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Data preparation
Data preparation is a crucial step in the ML process, as the quality and relevance of the data directly impact
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the model performance and accuracy . Data for ML models can be obtained through various methods,
including literature mining , computational data , experimental data , and database data [56,57] . While
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accurate data from literature or DFT calculations is highly valuable, it is often limited in quantity.
Conversely, databases can quickly provide large volumes of data, though specific materials may not always
be included. Experimental databases such as the Cambridge Structural Database (CSD) , the Inorganic
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Crystal Structure Database (ICSD) , PubChem, and the Crystallography Open Database (COD) offer
essential structural data for ML studies. For example, the ICSD contains over 240,000 crystal structures of
inorganic compounds. Key computational databases include AFLOW , the Materials Project (MP) , the
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Computational 2D Materials Database (C2DB) , and the Open Quantum Materials Database (OQMD) ,
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which provide computed properties such as optimized structures, electronic band structures, and densities
of states, supporting efficient ML predictions. MP alone includes ~144,000 inorganic compounds and
~63,000 molecules. These resources are invaluable for advancing material predictions through ML. After
acquiring the data, the initial step is to assess its quality to determine its suitability for building a ML model.
This assessment involves checking the representativeness of the samples, identifying outliers or erroneous
samples, ensuring consistent parameters for sample labels, and verifying that the distribution of label values
is balanced and close to a normal distribution. Following the quality assessment, data cleaning is essential to
remove noise, errors, and inconsistencies, ensuring the quality and reliability of data. To handle missing
values, one can either delete samples or features with missing values or use interpolation methods (such as
mean, median, or regression) to fill in the gaps. Normalizing and standardizing the data is crucial to
eliminate scale differences between features, ensuring comparability and consistency across the dataset.
These preprocessing steps are fundamental to prepare the data for effective ML, leading to more robust and
accurate models.

Feature engineering
Feature engineering is a preprocessing step in ML, referring to the process of transforming raw data into
features that better represent the underlying problem, thereby improving the predictive performance of the
model. It aids in representing the underlying problem to the predictive model more effectively, thus
enhancing the accuracy of the model on unseen data. Predictive models consist of predictor variables and
outcome variables, and the feature engineering process selects the most useful predictor variables for the
model. Feature engineering in ML primarily includes feature extraction, construction, and selection. Feature
extraction is the process of extracting useful information from raw data. The methods for feature extraction
vary depending on the type of data. For instance, extracting information about crystal elements, atomic
positions, atomic interactions, and local structures can help the model understand material properties [57,65,66] .
Feature construction involves creating new features through linear combinations of the original features to
provide richer information and improve the model performance. Effective feature construction requires a
deep understanding of the data and applying domain knowledge to innovate . In practical ML tasks, it is
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often necessary to repeatedly train ML models to evaluate the effectiveness of the current set of features and
iterate through the three stages of feature engineering. Feature selection is selecting the most relevant subset
of features from the original feature set by removing redundant, irrelevant, or noisy features. The goal is to
choose the features with the highest predictive power from the original set [68,69] . Common feature selection
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methods include filter methods , wrapper methods , and embedded methods .
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Model selection and training
Before building a ML model, it is essential to clearly define the type of addressed task and select the most
suitable model from multiple candidates for the specific problem. Different types of ML tasks, such as
regression, classification, and clustering, may correspond to different model selection strategies. There is