arXiv subjects

Michael Xu

Publications and source records attributed to Michael Xu.

30 records · Page 2Linked to original sources

arXiv2024

Atomic Structure of Self-Buffered BaZr(S,Se)$_3$ Epitaxial Thin Film Interfaces

Understanding and controlling the growth of chalcogenide perovskite thin films through interface design is important for tailoring film properties. Here, the film and interface structure of BaZr(S,Se)$_3$ thin films grown on LaAlO$_3$ by molecular beam epitaxy and post-growth anion exchange is resolved using aberration-corrected scanning transmission electron microscopy. Epitaxial films are achieved from self-assembly of an interface ``buffer'' layer, which accommodates the large film/substrate lattice mismatch of nearly 40\% for the alloy film studied here. The self-assembled buffer layer, occurring for both the as-grown sulfide and post-selenization alloy films, is shown to have rock-salt-like atomic stacking akin to a Ruddlesden-Popper phase. Above this buffer, the film quickly transitions to the perovskite structure. Overall, these results provide insights into oxide-chalcogenide heteroepitaxial film growth, illustrating a process that yields relaxed, crystalline, epitaxial chalcogenide perovskite films that support ongoing studies of optoelectronic and device properties.

cond-mat.mtrl-sci↗

arXiv2024

Collaborative Quest Completion with LLM-driven Non-Player Characters in Minecraft

The use of generative AI in video game development is on the rise, and as the conversational and other capabilities of large language models continue to improve, we expect LLM-driven non-player characters (NPCs) to become widely deployed. In this paper, we seek to understand how human players collaborate with LLM-driven NPCs to accomplish in-game goals. We design a minigame within Minecraft where a player works with two GPT4-driven NPCs to complete a quest. We perform a user study in which 28 Minecraft players play this minigame and share their feedback. On analyzing the game logs and recordings, we find that several patterns of collaborative behavior emerge from the NPCs and the human players. We also report on the current limitations of language-only models that do not have rich game-state or visual understanding. We believe that this preliminary study and analysis will inform future game developers on how to better exploit these rapidly improving generative AI models for collaborative roles in games.

cs.CL↗

arXiv2024

GENEVA: GENErating and Visualizing branching narratives using LLMs

Dialogue-based Role Playing Games (RPGs) require powerful storytelling. The narratives of these may take years to write and typically involve a large creative team. In this work, we demonstrate the potential of large generative text models to assist this process. \textbf{GENEVA}, a prototype tool, generates a rich narrative graph with branching and reconverging storylines that match a high-level narrative description and constraints provided by the designer. A large language model (LLM), GPT-4, is used to generate the branching narrative and to render it in a graph format in a two-step process. We illustrate the use of GENEVA in generating new branching narratives for four well-known stories under different contextual constraints. This tool has the potential to assist in game development, simulations, and other applications with game-like properties.

cs.CL↗

arXiv2024

Player-Driven Emergence in LLM-Driven Game Narrative

We explore how interaction with large language models (LLMs) can give rise to emergent behaviors, empowering players to participate in the evolution of game narratives. Our testbed is a text-adventure game in which players attempt to solve a mystery under a fixed narrative premise, but can freely interact with non-player characters generated by GPT-4, a large language model. We recruit 28 gamers to play the game and use GPT-4 to automatically convert the game logs into a node-graph representing the narrative in the player's gameplay. We find that through their interactions with the non-deterministic behavior of the LLM, players are able to discover interesting new emergent nodes that were not a part of the original narrative but have potential for being fun and engaging. Players that created the most emergent nodes tended to be those that often enjoy games that facilitate discovery, exploration and experimentation.

cs.CL↗

arXiv2024

A Processing Route to Chalcogenide Perovskites Alloys with Tunable Band Gap via Anion Exchange

