Meeting Notes 08-30-2021

Minutes of Meeting August 30, 2021

Present: Kamil Iskra, Vikram Jadhao, Geoffrey Fox, Deborah Penchoff, Xiaodong Yu, Jack Dongarra, Shantenu Jha, Piotr Luszczek, Pete Beckman, Baixi Sun

Updates

• Rutgers: Progress with recruiting problems. Highlighted a new paper https://doi.org/10.1021/acs.jcim.8b00839 on molecular benchmarks from Benevolent AI GuacaMol: Benchmarking Models for De Novo Molecular Design. Peter Coveney Company in London
• Tennessee continues work on the performance surrogate model. Tune hyperparameters. Build from small runs. Report in October. Works on simulations or data analytics. Unlike ATLAS aimed at problems with runs that take a large time
• **Argonne. **Pete noted by email a new paper Why AI is Harder Than We Think with a cautionary tale.
  • Baixi Sun from Washington State University was introduced as a new student on project
  • Xiaodong discussed their 3 use cases. Convert notebooks to python scripts and run in multinode fashion
  • Using ALCF the first usage mode is based on Jupyter notebooks and second usage mode is batch
  • ALCF likes Jupyter notebooks. Also note Jupyter notebooks at ORNL
• Indiana/Virginia. Vikram Jadhao presented on surrogates for soft materials
  • This reviewed highlights from the field and then focussed on his work
  • Word surrogate not often used in field
  • The review covered SorbNet from MInnesota, ab initio simulation from Toronto and pair correlation function of liquids from UIUC group Aluru
  • Vikram’s application was confined electrolytes where surrogate relates structure to attributes
  • Good use in education using nanoHUB deployment
  • Nice performance slide
  • EXTENDED Predictions were not as good as original ones
  • Need to quantify and improve accuracy – how? Average over all quantities but worse near the wall. COULD weight those points more in loss
    • Common in surrogates, that error is dominated by “special” regions – boundaries, singularities etc. as work of Geoffrey with James Glazier on diffusion equation for cell modelling.
  • Look at reducing needed training size
  • Will evaluate using Rutgers software infrastructure