:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Han, Zhichao, Pundir, Mohit, Fink, Olga, Kammer, David S.
Format:	Preprint
Published:	2024
Subjects:	Materials Science Machine Learning
Online Access:	https://arxiv.org/abs/2407.20273
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Simplifying FFT-based methods for solid mechanics with automatic differentiation
by: Pundir, Mohit, et al.
Published: (2024)

Collective Relational Inference for learning heterogeneous interactions
by: Han, Zhichao, et al.
Published: (2023)

Transonic and supershear crack propagation driven by geometric nonlinearities
by: Pundir, Mohit, et al.
Published: (2023)

Navigating the Complexities of Multiple Redox State Interactions in Aqueous Systems
by: Mundra, Shishir, et al.
Published: (2025)

Unsupervised Machine-Learning Pipeline for Data-Driven Defect Detection and Characterisation: Application to Displacement Cascades
by: Del Fré, Samuel, et al.
Published: (2025)

Physically Consistent Machine Learning for Melting Temperature Prediction of Refractory High-Entropy Alloys
by: Hasnain, Mohd
Published: (2026)

Bayesian Co-navigation: Dynamic Designing of the Materials Digital Twins via Active Learning
by: Slautin, Boris N., et al.
Published: (2024)

Eulerian framework for contact between solids represented as phase fields
by: Lorez, Flavio, et al.
Published: (2023)

A versatile FEM framework with native GPU scalability via globally-applied AD
by: Pundir, Mohit, et al.
Published: (2026)

SCALAR: Quantifying Structural Hallucination, Consistency, and Reasoning Gaps in Materials Foundation Models
by: Polat, Can, et al.
Published: (2026)

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties with Phonon-Informed Datasets
by: Benítez, Pol, et al.
Published: (2025)

Understanding the Capabilities of Molecular Graph Neural Networks in Materials Science Through Multimodal Learning and Physical Context Encoding
by: Polat, Can, et al.
Published: (2025)

Dynami-CAL GraphNet: A Physics-Informed Graph Neural Network Conserving Linear and Angular Momentum for Dynamical Systems
by: Sharma, Vinay, et al.
Published: (2025)

Learning Magnetic Order Classification from Large-Scale Materials Databases
by: Fahmy, Ahmed E.
Published: (2025)

Advanced Displacement Magnitude Prediction in Multi-Material Architected Lattice Structure Beams Using Physics Informed Neural Network Architecture
by: Mishra, Akshansh
Published: (2024)

Open Materials Generation with Inference-Time Reinforcement Learning
by: Hoellmer, Philipp, et al.
Published: (2026)

Quantum Kernel Machine Learning for Autonomous Materials Science
by: Adams, Felix, et al.
Published: (2026)

A Materials Map Integrating Experimental and Computational Data via Graph-Based Machine Learning for Enhanced Materials Discovery
by: Hashimoto, Yusuke, et al.
Published: (2025)

Multi-Task Multi-Fidelity Learning of Properties for Energetic Materials
by: Appleton, Robert J., et al.
Published: (2024)

Learning ORDER-Aware Multimodal Representations for Composite Materials Design
by: Li, Xinyao, et al.
Published: (2026)

A Critical Examination of Active Learning Workflows in Materials Science
by: Nair, Akhil S., et al.
Published: (2026)

Knowledge-Reuse Transfer Learning Methods in Molecular and Material Science
by: Chen, An, et al.
Published: (2024)

Interactive Symbolic Regression through Offline Reinforcement Learning: A Co-Design Framework
by: Tian, Yuan, et al.
Published: (2024)

Composite Material Design for Optimized Fracture Toughness Using Machine Learning
by: Jahromi, Mohammad Naqizadeh, et al.
Published: (2024)

UniMat: Unifying Materials Embeddings through Multi-modal Learning
by: Ock, Janghoon, et al.
Published: (2024)

Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling
by: Taj, Reyhaneh
Published: (2024)

Toward Multi-Fidelity Machine Learning Force Field for Cathode Materials
by: Dong, Guangyi, et al.
Published: (2025)

Identifying Constitutive Parameters for Complex Hyperelastic Materials using Physics-Informed Neural Networks
by: Song, Siyuan, et al.
Published: (2023)

A General Neural Network Potential for Energetic Materials with C, H, N, and O elements
by: Wen, Mingjie, et al.
Published: (2025)

Bridging Text and Crystal Structures: Literature-driven Contrastive Learning for Materials Science
by: Suzuki, Yuta, et al.
Published: (2025)

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

MoMa: A Modular Deep Learning Framework for Material Property Prediction
by: Wang, Botian, et al.
Published: (2025)

Foundation-Model Surrogates Enable Data-Efficient Active Learning for Materials Discovery
by: Hu, Jeffrey, et al.
Published: (2026)

Efficient and Accurate Spatial Mixing of Machine Learned Interatomic Potentials for Materials Science
by: Birks, Fraser, et al.
Published: (2025)

Extended Low-Rank Approximation Accelerates Learning of Elastic Response in Heterogeneous Materials
by: Karmakar, Prabhat, et al.
Published: (2025)

Materials Learning Algorithms (MALA): Scalable Machine Learning for Electronic Structure Calculations in Large-Scale Atomistic Simulations
by: Cangi, Attila, et al.
Published: (2024)

Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning
by: Kang, Kisung, et al.
Published: (2024)

Estimation of Electronic Band Gap Energy From Material Properties Using Machine Learning
by: Barad, Sagar Prakash, et al.
Published: (2024)

Bridging Theory and Experiment in Materials Discovery: Machine-Learning-Assisted Prediction of Synthesizable Structures
by: Xin, Yu, et al.
Published: (2025)

Accelerating Quantum Materials Characterization: Hybrid Active Learning for Autonomous Spin Wave Spectroscopy
by: Ratcliff II, William
Published: (2026)