Cover Image

Accurate and efficient predictions of keyhole dynamics in laser materials processing using machine learning-aided simulations

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhang, Jiahui, Jiang, Runbo, Li, Kangming, Chen, Pengyu, Bi, Shengbo, Shang, Xiao, Liu, Zhiying, Hattrick-Simpers, Jason, Simonds, Brian J., Wei, Qianglong, Wang, Hongze, Sun, Tao, Rollett, Anthony D., Zou, Yu
Format:	Preprint
Published:	2024
Subjects:	Materials Science Computational Engineering, Finance, and Science
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Efficient first principles based modeling via machine learning: from simple representations to high entropy materials
by: Li, Kangming, et al.
Published: (2024)

Probing out-of-distribution generalization in machine learning for materials
by: Li, Kangming, et al.
Published: (2024)

LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction
by: Rubungo, Andre Niyongabo, et al.
Published: (2024)

Reproducibility in Computational Materials Science: Lessons from 'A General-Purpose Machine Learning Framework for Predicting Properties of Inorganic Materials'
by: Persaud, Daniel, et al.
Published: (2023)

Evaluating the Performance and Robustness of LLMs in Materials Science Q&A and Property Predictions
by: Wang, Hongchen, et al.
Published: (2024)

When Active Learning Fails, Uncalibrated Out of Distribution Uncertainty Quantification Might Be the Problem
by: Dale, Ashley S., et al.
Published: (2025)

Building informative materials datasets beyond targeted objectives
by: Castañeda, Rafael Espinosa, et al.
Published: (2026)

Building Trustworthy AI for Materials Discovery: From Autonomous Laboratories to Z-scores
by: Amirian, Benhour, et al.
Published: (2025)

Kernel Learning Assisted Synthesis Condition Exploration for Ternary Spinel
by: Liu, Yutong, et al.
Published: (2025)

An Assessment of Commonly Used Equivalent Circuit Models for Corrosion Analysis: A Bayesian Approach to Electrochemical Impedance Spectroscopy
by: Zhang, Runze, et al.
Published: (2024)

Autonomous Sampling and SHAP Interpretation of Deposition-Rates in Bipolar HiPIMS
by: Wieczorek, Alexander, et al.
Published: (2026)

High-throughput validation of phase formability and simulation accuracy of Cantor alloys
by: Cheng, Changjun, et al.
Published: (2025)

Human-AI Synergy in Adaptive Active Learning for Continuous Lithium Carbonate Crystallization Optimization
by: Masouleh, Shayan S. Mousavi, et al.
Published: (2025)

Training-Free Active Learning Framework in Materials Science with Large Language Models
by: Wang, Hongchen, et al.
Published: (2025)

Nonlocal Linear Instability Drives the Initiation of Motion of Rational and Irrational Twin Interfaces
by: Lu, Chang-Tsan, et al.
Published: (2026)

Structured Extraction of Process Structure Properties Relationships in Materials Science
by: Verma, Amit K, et al.
Published: (2025)

Artificial Intelligence-Enabled Optimization of Battery-Grade Lithium Carbonate Production
by: Masouleh, S. Shayan Mousavi, et al.
Published: (2024)

Assessment of different loss functions for fitting equivalent circuit models to electrochemical impedance spectroscopy data
by: Jaberi, Ali, et al.
Published: (2025)

AutoREC: A software platform for developing reinforcement learning agents for equivalent circuit model generation from electrochemical impedance spectroscopy data
by: Jaberi, Ali, et al.
Published: (2026)

Digital generation of the 3-D pore architecture of isotropic membranes using 2-D cross-sectional scanning electron microscopy images
by: Danalou, Sima Zeinali, et al.
Published: (2025)

Accurate and efficient localized basis sets for two-dimensional materials
by: Bennett, Daniel, et al.
Published: (2024)

Developing and Validating a High-Throughput Robotic System for the Accelerated Development of Porous Membranes
by: Wang, Hongchen, et al.
Published: (2025)

Accurate prediction of structural and mechanical properties on amorphous materials enabled through machine-learning potentials: a case study of silicon nitride
by: Nayak, Ganesh Kumar, et al.
Published: (2024)

Accurate and efficient protocols for high-throughput first-principles materials simulations
by: Nascimento, Gabriel de Miranda, et al.
Published: (2025)

Materials Acceleration Platform for Electrochemistry: a Platform for Autonomous Electrochemistry
by: Persaud, Daniel, et al.
Published: (2026)

Accurate, transferable, and verifiable machine-learned interatomic potentials for layered materials
by: Georgaras, Johnathan D., et al.
Published: (2025)

Amorphous High Density Plutonium
by: Katz, J. K., et al.
Published: (2026)

Exploring the Limitations of kNN Noisy Feature Detection and Recovery for Self-Driving Labs
by: Shi, Qiuyu, et al.
Published: (2025)

Accurate and efficient machine learning interatomic potentials for finite temperature modeling of molecular crystals
by: Della Pia, Flaviano, et al.
Published: (2025)

Accurate Inverse Process Optimization Framework in Laser Directed Energy Deposition
by: Shang, Xiao, et al.
Published: (2024)

Fracture assessment of inclined double keyhole notches in polylactic acid specimens fabricated by fused deposition modeling and traditional method
by: Mohammad Taghi Fateri, et al.
Published: (2024)

Improving realistic material property prediction using domain adaptation based machine learning
by: Hu, Jeffrey, et al.
Published: (2023)

High-Tc superconductor candidates proposed by machine learning
by: Lee, Siwoo, et al.
Published: (2024)

Accurate predictive model of band gap with selected important features based on explainable machine learning
by: Lee, Joohwi, et al.
Published: (2025)

An experimental high-throughput to high-fidelity study towards discovering Al-Cr containing corrosion-resistant compositionally complex alloys
by: Sur, Debashish, et al.
Published: (2023)

Accelerating material melting temperature predictions by implementing machine learning potentials in the SLUSCHI package
by: CampBell, Audrey, et al.
Published: (2024)

Low dimensional fragment-based descriptors for property predictions in inorganic materials with machine learning
by: Islam, Md Mohaiminul
Published: (2024)

Variability in predicted deleterious mutations among barley accessions conserved ex situ
by: Yong‐Bi Fu
Published: (2024)

Towards reproducible machine learning-based process monitoring and quality prediction research for additive manufacturing
by: Xie, Jiarui, et al.
Published: (2024)

Rational Molecular Engineering of Amidonaphthoquinone Cathodes: Precise Hydrogen Bond and Size Control for High‐Performance Lithium Organic Batteries
by: Qianglong Chen, et al.
Published: (2025)