Latest Research in Machine Learning in Materials Science

Top Research Topics in Machine Learning in Materials Science

• Materials Science — 33 papers
• Machine Learning in Materials Science — 21 papers
• Chemical Physics — 11 papers
• Machine Learning — 9 papers
• Liquid Crystal Research Advancements — 4 papers
• Computational Physics — 4 papers
• Quasicrystal Structures and Properties — 3 papers
• Protein Structure and Dynamics — 3 papers
• Generative Adversarial Networks and Image Synthesis — 2 papers
• Soft Condensed Matter — 2 papers

Highest-Cited Papers

1. Resistive Switching and Space-Charge-Limited Transport in Ag/α-Phase Patterned PVDF/Ag Devices Fabricated by Soft Imprint Lithography
2. Crystal Structure of Form I of Poly[tris(ethylene sulfide)- alt -tris(ethylene oxide)]
3. An Exact Algebraic Reduction of the Hexagonal-Face Monostationarity Problem in Dual Phosphorylation
4. Training Energy-Based Models with Non-MCMC Samplers and Efficient Temperature Estimation
5. Optimized M‐ SHAKE Constraint Implementations for GPU ‐Accelerated Molecular Dynamics: Balancing Precision and Performance Across Architectures
6. Gas‐Phase Studies of 19 F NMR Magnetic Shielding of Simple Isolated Molecules
7. High-Performance Reverse-Mode Liquid Crystal Smart Windows via Co-Optimization of a Phosphate Self-Assembled Interface, a Polyurethane Acrylate Polymer Network, and a Patterned Microstructure
8. Truncated automatic sparse differentiation for machine learning interatomic potentials
9. Uncertainty quantification design principles for machine learning interatomic potentials: lessons learned from hierarchical Bayesian inference
10. Distortion-free scattering regime in polymer stabilized cholesteric liquid crystals: Textural, electro-optical, and dielectric behavior
11. Machine Learning Meets Spin–Orbit Coupling: Reconstruction of Spin–Orbit-Mixed Potential Energy Curves in MgCa
12. Tunable Circular Diattenuation and Passive Optical Isolation in Dye-Doped Cholesteric Glassy Liquid Crystals
13. Electroluminescent photoresists extending lithographic scaling to OLEDs
14. Agentic AI for Density-Functional Development: Revisiting r2SCAN
15. Load balancing for adaptive-precision interatomic potentials in materials science
16. ALIGNN 2.0: A Unified Line-Graph Neural Network Framework for Materials Screening, Force Fields, Inverse Design, Spectroscopy, and Microscopy
17. Machine Learning Electrostatic Interactions in Materials
18. Nitrogen Configuration‐Driven Modulation of Built‐in Electric Field Toward Synchronously Boosted Anti‐Corrosion and Electromagnetic Wave Absorption
19. Fixed-configuration inversion of interaction potentials from equilibrium configurations at fixed state points
20. Deep potential modeling of oxygen adsorption and surface reconstruction on silica