We demonstrate synthesis of BaZr(S,Se)3 chalcogenide perovskite alloys by selenization of BaZrS3 thin films. The anion-exchange process produces films with tunable composition and band gap without changing the orthorhombic perovskite crystal structure or the film microstructure. The direct band gap is tunable between 1.5 and 1.9 eV. The alloy films made in this way feature 100x stronger photoconductive response and a lower density of extended defects, compared to alloy films made by direct growth. The perovskite structure is stable in high-selenium-content thin films with and without epitaxy. The manufacturing-compatible process of selenization in H2Se gas may spur the development of chalcogenide perovskite solar cell technology.

cond-mat.mtrl-sci↗

arXiv2023

Validation of machine-learned interatomic potentials via temperature-dependent electron thermal diffuse scattering

Machine-learned interatomic potentials (MLIPs) show promise in accurately describing the physical properties of materials, but there is a need for a higher throughput method of validation. Here, we demonstrate using that MLIPs and molecular dynamics can accurately capture the potential energy landscape and lattice dynamics that are needed to describe electron thermal diffuse scattering. Using SrTiO$_3$ as a test-bed at cryogenic and room temperatures, we compare electron thermal diffuse scattering simulations using different approximations to incorporate thermal motion. Only when the simulations are based on quantum mechanically accurate MLIPs in combination with path-integral molecular dynamics that include nuclear quantum effects, there is excellent agreement with experiment\end{abstract}

cond-mat.mtrl-sci↗

arXiv2023

Determination of Local Short-Range Order in TiVNbHf(Al)

The presence of short-range chemical order can be a key factor in determining the mechanical behavior of metals, but directly and unambiguously determining its distribution in complex concentrated alloy systems can be challenging. Here, we directly identify and quantify chemical order in the globally single phase BCC-TiVNbHf(Al) system using aberration corrected scanning transmission electron microscopy (STEM) paired with spatial statistics methods. To overcome the difficulties of short-range order (SRO) quantification with STEM when the components of an alloy exhibit large atomic number differences and near equiatomic ratios, 'null hypothesis' tests are used to separate experiment from a random chemical distribution. Experiment is found to deviate from both the case of an ideal random solid solution and a fully ordered structure with statistical significance. We also identify local chemical order in TiVNbHf and confirm and quantify the enhancement of SRO with the addition of Al. These results provide insights into local chemical order in the promising TiVNbHf(Al) refractory alloys while highlighting the utility of spatial statistics in characterizing nanoscale SRO in compositionally complex systems.

cond-mat.mtrl-sci↗

arXiv2022

A new semiconducting perovskite alloy system made possible by gas-source molecular beam epitaxy

Optoelectronic technologies are based on families of semiconductor alloys. It is rare that a new semiconductor alloy family is developed to the point where epitaxial growth is possible; since the 1950s, this has happened approximately once per decade. Here we demonstrate epitaxial thin film growth of semiconducting chalcogenide perovskite alloys in the Ba-Zr-S-Se system by gas-source molecular beam epitaxy (MBE). We stabilize the full range y = 0 ... 3 of compositions BaZrS$_{(3-y)}$Se$_y$ in the perovskite structure, up to and including BaZrSe$_3$, by growing on BaZrS$_3$ epitaxial templates. The resulting films are environmentally stable and the direct band gap ($E_g$) varies strongly with Se content, as predicted by theory, covering the range $E_g$ = 1.9 ... 1.4 eV for $y$ = 0 ... 3. This creates possibilities for visible and near-infrared (VIS-NIR) optoelectronics, solid state lighting, and solar cells using chalcogenide perovskites.

cond-mat.mtrl-sci↗

arXiv2022

Correlating Local Chemical and Structural Order Using Geographic Information Systems-Based Spatial Statistics

