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In this study, we explore the use of a transformer-based language model as an encoder to predict the band gaps of semiconductor materials directly from their text descriptions. Quantum chemistry simulations, including Density Functional Theory (DFT), are computationally intensive and time-consuming, which limits their practicality for high-throughput material screening, particularly for complex systems. Shallow machine learning (ML) models, while effective, often require extensive data preprocessing to convert non-numerical material properties into numerical inputs. In contrast, our approach leverages textual data directly, bypassing the need for complex feature engineering. We generate material descriptions in two formats: formatted strings combining features and natural language text generated using the ChatGPT API. We demonstrate that the RoBERTa model, pre-trained on natural language processing tasks, performs effectively as an encoder for prediction tasks. With minimal fine-tuning, it achieves a mean absolute error (MAE) of approximately 0.33 eV, performing better than shallow machine learning models such as Support Vector Regression, Random Forest, and XGBoost. Even when only the linear regression head is trained while keeping the RoBERTa encoder layers frozen, the accuracy remains nearly identical to that of the fully trained model. This demonstrates that the pre-trained RoBERTa encoder is highly adaptable for processing domain-specific text related to material properties, such as the band gap, significantly reducing the need for extensive retraining. This study highlights the potential of transformer-based language models to serve as efficient and versatile encoders for semiconductor materials property prediction tasks.

Diffusion models have gained tremendous success in text-to-image generation, yet still lag behind with visual understanding tasks, an area dominated by autoregressive vision-language models. We propose a large-scale and fully end-to-end diffusion model for multi-modal understanding and generation that significantly improves on existing diffusion-based multimodal models, and is the first of its kind to support the full suite of vision-language modeling capabilities. Inspired by the multimodal diffusion transformer (MM-DiT) and recent advances in discrete diffusion language modeling, we leverage a cross-modal maximum likelihood estimation framework that simultaneously trains the conditional likelihoods of both images and text jointly under a single loss function, which is back-propagated through both branches of the diffusion transformer. The resulting model is highly flexible and capable of a wide range of tasks including image generation, captioning, and visual question answering. Our model attained competitive performance compared to recent unified image understanding and generation models, demonstrating the potential of multimodal diffusion modeling as a promising alternative to autoregressive next-token prediction models.

Neural surrogates for partial differential equations (PDEs) have become popular due to their potential to quickly simulate physics. With a few exceptions, neural surrogates generally treat the forward evolution of time-dependent PDEs as a black box by directly predicting the next state. While this is a natural and easy framework for applying neural surrogates, it can be an over-simplified and rigid framework for predicting physics. In this work, we propose an alternative framework in which neural solvers predict the temporal derivative and an ODE integrator forwards the solution in time, which has little overhead and is broadly applicable across model architectures and PDEs. We find that by simply changing the training target and introducing numerical integration during inference, neural surrogates can gain accuracy and stability. Predicting temporal derivatives also allows models to not be constrained to a specific temporal discretization, allowing for flexible time-stepping during inference or training on higher-resolution PDE data. Lastly, we investigate why this new framework can be beneficial and in what situations does it work well., Comment: 18 pages, 7 figures

In this study, we presented two innovative methods, which are Threshold-Based Derivative (TBD) and Adaptive Derivative Peak Detection(ADPD), that enhance the accuracy of Learning models for blood glucose estimation using Mid-Infrared (MIR) spectroscopy. In these presented methods, we have enhanced the model's accuracy by integrating absorbance data and its differentiation with critical points. Blood samples were characterized with Fourier Transform Infrared (FTIR) spectroscopy and advanced preprocessing steps. The learning models were Ridge Regression and Support Vector Regression(SVR) using Leave-One-out Cross-Validation. Results exhibited that TBD and ADPD significantly outperform basic used methods. For SVR, the TBD increased the r2 score by around 27%, and ADPD increased it by around 10%. these Ridge Regression values were between 36% and 24%. In addition, Results demonstrate that TBD and ADPD significantly outperform conventional methods, achieving lower error rates and improved clinical accuracy, validated through Clarke and Parkes Error Grid Analysis.

Deformable object manipulation remains a key challenge in developing autonomous robotic systems that can be successfully deployed in real-world scenarios. In this work, we explore the challenges of deformable object manipulation through the task of sculpting clay into 3D shapes. We propose the first coarse-to-fine autonomous sculpting system in which the sculpting agent first selects how many and where to place discrete chunks of clay into the workspace to create a coarse shape, and then iteratively refines the shape with sequences of deformation actions. We leverage large language models for sub-goal generation, and train a point cloud region-based action model to predict robot actions from the desired point cloud sub-goals. Additionally, our method is the first autonomous sculpting system that is a real-world text-to-3D shaping pipeline without any explicit 3D goals or sub-goals provided to the system. We demonstrate our method is able to successfully create a set of simple shapes solely from text-based prompting. Furthermore, we explore rigorously how to best quantify success for the text-to-3D sculpting task, and compare existing text-image and text-point cloud similarity metrics to human evaluations for this task. For experimental videos, human evaluation details, and full prompts, please see our project website: https://sites.google.com/andrew.cmu.edu/hierarchicalsculpting

