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Classroom Contents
Extending the Reach of Quantum Monte Carlo Methods via Machine Learning
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- 1 Intro
- 2 A QMC DREAM Heterogeneous Catalysis
- 3 PREDICTION WORKFLOW
- 4 COMPARISONS WITH BENCHMARKS
- 5 EXTRAPOLATION COMPARISONS
- 6 COMPARISONS WITH SUBTRACTION TRIC • Comparison with the
- 7 AB INITIO MOLECULAR DYNAMICS AND RELAXATION
- 8 LEARNING FORCE FIELDS
- 9 MOLECULAR CASE STUDIES Carbon Dimer, Water, H, 0
- 10 MACHINE LEARNING WORKFLOW
- 11 C, ENERGY AND FORCE PREDICTIONS Challenges at Short Bond Lengths
- 12 C, MOLECULAR DYNAMICS Does Averaging Help? NVE Bond Distance vs. Time
- 13 EFFECTS OF STATISTICAL ERROR BARS HO Modeled via AMPTorch-DMC
- 14 CH CI: A MORE SOPHISTICATED EXAMPLE Generalization to 9 Degrees of Freedom
- 15 CONCLUSIONS AND OUTLOOK Machine Learning Methods Can Be Coupled with Quantum Monte Carlo Methods to Enable and Accelerate Calculations Difficult to Perform Using QMC Alone.