Analysis of nanoscale short-range chemical and/or structural order via (scanning) transmission electron microscopy (S/TEM) imaging is fundamentally limited by projection of the three dimensional sample, which averages informational along the beam direction. Extracting statistically significant spatial correlations between the structure and chemistry determined from these two-dimensional datasets thus remains challenging. Here, we apply methods commonly used in Geographic Information Systems (GIS) to determine the spatial correlation between measures of local chemistry and structure from atomic-resolution STEM imaging of a compositionally complex relaxor, Pb(Mg$_{1/3}$Nb$_{2/3}$)O$_{3}$ (PMN). The approach is used to determine the type of ordering present and to quantify the spatial variation of chemical order, oxygen octahedral distortions, and oxygen octahedral tilts. The extent of autocorrelation and inter-feature correlation among these short-range ordered regions are then evaluated through a spatial covariance analysis, showing correlation as a function of distance. The results demonstrate that integrating GIS tools for analyzing microscopy datasets can serve to unravel subtle relationships among chemical and structural features in complex materials that can be hidden when ignoring their spatial distributions.

cond-mat.mtrl-sci↗

arXiv2022

Towards Augmented Microscopy with Reinforcement Learning-Enhanced Workflows

Here, we report a case study implementation of reinforcement learning (RL) to automate operations in the scanning transmission electron microscopy (STEM) workflow. To do so, we design a virtual, prototypical RL environment to test and develop a network to autonomously align the electron beam without prior knowledge. Using this simulator, we evaluate the impact of environment design and algorithm hyperparameters on alignment accuracy and learning convergence, showing robust convergence across a wide hyperparameter space. Additionally, we deploy a successful model on the microscope to validate the approach and demonstrate the value of designing appropriate virtual environments. Consistent with simulated results, the on-microscope RL model achieves convergence to the goal alignment after minimal training. Overall, the results highlight that by taking advantage of RL, microscope operations can be automated without the need for extensive algorithm design, taking another step towards augmenting electron microscopy with machine learning methods.

physics.ins-det↗

arXiv2021

Making BaZrS3 chalcogenide perovskite thin films by molecular beam epitaxy

We demonstrate the making of BaZrS3 thin films by molecular beam epitaxy (MBE). BaZrS3 forms in the orthorhombic distorted-perovskite structure with corner-sharing ZrS6 octahedra. The single-step MBE process results in films smooth on the atomic scale, with near-perfect BaZrS3 stoichiometry and an atomically-sharp interface with the LaAlO3 substrate. The films grow epitaxially via two, competing growth modes: buffered epitaxy, with a self-assembled interface layer that relieves the epitaxial strain, and direct epitaxy, with rotated-cube-on-cube growth that accommodates the large lattice constant mismatch between the oxide and the sulfide perovskites. This work sets the stage for developing chalcogenide perovskites as a family of semiconductor alloys with properties that can be tuned with strain and composition in high-quality epitaxial thin films, as has been long-established for other systems including Si-Ge, III-Vs, and II-Vs. The methods demonstrated here also represent a revival of gas-source chalcogenide MBE.

cond-mat.mtrl-sci↗

arXiv2021

A Non-Nested Multilevel Method for Meshless Solution of the Poisson Equation in Heat Transfer and Fluid Flow

We present a non-nested multilevel algorithm for solving the Poisson equation discretized at scattered points using polyharmonic radial basis function (PHS-RBF) interpolations. We append polynomials to the radial basis functions to achieve exponential convergence of discretization errors. The interpolations are performed over local clouds of points and the Poisson equation is collocated at each of the scattered points, resulting in a sparse set of discrete equations for the unkown variables. To solve this set of equations, we have developed a non-nested multilevel algorithm utilizing multiple independently generated coarse sets of points. The restriction and prolongation operators are also constructed with the same RBF interpolations procedure. The performance of the algorithm for Dirichlet and all-Neumann boundary conditions is evaluated in three model geometries using a manufactured solution. For Dirichlet boundary conditions, rapid convergence is observed using SOR point solver as the relaxation scheme. For cases of all-Neumann boundary conditions, convergence is seen to slow down with the degree of the appended polynomial. However, when the multilevel procedure is combined with a GMRES algorithm, the convergence is seen to significantly improve. The GMRES accelerated multilevel algorithm is included in a fractional step method to solve incompressible Navier-Stokes equations.

math.NA↗