SynthGPT

This repository contains the data and code for Large Language Models for Inorganic Synthesis Predictions by Seongmin Kim, Yousung Jung, and Joshua Schrier.

Organization

Input data and pre-defined training and cross-validation and train/test splits are found in the data_MP and data folders, for the synthesizability and precursor selection tasks, respectively.

Results are in the results_MP and results folders, for the synthesizability and precursor selection tasks, respectively. We have used a JSON format to facilitate interpretation of the results.

Prompts for the LLM are in the prompts folder as plain text files; they can also be found in the online Supporting Information file.

Source code is in the src folder; some haphazard tests are included in tests.

Instructions

Run the notebooks in the top-level directory in order. Mathematica code (.wls) uses Mathematica 14.0 and no other libraries. Python code (.py) uses python 3.8.13 and requires libraries; Numpy (version == 1.22.3), PyTorch (version == 1.11.0), and Pymatgen (version == 2022.9.21).

The directory is organized around the order in which we performed the work, dividing the work into discrete tasks:

• Precursor selection (scripts 00_Data_Curation.py - 07_Estimate_Perfect_Elemwise.py)
• Synthesizability prediction (08_Data_Preparation_Synthesizability.wls - 11_Score_GPT_Outputs_Synthesizability.wls)
• Evaluation of precursor rescoring results with GPT-4 (12a_SetupData_Combined.wls and 12b_Evaluate_Combined.wls ) and by removing recommendations that do not consist of only allowed precursors (13_Precursor_Compliance.wls and 14_Evaluate_Combination_Retaining_Only_Allowed_Precursors.wls)

Yes, this is different from the order the paper. "Life can only be understood backwards; but it must be lived forwards." --Søren Kierkegaard

Cite

A preprint appears on the ChemRXiv as doi:10.26434/chemrxiv-2024-9bmfj