Category: physics.chem-ph
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Gradient-Guided Furthest Point Sampling for Robust Training Set Selection
Gradient-Guided Furthest Point Sampling for Robust Training Set Selection arXiv:2510.08906v1 Announce Type: new Abstract: Smart training set selections procedures enable the reduction of data needs and improves predictive robustness in machine learning problems relevant to chemistry. We introduce Gradient Guided Furthest Point Sampling (GGFPS), a simple extension of Furthest Point Sampling (FPS) that leverages molecular…
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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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FEAT: Free energy Estimators with Adaptive Transport
FEAT: Free energy Estimators with Adaptive Transport arXiv:2504.11516v1 Announce Type: new Abstract: We present Free energy Estimators with Adaptive Transport (FEAT), a novel framework for free energy estimation — a critical challenge across scientific domains. FEAT leverages learned transports implemented via stochastic interpolants and provides consistent, minimum-variance estimators based on escorted Jarzynski equality and controlled…