Alejandro Strachan
Engineering · Purdue University West Lafayette
Publications
442
Citations
10,397
Est. group size
~23
Recurring co-author estimate
Active years
31
Publishing since 1996
Alejandro Strachan's research uses computational modeling and simulation, including molecular dynamics and machine learning, to understand and predict how materials behave under extreme conditions such as shock waves, high pressure, and impact. His work spans energetic materials (explosives), 2D materials, polymers, and metals, with an emphasis on building predictive computational tools that connect atomic-scale physics to real-world material performance. This research is relevant to students interested in materials engineering, computational physics, and applying machine learning to physical sciences.
Publication output has fluctuated over the last decade with a peak in 2017, a dip in 2018, and generally steady activity averaging about 22 papers per year over the last five years.
Generated by claude-sonnet-5 from public bibliographic data · Jul 20, 2026
- A deep learning approach to searching property spaces of materials
Digital Discovery · 2026
- Predictive Models for Strain Energy in Condensed Phase Reactions
Macromolecules · 2026
- Multi-Fidelity Predictive Model for Shock Response of Energetic Materials Using Conditional U-Net
arXiv (Cornell University) · 2026
- Multi-Fidelity Predictive Model for Shock Response of Energetic Materials Using Conditional U-Net
arXiv (Cornell University) · 2026
- Physics-constrained Gaussian Processes for Predicting Shockwave Hugoniot Curves
arXiv (Cornell University) · 2026
- Physics-constrained Gaussian Processes for Predicting Shockwave Hugoniot Curves
arXiv (Cornell University) · 2026
- Nuclear Quantum Effects in Multi-Step Condensed Matter Chemistry: A Path Integral Molecular Dynamics Study of Thermal Decomposition
arXiv (Cornell University) · 2026
- Nuclear Quantum Effects in Multi-Step Condensed Matter Chemistry: A Path Integral Molecular Dynamics Study of Thermal Decomposition
arXiv (Cornell University) · 2026
- Evaluating LLM-generated code for domain-specific languages: Molecular dynamics with LAMMPS
Computational Materials Science · 2026
- Mechanisms of spall failure in niobium subjected to high-throughput laser-driven micro-flyer impact
Acta Materialia · 2025
- Data Fusion of Deep Learned Molecular Embeddings for Property Prediction
Journal of Chemical Information and Modeling · 2025
- Large scale polymer toughening of two-dimensional materials revealed by in situ TEM fracture tests and multiscale simulations
European Journal of Mechanics - A/Solids · 2025
- Steady-state elastic plastic shock waves in a low-symmetry molecular crystal
Physical review. B./Physical review. B · 2025
- Modeling Framework to Predict Melting Dynamics at Microstructural Defects in TNT-HMX High Explosive Composites
The Journal of Physical Chemistry C · 2025
- Spall strength of symmetric tilt grain boundaries in 6H silicon carbide
Journal of Applied Physics · 2025
- arXiv (Cornell University)×46
- Journal of Applied Physics×26
- Bulletin of the American Physical Society×18
- The Journal of Physical Chemistry C×15
- HubZero×10
- Brenden W. Hamilton
Engineering · Purdue University West Lafayette
- Michael Sakano
Engineering · Purdue University West Lafayette
- Chunyu Li
Engineering · Purdue University West Lafayette
- Timothy D. Manship
Engineering · Purdue University West Lafayette
- Alex Casey
Engineering · Purdue University West Lafayette
This profile was generated automatically from public scholarly data (OpenAlex). Group size and activity levels are estimates derived from co-authorship patterns.
Last updated Jul 20, 2026.
Claim or correct this profile