COMPANY PROFILE / MATERIALS INFORMATICS
Mitsui Chemicals
A chemical maker that folds MI into its R&D digital transformation and uses it together with partners such as Hitachi and NEC
Mitsui Chemicals is a major Japanese chemical company whose products range from plastic raw materials to functional and healthcare materials. From the standpoint of materials informatics (MI, the use of data and computation to narrow down candidate materials and conditions), it is not a company that sells MI software or materials AI. It is an industrial user that applies MI to speed up its own materials development. The public record shows three threads: collaboration with Hitachi (since 2017), the Digital Science Lab., a computing hub for R&D DX (completed December 2024), and MI for recycled plastics with NEC and others (from October 2025). This profile keeps the whole-company business brief and concentrates on the part that bears on MI.
- Mitsui Chemicals in 30 seconds, and where it stands in materials informatics
- The logo and how the name is written
- Where the company sits in the materials informatics field
- Scale of the company (the whole company in brief)
- Resources committed to materials informatics
- What it has done so far
- What comes next
- What the company is aiming for in materials informatics
- Map of partnerships in materials informatics
- How MI is used, and the technologies and businesses involved
- The risks this company carries
- Glossary, references and claim-to-source audit
1. Mitsui Chemicals in 30 seconds, and where it stands in materials informatics
NECHitachi since 2017. NEC from October 2025
(company-wide)Reached in March 2026. MI-dedicated headcount is undisclosed
At Mitsui Chemicals, MI is described not as a stand-alone MI business but as one tool within company-wide DX (changing work and research with digital technology). The securities report mentions MI in three places: battery materials, formulation of polyolefin resins and corporate research (advanced foundation technologies). It does not name any product developed with MI, nor any shortening of development time actually achieved Sourced. The only figures the company puts forward are the 2021 test with Hitachi (about one quarter of the experiments, on past data) and the target of the 2025 collaboration with NEC and others (25% less production time). So it is clear that the company is investing in MI, but what MI has achieved is still hard to check from outside.
2. The logo and how the name is written
A note on names: the legal entity is Mitsui Chemicals, Inc. It is a separate company from other firms that carry the Mitsui name, such as Mitsui & Co. Its subsidiaries include Prime Polymer and Kulzer, and group-wide (consolidated) figures differ from those of Mitsui Chemicals on its own. The logo is the English-language logo shown in the header of the company's official website.
Official site: https://jp.mitsuichemicals.com/jp/index.htm3. Where the company sits in the materials informatics field
MI is a flow in which past data and computation narrow down "promising candidate materials and conditions", and experiments then confirm them (see our explainer on materials informatics for an overview). Companies involved in MI fall into four types, depending on which part of that flow they own.
Mitsui Chemicals is an industrial user. It is not a company that sells MI software; it sits on the side that borrows AI from Hitachi and MI technology from NEC to use in its own materials development and manufacturing. In terms of the MI flow, it starts from its own experimental and production data, lets outside AI narrow candidates and conditions, and takes on the part where results are confirmed in the lab or on the factory floor. For this type, the strength is "real materials and markets" and the weakness is "limited in-house data" (the general tendency in Fig. 1), and Mitsui Chemicals' development of 165 data scientists and its build-out of a computing hub can be read as moves against that weakness.
4. Scale of the company (the whole company in brief)
Mitsui Chemicals is a large company, so only the scale of the whole company is shown, briefly Sourced. No figure for MI alone is disclosed.
| Measure (consolidated, IFRS) | Year to Mar 2024 | Year to Mar 2025 | Year to Mar 2026 |
|---|---|---|---|
| Revenue (¥ million) | 1,749,743 | 1,809,164 | 1,668,754 |
| Profit attributable to owners of the parent (¥ million) | 49,999 | 32,242 | 34,378 |
| Consolidated headcount | 19,861 | 17,320 | 16,967 |
| R&D (year to March 2026) | Figure | Note |
|---|---|---|
| R&D expense (company and consolidated subsidiaries) | ¥46.4 billion | About 2.8% of revenue (our calculation) |
| Of which Life & Healthcare Solutions / Mobility / ICT / Basic & Green Materials | ¥11.7 / 8.8 / 13.1 / 4.8 billion | Separately, new-business creation at ¥0.5 billion and corporate research at ¥7.5 billion (the report says it is allocated to all reportable segments) |
| R&D structure | Changed in October 2025 | Development departments set up in the business divisions of the three growth fields, joining business and development |
The figures above are for the whole company. How much is spent on MI, and how many people work on it full time, is not disclosed Not yet confirmed. The MI share of the ¥46.4 billion of R&D cannot be separated out from the report. When reading a profile of a chemical company in this field, keep in mind that the scale of the company and the scale of its commitment to MI are two different things.