Identifying drug-target interactions is essential for developing effective therapeutics. Binding affinity quantifies these interactions, and traditional approaches rely on computationally intensive 3D structural data. In contrast, language models can efficiently process sequential data, offering an alternative approach to molecular representation. In the current study, we introduce BAPULM, an innovative sequence-based framework that leverages the chemical latent representations of proteins via ProtT5-XL-U50 and ligands through MolFormer, eliminating reliance on complex 3D configurations. Our approach was validated extensively on benchmark datasets, achieving scoring power (R) values of 0.925 $\pm$ 0.043, 0.914 $\pm$ 0.004, and 0.8132 $\pm$ 0.001 on benchmark1k2101, Test2016_290, and CSAR-HiQ_36, respectively. These findings indicate the robustness and accuracy of BAPULM across diverse datasets and underscore the potential of sequence-based models in-silico drug discovery, offering a scalable alternative to 3D-centric methods for screening potential ligands.

We investigate a gapped two-dimensional nodal-line semimetal subjected to a perpendicular magnetic field. We identify an unusual pattern for Landau levels, where the energy of Landau levels first decreases and then starts increasing versus the level index. These descending/ascending Landau levels form a stretched check-mark shape. Tuning the magnitude of the magnetic field changes the spacing between Landau levels. At fixed chemical potential in the low doped regime, the Landau levels move below the chemical potential and get filled from one side, while they move out and get vacant from the other side. Using the Kubo formalism in the linear response theory, we analytically study a gapped nodal-line semimetal's longitudinal and Hall conductivities. In the DC limit, we observe that as the magnetic field is varied at a fixed chemical potential, the Chern number swings back and forth between two consecutive integer numbers, giving rise to bumpy Hall plateaus in a low-doped system. We also find that the longitudinal optical conductivity displays different series of the Shubnikov-de Haas oscillations, corresponding to inter-band and intra-band transition among the descending and ascending sets of the Landau levels., Comment: 9 pages, 5 figures, submitted

In recent years, natural language processing (NLP) models have demonstrated remarkable capabilities in various domains beyond traditional text generation. In this work, we introduce PeptideGPT, a protein language model tailored to generate protein sequences with distinct properties: hemolytic activity, solubility, and non-fouling characteristics. To facilitate a rigorous evaluation of these generated sequences, we established a comprehensive evaluation pipeline consisting of ideas from bioinformatics to retain valid proteins with ordered structures. First, we rank the generated sequences based on their perplexity scores, then we filter out those lying outside the permissible convex hull of proteins. Finally, we predict the structure using ESMFold and select the proteins with pLDDT values greater than 70 to ensure ordered structure. The properties of generated sequences are evaluated using task-specific classifiers - PeptideBERT and HAPPENN. We achieved an accuracy of 76.26% in hemolytic, 72.46% in non-hemolytic, 78.84% in non-fouling, and 68.06% in solubility protein generation. Our experimental results demonstrate the effectiveness of PeptideGPT in de novo protein design and underscore the potential of leveraging NLP-based approaches for paving the way for future innovations and breakthroughs in synthetic biology and bioinformatics. Codes, models, and data used in this study are freely available at: https://github.com/aayush-shah14/PeptideGPT.

Adsorption energy is a key reactivity descriptor in catalysis, enabling efficient screening for optimal catalysts. However, determining adsorption energy typically requires evaluating numerous adsorbate-catalyst configurations. Current algorithmic approaches rely on exhaustive enumeration of adsorption sites and configurations, which makes the process computationally intensive and does not inherently guarantee the identification of the global minimum energy. In this work, we introduce Adsorb-Agent, a Large Language Model (LLM) agent designed to efficiently identify system-specific stable adsorption configurations corresponding to the global minimum adsorption energy. Adsorb-Agent leverages its built-in knowledge and emergent reasoning capabilities to strategically explore adsorption configurations likely to hold adsorption energy. By reducing the reliance on exhaustive sampling, it significantly decreases the number of initial configurations required while improving the accuracy of adsorption energy predictions. We evaluate Adsorb-Agent's performance across twenty representative systems encompassing a range of complexities. The Adsorb-Agent successfully identifies comparable adsorption energies for 83.7% of the systems and achieves lower energies, closer to the actual global minimum, for 35% of the systems, while requiring significantly fewer initial configurations than conventional methods. Its capability is particularly evident in complex systems, where it identifies lower adsorption energies for 46.7% of systems involving intermetallic surfaces and 66.7% of systems with large adsorbate molecules. These results demonstrate the potential of Adsorb-Agent to accelerate catalyst discovery by reducing computational costs and improving the reliability of adsorption energy predictions.

