Amorphous silicon nitride (a-SiN) is a material which has found wide application due to its excellent mechanical and electrical properties. Despite the significant effort devoted in understanding how the microscopic structure influences the material performance, many aspects still remain elusive. If on the one hand \textit{ab initio} calculations respresent the technique of election to study such a system, they present severe limitations in terms of the size of the system that can be simulated. Such an aspect plays a determinant role, particularly when amorphous structure are to be investigated, as often results depend dramatically on the size of the system. Here, we overcome this limitation by training a machine-learning (ML) interatomic model to \textit{ab initio} data. We show that molecular dynamics simulations using the ML model on much larger systems can reproduce experimental measurements of elastic properties, including elastic isotropy. Our study demonstrates the broader impact of machine-learning potentials for predicting structural and mechanical properties, even for complex amorphous structures.

We consider reinforcement learning in changing Markov Decision Processes where both the state-transition probabilities and the reward functions may vary over time. For this problem setting, we propose an algorithm using a sliding window approach and provide performance guarantees for the regret evaluated against the optimal non-stationary policy. We also characterize the optimal window size suitable for our algorithm. These results are complemented by a sample complexity bound on the number of sub-optimal steps taken by the algorithm. Finally, we present some experimental results to support our theoretical analysis.

Automated rock classification from mineral composition presents a significant challenge in geological applications, with critical implications for material recycling, resource management, and industrial processing. While existing methods using One dimensional Convolutional Neural Network (1D-CNN) excel at mineral identification through Raman spectroscopy, the crucial step of determining rock types from mineral assemblages remains unsolved, particularly because the same minerals can form different rock types depending on their proportions and formation conditions. This study presents a novel knowledge-enhanced deep learning approach that integrates geological domain expertise with spectral analysis. The performance of five machine learning methods were evaluated out of which the 1D-CNN and its uncertainty-aware variant demonstrated excellent mineral classification performance (98.37+-0.006% and 97.75+-0.010% respectively). The integrated system's evaluation on rock samples revealed variable performance across lithologies, with optimal results for limestone classification but reduced accuracy for rocks sharing similar mineral assemblages. These findings not only show critical challenges in automated geological classification systems but also provide a methodological framework for advancing material characterization and sorting technologies.

The increasing demand for sustainable energy solutions necessitates advancements in hydrogen storage technologies. This study investigates the hydrogen adsorption characteristics of graphene and a (8,0) carbon nanotube (CNT) decorated with adatoms of various elements. Using molecular dynamics (MD) simulations and the universal interatomic potential 'PreFerred Potential' (PFP) implemented in the Matlantis framework, we explore the hydrogen storage capabilities of these doped carbon structures at 77K. We analyze the adsorption efficiency based on the position of adatoms (top, bridge, and hollow sites) and find that the group II elements, such as calcium and strontium, exhibit significant hydrogen uptake. Additionally, light elements like lithium and sodium demonstrate enhanced gravimetric hydrogen storage due to their low atomic mass. Our findings provide insights into the potential of doped graphene and CNTs for efficient hydrogen storage applications.

We consider undiscounted reinforcement learning in Markov decision processes (MDPs) where both the reward functions and the state-transition probabilities may vary (gradually or abruptly) over time. For this problem setting, we propose an algorithm and provide performance guarantees for the regret evaluated against the optimal non-stationary policy. The upper bound on the regret is given in terms of the total variation in the MDP. This is the first variational regret bound for the general reinforcement learning setting.

Hydrogen embrittlement can result in a sudden failure in metallic materials, which is particularly harmful in industrially relevant alloys, such as steels. A more comprehensive understanding of hydrogen interactions with microstructural features is critical for preventing hydrogen-induced damage and promoting a hydrogen-based environment-benign economy. We use the Kelvin probe-based potentiometric hydrogen electrode method and thermal desorption spectroscopy to investigate hydrogen interactions with different hydrogen traps in ferritic FeCr alloys with different chromium contents, dislocation densities, and grain sizes. In addition, we confirm the validity of a novel nanohardness-based diffusion coefficient approach by performing in situ nanoindentation testing. Simultaneous acquisition of the dynamic time-resolved mechanical response of FeCr alloys to hydrogen and the hydrogen diffusivities in these alloys is possible during continuous hydrogen supply. Dislocations, grain boundaries and Cr atoms induce reversible hydrogen trapping sites in these ferritic alloys, leading to the reduction of the hydrogen diffusion coefficients and the increase of the absorbed hydrogen.