5. Resources committed to materials informatics
The table shows, for people, money, outside investment and links with universities or public bodies, what could and could not be confirmed from primary sources. "Not stated" is also a finding.
| Type of resource | What is there | Status |
|---|---|---|
| People (company-wide data talent) | 165 data scientists reached in March 2026, assigned to R&D, production, business and corporate divisions. A "Data Scientist Specialist" system started in 2025 (Mitsui Chemicals Report 2025) | Sourced |
| People (research site) | The Digital Science Lab. is meant to gather digital-science experts from R&D and from production and technology. The Sodegaura site brings together more than 1,000 researchers (2022 announcement) | Sourced |
| People (MI only) | Headcount dedicated to MI | Not disclosed |
| Money (facility) | Investment in the Digital Science Lab.: in the 2022 announcement the Japanese version says about ¥5 billion and the English version says about ¥4 billion, so one announcement gives two figures. The actual cost could not be confirmed | Sourced (with a discrepancy) |
| Money (R&D) | R&D expense of ¥46.4 billion (whole company). The MI share is not disclosed | Not disclosed |
| Compute | A multi-architecture high-performance computing environment with petaflops-class total performance (theoretical double-precision peak) (2022 and 2024 announcements) | Sourced |
| Corporate co-creation | Hitachi (since 2017, joint demonstration of AI-based MI technology) and NEC, Hagihara Industries and Maruki Sangyo (from October 2025, recycled plastics) | Sourced |
| Investments, acquisitions, joint ventures | No investment, acquisition or joint venture relating to MI appears in the material checked | Could not be confirmed |
| Ties with universities, institutes and public funding | No joint research with universities or public bodies, and no public funding for MI, appears in the material checked. The securities report has only a general line about "taking in outside technology and knowledge through open innovation" | Could not be confirmed |
Status of the main technologies and facilities
The table covers MI-related facilities, technology and collaborations, as far as official material confirms them. Mitsui Chemicals does not release its own models or software, so the "form of release" column says things like "internal use".
| Name | Kind | Main specifics (as disclosed) | Form of release | Stage and date | Source |
|---|---|---|---|---|---|
| Digital Science Lab. | Research facility and computing environment | Petaflops-class HPC environment. Building area 1,471.98 sq m and floor area 2,707.84 sq m. A project room is planned for outside co-creators | Internal use (co-creation is Not yet confirmed) | Completion ceremony on 16 December 2024. Opened the same month | Completion announcement, 2022 announcement |
| Hitachi's nested AI | MI technology (developed by Hitachi) | An AI trained on about 500,000 entries of public formula data with a second AI, trained on experimental data, embedded inside it. Patent pending | Hitachi plans to bring it into its "Lumada" solutions (Hitachi's side) | Demonstration trial began in June 2021 | Joint release with Hitachi |
| NEC's MI technology for recycled plastics | Collaboration | Viscosity data collected at Maruki Sangyo's plant is analyzed with MI so that the amount of resin added can be adjusted in real time. The target is 25% less production time than the conventional process | Could not be confirmed | Collaboration began on 7 October 2025. The target is Not yet confirmed | Joint release |
| Single-extruder viscosity-uniformizing device | Equipment (co-developed with Hagihara Industries) | Measures viscosity during recycled-plastic production and adjusts the amount of resin added | Hagihara Industries began making and selling it in October 2025 | Offered | Joint release |
The approach taken with Hitachi is designed for situations where experimental data is scarce Sourced. Until now, AI that directly generates chemical formulas needed large numbers of pairs of "formula and performance value". Hitachi's method trains the outer AI on public formula data (about 500,000 entries) and trains only the inner AI on the company's own small set of experiments. The aim is that it can be used even for material systems with only dozens or hundreds of experimental results. There are caveats. First, "about one quarter of the experiments" is the result of testing on Mitsui Chemicals' past organic-materials data, not the result of developing a real new material. Second, the formulas the AI proposes are candidates; whether they can be synthesized and reach the target performance has to be settled by experiment. Third, no material was found that reports the results of the demonstration trial after that test Not yet confirmed. If you want to apply this to your own work, a practical first step is to run the same kind of test on your own past data.
