Cover Image

Jobflow: Computational Workflows Made Simple

Fuente: Zenodo

Saved in:

Bibliographic Details
Main Authors:	Rosen, Andrew S., Gallant, Max, George, Janine, Riebesell, Janosh, Sahasrabuddhe, Hrushikesh, Shen, Jimmy-Xuan, Wen, Mingjian, Evans, Matthew L., Petretto, Guido, Waroquiers, David, Rignanese, Gian-Marco, Persson, Kristin A., Jain, Anubhav, Ganose, Alex M.
Format:	Recurso digital
Published:	Zenodo 2025
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Jobflow: Computational Workflows Made Simple
by: Rosen, Andrew S., et al.
Published: (2026)

LLaMP: Large Language Model Made Powerful for High-fidelity Materials Knowledge Retrieval and Distillation
by: Chiang, Yuan, et al.
Published: (2024)

An Investigation in the Kinetic Persistence of TiO$_2$ Polymorphs using Machine Learning Driven Pathfinding in Crystal Configuration Space
by: Gallant, Max C., et al.
Published: (2026)

Matbench Discovery -- A framework to evaluate machine learning crystal stability predictions
by: Riebesell, Janosh, et al.
Published: (2023)

Ion correlations explain kinetic selectivity in diffusion-limited solid state synthesis reactions
by: Karan, Vir, et al.
Published: (2025)

ReactCA: A Cellular Automaton for Predicting Phase Evolution in Solid-State Reactions
by: Gallant, Max C., et al.
Published: (2024)

AlabOS: A Python-based Reconfigurable Workflow Management Framework for Autonomous Laboratories
by: Fei, Yuxing, et al.
Published: (2024)

On the Origin of Extrinsic Size Effects on the Fracture Resistance of Thin ETP Copper Sheets
by: Akshaye Prasad, et al.
Published: (2024)

Towards Automated Discovery: A Review of Generative Models, Multimodal Learning and Closed-Loop Workflows in Inverse Materials Design
by: Babu, Anand, et al.
Published: (2026)

Accelerating the discovery of high-performance nonlinear optical materials using active learning and high-throughput screening
by: Trinquet, Victor, et al.
Published: (2025)

Overcoming systematic softening in universal machine learning interatomic potentials by fine-tuning
by: Deng, Bowen, et al.
Published: (2024)

A Foundational Potential Energy Surface Dataset for Materials
by: Kaplan, Aaron D., et al.
Published: (2025)

Basilisk and Docker for Reproducible GN&C Simulation: A Workflow Reference
by: Gupta, Anubhav
Published: (2026)

Imprenditore ed Università nello start-up di impresa: ruoli e relazioni critiche
by: Petretto, Lorenzo
Published: (2022)

Datatractor: Metadata, automation, and registries for extractor interoperability in the chemical and materials sciences
by: Evans, Matthew L., et al.
Published: (2024)

The ab initio amorphous materials database: Empowering machine learning to decode diffusivity
by: Zheng, Hui, et al.
Published: (2024)

Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials
by: Riebesell, Janosh, et al.
Published: (2024)

Local Thermal Conductivity Patterning in Rotating Lattice Crystals of Anisotropic Sb 2 S 3
by: Eleonora Isotta, et al.
Published: (2025)

VibroML: an automated toolkit for high-throughput vibrational analysis and dynamic instability remediation of crystalline materials using machine-learned potentials
by: Gouvêa, Rogério Almeida, et al.
Published: (2026)

The open source software revolution in actuarial science / Rajesh Sahasrabuddhe, Steve Berman
by: Sahasrabuddhe, Rajesh

Short-range order in the CoCrFeMnNi high-entropy alloy from cluster expansion
by: Chen, Wei, et al.
Published: (2026)

Calibeating Made Simple
by: Chen, Yurong, et al.
Published: (2026)

Bookmaking Made Simple.
by: Marzollo, Jean
Published: (1991)

An equivariant graph neural network for the elasticity tensors of all seven crystal systems
by: Wen, Mingjian, et al.
Published: (2023)

From Microscale to Road Scale: Bridging the Gaps of Predictive Aluminum Corrosion Using SECM (Adv. Mater. Interfaces 8/2025)
by: Vikram Singh, et al.
Published: (2025)

From Microscale to Road Scale: Bridging the Gaps of Predictive Aluminum Corrosion Using SECM
by: Vikram Singh, et al.
Published: (2025)

Face Anonymization Made Simple
by: Kung, Han-Wei, et al.
Published: (2024)

Hydrogen Evolution Reaction of Electrodeposited Ni‐W Films in Acidic Medium and Performance Optimization Using Machine Learning
by: Roger de Paz‐Castany, et al.
Published: (2024)

Ensemble Deep Learning for Medical Image Classification Across Diverse Modalities: A Multi-Architecture Evaluation With Uncertainty Quantification on MedMNIST
by: Sanap, Hrushikesh
Published: (2026)

Approximation by non-symmetric networks for cross-domain learning
by: Mhaskar, Hrushikesh
Published: (2023)

Wilkins: HPC In Situ Workflows Made Easy
by: Yildiz, Orcun, et al.
Published: (2024)

A critical assessment of bonding descriptors for predicting materials properties
by: Naik, Aakash Ashok, et al.
Published: (2026)

Minimal isometric dilations and operator models for the polydisc
by: Pal, Sourav, et al.
Published: (2022)

Structure-aware divergences for comparing probability distributions
by: Sahasrabuddhe, Rohit, et al.
Published: (2026)

Minimal unitary dilations for commuting contractions
by: Pal, Sourav, et al.
Published: (2022)

From First Principles to Multi-scale Decomposition:Mutual Information as a Segregation Index
by: Sahasrabuddhe, Rohit, et al.
Published: (2025)

High-throughput determination of Hubbard U and Hund J values for transition metal oxides via linear response formalism
by: Moore, Guy C., et al.
Published: (2022)

Machine Learning for Electrode Materials: Property Prediction via Composition
by: Wu, Hao, et al.
Published: (2026)

Native point defects in HgCdTe infrared detector material: Identifying deep centers from first principles
by: Chen, Wei, et al.
Published: (2023)

Server-Side Includes Made Simple.
by: Fagan, Jody Condit
Published: (2002)