Zobrazeno 1 - 10
of 120 164
pro vyhledávání: '"Force fields"'
Autor:
Wines, Daniel, Choudhary, Kamal
In this work, we introduce CHIPS-FF (Computational High-Performance Infrastructure for Predictive Simulation-based Force Fields), a universal, open-source benchmarking platform for machine learning force fields (MLFFs). This platform provides robust
Externí odkaz:
http://arxiv.org/abs/2412.10516
Autor:
MacLean, Thomas, Barr, Alan H.
Accurate gravity field calculations are necessary for landing on planets, moons, asteroids, minimoons, or other irregularly shaped bodies, but current methods become increasingly inaccurate and slow near the surface. We present high accuracy, fast me
Externí odkaz:
http://arxiv.org/abs/2411.15728
In moir\'e systems, the impact of lattice relaxation on electronic band structures is significant, yet the computational demands of first-principles relaxation are prohibitively high due to the large number of atoms involved. To address this challeng
Externí odkaz:
http://arxiv.org/abs/2412.19333
Autor:
Greener, Joe G
The next generation of force fields for molecular dynamics will be developed using a wealth of data. Training systematically with experimental data remains a challenge, however, especially for machine learning potentials. Differentiable molecular sim
Externí odkaz:
http://arxiv.org/abs/2412.04374
Sodium-ion batteries (SIBs) have garnered significant attention in recent years as a promising alternative to lithium-ion batteries (LIBs) due to their low cost, abundant sodium resources, and excellent cycling performance. Hard carbon materials, cha
Externí odkaz:
http://arxiv.org/abs/2412.00340
The origin of the chirality of single-walled carbon nanotubes (SWCNTs) has been a long-standing dispute. Molecular dynamics (MD) simulations driven by machine-learning force fields (MLFF), which can study the interface dynamics under near ab-initio a
Externí odkaz:
http://arxiv.org/abs/2411.19764
Autor:
Shen, Chen, Attarian, Siamak, Zhang, Yixuan, Zhang, Hongbin, Asta, Mark, Szlufarska, Izabela, Morgan, Dane
Molten salts are crucial for clean energy applications, yet exploring their thermophysical properties across diverse chemical space remains challenging. We present the development of a machine learning interatomic potential (MLIP) called SuperSalt, w
Externí odkaz:
http://arxiv.org/abs/2412.19353
Point defects dictate the properties of many functional materials. The standard approach to modelling the thermodynamics of defects relies on a static description, where the change in Gibbs free energy is approximated by the internal energy. This app
Externí odkaz:
http://arxiv.org/abs/2412.16741
Halide perovskite optoelectronic devices suffer from chemical degradation and current-voltage hysteresis induced by migration of highly mobile charged defects. Atomic scale molecular dynamics simulations can capture the motion of these ionic defects,
Externí odkaz:
http://arxiv.org/abs/2409.16051