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From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
Announce Type: replace Abstract: Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (PES), leading to erroneous behavior in downstream simulations that standard energy and force regression evaluations can miss. Existing evaluations, such as microcanonical molecular dynamics (MD), are computationally expensive and primarily probe near-equilibrium states. To improve evaluation metrics for MLIPs, we...
From Evaluation to Design: Using Potential Energy Surface Smoothness Metrics to Guide Machine Learning Interatomic Potential Architectures
Announce Type: replace-cross Abstract: Machine Learning Interatomic Potentials (MLIPs) sometimes fail to reproduce the physical smoothness of the quantum potential energy surface (PES), leading to erroneous behavior in downstream simulations that standard energy and force regression evaluations can miss. Existing evaluations, such as microcanonical molecular dynamics (MD), are computationally expensive and primarily probe near-equilibrium states. To improve evaluation metrics for MLIPs, we...
Scalable Prediction of Complex Surface Reconstructions under Operating Conditions via Harmony-Search-Based Global Optimization
Announce Type: cross Abstract: The dynamic structural evolution of catalyst surfaces under operating conditions dictates catalytic performance, yet capturing these reconstructions atomically remains challenging. Global optimization based on machine learning interatomic potentials (MLIPs) is promising, but scaling to large-scale, low-symmetry operando systems is hindered by expansive search spaces and potential energy surface (PES) inaccuracies. Herein, we present Harmony-search-based Atomic...
Improving Bayesian Optimization via Training-Aware Conditional Diffusion Models
arXiv:2606.08438v1 Announce Type: cross Abstract: Bayesian optimization (BO) is a widely used approach for black-box optimization that uses a Gaussian process (GP) as a surrogate and guides sequential evaluations via an acquisition function, with the ultimate goal of locating the global optimum $\mathbf{x}^{\star}$. To align with this goal, information-based acquisition functions such as Predictive Entropy Search (PES) model $\mathbf{x}^{\star}$ as a random variable and reduce the entropy of...
Costa Rica paid landowners to restore forests and biodiversity—bioacoustics indicate that it worked
June 4, 2026 report Costa Rica paid landowners to restore forests and biodiversity—bioacoustics indicate that it worked Krystal Kasal Author Gaby Clark Scientific Editor Robert Egan Associate Editor Forest restoration can help fight climate change and restore lost biodiversity, but the satellite-based techniques used to measure successful forest restoration have been less-than-helpful for measuring changes in biodiversity. Instead, a team of researchers listened to the sounds of life in the...
What Your Posts Reveal: A Benchmark and Agentic Framework for User-Level Privacy Leakage on Social Media
arXiv:2606.06784v1 Announce Type: new Abstract: Public social media posts can reveal private information through weak cues scattered across text, images, or metadata. Such leakage is often cumulative and cross-post: cues that appear harmless in isolation may jointly expose a user's home, workplace, or routine. However, current research lacks a unified benchmark for user-level multimodal privacy leakage and an evaluation metric that captures exposure severity beyond binary accuracy.
OpenEye: A Scalable Open-Source Hardware Accelerator for DNNs
arXiv:2606.01450v1 Announce Type: new Abstract: The increasing computational complexity of deep neural network inference poses significant challenges for efficient hardware acceleration on embedded platforms, particularly with respect to resource consumption and scalability. This work presents OpenEye, a scalable and sparsity-aware FPGA-based hardware accelerator designed to efficiently execute common neural network operations such as convolutions, dense layers, and pooling. OpenEye is based...
Static Electric Fields as a Model for Hydrogen-Bond-Induced Dissociation of HF and HCl
arXiv:2606.09011v1 Announce Type: new Abstract: The influence of static electric fields on the electronic structure and dissociation behavior of the polar diatomics \ce{HF} and \ce{HCl} is investigated using quantum chemical calculations. Ground- and excited-state potential energy surfaces (PESs) are computed as a function of bond distance and external electric field strength to examine field-induced modifications of chemical bonding. The calculations reveal pronounced bond softening and...