Tootfinder

Graph Visualization for Blockchain Data
Marcell Dietl, Andre Gem\"und, Daniel Oeltz, Felix M. Thiele, Christian Werner
https://arxiv.org/abs/2403.03504

Graph Visualization for Blockchain Data
In this report, we introduce a novel approach to visualize extremely large graphs efficiently. Our method combines two force-directed algorithms, Kamada-Kawai and ForceAtlas2, to handle different graph components based on their node count. Additionally, we suggest utilizing the Fast Multipole method to enhance the speed of ForceAtlas2. Although initially designed for analyzing bitcoin transaction graphs, for which we present results here, this algorithm can also be applied to other crypto curre…

Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces
Bruno Focassio, Luis Paulo Mezzina Freitas, Gabriel R. Schleder
https://arxiv.org/abs/2403.04217

Performance Assessment of Universal Machine Learning Interatomic Potentials: Challenges and Directions for Materials' Surfaces
Machine learning interatomic potentials (MLIPs) are one of the main techniques in the materials science toolbox, able to bridge ab initio accuracy with the computational efficiency of classical force fields. This allows simulations ranging from atoms, molecules, and biosystems, to solid and bulk materials, surfaces, nanomaterials, and their interfaces and complex interactions. A recent class of advanced MLIPs, which use equivariant representations and deep graph neural networks, is known as uni…

Exploring the Influence of Graph Operations on Zero Forcing Sets
Krishna Menon, Anurag Singh
https://arxiv.org/abs/2405.01423 https://

Exploring the Influence of Graph Operations on Zero Forcing Sets
Zero forcing in graphs is a coloring process where a colored vertex can force its unique uncolored neighbor to be colored. A zero forcing set is a set of initially colored vertices capable of eventually coloring all vertices of the graph. In this paper, we focus on the numbers $z(G; i)$, which is the number of zero forcing sets of size $i$ of the graph $G$. These numbers were initially studied by Boyer et al. where they conjectured that for any graph $G$ on $n$ vertices, $z(G; i) \leq z(P_n; i)…

Graph neural network coarse-grain force field for the molecular crystal RDX
Brian H. Lee, James P. Larentzos, John K. Brennan, Alejandro Strachan
https://arxiv.org/abs/2403.15266 …

Graph neural network coarse-grain force field for the molecular crystal RDX
Condense phase molecular systems organize in wide range of distinct molecular configurations, including amorphous melt and glass as well as crystals often exhibiting polymorphism, that originate from their intricate intra- and intermolecular forces. While accurate coarse-grain (CG) models for these materials are critical to understand phenomena beyond the reach of all-atom simulations, current models cannot capture the diversity of molecular structures. We introduce a generally applicable appro…

Creating spanning trees in Waiter-Client games
Grzegorz Adamski, Sylwia Antoniuk, Ma{\l}gorzata Bednarska-Bzd\k{e}ga, Dennis Clemens, Fabian Hamann, Yannick Mogge
https://arxiv.org/abs/2403.18534

Creating spanning trees in Waiter-Client games
For a positive integer $n$ and a tree $T_n$ on $n$ vertices, we consider an unbiased Waiter-Client game $\textrm{WC}(n,T_n)$ played on the complete graph~$K_n$, in which Waiter's goal is to force Client to build a copy of $T_n$. We prove that for every constant $c<1/3$, if $Δ(T_n)\le cn$ and $n$ is sufficiently large, then Waiter has a winning strategy in $\textrm{WC}(n,T_n)$. On the other hand, we show that there exist a positive constant $c'<1/2$ and a family of trees $T_{n}$ with $Δ(T_n)\l…

This https://arxiv.org/abs/2404.07181 has been replaced.
initial toot: https://mastoxiv.page/@a…

BAMBOO: a predictive and transferable machine learning force field framework for liquid electrolyte development
Despite the widespread applications of machine learning force field (MLFF) on solids and small molecules, there is a notable gap in applying MLFF to complex liquid electrolytes. In this work, we introduce BAMBOO (ByteDance AI Molecular Simulation Booster), a novel framework for molecular dynamics (MD) simulations, with a demonstration of its capabilities in the context of liquid electrolytes for lithium batteries. We design a physics-inspired graph equivariant transformer architecture as the ba…

Phonon predictions with E(3)-equivariant graph neural networks
Shiang Fang, Mario Geiger, Joseph G. Checkelsky, Tess Smidt
https://arxiv.org/abs/2403.11347

Phonon predictions with E(3)-equivariant graph neural networks
We present an equivariant neural network for predicting vibrational and phonon modes of molecules and periodic crystals, respectively. These predictions are made by evaluating the second derivative Hessian matrices of the learned energy model that is trained with the energy and force data. Using this method, we are able to efficiently predict phonon dispersion and the density of states for inorganic crystal materials. For molecules, we also derive the symmetry constraints for IR/Raman active mo…

BAMBOO: a predictive and transferable machine learning force field framework for liquid electrolyte development
Sheng Gong, Yumin Zhang, Zhenliang Mu, Zhichen Pu, Hongyi Wang, Zhiao Yu, Mengyi Chen, Tianze Zheng, Zhi Wang, Lifei Chen, Xiaojie Wu, Shaochen Shi, Weihao Gao, Wen Yan, Liang Xiang
https://arxiv.org/abs/2404.07181

BAMBOO: a predictive and transferable machine learning force field framework for liquid electrolyte development
Despite the widespread applications of machine learning force field (MLFF) on solids and small molecules, there is a notable gap in applying MLFF to complex liquid electrolytes. In this work, we introduce BAMBOO (ByteDance AI Molecular Simulation Booster), a novel framework for molecular dynamics (MD) simulations, with a demonstration of its capabilities in the context of liquid electrolytes for lithium batteries. We design a physics-inspired graph equivariant transformer architecture as the ba…

This https://arxiv.org/abs/2404.07181 has been replaced.
initial toot: https://mastoxiv.page/@a…

BAMBOO: a predictive and transferable machine learning force field framework for liquid electrolyte development
Despite the widespread applications of machine learning force field (MLFF) on solids and small molecules, there is a notable gap in applying MLFF to complex liquid electrolytes. In this work, we introduce BAMBOO (ByteDance AI Molecular Simulation Booster), a novel framework for molecular dynamics (MD) simulations, with a demonstration of its capabilities in the context of liquid electrolytes for lithium batteries. We design a physics-inspired graph equivariant transformer architecture as the ba…

This https://arxiv.org/abs/2404.07181 has been replaced.
initial toot: https://mastoxiv.page/@a…

BAMBOO: a predictive and transferable machine learning force field framework for liquid electrolyte development
Despite the widespread applications of machine learning force field (MLFF) on solids and small molecules, there is a notable gap in applying MLFF to complex liquid electrolytes. In this work, we introduce BAMBOO (ByteDance AI Molecular Simulation Booster), a novel framework for molecular dynamics (MD) simulations, with a demonstration of its capabilities in the context of liquid electrolytes for lithium batteries. We design a physics-inspired graph equivariant transformer architecture as the ba…