6. What it has done so far
| When | What happened | What it means |
|---|---|---|
| 2017 | Begins co-creating materials development with Hitachi using MI | The starting point of its MI work (footnote of the joint release) |
| April 2021 | Masao Sambe becomes CDO (chief digital officer) | A head for company-wide DX is put in place (2023 announcement) |
| 28 June 2021 | Starts a demonstration trial of the AI-based MI technology developed by Hitachi. Tests on past data cut the experimental runs to about one quarter of conventional MI | The only quantitative result the company cites (a test on past data) |
| 15 November 2022 | Announces plans for the Digital Science Lab. | The plan for a computing hub for R&D DX |
| 22 March 2023 | Describes its DX work as including DX in R&D, such as MI | MI sits inside DX |
| 16 December 2024 | Completion ceremony for the Digital Science Lab. (the plan was completion and start of use in October 2024) | Opened about two months later than the 2022 plan (our comparison) |
| 2025 | Starts operating the "Data Scientist Specialist" system | A way to reward specialist talent |
| 7 October 2025 | Mitsui Chemicals, Hagihara Industries, Maruki Sangyo and NEC begin a collaboration using MI for recycled plastics | MI's scope widens from molecular design to the manufacturing process |
| October 2025 | Sets up development departments in the business divisions of the three growth fields and changes its R&D structure (securities report) | Brings development closer to the business |
| March 2026 | Reaches its target of 165 data scientists | The talent base is in place |
| 22 June 2026 | The securities report describes MI use in battery materials, resin formulation and corporate research | MI appears in a business-strategy document |
7. What comes next
Everything below is a company target or policy, not an achieved fact Not yet confirmed.
None of the following appears in the material checked Not yet confirmed.
Names of products developed with MI / shorter development time actually achieved with MI / MI's contribution to sales / MI-dedicated headcount and spending / results of the trial with Hitachi / outside co-creators to be hosted at the Digital Science Lab. Figures taken from news reports are not used.
8. What the company is aiming for in materials informatics
This chapter includes points the company does not state directly. They are inferences from what the primary sources say, set out with their grounds.
| What can be read | Primary source behind it | Status |
|---|---|---|
| MI is being pursued as part of company-wide DX | The 2023 announcement lists DX in R&D, such as MI, among its DX work, and the 2026 announcement describes building a company-wide base of 165 data talents. Nothing describes an MI-only organization | Inference |
| It does not build MI software itself, but uses it together with outside partners | Collaborations that use technology from Hitachi (AI) and NEC (MI) are confirmed in official releases. Nothing states that it sells MI software of its own | Inference |
| The focus is battery materials, resin formulation, catalysts and recycled plastics | The securities report mentions MI or DX in battery-materials solutions and in Basic & Green Materials (formulation development, and polymerization catalysts developed with DX) | Sourced (as far as the text goes) |
| It is widening MI's target from molecular design to the manufacturing process | The October 2025 collaboration uses viscosity data to adjust additive amounts in real time and shorten production time | Inference |
The October 2025 collaboration starts from a problem: in recycled plastics the amount and quality of waste plastic vary from day to day, so the kinds and amounts of additives needed to meet viscosity and strength have been decided case by case from the experience of skilled workers Sourced. The job MI has here is not designing a new molecule but finding the best conditions from data gathered during manufacturing. The 2021 work with Hitachi pointed toward "generating molecular candidates with AI", while 2025 points toward "tuning manufacturing conditions with factory data". Putting the two side by side, it reads as though Mitsui Chemicals means to use MI for both materials design and manufacturing Not yet confirmed. That is our interpretation, though, and not something the company has declared as strategy.
