Predicting the bandgap of hybrid halide perovskite monolayers with machine learning

AI editorPublished 2026.10.28Posted 2026.10.29 00:02 KST

The paper published in J. Phys. Chem. Lett. on 2026-10-28 is a study that predicts the bandgap of hybrid halide perovskite monolayers with machine learning. As far as currently available information shows, the specific model type and quantitative performance are not disclosed, but it can be regarded as an example of applying ML to materials property prediction. [1]

1/ AI 활용 방법: 머신러닝 회귀 예측
2/ 적용 과제: 하이브리드 할라이드 페로브스카이트 단층의 밴드갭 예측
3/ 보고된 결과: 밴드갭 예측 연구로 게재됨
4/ This paper is a study that predicts the bandgap of hybrid halide perovskite monolayers with machine learning, and it is confirmed to have been published on 2026-10-28.
5/ Based on the title and source information, this is an example of applying ML to a materials property prediction problem, and the prediction target is the bandgap.
6/ From a materials research perspective, its significance is that ML was used to rapidly estimate the electronic structure property of a monolayer perovskite.
7/ Such an approach can be useful for candidate material screening or computational screening.
8/ However, based only on the currently available information, the type of model used, the training data composition, the validation method, and the performance metrics cannot be confirmed.
9/ Therefore, this result should not be extrapolated into general predictive performance. [1]

How to apply this

To predict the bandgap of hybrid halide perovskite monolayers with a regression model.

Collect a dataset paired with structures and bandgaps, and organize missing values and outliers.
Choose structural descriptors or graph representations and convert them into inputs for training.
Train a regression model with a training, validation, and test split or cross-validation.
Calculate error metrics on the test set and check the scatter plot of predicted versus actual values.

Data needed: A dataset of hybrid halide perovskite monolayers with structural information and corresponding bandgap values is needed.
Checking the result: Check MAE, RMSE, and R2 on a hold-out test set or through cross-validation, and verify that the predictions do not deviate greatly from known physical trends.
What can go wrong: Overfitting can easily occur on small datasets, and performance can drop sharply when extrapolating to compositions or structures outside the training distribution.

이 가이드는 원문의 방법을 바탕으로 AI가 제안한 적용 방향입니다. 원문에 적힌 내용이 아니며 같은 결과가 재현된다는 보장도 아닙니다.

웹 자료를 바탕으로 AI가 자동 요약한 글입니다. 연구의 세부 조건과 결과는 연결된 원문을 확인해주세요.

Predicting the bandgap of hybrid halide perovskite monolayers with machine learning

How to apply this

Related concepts and practice

Sources