Publication date: Feb 22, 2021
The design rules for materials are clear for applications with a single objective. For most applications, however, there are often multiple, sometimes competing objectives where there is no single best material, and the design rules change to finding the set of Pareto optimal materials. In this work, we introduce an active learning algorithm that directly uses the Pareto dominance relation to compute the set of Pareto optimal materials with desirable accuracy. We apply our algorithm to de novo polymer design with a prohibitively large search space. Using molecular simulations, we compute key descriptors for dispersant applications and reduce the number of materials that need to be evaluated to reconstruct the Pareto front with a desired confidence by over 98% compared to random search. This work showcases how simulation and machine learning techniques can be coupled to discover materials within a design space that would be intractable using conventional screening approaches.
No Explore or Discover sections associated with this archive record.
File name | Size | Description |
---|---|---|
ml_data.zip
MD5md5:9df8b9ec233d1f551e1e35e21399cf27
|
430.5 KiB | Features and labels for machine learning (zipped folder of csv files) |
rg_runs.zip
MD5md5:41825511b8ef9210b91814f3b376f810
|
416.6 MiB | LAMMPS input files for the calculation of the radii of gyration. |
README.txt
MD5md5:4d7e5be16e378137f0aab6f62194f620
|
1.1 KiB | Detailed description of the filecontents. |
dimer_runs2.zip
MD5md5:2b7b37bbb2623a6f94469b2a0e20a88d
|
3.4 GiB | LAMMPS and SSAGES input files for the calculation of the dimer free energy. |
adsorption_runs.zip
MD5md5:b8f36997b0b3fbf0898110c30c927d75
|
3.4 GiB | LAMMPS and SSAGES input files for the calculation of the free energy of adsorption. |
2021.34 (version v1) [This version] | Feb 22, 2021 | DOI10.24435/materialscloud:8m-6d |