Recent advances in deep learning have inspired numerous works on data-driven solutions to partial differential equation (PDE) problems. These neural PDE solvers can often be much faster than their numerical counterparts; however, each presents its unique limitations and generally balances training cost, numerical accuracy, and ease of applicability to different problem setups. To address these limitations, we introduce several methods to apply latent diffusion models to physics simulation. Firstly, we introduce a mesh autoencoder to compress arbitrarily discretized PDE data, allowing for efficient diffusion training across various physics. Furthermore, we investigate full spatio-temporal solution generation to mitigate autoregressive error accumulation. Lastly, we investigate conditioning on initial physical quantities, as well as conditioning solely on a text prompt to introduce text2PDE generation. We show that language can be a compact, interpretable, and accurate modality for generating physics simulations, paving the way for more usable and accessible PDE solvers. Through experiments on both uniform and structured grids, we show that the proposed approach is competitive with current neural PDE solvers in both accuracy and efficiency, with promising scaling behavior up to $\sim$3 billion parameters. By introducing a scalable, accurate, and usable physics simulator, we hope to bring neural PDE solvers closer to practical use., Comment: 25 pages, 7 figures

Solving Partial Differential Equations (PDEs) is ubiquitous in science and engineering. Computational complexity and difficulty in writing numerical solvers has motivated the development of machine learning techniques to generate solutions quickly. Many existing methods are purely data driven, relying solely on numerical solution fields, rather than known system information such as boundary conditions and governing equations. However, the recent rise in popularity of Large Language Models (LLMs) has enabled easy integration of text in multimodal machine learning models. In this work, we use pretrained LLMs to integrate various amounts known system information into PDE learning. Our multimodal approach significantly outperforms our baseline model, FactFormer, in both next-step prediction and autoregressive rollout performance on the 2D Heat, Burgers, Navier-Stokes, and Shallow Water equations. Further analysis shows that pretrained LLMs provide highly structured latent space that is consistent with the amount of system information provided through text., Comment: 22 pages, 15 figures, 7 tables

The stochastic formation of defects during Laser Powder Bed Fusion (L-PBF) negatively impacts its adoption for high-precision use cases. Optical monitoring techniques can be used to identify defects based on layer-wise imaging, but these methods are difficult to scale to high resolutions due to cost and memory constraints. Therefore, we implement generative deep learning models to link low-cost, low-resolution images of the build plate to detailed high-resolution optical images of the build plate, enabling cost-efficient process monitoring. To do so, a conditional latent probabilistic diffusion model is trained to produce realistic high-resolution images of the build plate from low-resolution webcam images, recovering the distribution of small-scale features and surface roughness. We first evaluate the performance of the model by analyzing the reconstruction quality of the generated images using peak-signal-to-noise-ratio (PSNR), structural similarity index measure (SSIM) and wavelet covariance metrics that describe the preservation of high-frequency information. Additionally, we design a framework based upon the Segment Anything foundation model to recreate the 3D morphology of the printed part and analyze the surface roughness of the reconstructed samples. Finally, we explore the zero-shot generalization capabilities of the implemented framework to other part geometries by creating synthetic low-resolution data.

As robotic systems become increasingly integrated into complex real-world environments, there is a growing need for approaches that enable robots to understand and act upon natural language instructions without relying on extensive pre-programmed knowledge of their surroundings. This paper presents PLATO, an innovative system that addresses this challenge by leveraging specialized large language model agents to process natural language inputs, understand the environment, predict tool affordances, and generate executable actions for robotic systems. Unlike traditional systems that depend on hard-coded environmental information, PLATO employs a modular architecture of specialized agents to operate without any initial knowledge of the environment. These agents identify objects and their locations within the scene, generate a comprehensive high-level plan, translate this plan into a series of low-level actions, and verify the completion of each step. The system is particularly tested on challenging tool-use tasks, which involve handling diverse objects and require long-horizon planning. PLATO's design allows it to adapt to dynamic and unstructured settings, significantly enhancing its flexibility and robustness. By evaluating the system across various complex scenarios, we demonstrate its capability to tackle a diverse range of tasks and offer a novel solution to integrate LLMs with robotic platforms, advancing the state-of-the-art in autonomous robotic task execution. For videos and prompt details, please see our project website: https://sites.google.com/andrew.cmu.edu/plato, Comment: 7 pages, 4 figures, submitted to ICRA 2025

The adaptive immune response, largely mediated by B-cell receptors (BCRs), plays a crucial role for effective pathogen neutralization due to its diversity and antigen specificity. Designing BCRs de novo, or from scratch, has been challenging because of their complex structure and diverse binding requirements. Protein language models (PLMs) have shown remarkable performance in contextualizing and performing various downstream tasks without relying on structural information. However, these models often lack a comprehensive understanding of the entire protein space, which limits their application in antibody design. In this study, we introduce Antibody Generative Pretrained Transformer (AbGPT), a model fine-tuned from a foundational PLM to enable a more informed design of BCR sequences. Using a custom generation and filtering pipeline, AbGPT successfully generated a high-quality library of 15,000 BCR sequences, demonstrating a strong understanding of the intrinsic variability and conserved regions within the antibody repertoire.

