Improving efficiency and accelerating new material development is a major challenge for researchers in the field of materials science. Many researchers hope to overcome this challenge by utilizing Materials Informatics (MI). However, even with the implementation of MI, achieving the desired results is not always guaranteed.
To successfully leverage MI, it is essential to understand the challenges associated with MI, such as data collection and securing skilled personnel.
In this article, CrowdChem, a company with expertise in both chemistry and data science, provides a detailed explanation of the process of new material development using MI. Additionally, we introduce key challenges in MI implementation and propose potential solutions.
The Potential of MI That Materials Researchers Should Know
Materials Informatics (MI) is an emerging interdisciplinary field that integrates materials science with data science. By leveraging vast amounts of materials-related data and using machine learning models for predictions, MI offers the following key benefits:
- Significantly reducing the development time of new materials
- Substantially cutting experimental costs
These advantages have the potential to bring transformative changes to the field of materials development.
In the following sections, we will take a closer look at the two major benefits that MI offers.
Utilizing Data to Shorten the Development Period of New Materials
MI utilizes machine learning models to analyze vast experimental datasets, extract key features, and efficiently narrow down promising raw material candidates. This drastically reduces the number of required experiments.
For instance, Mitsubishi Gas Chemical implemented MI and successfully shortened new material development time by 30–50%. By optimizing researchers’ time and resources, MI accelerates the development process, potentially leading to the discovery of groundbreaking new materials that might have been overlooked using conventional methods.
Reducing Costs for Materials and Experiments
Another major advantage of MI is cost reduction in experiments. Predicting material properties through machine learning models is far more cost-effective than conducting actual experiments.
By minimizing the costs associated with materials, labor, and other expenses, some companies have reported up to a 30% reduction in development costs. For materials development teams striving to maximize results within limited budgets, MI holds immense potential in reducing costs.
The Process of an MI Project for New Material Development
Although MI is known for improving efficiency and accelerating new material development, many researchers still struggle to envision how to apply it in actual research projects. To successfully implement an MI project, it is crucial to understand the entire workflow, from data collection to machine learning model development and experimental validation, and to grasp the key points of each step.
The following sections break down the general MI project workflow into four key steps, explaining the role and significance of each step in detail. As you read through, consider how these concepts might apply to your specific material development needs.
Laying the Foundation of the Project: Data Collection
The first step in an MI project is collecting high-quality data. This involves organizing existing data and processing it for use in the project. Additionally, researchers can conduct new experiments to gather more data or generate virtual datasets using computational methods to enhance data quantity and improve analysis accuracy. However, not all collected data is usable—some datasets may contain missing or abnormal values. Data cleansing and verification are essential to ensure reliability.
Building Machine Learning Models to Predict the Properties of New Materials
Using the collected data, researchers select appropriate machine learning techniques that align with the project’s goals and develop models to predict new material properties. These models help filter and identify the most promising candidates for new materials.
Improving Models Through Feedback from Experimental Results
Once promising candidates are identified through the prediction model, minimal experimental verification is conducted. The goal here is to assess the model’s accuracy and efficiently gather data to improve it.
By comparing experimental results with predictions, researchers can evaluate model accuracy. The feedback from experiments is then used to refine and enhance the machine learning model, leading to more precise property predictions for new materials.
This iterative process allows for a significant reduction in the number of required experiments while continuously improving the predictive power of the MI model.
Experimental Validation of New Material Candidates Narrowed Down by the Prediction Model
The refined prediction model is used to further narrow down the most promising new material candidates, which are then validated through real-world experiments.
The new experimental data obtained should be incorporated into the existing dataset, enabling the machine learning model to undergo retraining for even greater accuracy. MI projects follow this cyclical process to continuously improve efficiency and accelerate new material development.
However, despite MI’s potential, practical implementation still presents various challenges in materials research.
In the next section, we will explore the key challenges that chemical manufacturers face when adopting MI.
Challenges Faced by Chemical Manufacturers in Utilizing MI
While MI offers great potential for new material development, its implementation comes with several challenges. Chemical manufacturers often encounter the following obstacles when integrating MI into their research and development processes:
- The challenge of data collection and managing large-scale data
- Securing talent proficient in both chemistry and data science
Overcoming these challenges is not easy, but it is a necessary step toward the successful adoption of MI. In the following sections, we will explore these challenges in detail and discuss potential solutions.
The Challenge of Data Collection and Managing Large-Scale Data
For an MI project to succeed, large volumes of high-quality data must be collected and organized. However, consolidating scattered internal data into a single repository and converting it into an analyzable format requires significant time and effort. Even after investing resources into gathering internal data, companies often find that their dataset is still insufficient.
Moreover, ensuring data quality through cleansing and validation is essential, adding another layer of complexity. Managing large-scale data requires considerable manpower, and attempting to handle it entirely in-house can disrupt core business operations.
If internal data management is not feasible, leveraging external resources is a viable option. Collaborating with external partners can help address data shortages by incorporating externally sourced datasets. This approach not only facilitates a smoother transition to MI implementation but also provides an opportunity to acquire expertise in data collection, organization, and management from external specialists, benefiting long-term internal data strategies.
The Need for Talent Skilled in Both Chemistry and Data Science
As emphasized in the previous section, effective data management is crucial for MI projects. To drive these projects forward, companies require professionals with expertise in both chemistry and data science. However, while chemical manufacturers have many chemistry experts, they often lack personnel with data science skills. Two main approaches can address this issue:
- Training in-house personnel
- Utilizing external resources
Training In-House Personnel
Developing data science expertise within the company has several advantages:
- Employees are already familiar with internal processes and challenges.
- Security risks are lower since sensitive company data remains internal.
- However, training takes time and investment, making it difficult to solve immediate challenges.
Utilizing External Resources
Relying on external specialists offers immediate benefits:
- Access to experienced professionals who can apply cutting-edge data science techniques.
- Faster implementation of MI without the long lead time required for training in-house personnel.
The downsides include limited internal knowledge retention and potential security risks, but these can be mitigated through careful management and communication. To maximize efficiency, companies should balance both approaches—developing internal talent for long-term sustainability while leveraging external experts for short-term problem-solving. Collaborating with specialists can also accelerate the internal training process by incorporating their expertise into the organization.
CrowdChem’s MI Experts Support Problem-Solving
Given the challenges of data management and talent shortages, utilizing external resources is an effective solution for chemical manufacturers implementing MI. CrowdChem specializes in collecting data from patents, research papers, and product catalogs, building high-quality datasets essential for MI projects. These comprehensive datasets enhance machine learning model accuracy, significantly contributing to achieving the desired outcomes.
Additionally, CrowdChem has a team of experts proficient in both chemistry and data science. With deep knowledge of the unique challenges faced by chemical manufacturers, these specialists provide the expertise and experience necessary for MI project success.
If you are considering MI implementation, we encourage you to consult with CrowdChem for expert guidance and support.