9. Map of partnerships in materials informatics
10. How MI is used, and the technologies and businesses involved
The securities report mentions MI in three contexts (Part 1, "Business overview" and "Research and development"). It gives no numbers for what MI has achieved.
| Area | What the securities report says (in short) | Related products named in the report |
|---|---|---|
| Battery-materials solutions | Customizing products to each customer's battery design, with fast product development using materials informatics and more | Electrolyte for lithium-ion batteries (Millet), encapsulation sheet for solar cells (Solar Ace) |
| Basic & Green Materials | To strengthen high-function products: materials development starting from the end user, and new grades and formulations through wider use of MI. Also development of high-performance polymerization catalysts for polyolefin resins using DX | Polyolefin resins and olefin polymerization catalysts |
| Corporate research | To create future value, it applies advanced foundation technologies such as materials informatics and sensory-evaluation technology, and takes in outside technology through open innovation | Carried out by laboratories such as the Advanced Materials and Solutions Laboratory |
| Recycled plastics (collaboration) | The October 2025 joint release. Viscosity data is analyzed with NEC's MI and the amount of additive is adjusted in real time | Single-extruder viscosity-uniformizing device (Hagihara Industries) |
From the securities report, the places where MI is used overlap with optimizing blends and formulations and developing catalysts (Sourced: as far as the text goes). Battery electrolytes have a huge number of combinations of solvents, salts and additives, and testing them one by one takes a long time. New grades and formulations of polyolefin resins are a many-variable problem in which the catalyst, the polymerization conditions and the additives all affect one another. In such settings MI helps by narrowing the order in which to test. The Mitsui Chemicals material does not say, however, which material systems used which methods (machine learning, simulation, design of experiments and so on). How trustworthy an MI result is depends on the quality and amount of your own data and on how you evaluate it (see our explainer on how to read MI results and their reproducibility). If you take Mitsui Chemicals' work as a reference, a practical approach is to decide which problem, using which data, and what counts as success before you adopt MI.
11. The risks this company carries
| Risk | What it is | Status |
|---|---|---|
| Results are hard to check from outside | No product developed with MI, no shortening of development time and no contribution to sales is disclosed. The only quantitative figures the company gives are the 2021 test on past data and the 2025 collaboration target | Not yet confirmed |
| Gap between testing and deployment | "About one quarter of the experiments" is a result of testing on past data. No result from developing a real new material could be confirmed | Sourced |
| Delay in using DX and AI, and security | The securities report lists as risks that late adoption of digital technology and AI may keep work reform and development from improving, and cyberattacks. It says the company sets company-wide guidelines for generative AI | Sourced |
| Schedule slips | The Digital Science Lab. was planned for completion and use from October 2024 in the 2022 announcement, while the completion ceremony took place on 16 December 2024 | Sourced |
| Dependence on outside technology | It relies on outside companies such as Hitachi and NEC for AI technology for MI. Its own data combined with outside technology is what gives it an edge, but nothing says what it would do if outside technology became unavailable | Inference |
| Talent spread across the company | The 165 data scientists are assigned to R&D, production, business and corporate divisions and are not dedicated to MI. How many people can focus on MI cannot be read from the material | Inference |
| A harsh business environment | In the securities report's description of its environment, restructuring the Basic & Green Materials business and reorganizing with other companies are named as priorities. How resources are allocated to MI cannot be read from it | Inference |
12. Glossary
- Materials informatics (MI)
- Using data and computation to narrow down promising candidate materials and conditions, then confirming them by experiment.
- DX
- Digital transformation. Changing how work and research are done by using digital technology.
- CDO
- Chief digital officer. The executive in charge of digital-driven change.
- Digital science
- Research that combines computational science, data science and high-performance computing (Mitsui Chemicals' explanation).
- HPC / petaFLOPS
- HPC is a large-scale computing environment. A petaFLOPS is a thousand trillion calculations per second.
- SMILES
- A way of writing a molecule as a string of characters. Often used when AI handles chemical formulas as text.
- Deep learning
- A machine-learning method that learns features from data with many-layered neural networks.
- Descriptor
- A number that captures a feature of a molecule or material. It serves as input when AI predicts a material's performance.
- Demonstration trial
- Trying a developed technology in real operations or real materials development, a stage apart from checking it in the lab.
- Recycled plastic
- Used plastic that is collected and processed to be used as a new material. The quality of the feedstock changes from day to day.
