:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Author:	Sharma, Gaurav Kumar
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Materials Science
Online Access:	https://arxiv.org/abs/2512.19732
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Featurized Materials Dataset for Formation Energy Prediction (derived from Materials Project)
by: Torlao, Virginio
Published: (2025)

Representation of Inorganic Synthesis Reactions and Prediction: Graphical Framework and Datasets
by: Andrello, Samuel, et al.
Published: (2025)

Thermodynamic Prediction Enabled by Automatic Dataset Building and Machine Learning
by: Liu, Juejing, et al.
Published: (2025)

Predicting and Accelerating Nanomaterials Synthesis Using Machine Learning Featurization
by: Price, Christopher C., et al.
Published: (2024)

Industrial-scale Prediction of Cement Clinker Phases using Machine Learning
by: Fayaz, Sheikh Junaid, et al.
Published: (2024)

MatQnA: A Benchmark Dataset for Multi-modal Large Language Models in Materials Characterization and Analysis
by: Weng, Yonghao, et al.
Published: (2025)

Revealing the dynamic responses of Pb under shock loading based on DFT-accuracy machine learning potential
by: Hou, Enze, et al.
Published: (2025)

ReciNet: Reciprocal Space-Aware Long-Range Modeling for Crystalline Property Prediction
by: Nie, Jianan, et al.
Published: (2025)

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

Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks
by: Park, Eunjeong, et al.
Published: (2026)

OBELiX: A Curated Dataset of Crystal Structures and Experimentally Measured Ionic Conductivities for Lithium Solid-State Electrolytes
by: Therrien, Félix, et al.
Published: (2025)

Uncertainty-Aware Machine-Learning Framework for Predicting Dislocation Plasticity and Stress-Strain Response in FCC Alloys
by: Luo, Jing, et al.
Published: (2025)

Accelerating CALPHAD-based Phase Diagram Predictions in Complex Alloys Using Universal Machine Learning Potentials: Opportunities and Challenges
by: Zhu, Siya, et al.
Published: (2024)

Accurate and Uncertainty-Aware Multi-Task Prediction of HEA Properties Using Prior-Guided Deep Gaussian Processes
by: Alvi, Sk Md Ahnaf Akif, et al.
Published: (2025)

InSpecLearn4SDL: Interpretable Spectral Features Predict Conductivity in Self-Driving Doped Conjugated Polymer Labs
by: Mishra, Ankush Kumar, et al.
Published: (2025)

Capabilities of Auto-encoders and Principal Component Analysis of the Reduction of Microstructural Images; Application on the Acceleration of Phase-Field Simulations
by: Fetni, Seifallah, et al.
Published: (2026)

Energy & Force Regression on DFT Trajectories is Not Enough for Universal Machine Learning Interatomic Potentials
by: Miret, Santiago, et al.
Published: (2025)

Benchmark Dataset for Catalysis on 2D MXenes
by: Melnyk, Pavlo, et al.
Published: (2026)

LeMat-Traj: A Scalable and Unified Dataset of Materials Trajectories for Atomistic Modeling
by: Ramlaoui, Ali, et al.
Published: (2025)

JARVIS-Leaderboard: A Large Scale Benchmark of Materials Design Methods
by: Choudhary, Kamal, et al.
Published: (2023)

A Geometry-Adaptive Deep Variational Framework for Phase Discovery in the Landau-Brazovskii Model
by: Xie, Yuchen, et al.
Published: (2026)

A Temporal Filter to Extract Doped Conducting Polymer Information Features from an Electronic Nose
by: Ammar, Wiem Haj, et al.
Published: (2024)

Protocol for Clustering 4DSTEM Data for Phase Differentiation in Glasses
by: Kumar, Mridul, et al.
Published: (2025)

Supply Risk-Aware Alloy Discovery and Design
by: Mulukutla, Mrinalini, et al.
Published: (2024)

The JARVIS Infrastructure is All You Need for Materials Design
by: Choudhary, Kamal
Published: (2025)

Maximizing Efficiency of Dataset Compression for Machine Learning Potentials With Information Theory
by: Yu, Benjamin, et al.
Published: (2025)

Symmetry-Aware Bayesian Flow Networks for Crystal Generation
by: Ruple, Laura, et al.
Published: (2025)

A Unified Generative-Predictive Framework for Deterministic Inverse Design
by: Batley, Reza T., et al.
Published: (2025)

Physics-Informed Gaussian Process Regression for the Constitutive Modeling of Concrete: A Data-Driven Improvement to Phenomenological Models
by: Li, Chenyang, et al.
Published: (2026)

HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction
by: Han, Xiao-Qi, et al.
Published: (2025)

A Machine Learning Approach for Design of Frequency Selective Surface based Radar Absorbing Material via Image Prediction
by: Sutrakar, Vijay Kumar, et al.
Published: (2025)

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

Predicting Atomistic Transitions with Transformers
by: Tischler, Henry, et al.
Published: (2026)

Boosting SISSO Performance on Small Sample Datasets by Using Random Forests Prescreening for Complex Feature Selection
by: Jiang, Xiaolin, et al.
Published: (2024)

Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design
by: Hardcastle, Christofer, et al.
Published: (2025)

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

Equivariant Diffusion for Crystal Structure Prediction
by: Lin, Peijia, et al.
Published: (2025)

Efficient Symmetry-Aware Materials Generation via Hierarchical Generative Flow Networks
by: Nguyen, Tri Minh, et al.
Published: (2024)

SynCoTrain: A Dual Classifier PU-learning Framework for Synthesizability Prediction
by: Amariamir, Sasan, et al.
Published: (2024)

Crystal Structure Prediction by Joint Equivariant Diffusion
by: Jiao, Rui, et al.
Published: (2023)