The design of aerodynamic shapes, such as airfoils, has traditionally required significant computational resources and relied on predefined design parameters, which limit the potential for novel shape synthesis. In this work, we introduce a data-driven methodology for airfoil generation using a diffusion model. Trained on a dataset of preexisting airfoils, our model can generate an arbitrary number of new airfoils from random vectors, which can be conditioned on specific aerodynamic performance metrics such as lift and drag, or geometric criteria. Our results demonstrate that the diffusion model effectively produces airfoil shapes with realistic aerodynamic properties, offering substantial improvements in efficiency, flexibility, and the potential for discovering innovative airfoil designs. This approach significantly expands the design space, facilitating the synthesis of high-performance aerodynamic shapes that transcend the limitations of traditional methods., Comment: 20 Pages, 6 figures

Industry 4.0 has revolutionized manufacturing by driving digitalization and shifting the paradigm toward additive manufacturing (AM). Fused Deposition Modeling (FDM), a key AM technology, enables the creation of highly customized, cost-effective products with minimal material waste through layer-by-layer extrusion, posing a significant challenge to traditional subtractive methods. However, the susceptibility of material extrusion techniques to errors often requires expert intervention to detect and mitigate defects that can severely compromise product quality. While automated error detection and machine learning models exist, their generalizability across diverse 3D printer setups, firmware, and sensors is limited, and deep learning methods require extensive labeled datasets, hindering scalability and adaptability. To address these challenges, we present a process monitoring and control framework that leverages pre-trained Large Language Models (LLMs) alongside 3D printers to detect and address printing defects. The LLM evaluates print quality by analyzing images captured after each layer or print segment, identifying failure modes and querying the printer for relevant parameters. It then generates and executes a corrective action plan. We validated the effectiveness of the proposed framework in identifying defects by comparing it against a control group of engineers with diverse AM expertise. Our evaluation demonstrated that LLM-based agents not only accurately identify common 3D printing errors, such as inconsistent extrusion, stringing, warping, and layer adhesion, but also effectively determine the parameters causing these failures and autonomously correct them without any need for human intervention.

The success of diffusion probabilistic models in generative tasks, such as text-to-image generation, has motivated the exploration of their application to regression problems commonly encountered in scientific computing and various other domains. In this context, the use of diffusion regression models for ensemble prediction is becoming a practice with increasing popularity. Under such background, we conducted a study to quantitatively evaluate the effectiveness of ensemble methods on solving different regression problems using diffusion models. We consider the ensemble prediction of a diffusion model as a means for zero-shot uncertainty quantification, since the diffusion models in our study are not trained with a loss function containing any uncertainty estimation. Through extensive experiments on 1D and 2D data, we demonstrate that ensemble methods consistently improve model prediction accuracy across various regression tasks. Notably, we observed a larger accuracy gain in auto-regressive prediction compared with point-wise prediction, and that enhancements take place in both the mean-square error and the physics-informed loss. Additionally, we reveal a statistical correlation between ensemble prediction error and ensemble variance, offering insights into balancing computational complexity with prediction accuracy and monitoring prediction confidence in practical applications where the ground truth is unknown. Our study provides a comprehensive view of the utility of diffusion ensembles, serving as a useful reference for practitioners employing diffusion models in regression problem-solving.

Medical Internet of Things (IoMT) is the next frontier in the digital revolution and is utilized in healthcare. In this context, IoT enables individuals to remotely manage their essential activities with minimal interaction. However, the limitations of network resources and the challenges of establishing a secure channel, as well as sharing and collecting sensitive information through an insecure public channel, pose security challenges for the medical IoT. This paper presents a lightweight multi-factor authentication and anonymous user authentication scheme to access real-time data in a blockchain-based environment. The scheme utilizes an insecure channel called L2AI. L2AI ensures security and efficiency while enhancing user anonymity through the use of pseudo-identity and dynamic indexing. The proposed method supports highly scalable systems with an efficient user registration process, allowing authenticated users to access both existing and newly added system entities without additional processes. Although the scheme is primarily designed for large systems, such as health infrastructure, it is also suitable for resource-constrained devices. The scheme relies on one-way cryptographic hashing functions and bitwise XOR operations. Additionally, a fuzzy mining algorithm is employed on the user side to verify the user's biometric information. L2AI adopts the "Real-Or-Random (ROR)" model for security proof and employs BAN logic for proof of authenticity. Formal security verification is conducted using the "Automatic Validation of Internet Security Protocols and Programs" (Proverif) tool, complemented by informal security analysis demonstrating the proper functionality of L2AI.

There has recently been increasing attention towards developing foundational neural Partial Differential Equation (PDE) solvers and neural operators through large-scale pretraining. However, unlike vision and language models that make use of abundant and inexpensive (unlabeled) data for pretraining, these neural solvers usually rely on simulated PDE data, which can be costly to obtain, especially for high-dimensional PDEs. In this work, we aim to Pretrain neural PDE solvers on Lower Dimensional PDEs (PreLowD) where data collection is the least expensive. We evaluated the effectiveness of this pretraining strategy in similar PDEs in higher dimensions. We use the Factorized Fourier Neural Operator (FFNO) due to having the necessary flexibility to be applied to PDE data of arbitrary spatial dimensions and reuse trained parameters in lower dimensions. In addition, our work sheds light on the effect of the fine-tuning configuration to make the most of this pretraining strategy. Code is available at https://github.com/BaratiLab/PreLowD.

