Accelerating active learning materials discovery with FAIR data and workflows: a case study for alloy melting temperatures
Fuente:
arXiv
Saved in:
| Main Authors: | Harwani, Mohnish, Verduzco, Juan C., Lee, Brian H., Strachan, Alejandro |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A collaborative digital twin built on FAIR data and compute infrastructure
by: Deucher, Thomas M., et al.
Published: (2025)
by: Deucher, Thomas M., et al.
Published: (2025)
Mass uptake during oxidation of metallic alloys: literature data collection, analysis, and FAIR sharing
by: Mishra, Saswat, et al.
Published: (2023)
by: Mishra, Saswat, et al.
Published: (2023)
Machine learning descriptors for predicting the high temperature oxidation of refractory complex concentrated alloys
by: Bejjipurapu, Akhil, et al.
Published: (2025)
by: Bejjipurapu, Akhil, et al.
Published: (2025)
Evaluating LLM-generated code for domain-specific languages: molecular dynamics with LAMMPS
by: Holbrook, Ethan, et al.
Published: (2026)
by: Holbrook, Ethan, 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)
by: Trinquet, Victor, et al.
Published: (2025)
Data-driven active learning approaches for accelerating materials discovery
by: Chen, Jiaxin, et al.
Published: (2026)
by: Chen, Jiaxin, et al.
Published: (2026)
Accelerating material melting temperature predictions by implementing machine learning potentials in the SLUSCHI package
by: CampBell, Audrey, et al.
Published: (2024)
by: CampBell, Audrey, et al.
Published: (2024)
Accelerating ab initio melting property calculations with machine learning: Application to the high entropy alloy TaVCrW
by: Zhu, Li-Fang, et al.
Published: (2024)
by: Zhu, Li-Fang, et al.
Published: (2024)
Uncertainty-aware phase fraction prediction and active-learning-guided out-of-domain discovery of refractory multi-principal element alloys
by: Shargh, A. K., et al.
Published: (2026)
by: Shargh, A. K., et al.
Published: (2026)
Machine learning accelerated discovery of corrosion-resistant high-entropy alloys
by: Zeng, Cheng, et al.
Published: (2023)
by: Zeng, Cheng, et al.
Published: (2023)
Accelerating discovery across scientific disciplines through reproducible workflows with AiiDAlab
by: Yakutovich, Aliaksandr V., et al.
Published: (2025)
by: Yakutovich, Aliaksandr V., et al.
Published: (2025)
Temperature-dependent discovery of BCC refractory multi-principal element alloys: Integrating deep learning and CALPHAD calculations
by: Shargh, A. K., et al.
Published: (2024)
by: Shargh, A. K., et al.
Published: (2024)
Spall strength of symmetric tilt grain boundaries in 6H silicon carbide
by: Li, Chunyu, et al.
Published: (2025)
by: Li, Chunyu, et al.
Published: (2025)
Preferential Composition during Nucleation and Growth in Multi-Principal Elements Alloys
by: Mishra, Saswat, et al.
Published: (2023)
by: Mishra, Saswat, et al.
Published: (2023)
Data Fusion of Deep Learned Molecular Embeddings for Property Prediction
by: Appleton, Robert J, et al.
Published: (2025)
by: Appleton, Robert J, et al.
Published: (2025)
Optical materials discovery and design with federated databases and machine learning
by: Trinquet, Victor, et al.
Published: (2024)
by: Trinquet, Victor, et al.
Published: (2024)
Exploration of Hexagonal, Layered Carbides and Nitrides as Ultra-High Temperature Ceramics
by: Nykiel, Kat, et al.
Published: (2025)
by: Nykiel, Kat, et al.
Published: (2025)
InvDesFlow-AL: active learning-based workflow for inverse design of functional materials
by: Han, Xiao-Qi, et al.
Published: (2025)
by: Han, Xiao-Qi, et al.
Published: (2025)
Machine-learned accelerated discovery of oxidation-resistant NiCoCrAl high-entropy alloys
by: Boakye, Dennis, et al.
Published: (2025)
by: Boakye, Dennis, et al.
Published: (2025)
Graph neural network coarse-grain force field for the molecular crystal RDX
by: Lee, Brian H., et al.
Published: (2024)
by: Lee, Brian H., et al.
Published: (2024)
The carbon cost of materials discovery: Can machine learning really accelerate the discovery of new photovoltaics?
by: Walker, Matthew, et al.
Published: (2025)
by: Walker, Matthew, et al.
