirmed by E‐BeamSim. At elevated temperatures, after removal of the surface functional groups and with the increased mobility of atoms results in atomic transformations that lead to the selective removal of atoms layer by layer. This work can lead to the development of defect engineering techniques within functionalized MXene layers and other 2D materials. 

For over three decades, scanning probe microscopy (SPM) has been a key method for exploring material structures and functionalities at nanometer and often atomic scales in ambient, liquid, and vacuum environments. Historically, SPM applications have predominantly been downstream, with images and spectra serving as a qualitative source of data on the microstructure and properties of materials, and in rare cases of fundamental physical knowledge. However, the fast-growing developments in accelerated material synthesis via self-driving labs and established applications such as combinatorial spread libraries are poised to change this paradigm. Rapid synthesis demands matching capabilities to probe the structure and functionalities of materials on small scales and with high throughput. SPM inherently meets these criteria, offering a rich and diverse array of data from a single measurement. Here, we overview SPM methods applicable to these emerging applications and emphasize their quantitativeness, focusing on piezoresponse force microscopy, electrochemical strain microscopy, conductive, and surface photovoltage measurements. We discuss the challenges and opportunities ahead, asserting that SPM will play a crucial role in closing the loop from material prediction and synthesis to characterization. 

It is widely accepted that the interaction of swift heavy ions with many complex oxides is predominantly governed by the electronic energy loss that gives rise to nanoscale amorphous ion tracks along the penetration direction.