We present a comprehensive study on the formation of micrometer-sized, textured hexagonal diamond silicon (hd-Si) crystals via nanoindentation followed by annealing. Utilizing advanced characterization techniques such as polarized Raman spectroscopy, high-resolution transmission electron microscopy, and electron energy-loss spectroscopy, we demonstrate the successful transformation of silicon into high-quality hd-Si. The experimental results are further supported by first-principles calculations and molecular dynamics simulations. Notably, the hd-Si phase consists of nanometer-sized grains with slight misorientations, organized into large micrometer-scale textured domains. These findings underscore the potential of nanoindentation as a precise and versatile tool for inducing pressure-driven phase transformations, particularly for the stabilization of hexagonal silicon. The textured nature of hd-Si also presents a unique opportunity to tailor its optical properties, opening new avenues for its application in semiconductor and optoelectronic devices.

To ensure the efficiency of robot autonomy under diverse real-world conditions, a high-quality heterogeneous dataset is essential to benchmark the operating algorithms' performance and robustness. Current benchmarks predominantly focus on urban terrains, specifically for on-road autonomous driving, leaving multi-degraded, densely vegetated, dynamic and feature-sparse environments, such as underground tunnels, natural fields, and modern indoor spaces underrepresented. To fill this gap, we introduce EnvoDat, a large-scale, multi-modal dataset collected in diverse environments and conditions, including high illumination, fog, rain, and zero visibility at different times of the day. Overall, EnvoDat contains 26 sequences from 13 scenes, 10 sensing modalities, over 1.9TB of data, and over 89K fine-grained polygon-based annotations for more than 82 object and terrain classes. We post-processed EnvoDat in different formats that support benchmarking SLAM and supervised learning algorithms, and fine-tuning multimodal vision models. With EnvoDat, we contribute to environment-resilient robotic autonomy in areas where the conditions are extremely challenging. The datasets and other relevant resources can be accessed through this https URL

Deployment of deep neural networks in resource-constrained embedded systems requires innovative algorithmic solutions to facilitate their energy and memory efficiency. To further ensure the reliability of these systems against malicious actors, recent works have extensively studied adversarial robustness of existing architectures. Our work focuses on the intersection of adversarial robustness, memory- and energy-efficiency in neural networks. We introduce a neural network conversion algorithm designed to produce sparse and adversarially robust spiking neural networks (SNNs) by leveraging the sparse connectivity and weights from a robustly pretrained artificial neural network (ANN). Our approach combines the energy-efficient architecture of SNNs with a novel conversion algorithm, leading to state-of-the-art performance with enhanced energy and memory efficiency through sparse connectivity and activations. Our models are shown to achieve up to 100x reduction in the number of weights to be stored in memory, with an estimated 8.6x increase in energy efficiency compared to dense SNNs, while maintaining high performance and robustness against adversarial threats.

In this paper, we extended the method proposed in [21] to enable humans to interact naturally with autonomous agents through vocal and textual conversations. Our extended method exploits the inherent capabilities of pre-trained large language models (LLMs), multimodal visual language models (VLMs), and speech recognition (SR) models to decode the high-level natural language conversations and semantic understanding of the robot's task environment, and abstract them to the robot's actionable commands or queries. We performed a quantitative evaluation of our framework's natural vocal conversation understanding with participants from different racial backgrounds and English language accents. The participants interacted with the robot using both spoken and textual instructional commands. Based on the logged interaction data, our framework achieved 87.55% vocal commands decoding accuracy, 86.27% commands execution success, and an average latency of 0.89 seconds from receiving the participants' vocal chat commands to initiating the robot's actual physical action. The video demonstrations of this paper can be found at this https URL