We use semiclassical Boltzmann transport theory to analytically study the electronic contribution to the linear thermoelectric response of anisotropic two-dimensional materials subjected to a perpendicular magnetic field. Conventional methods, such as the relaxation-time approximation within the Boltzmann formalism, often yield qualitatively incorrect results when applied to anisotropic mediums. On the other hand, the vector mean free path provides exact solutions for the linearized Boltzmann transport equation, and in principle, we can systematically evaluate it to any desired order in the external magnetic field. After laying out the general formalism of the magneto-thermoelectric responses of anisotropic systems based on the vector mean free path approach, we study the longitudinal and Hall charge conductivities, as well as the Seebeck and Nernst coefficients of two representative anisotropic two-dimensional electronic systems: a semi-Dirac semimetal and a two-dimensional system of tilted massless Dirac fermions., Comment: 10 pages, 3 figures, submitted

Peptides are essential in biological processes and therapeutics. In this study, we introduce Multi-Peptide, an innovative approach that combines transformer-based language models with Graph Neural Networks (GNNs) to predict peptide properties. We combine PeptideBERT, a transformer model tailored for peptide property prediction, with a GNN encoder to capture both sequence-based and structural features. By employing Contrastive Language-Image Pre-training (CLIP), Multi-Peptide aligns embeddings from both modalities into a shared latent space, thereby enhancing the model's predictive accuracy. Evaluations on hemolysis and nonfouling datasets demonstrate Multi-Peptide's robustness, achieving state-of-the-art 86.185% accuracy in hemolysis prediction. This study highlights the potential of multimodal learning in bioinformatics, paving the way for accurate and reliable predictions in peptide-based research and applications.

Tactile perception is a critical component of solving real-world manipulation tasks, but tactile sensors for manipulation have barriers to use such as fragility and cost. In this work, we engage a robust, low-cost tactile sensor, BeadSight, as an alternative to precise pre-calibrated sensors for a pretraining approach to manipulation. We show that tactile pretraining, even with a low-fidelity sensor as BeadSight, can improve an imitation learning agent's performance on complex manipulation tasks. We demonstrate this method against a baseline USB cable plugging task, previously achieved with a much higher precision GelSight sensor as the tactile input to pretraining. Our best BeadSight pretrained visuo-tactile agent completed the task with 70\% accuracy compared to 85\% for the best GelSight pretrained visuo-tactile agent, with vision-only inference for both.

Alternative current optimal power flow (ACOPF) problems have been studied for over fifty years, and yet the development of an optimal algorithm to solve them remains a hot and challenging topic for researchers because of their nonlinear and nonconvex nature. A number of methods based on linearization and convexification have been proposed to solve to ACOPF problems, which result in near-optimal or local solutions, not optimal solutions. Nowadays, with the prevalence of machine learning, some researchers have begun to utilize this technology to solve ACOPF problems using the historical data generated by the grid operators. The present paper reviews the research on solving ACOPF problems using machine learning and neural networks and proposes future studies. This body of research is at the beginning of this area, and further exploration can be undertaken into the possibilities of solving ACOPF problems using machine learning.

This paper presents a new method for enhancing Alternating Current Power Flow (ACPF) analysis. The method integrates the Newton-Raphson (NR) method with Enhanced-Gradient Descent (GD) and computational graphs. The integration of renewable energy sources in power systems introduces variability and unpredictability, and this method addresses these challenges. It leverages the robustness of NR for accurate approximations and the flexibility of GD for handling variable conditions, all without requiring Jacobian matrix inversion. Furthermore, computational graphs provide a structured and visual framework that simplifies and systematizes the application of these methods. The goal of this fusion is to overcome the limitations of traditional ACPF methods and improve the resilience, adaptability, and efficiency of modern power grid analyses. We validate the effectiveness of our advanced algorithm through comprehensive testing on established IEEE benchmark systems. Our findings demonstrate that our approach not only speeds up the convergence process but also ensures consistent performance across diverse system states, representing a significant advancement in power flow computation., Comment: 10 pages, 3 figures, Submitted to IEEE Transactions on Industry Applications

We formulate the Alternating Current Optimal Power Flow Problem (ACOPF) as a Linear Constrained Quadratic Program (LCQP) with many negative eigenvalues ($r$) and linear constraints, making it NP-hard. We propose two algorithms, Feasible Successive Linear Programming (FSLP) and Feasible Branch-and-Bound (FBB), for a global optimal solution. These use optimization strategies like bounded successive linear programming, convex relaxation, initialization, and branch-and-bound to find a globally optimal solution within a predefined $\epsilon$-tolerance. The complexity of FSLP and FBB is $\mathcal{O}\left(N \prod_{i=1}^r\left\lceil\frac{\sqrt{r}(t_u^i-t_l^i)}{2 \sqrt{\epsilon}}\right\rceil\right)$, where $N$ is the complexity of solving subproblems at each FBB node. Variables $t_l$ and $t_u$ are the lower and upper bounds of $t$, respectively, and $-|t|^2$ is the negative quadratic component in the ACOPF objective function. We use penalized semidefinite modeling, convex relaxation, and line search to design a globally feasible branch-and-bound algorithm for the LCQP form of ACOPF, finding an optimal solution within $\epsilon$-tolerance. Initial results show FSLP and FBB can find global optimal solutions for large-scale ACOPF instances, even with large $r$, and outperform other methods in most PG-lib tests.

