Method and System for Material Screening
A method for screening materials may include obtaining materials from a database. The method may include screening the materials to obtain a one or more screened materials. The method may include generating a training set based on the screened materials, validated experimental data, or both. The method may include establishing a machine learning screening model based on the training set, one or more target parameters, or both. The method may include applying the machine learning screening model to uncharacterized materials. The method may include outputting one or more materials having characteristics matching the target parameters.
1 . A method comprising:
obtaining a plurality of materials from a database;
screening the plurality of materials to obtain a plurality of screened materials;
generating a training set based on the plurality of screened materials and validated experimental data;
establishing a machine learning screening model based on the training set and target parameters;
applying the machine learning screening model to uncharacterized materials; and
outputting one or more materials having characteristics matching the target parameters.
2 . The method of claim 1 , wherein screening the plurality of materials comprises constructing a canonical phase diagram for each of the plurality of materials.
3 . The method of claim 2 , further comprising:
computing an electrochemical stability for each of the plurality of materials based on a respective canonical phase diagram.
4 . The method of claim 3 , further comprising:
filtering the plurality of materials based on a target electrochemical stability range to obtain a plurality of pre-screened materials.
5 . The method of claim 4 , further comprising:
filtering the plurality of pre-screened materials for oxides, halides, or nitrides to obtain the plurality of screened materials.
6 . The method of claim 1 , further comprising:
computing an ionic conductivity for each of the plurality of materials.
7 . The method of claim 6 , wherein computing the ionic conductivity is based on text mining and a manual search.
8 . The method of claim 6 , wherein computing the ionic conductivity is based on an activation energy calculation.
9 . The method of claim 1 , further comprising:
computing a dendrite suppression value for each of the plurality of materials.
10 . The method of claim 1 , further comprising:
computing a thickness for each of the plurality of materials.
11 . The method of claim 1 , wherein the machine learning screening model is a linear regression model, a random forest model, or an Xgboost model.
12 . A method comprising:
establishing a machine learning screening model based on a training set and target parameters, wherein the training set is based on a plurality of screened materials and validated experimental data;
applying the machine learning screening model to uncharacterized materials;
outputting one or more materials having characteristics matching the target parameters; and
updating the machine learning screening model based on validated experimental data of the one or more materials having characteristics matching the target parameters.
13 . The method of claim 12 , further comprising:
computing an electrochemical stability for each of the one or more materials having characteristics matching the target parameters.
14 . The method of claim 13 , wherein computing the electrochemical stability is based on a canonical phase diagram.
15 . The method of claim 12 , further comprising:
computing an ionic conductivity for each of the one or more materials having characteristics matching the target parameters.
16 . The method of claim 15 , wherein computing the ionic conductivity is based on text mining and a manual search.
17 . The method of claim 15 , wherein computing the ionic conductivity is based on an activation energy calculation.
18 . The method of claim 12 , further comprising:
computing a dendrite suppression value for each of the one or more materials having characteristics matching the target parameters.
19 . The method of claim 12 , further comprising:
computing a thickness for each of the one or more materials having characteristics matching the target parameters.
20 . The method of claim 12 , wherein the machine learning screening model is a linear regression model, a random forest model, or an Xgboost model.