Four-dimensional scanning transmission electron microscopy (4D-STEM) is a powerful tool that allows for the simultaneous acquisition of spatial and diffraction information, driven by recent advancements in direct electron detector technology. Although 4D-STEM has been predominantly developed for and used in conventional TEM and STEM, efforts are being made to implement the technique in scanning electron microscopy (SEM). In this paper, we push the boundaries of 4D-STEM in SEM and extend its capabilities in three key aspects: (1) faster acquisition rate with reduced data size, (2) higher angular resolution, and (3) application to various materials including conventional alloys and focused ion beam (FIB) lamella. Specifically, operating the MiniPIX Timepix3 detector in the event-driven mode significantly improves the acquisition rate by a factor of a few tenths compared to conventional frame-based mode, thereby opening up possibilities for integrating 4D-STEM into various in situ SEM testing. Furthermore, with a novel stage-detector geometry, a camera length of 160 mm is achieved which improves the angular resolution amplifying its utility, for example, magnetic or electric field imaging. Lastly, we successfully imaged a nanostructured platinum-copper thin film with a grain size of 16 nm and a thickness of 20 nm, and identified annealing twins in FIB-prepared polycrystalline copper using virtual darkfield imaging and orientation mapping. This work demonstrates the potential of synergetic combination of 4D-STEM with in situ experiments, and broadening its applications across a wide range of materials.

Let $s(n)$ denote the number of ones in the binary expansion of the nonnegative integer $n$. How does $s$ behave under addition of a constant $t$? In order to study the differences $$s(n+t)-s(n),$$ for all $n\ge0$, we consider the associated characteristic function $\gamma_t$. Our main theorem is a structural result on the decomposition of $\gamma_t$ into a sum of \emph{components}. We also study in detail the case that $t$ contains at most two blocks of consecutive $1$s. The results in this paper are motivated by \emph{Cusick's conjecture} on the sum-of-digits function. This conjecture is concerned with the \emph{central tendency} of the corresponding probability distributions, and is still unsolved.

In this article we quantify almost sure martingale convergence theorems in terms of the tradeoff between asymptotic almost sure rates of convergence (error tolerance) and the respective modulus of convergence. For this purpose we generalize {an} elementary quantitative version of the first Borel-Cantelli lemma on the statistics of the deviation frequencies (error incidence), which was recently established by the authors. First we study martingale convergence in $L^2$, and in the setting of the Azuma-Hoeffding inequality. In a second step we study the strong law of large numbers for martingale differences in two settings: uniformly bounded increments in $L^p$, $p\geq 2$, using the respective Baum-Katz-Stoica theorems, and uniformly bounded exponential moments with the help of the martingale estimates by Lesigne and Volný. We also present applications for the tradeoff for the multicolor generalized Pólya urn process, the Generalized Chinese restaurant process, statistical M-estimators, as well as the a.s.~excursion frequencies of the Galton-Watson branching process. Finally, we relate the tradeoff concept to the convergence in the Ky Fan metric.

High entropy alloys (HEAs) have captured much attention in recent years due to their conceivably improved radiation resistance compared to pure metals and traditional alloys. However, among HEAs, there are millions of design possibilities considering all potential compositions. In this study, we develop criteria to design HEAs with improved radiation resilience taking into consideration defect properties to promote interstitial-vacancy recombination. First, we conduct rate theory calculations on defects followed by molecular dynamics (MD) simulations on pure W and W-based multicomponent concentrated alloys. It is found that when the diffusion coefficients for single vacancies and interstitials become similar and the effective migration energies of defects is minimum (maximum diffusivities), defect recombination becomes optimal, and the concentration of defects is significantly reduced. This is supported by MD simulations indicating improved radiation resistance of V- and Cr-based alloys, which satisfy the above-stated criteria. Furthermore, experimental observations also reinforce the proposed approach. This study sheds light on the design criteria for improved radiation resistance and helps material selection without the need of extensive experimental work.

We consider the sum-of-digits functions $s_2$ and $s_3$ in bases $2$ and $3$. These functions just return the minimal numbers of powers of two (resp. three) needed in order to represent a nonnegative integer as their sum. A result of the second author states that there are infinitely many \emph{collisions} of $s_2$ and $s_3$, that is, positive integers $n$ such that $$s_2(n)=s_3(n).$$ This resolved a long-standing folklore conjecture. In the present paper, we prove a strong generalization of this statement, stating that $(s_2(n),s_3(n))$ attains almost all values in $\mathbb N^2$, in the sense of asymptotic density. In particular, this yields \emph{generalized collisions}: for any pair $(a,b)$ of positive integers, the equation $$as_2(n)=bs_3(n)$$ admits infinitely many solutions in $n$.

