Image de couverture de livre

vasp-mace: a VASP-style workflow interface for MACE machine-learning interatomic potentials

Fuente: Zenodo

Enregistré dans:

Détails bibliographiques
Auteur principal:	Grau-Crespo, Ricardo
Format:	Recurso digital
Publié:	Zenodo 2026
Sujets:	MACE machine learning potentials molecular dynamics geometry relaxation VASP atomistic simulation materials science
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

vasp-mace: a VASP-style workflow interface for MACE machine-learning interatomic potentials
par: Grau-Crespo, Ricardo
Publié: (2026)

vasp-mace: a VASP simulator based on the MACE machine-learning interatomic potential
par: Grau-Crespo, Ricardo
Publié: (2026)

Simulation data and codes for "Theory and Discovery of Electrides"
par: Xiao, Chengcheng, et autres
Publié: (2026)

Molecular dynamics simulation of a nanocluster obtained from the mining industry
par: Karen Isela Vargas-Rubio
Publié: (2021)

SiliconAI
par: Novak, Tadej
Publié: (2025)

Supplementary information for the article: Isobutanol Dehydration Catalyzed by (100) Surface of Ferrierite: From Static DFT to Machine Learning Accelerated Molecular Dynamics
par: Skladanová, Katarína, et autres
Publié: (2026)

Data for "Dynamics of protein – water mixtures: insight from combined 1H spin-lattice and spin-spin relaxation studies for myoglobin""
par: Kamau, Kahinga, et autres
Publié: (2024)

Advanced Engineering Research
Publié: (2021)

On the morse potential in liquid phase and at liquid-vapor interface
par: U.F. Galicia-Pimentel
Publié: (2006)

Conformational Ensemble Dynamics of Intrinsically Disordered Full-Length α- and β-Synuclein Monomers
par: Zhongyue, Lv, et autres
Publié: (2025)

Towards the design of new and improved drilling fluid additives using molecular dynamics simulations
par: Richard L. Anderson
Publié: (2010)

An Approach from Software Engineering to an IoT and Machine Learning Technological Solution that Allows Monitoring and Controlling Environmental Variables in a Coffee Crop
par: William Ruiz-Martínez
Publié: (2021)

Deep material networks for efficient scale-bridging in thermomechanical simulations of solids
par: Gajek, Sebastian
Publié: (2023)

Traversal-Induced Geometry: A Discrete Path-Based Measurement Framework for Circular Metrics
par: Tu, Kai-Jie
Publié: (2026)

A relax and cut approach using the multi-commodity flow formulation for the traveling salesman problem
par: Makswell Seyiti Kawashima
Publié: (2015)

Explicit and implicit modeling of nanobubbles in hydrophobic confinement
par: Joachim Dzubiella
Publié: (2010)

Molecular dynamics simulation of the truncated NMDA receptor in the open state
par: Zheng, Wenjun
Publié: (2025)

Emergence Without Scale: Inductive Resonance and Invariant Dynamics in Tiny Recurrent Networks
par: Giebelhaus, M. Axel
Publié: (2025)

Computational Study of Allotropic Structures of Carbon by Density Functional Theory (DTF)
par: J. M. González
Publié: (2014)

Вестник Донского государственного технического университета
Publié: (2018)

Dataset for Melting Behavior and Phase Stability of CaO from Neural Network Potentials
par: Menescardi, Francesca
Publié: (2026)

Complex Manifolds
Publié: (2016)

Scientific and Mathematical Framework for Modeling Angelic Manifestation in Human Form
par: Pakgohar, Amirpouya
Publié: (2025)

eunhanka/mirage-eebl-detector: VehicleSec 2026 AE Final Submission (v2)
par: Eunhan
Publié: (2026)

Virtual screening using machine learning techniques and ensemble docking-based molecular descriptors
par: Joel Ricci López
Publié: (2023)

AWSC: An approach to Web service classification based on machine learning techniques
par: Marco Crasso
Publié: (2008)

Quantitative and qualitative analysis of segmental dielectric relaxations and space charge by TSDC in nanocomposites of natural rubber/clay
par: Luis Alfredo Martínez
Publié: (2020)

What is the best muscle relaxant for muscle injuries?
par: Tripdatabase
Publié: (2025)

A Controlled Evaluation of Transfer Learning and Federated Meta-Learning for Privacy-Preserving Photonic Inverse Design
par: Ray, Rahul D
Publié: (2026)

OPEN LOOP CONTROL OF THE FIVE-AXIS MISSILE AND TARGET FLIGHT MOTION SIMULATOR IMPLEMENTATION
par: Nataša D. Kljajić
Publié: (2016)

The Bottleneck Primitive: Statistics as the Study of Information Compression
par: Close, Larsen James
Publié: (2026)

Quarterly multidimensional poverty estimates in Mexico using machine learning algorithms
par: Ratzanyel Rincón
Publié: (2023)

Assessing the behavior of machine learning methods to predict the activity of antimicrobial peptides
par: Francy Liliana Camacho
Publié: (2017)

Robust Sensor Fusion for Autonomous Navigation in Dynamically Changing Environments
par: Liam O'Connor
Publié: (2026)

A Proximal Gradient Framework for Optimization Under Uncertainty with Applications to Sparse Data Representation
par: Jordan Smith
Publié: (2026)

Addressing Exploration Challenges in Sparse Reward Reinforcement Learning Environments via Intrinsic Curiosity Modules and Reward Shaping
par: Elena Rossi
Publié: (2026)

Scalable and Efficient Distributed Training of Deep Learning Models via Hybrid Parallelism
par: Yuki Tanaka
Publié: (2026)

Leveraging Graph Neural Networks for Enhanced Node Representation Learning in Sparse Interaction Networks
par: Sasha Petrova
Publié: (2026)

Proximal Gradient Methods for Non-Convex Optimization with Applications to Robust Computer Vision
par: Jamie Chen
Publié: (2026)

Adaptive Regularization for Robust Optimization Under Data Distribution Shift
par: Yuki Tanaka
Publié: (2026)