When humans create sculptures, we are able to reason about how geometrically we need to alter the clay state to reach our target goal. We are not computing point-wise similarity metrics, or reasoning about low-level positioning of our tools, but instead determining the higher-level changes that need to be made. In this work, we propose LLM-Craft, a novel pipeline that leverages large language models (LLMs) to iteratively reason about and generate deformation-based crafting action sequences. We simplify and couple the state and action representations to further encourage shape-based reasoning. To the best of our knowledge, LLM-Craft is the first system successfully leveraging LLMs for complex deformable object interactions. Through our experiments, we demonstrate that with the LLM-Craft framework, LLMs are able to successfully reason about the deformation behavior of elasto-plastic objects. Furthermore, we find that LLM-Craft is able to successfully create a set of simple letter shapes. Finally, we explore extending the framework to reaching more ambiguous semantic goals, such as "thinner" or "bumpy". For videos please see our website: https://sites.google.com/andrew.cmu.edu/llmcraft.

Pretraining for partial differential equation (PDE) modeling has recently shown promise in scaling neural operators across datasets to improve generalizability and performance. Despite these advances, our understanding of how pretraining affects neural operators is still limited; studies generally propose tailored architectures and datasets that make it challenging to compare or examine different pretraining frameworks. To address this, we compare various pretraining methods without optimizing architecture choices to characterize pretraining dynamics on different models and datasets as well as to understand its scaling and generalization behavior. We find that pretraining is highly dependent on model and dataset choices, but in general transfer learning or physics-based pretraining strategies work best. In addition, pretraining performance can be further improved by using data augmentations. Lastly, pretraining can be additionally beneficial when fine-tuning in scarce data regimes or when generalizing to downstream data similar to the pretraining distribution. Through providing insights into pretraining neural operators for physics prediction, we hope to motivate future work in developing and evaluating pretraining methods for PDEs., Comment: 29 pages, 5 figures

Computer Aided Design (CAD) engineers typically do not achieve their best prototypes in a single attempt. Instead, they iterate and refine their designs to achieve an optimal solution through multiple revisions. This traditional approach, though effective, is time-consuming and relies heavily on the expertise of skilled engineers. To address these challenges, we introduce Query2CAD, a novel framework to generate CAD designs. The framework uses a large language model to generate executable CAD macros. Additionally, Query2CAD refines the generation of the CAD model with the help of its self-refinement loops. Query2CAD operates without supervised data or additional training, using the LLM as both a generator and a refiner. The refiner leverages feedback generated by the BLIP2 model, and to address false negatives, we have incorporated human-in-the-loop feedback into our system. Additionally, we have developed a dataset that encompasses most operations used in CAD model designing and have evaluated our framework using this dataset. Our findings reveal that when we used GPT-4 Turbo as our language model, the architecture achieved a success rate of 53.6\% on the first attempt. With subsequent refinements, the success rate increased by 23.1\%. In particular, the most significant improvement in the success rate was observed with the first iteration of the refinement. With subsequent refinements, the accuracy of the correct designs did not improve significantly. We have open-sourced our data, model, and code (github.com/akshay140601/Query2CAD)., Comment: 8 pages, 5 figures

The increasing popularity of machine learning (ML) in catalysis has spurred interest in leveraging these techniques to enhance catalyst design. Our study aims to bridge the gap between physics-based studies and data-driven methodologies by integrating ML techniques with eXplainable AI (XAI). Specifically, we employ two XAI techniques: Post-hoc XAI analysis and Symbolic Regression. These techniques help us unravel the correlation between adsorption energy and the properties of the adsorbate-catalyst system. Leveraging a large dataset such as the Open Catalyst Dataset (OC20), we employ a combination of shallow ML techniques and XAI methodologies. Our investigation involves utilizing multiple shallow machine learning techniques to predict adsorption energy, followed by post-hoc analysis for feature importance, inter-feature correlations, and the influence of various feature values on the prediction of adsorption energy. The post-hoc analysis reveals that adsorbate properties exert a greater influence than catalyst properties in our dataset. The top five features based on higher Shapley values are adsorbate electronegativity, the number of adsorbate atoms, catalyst electronegativity, effective coordination number, and the sum of atomic numbers of the adsorbate molecule. There is a positive correlation between catalyst and adsorbate electronegativity with the prediction of adsorption energy. Additionally, symbolic regression yields results consistent with SHAP analysis. It deduces a mathematical relationship indicating that the square of the catalyst electronegativity is directly proportional to the adsorption energy. These consistent correlations resemble those derived from physics-based equations in previous research. Our work establishes a robust framework that integrates ML techniques with XAI, leveraging large datasets like OC20 to enhance catalyst design through model explainability.

