3 papers
cs.LG2025
Data Fusion of Deep Learned Molecular Embeddings for Property Prediction
Robert J Appleton, Brian C Barnes, Alejandro Strachan
Data-driven approaches such as deep learning can result in predictive models for material properties with exceptional accuracy and efficiency. However, in many applications, data i…
cond-mat.mtrl-sci2025
Harnessing Machine Learning for Quantum-Accurate Predictions of Non-Equilibrium Behavior in 2D Materials
Yue Zhang, Robert J. Appleton, Kui Lin +5
Accurately predicting the non-equilibrium mechanical properties of two-dimensional (2D) materials is essential for understanding their deformation, thermo-mechanical properties, an…
cs.LG2024
Multi-Task Multi-Fidelity Learning of Properties for Energetic Materials
Robert J. Appleton, Daniel Klinger, Brian H. Lee +6
Data science and artificial intelligence are playing an increasingly important role in the physical sciences. Unfortunately, in the field of energetic materials data scarcity limit…