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Alejandro Strachan

Alejandro Strachan

Apr 07, 20261 min read

Publications

  • Design of Atomic Ordering in Mo2Nb2C3Tx MXenes for Hydrogen Evolution Electrocatalysis
  • High-throughput density functional theory screening of double transition metal MXene precursors
  • How accurate is density functional theory at high pressures?
  • Discovery of new high-pressure phases – integrating high-throughput DFT simulations, graph neural networks, and active learning
  • Thermodynamic fidelity of generative models for Ising system
  • Cyber-Enabled Simulations in Nanoscale Science and Engineering
  • Sim2Ls: FAIR simulation workflows and data
  • 2D rare-earth metal carbides (MXenes) Mo2NdC2T2 electronic structure and magnetic properties: A DFT + U study
  • Phase diagram of MgO from density-functional theory and molecular-dynamics simulations
  • An Active Learning Approach for the Design of Doped LLZO Ceramic Garnets for Battery Applications
  • Active learning and molecular dynamics simulations to find high melting temperature alloys
  • Multi‐Task Multi‐Fidelity Learning of Properties for Energetic Materials
  • Unsupervised Learning-Based Multiscale Model of Thermochemistry in 1,3,5-Trinitro-1,3,5-triazinane (RDX)
  • Neural network reactive force field for C, H, N, and O systems
  • Mapping microstructure to shock-induced temperature fields using deep learning

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Backlinks

  • Discovery of new high-pressure phases – integrating high-throughput DFT simulations, graph neural networks, and active learning
  • High-throughput density functional theory screening of double transition metal MXene precursors
  • 2D rare-earth metal carbides (MXenes) Mo2NdC2T2 electronic structure and magnetic properties: A DFT + U study
  • Active learning and molecular dynamics simulations to find high melting temperature alloys
  • An Active Learning Approach for the Design of Doped LLZO Ceramic Garnets for Battery Applications
  • Cyber-Enabled Simulations in Nanoscale Science and Engineering
  • High-Throughput Density Functional Theory Screening of Double Transition Metal MXene Precursors
  • How accurate is density functional theory at high pressures?
  • Mapping microstructure to shock-induced temperature fields using deep learning
  • Multi‐Task Multi‐Fidelity Learning of Properties for Energetic Materials
  • Neural network reactive force field for C, H, N, and O systems
  • Parsimonious neural networks learn interpretable physical laws
  • Phase diagram of MgO from density-functional theory and molecular-dynamics simulations
  • Sim2Ls: FAIR simulation workflows and data
  • Unsupervised Learning-Based Multiscale Model of Thermochemistry in 1,3,5-Trinitro-1,3,5-triazinane (RDX)
  • Design of Atomic Ordering in Mo2Nb2C3Tx MXenes for Hydrogen Evolution Electrocatalysis
  • Discovery of new high-pressure phases – integrating high-throughput DFT simulations, graph neural networks, and active learning
  • High-throughput density functional theory screening of double transition metal MXene precursors
  • How accurate is density functional theory at high pressures?
  • Thermodynamic fidelity of generative models for Ising system

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