- Extruder and viscosity
- An extruder melts resin and pushes it out. Viscosity is how resistant the molten resin is to flowing, and is a quality indicator.
- Securities report
- The annual report that a Japanese listed company files with the government. It is audited and carries legal responsibility.
- IFRS
- International Financial Reporting Standards. Mitsui Chemicals' consolidated statements follow them.
13. References
- Mitsui Chemicals Securities Report (yuka shoken hokokusho) for the 29th fiscal year, 1 April 2025 to 31 March 2026 (in Japanese; filed with the Kanto Local Finance Bureau on 22 June 2026). Revenue, headcount, R&D expense, MI in the battery-materials and basic-materials strategies, corporate research and the risk of DX. https://jp.mitsuichemicals.com/content/dam/mitsuichemicals/sites/mci/documents/release/2026/ms_260622.pdf
- Mitsui Chemicals and Hitachi "Mitsui Chemicals and Hitachi begin demonstration trial toward practical use of MI technology to speed up materials development" (joint news release, in Japanese, 28 June 2021). How Hitachi's MI technology works, the result of testing on past data, and when the collaboration began. https://www.hitachi.co.jp/New/cnews/month/2021/06/0628.pdf
- Mitsui Chemicals "Mitsui Chemicals to open Digital Science Lab. to step up DX in R&D" (Japanese version, 15 November 2022). Purpose, computing environment, investment and schedule of the facility. https://jp.mitsuichemicals.com/jp/release/2022/2022_1115/index.htm
- Mitsui Chemicals "Mitsui Chemicals Opens Digital Science Lab. to Step up DX in R&D" (English version of the same announcement, 15 November 2022). Cited because its investment figure differs from the Japanese version. https://jp.mitsuichemicals.com/en/release/2022/2022_1115.htm
- Mitsui Chemicals "Mitsui Chemicals holds completion ceremony for Digital Science Lab., accelerating DX in R&D" (in Japanese, 17 December 2024). Date of the ceremony, computing environment and size of the building. https://jp.mitsuichemicals.com/content/dam/mitsuichemicals/sites/mci/documents/release/2024/241217.pdf
- Mitsui Chemicals "Briefing on Mitsui Chemicals' Outcomes for Digital Transformation" (22 March 2023). That the company's DX work covers DX in R&D, such as materials informatics. https://jp.mitsuichemicals.com/content/dam/mitsuichemicals/sites/mci/documents/release/2023/230322.pdf
- Mitsui Chemicals, Hagihara Industries, Maruki Sangyo and NEC "Mitsui Chemicals, Hagihara Industries, Maruki Sangyo, and NEC to utilize materials informatics for stabilizing recycled plastic quality and improving manufacturing efficiency" (joint news release, English version on the NEC site, 7 October 2025). https://www.nec.com/en/press/202510/global_20251007_05.html
- Mitsui Chemicals "Mitsui Chemicals moves company-wide transformation to the next stage, built on its DX talent base" (in Japanese, 30 March 2026). Reaching 165 data scientists. https://jp.mitsuichemicals.com/jp/release/2026/2026_0330/index.htm
- Mitsui Chemicals Mitsui Chemicals Report 2025 (integrated report, in Japanese, published 30 September 2025). The target for the number of data scientists and the mention of MI in technology management under the CTO. https://jp.mitsuichemicals.com/content/dam/mitsuichemicals/sites/mci/documents/ir/ar/ar25_all_jp.pdf
14. Claim-to-source audit
| Claim in the text | Status | Source |
|---|---|---|
| Legal name, registered office (2-2-1 Yaesu, Chuo-ku, Tokyo), president and CEO, and listing on the Tokyo Stock Exchange Prime Market | Sourced | Reference 1 https://jp.mitsuichemicals.com/content/dam/mitsuichemicals/sites/mci/documents/release/2026/ms_260622.pdf |
| Revenue for the years to March 2024, 2025 and 2026 of ¥1,749,743 / 1,809,164 / 1,668,754 million, and profit attributable to owners of the parent of ¥49,999 / 32,242 / 34,378 million | Sourced | Reference 1 https://jp.mitsuichemicals.com/content/dam/mitsuichemicals/sites/mci/documents/release/2026/ms_260622.pdf |
