Category: cond-mat.mtrl-sci
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Towards universal property prediction in Cartesian space: TACE is all you need
Towards universal property prediction in Cartesian space: TACE is all you need arXiv:2509.14961v1 Announce Type: new Abstract: Machine learning has revolutionized atomistic simulations and materials science, yet current approaches often depend on spherical-harmonic representations. Here we introduce the Tensor Atomic Cluster Expansion and Tensor Moment Potential, the first unified framework formulated entirely in Cartesian space…
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Reduced Order Modeling of Energetic Materials Using Physics-Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC)
Reduced Order Modeling of Energetic Materials Using Physics-Aware Recurrent Convolutional Neural Networks in a Latent Space (LatentPARC) arXiv:2509.12401v1 Announce Type: cross Abstract: Physics-aware deep learning (PADL) has gained popularity for use in complex spatiotemporal dynamics (field evolution) simulations, such as those that arise frequently in computational modeling of energetic materials (EM). Here, we show that…
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Differentiable neural network representation of multi-well, locally-convex potentials
Differentiable neural network representation of multi-well, locally-convex potentials arXiv:2506.17242v1 Announce Type: new Abstract: Multi-well potentials are ubiquitous in science, modeling phenomena such as phase transitions, dynamic instabilities, and multimodal behavior across physics, chemistry, and biology. In contrast to non-smooth minimum-of-mixture representations, we propose a differentiable and convex formulation based on a log-sum-exponential (LSE) mixture of…
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Batch Bayesian Optimization for High-Dimensional Experimental Design: Simulation and Visualization
Batch Bayesian Optimization for High-Dimensional Experimental Design: Simulation and Visualization arXiv:2504.03943v1 Announce Type: new Abstract: Bayesian Optimization (BO) is increasingly used to guide experimental optimization tasks. To elucidate BO behavior in noisy and high-dimensional settings typical for materials science applications, we perform batch BO of two six-dimensional test functions: an Ackley function representing a needle-in-a-haystack…