Generalized large language models (LLMs) such as GPT-4 may not provide specific answers to queries formulated by materials science researchers. These models may produce a high-level outline but lack the capacity to return detailed instructions on manufacturing and material properties of novel alloys. Enhancing a smaller model with specialized domain knowledge may provide an advantage over large language models which cannot be retrained quickly enough to keep up with the rapid pace of research in metal additive manufacturing (AM). We introduce "AMGPT," a specialized LLM text generator designed for metal AM queries. The goal of AMGPT is to assist researchers and users in navigating the extensive corpus of literature in AM. Instead of training from scratch, we employ a pre-trained Llama2-7B model from Hugging Face in a Retrieval-Augmented Generation (RAG) setup, utilizing it to dynamically incorporate information from $\sim$50 AM papers and textbooks in PDF format. Mathpix is used to convert these PDF documents into TeX format, facilitating their integration into the RAG pipeline managed by LlamaIndex. Expert evaluations of this project highlight that specific embeddings from the RAG setup accelerate response times and maintain coherence in the generated text., Comment: 54 pages, 4 figures

In robotic manipulation, tactile sensors are indispensable, especially when dealing with soft objects, objects of varying dimensions, or those out of the robot's direct line of sight. Traditional tactile sensors often grapple with challenges related to cost and durability. To address these issues, our study introduces a novel approach to visuo-tactile sensing with an emphasis on economy and replacablity. Our proposed sensor, BeadSight, uses hydro-gel beads encased in a vinyl bag as an economical, easily replaceable sensing medium. When the sensor makes contact with a surface, the deformation of the hydrogel beads is observed using a rear camera. This observation is then passed through a U-net Neural Network to predict the forces acting on the surface of the bead bag, in the form of a pressure map. Our results show that the sensor can accurately predict these pressure maps, detecting the location and magnitude of forces applied to the surface. These abilities make BeadSight an effective, inexpensive, and easily replaceable tactile sensor, ideal for many robotics applications., Comment: BeadSight code is available at: https://github.com/Abraham190137/BeadSight 7 pages, 8 figures

Laser powder bed fusion (LPBF) has shown promise for wide range of applications due to its ability to fabricate freeform geometries and generate a controlled microstructure. However, components generated by LPBF still possess sub-optimal mechanical properties due to the defects that are created during laser-material interactions. In this work, we investigate mechanism of spatter formation, using a high-fidelity modelling tool that was built to simulate the multi-physics phenomena in LPBF. The modelling tool have the capability to capture the 3D resolution of the meltpool and the spatter behavior. To understand spatter behavior and formation, we reveal its properties at ejection and evaluate its variation from the meltpool, the source where it is formed. The dataset of the spatter and the meltpool collected consist of 50 % spatter and 50 % melt pool samples, with features that include position components, velocity components, velocity magnitude, temperature, density and pressure. The relationship between the spatter and the meltpool were evaluated via correlation analysis and machine learning (ML) algorithms for classification tasks. Upon screening different ML algorithms on the dataset, a high accuracy was observed for all the ML models, with ExtraTrees having the highest at 96 % and KNN having the lowest at 94 %.

Accurate weather forecasting is crucial in various sectors, impacting decision-making processes and societal events. Data-driven approaches based on machine learning models have recently emerged as a promising alternative to numerical weather prediction models given their potential to capture physics of different scales from historical data and the significantly lower computational cost during the prediction stage. Renowned for its state-of-the-art performance across diverse domains, the Transformer model has also gained popularity in machine learning weather prediction. Yet applying Transformer architectures to weather forecasting, particularly on a global scale is computationally challenging due to the quadratic complexity of attention and the quadratic increase in spatial points as resolution increases. In this work, we propose a factorized-attention-based model tailored for spherical geometries to mitigate this issue. More specifically, it utilizes multi-dimensional factorized kernels that convolve over different axes where the computational complexity of the kernel is only quadratic to the axial resolution instead of overall resolution. The deterministic forecasting accuracy of the proposed model on $1.5^\circ$ and 0-7 days' lead time is on par with state-of-the-art purely data-driven machine learning weather prediction models. We also showcase the proposed model holds great potential to push forward the Pareto front of accuracy-efficiency for Transformer weather models, where it can achieve better accuracy with less computational cost compared to Transformer based models with standard attention., Comment: Preprint

Mathematical equations have been unreasonably effective in describing complex natural phenomena across various scientific disciplines. However, discovering such insightful equations from data presents significant challenges due to the necessity of navigating extremely high-dimensional combinatorial and nonlinear hypothesis spaces. Traditional methods of equation discovery, commonly known as symbolic regression, largely focus on extracting equations from data alone, often neglecting the rich domain-specific prior knowledge that scientists typically depend on. To bridge this gap, we introduce LLM-SR, a novel approach that leverages the extensive scientific knowledge and robust code generation capabilities of Large Language Models (LLMs) to discover scientific equations from data in an efficient manner. Specifically, LLM-SR treats equations as programs with mathematical operators and combines LLMs' scientific priors with evolutionary search over equation programs. The LLM iteratively proposes new equation skeleton hypotheses, drawing from its physical understanding, which are then optimized against data to estimate skeleton parameters. We demonstrate LLM-SR's effectiveness across three diverse scientific domains, where it discovers physically accurate equations that provide significantly better fits to in-domain and out-of-domain data compared to the well-established symbolic regression baselines. Incorporating scientific prior knowledge also enables LLM-SR to search the equation space more efficiently than baselines. Code is available at: https://github.com/deep-symbolic-mathematics/LLM-SR