Published: (2025)
Computational discovery of cathode materials for rechargeable aqueous zinc-ion batteries
by: Miliante, Caio Miranda, et al.
Published: (2026)
by: Miliante, Caio Miranda, et al.
Published: (2026)
Nuclear Quantum Effects in Multi-Step Condensed Matter Chemistry: A Path Integral Molecular Dynamics Study of Thermal Decomposition
by: Macatangay, Jalen, et al.
Published: (2026)
by: Macatangay, Jalen, et al.
Published: (2026)
From electrons to phase diagrams with classical and machine learning potentials: automated workflows for materials science with pyiron
by: Menon, Sarath, et al.
Published: (2024)
by: Menon, Sarath, et al.
Published: (2024)
Reproducible container solutions for codes and workflows in materials science
by: Bissuel, Dylan, et al.
Published: (2025)
by: Bissuel, Dylan, et al.
Published: (2025)
Multi-Fidelity Predictive Model for Shock Response of Energetic Materials Using Conditional U-Net
by: Lee, Brian H., et al.
Published: (2026)
by: Lee, Brian H., et al.
Published: (2026)
Electronic manifolds for extrapolative alloy discovery
by: Ray, Pranoy, et al.
Published: (2026)
by: Ray, Pranoy, et al.
Published: (2026)
Accelerating computational materials discovery with artificial intelligence and cloud high-performance computing: from large-scale screening to experimental validation
by: Chen, Chi, et al.
Published: (2024)
by: Chen, Chi, et al.
Published: (2024)
Molecular Dynamics simulations of Al-Ti metallic alloy melts using a transferable machine-learning potential
by: Kato, Yuna, et al.
Published: (2026)
by: Kato, Yuna, et al.
Published: (2026)
Developments and applications of the OPTIMADE API for materials discovery, design, and data exchange
by: Evans, Matthew L., et al.
Published: (2024)
by: Evans, Matthew L., et al.
Published: (2024)
A case study: the savings potential thanks to FAIR data in one Materials Science PhD project
by: Seitz, Michael, et al.
Published: (2025)
by: Seitz, Michael, et al.
Published: (2025)
Accelerating discovery of infrared nonlinear optical materials with large shift current via high-throughput screening
by: Yang, Aiqin, et al.
Published: (2025)
by: Yang, Aiqin, et al.
Published: (2025)
Are diffusion models ready for materials discovery in unexplored chemical space?
by: Kim, Sanghyun, et al.
Published: (2025)
by: Kim, Sanghyun, et al.
Published: (2025)
Computational discovery of high-refractive-index van der Waals materials: The case of HfS$_2$
by: Zambrana-Puyalto, Xavier, et al.
Published: (2025)
by: Zambrana-Puyalto, Xavier, et al.
Published: (2025)
Machine learning assisted screening of metal binary alloys for anode materials
by: Shi, Xingyue, et al.
Published: (2024)
by: Shi, Xingyue, et al.
Published: (2024)
OptiMat Alloys: a FAIR, living database of multi-principal element alloys enabled by a conversational agent
by: Hu, Yang, et al.
Published: (2026)
by: Hu, Yang, et al.
Published: (2026)
fair_data.py: implementing FAIR data compliance in Tribchem
by: Berghenti, Lucrezia, et al.
Published: (2025)
by: Berghenti, Lucrezia, et al.
Published: (2025)
Screening 39 billion protostructures for materials discovery
by: Parackal, Abhijith S, et al.
Published: (2026)
by: Parackal, Abhijith S, et al.
Published: (2026)
Incorporating quasiparticle and excitonic properties into material discovery
by: Biswas, Tathagata, et al.
Published: (2024)
by: Biswas, Tathagata, et al.
Published: (2024)
Accelerated discovery of cost-effective photoabsorber materials for near-infrared (λ=1600 nm) photodetector applications
by: Zhao, Wayne, et al.
Published: (2025)
by: Zhao, Wayne, et al.
Published: (2025)
Similar Items
-
A collaborative digital twin built on FAIR data and compute infrastructure
by: Deucher, Thomas M., et al.
Published: (2025) -
Mass uptake during oxidation of metallic alloys: literature data collection, analysis, and FAIR sharing
by: Mishra, Saswat, et al.
Published: (2023) -
Machine learning descriptors for predicting the high temperature oxidation of refractory complex concentrated alloys
by: Bejjipurapu, Akhil, et al.
Published: (2025) -
Evaluating LLM-generated code for domain-specific languages: molecular dynamics with LAMMPS
by: Holbrook, Ethan, 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)