The segregation of solutes to grain boundaries can significantly influence material behavior. Most previous computational studies have concentrated on substitutional solute segregation, neglecting interstitial segregation due to its increased complexity. The site preference, interstitial or substitutional, for P segregation in $\alpha$-Fe still remains under debate. In this work, we investigate the full GB-segregation spectrum for both substitutional and interstitial GB sites in a polycrystalline atomistic structure of ferrite with the aid of classical interatomic potentials combined with machine learning techniques. The method is qualitatively tested for H and Ni, where the segregation behavior in $\alpha$-Fe is well understood. Our findings for P show that segregation to both types of GB sites is possible, with a preference for the substitutional sites based on the mean segregation energy. However, due to the much larger number of interstitial sites, interstitial segregation significantly contributes to the GB enrichment with P. This underscores the importance of considering interstitial P segregation in addition to the substitutional one. Furthermore, we also argue that equally important for quantitative predictions (that agree with experimental data) is to get a representative spectrum of the segregation energies.

Hydrogen is key in reducing greenhouse gas emissions in materials production. At the same time, it significantly affects mechanical properties, often causing unwanted embrittlement. However, rather than solely addressing these disadvantages, hydrogens inevitable role in sustainable metallurgy should be leveraged to create new and potentially superior materials. Here, we show that using hydrogen in the form of metal hydrides introduces a barrier to mechanical alloying, stabilizing otherwise unattainable microstructures. Severe plastic deformation of a composite of the high entropy alloy (HEA) TiVZrNbHf and Cu leads to amorphization while substituting the HEA by its hydride preserves the two-phase structure. Monte Carlo simulations confirm that the significantly different hydrogen affinities, together with the restricted dislocation motion in the hydride, create a barrier to mechanical alloying. This hydride route enables new microstructural states, even in well-studied material systems. It opens an additional dimension in designing materials with diverging hydrogen affinities, offering tighter control over mechanical alloying.

In order to establish the thermodynamic stability of a system, knowledge of its Gibbs free energy is essential. Most often, the Gibbs free energy is predicted within the CALPHAD framework using models employing thermodynamic properties, such as the mixing enthalpy, heat capacity, and activity coefficients. Here, we present a deep-learning approach capable of predicting the mixing enthalpy of liquid phases of binary systems that were not present in the training dataset. Therefore, our model allows for a system-informed enhancement of the thermodynamic description to unknown binary systems based on information present in the available thermodynamic assessment. Thereby, significant experimental efforts in assessing new systems can be spared. We use an open database for steels containing 91 binary systems to generate our initial training (and validation) and amend it with several direct experimental reports. The model is thoroughly tested using different strategies, including a test of its predictive capabilities. The model shows excellent predictive capabilities outside of the training dataset as soon as some data containing species of the predicted system is included in the training dataset. The estimated uncertainty of the model is below 1 kJ/mol for the predicted mixing enthalpy. Subsequently, we used our model to predict the enthalpy of mixing of all binary systems not present in the original database and extracted the Redlich-Kister parameters, which can be readily reintegrated into the thermodynamic database file.

Transformations in bcc-$\beta$, hcp-$\alpha$, and the $\omega$ phases of Ti alloys are studied using Density Functional Theory for pure Ti and Ti alloyed with Al, Si, V, Cr, Fe, Cu, Nb, Mo, and Sn. The $\beta$-stabilization caused by alloying Si, Fe, Cr, and Mo was observed, but the most stable phase appears between the $\beta$ and the $\alpha$ phases, corresponding to the martensitic $\alpha''$ phase. Next, the $\{112\}\langle11\bar1\rangle$ bcc twins are separated by a positive barrier, which further increases by alloying w.r.t. pure Ti. The $\{332\}\langle11\bar3\rangle$ twinning yields negative barriers for all species but Mo and Fe. This is because the transition state is structurally similar to the $\alpha$ phase, which is preferred over the $\beta$ phase for the majority of alloying elements. Lastly, the impact of alloying on twin boundary energies is discussed. These results may serve as design guidelines for novel Ti-based alloys with specific application areas.