Insufficient overlap between the melt pools produced during Laser Powder Bed Fusion (L-PBF) can lead to lack-of-fusion defects and deteriorated mechanical and fatigue performance. In-situ monitoring of the melt pool subsurface morphology requires specialized equipment that may not be readily accessible or scalable. Therefore, we introduce a machine learning framework to correlate in-situ two-color thermal images observed via high-speed color imaging to the two-dimensional profile of the melt pool cross-section. Specifically, we employ a hybrid CNN-Transformer architecture to establish a correlation between single bead off-axis thermal image sequences and melt pool cross-section contours measured via optical microscopy. In this architecture, a ResNet model embeds the spatial information contained within the thermal images to a latent vector, while a Transformer model correlates the sequence of embedded vectors to extract temporal information. Our framework is able to model the curvature of the subsurface melt pool structure, with improved performance in high energy density regimes compared to analytical melt pool models. The performance of this model is evaluated through dimensional and geometric comparisons to the corresponding experimental melt pool observations.

Conventional mechanical design paradigms rely on experts systematically refining concepts through experience-guided modification and FEA to meet specific requirements. However, this approach can be time-consuming and heavily dependent on prior knowledge and experience. While numerous machine learning models have been developed to streamline this intensive and expert-driven iterative process, these methods typically demand extensive training data and considerable computational resources. Furthermore, methods based on deep learning are usually restricted to the specific domains and tasks for which they were trained, limiting their applicability across different tasks. This creates a trade-off between the efficiency of automation and the demand for resources. In this study, we present a novel approach that integrates pre-trained LLMs with a FEM module. The FEM module evaluates each design and provides essential feedback, guiding the LLMs to continuously learn, plan, generate, and optimize designs without the need for domain-specific training. We demonstrate the effectiveness of our proposed framework in managing the iterative optimization of truss structures, showcasing its capability to reason about and refine designs according to structured feedback and criteria. Our results reveal that these LLM-based agents can successfully generate truss designs that comply with natural language specifications with a success rate of up to 90%, which varies according to the applied constraints. By employing prompt-based optimization techniques we show that LLM based agents exhibit optimization behavior when provided with solution-score pairs to iteratively refine designs to meet specifications. This ability of LLM agents to produce viable designs and optimize them based on their inherent reasoning capabilities highlights their potential to develop and implement effective design strategies autonomously.

We present a deep learning approach for quantifying and localizing ex-situ porosity within Laser Powder Bed Fusion fabricated samples utilizing in-situ thermal image monitoring data. Our goal is to build the real time porosity map of parts based on thermal images acquired during the build. The quantification task builds upon the established Convolutional Neural Network model architecture to predict pore count and the localization task leverages the spatial and temporal attention mechanisms of the novel Video Vision Transformer model to indicate areas of expected porosity. Our model for porosity quantification achieved a $R^2$ score of 0.57 and our model for porosity localization produced an average IoU score of 0.32 and a maximum of 1.0. This work is setting the foundations of part porosity "Digital Twins" based on additive manufacturing monitoring data and can be applied downstream to reduce time-intensive post-inspection and testing activities during part qualification and certification. In addition, we seek to accelerate the acquisition of crucial insights normally only available through ex-situ part evaluation by means of machine learning analysis of in-situ process monitoring data.

The pursuit of novel alloys tailored to specific requirements poses significant challenges for researchers in the field. This underscores the importance of developing predictive techniques for essential physical properties of alloys based on their chemical composition and processing parameters. This study introduces AlloyBERT, a transformer encoder-based model designed to predict properties such as elastic modulus and yield strength of alloys using textual inputs. Leveraging the pre-trained RoBERTa encoder model as its foundation, AlloyBERT employs self-attention mechanisms to establish meaningful relationships between words, enabling it to interpret human-readable input and predict target alloy properties. By combining a tokenizer trained on our textual data and a RoBERTa encoder pre-trained and fine-tuned for this specific task, we achieved a mean squared error (MSE) of 0.00015 on the Multi Principal Elemental Alloys (MPEA) data set and 0.00611 on the Refractory Alloy Yield Strength (RAYS) dataset. This surpasses the performance of shallow models, which achieved a best-case MSE of 0.00025 and 0.0076 on the MPEA and RAYS datasets respectively. Our results highlight the potential of language models in material science and establish a foundational framework for text-based prediction of alloy properties that does not rely on complex underlying representations, calculations, or simulations., Comment: 20 pages, 3 figures