• 한국전기전자재료학회논문지
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• 제35권6호
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• pp.556-561
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• 2022

밀도범함수이론(density functional theory, DFT)이 등장한 이래로, 이를 재료과학에 적용하여 에너지 재료 및 반도체와 같은 전자재료들의 연구개발에 활발하게 활용되고 있다. 하지만 DFT 계산 프로그램을 실행할 때 필요한 입력 파일 생성 시 여러 가지 소재들에 대해 동일한 계산 조건을 맞춰 주고 파라미터들을 알맞게 설정해 줘야 올바른 계산 결과 비교가 가능한데, 이런 부분들에 대해 진입 장벽이 높다는 어려움이 있다. 이에 본 논문에서는 Python Materials Genomics (pymatgen) 파이썬 패키지를 이용해 분자 및 결정구조를 다루고 널리 사용되는 DFT 계산 프로그램인 Vienna Ab initio Simulation Package (VASP) 및 Gaussian 입력 파일 생성에 대해 소개하고자 한다. 이를 통해 해당 프로그램에 대한 전문적인 지식이 많지 않더라도 보다 일관적인 계산 조건에서 결과들을 손쉽게 수행할 수 있게 되기를 기대한다.

• 한국재료학회지
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• 제33권4호
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• pp.164-174
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• 2023

The bandgap characteristics of semiconductor materials are an important factor when utilizing semiconductor materials for various applications. In this study, based on data provided by AFLOW (Automatic-FLOW for Materials Discovery), the bandgap of a semiconductor material was predicted using only the material's compositional features. The compositional features were generated using the python module of 'Pymatgen' and 'Matminer'. Pearson's correlation coefficients (PCC) between the compositional features were calculated and those with a correlation coefficient value larger than 0.95 were removed in order to avoid overfitting. The bandgap prediction performance was compared using the metrics of R² score and root-mean-squared error. By predicting the bandgap with randomforest and xgboost as representatives of the ensemble algorithm, it was found that xgboost gave better results after cross-validation and hyper-parameter tuning. To investigate the effect of compositional feature selection on the bandgap prediction of the machine learning model, the prediction performance was studied according to the number of features based on feature importance methods. It was found that there were no significant changes in prediction performance beyond the appropriate feature. Furthermore, artificial neural networks were employed to compare the prediction performance by adjusting the number of features guided by the PCC values, resulting in the best R² score of 0.811. By comparing and analyzing the bandgap distribution and prediction performance according to the material group containing specific elements (F, N, Yb, Eu, Zn, B, Si, Ge, Fe Al), various information for material design was obtained.