| Consolidated headcount of 16,967 (31 March 2026) and 5,198 for the company alone | Sourced | Reference 1 https://jp.mitsuichemicals.com/content/dam/mitsuichemicals/sites/mci/documents/release/2026/ms_260622.pdf |
| R&D expense of ¥46.4 billion for the company and its consolidated subsidiaries (year to March 2026) | Sourced | Reference 1 https://jp.mitsuichemicals.com/content/dam/mitsuichemicals/sites/mci/documents/release/2026/ms_260622.pdf |
| R&D expense as about 2.8% of revenue | Our calculation | Our own division in this article (¥46.4 billion divided by ¥1,668.8 billion revenue). |
| In October 2025 it set up development departments in the business divisions of its three growth fields and changed its R&D structure | Sourced | Reference 1 https://jp.mitsuichemicals.com/content/dam/mitsuichemicals/sites/mci/documents/release/2026/ms_260622.pdf |
| MI in the battery-materials and basic-and-green-materials strategies, DX in polymerization-catalyst development, and the spread of MI in corporate research | Sourced | Reference 1 https://jp.mitsuichemicals.com/content/dam/mitsuichemicals/sites/mci/documents/release/2026/ms_260622.pdf |
| Mitsui Chemicals and Hitachi have collaborated on materials development using MI since 2017 | Sourced | Reference 2 https://www.hitachi.co.jp/New/cnews/month/2021/06/0628.pdf |
| A demonstration trial of Hitachi's AI-based MI technology began in June 2021; testing on past organic-materials data cut the experimental runs needed to about one quarter of conventional MI | Sourced | Reference 2 https://www.hitachi.co.jp/New/cnews/month/2021/06/0628.pdf |
| How Hitachi's "nested" AI works (public formula data of about 500,000 entries; patent pending) | Sourced | Reference 2 https://www.hitachi.co.jp/New/cnews/month/2021/06/0628.pdf |
| Digital Science Lab.: blends computational science, data science and HPC, petaFLOPS-class computing, gathers experts from R&D and production-technology divisions | Sourced | References 3, 5 https://jp.mitsuichemicals.com/jp/release/2022/2022_1115/index.htm https://jp.mitsuichemicals.com/content/dam/mitsuichemicals/sites/mci/documents/release/2024/241217.pdf |
| Investment in the Digital Science Lab.: the Japanese announcement says about ¥5 billion and the English version says about ¥4 billion, so the two do not agree | Sourced | References 3, 4 https://jp.mitsuichemicals.com/jp/release/2022/2022_1115/index.htm https://jp.mitsuichemicals.com/en/release/2022/2022_1115.htm |
| Completion ceremony of the Digital Science Lab. on 16 December 2024 (the plan was completion and start of use in October 2024); building area 1,471.98 sq m and floor area 2,707.84 sq m | Sourced | References 3, 5 https://jp.mitsuichemicals.com/jp/release/2022/2022_1115/index.htm https://jp.mitsuichemicals.com/content/dam/mitsuichemicals/sites/mci/documents/release/2024/241217.pdf |
| March 2023: described its DX work as covering DX in R&D, including materials informatics | Sourced | Reference 6 https://jp.mitsuichemicals.com/content/dam/mitsuichemicals/sites/mci/documents/release/2023/230322.pdf |
| On 7 October 2025 Mitsui Chemicals, Hagihara Industries, Maruki Sangyo and NEC began a collaboration using MI for recycled plastics | Sourced | Reference 7 https://www.nec.com/en/press/202510/global_20251007_05.html |
| Viscosity data collected at Maruki Sangyo's plant is analyzed with NEC's MI technology; the target is to cut production time by 25% against the conventional process | Not yet confirmed | Reference 7 https://www.nec.com/en/press/202510/global_20251007_05.html |
| Reached 165 company-wide data scientists in March 2026 (assigned to R&D, production, business and corporate divisions) | Sourced | Reference 8 https://jp.mitsuichemicals.com/jp/release/2026/2026_0330/index.htm |
| The target of 165 or more data scientists by the end of fiscal 2025, and the mention of "MI use" in technology management under the CTO | Sourced | Reference 9 https://jp.mitsuichemicals.com/content/dam/mitsuichemicals/sites/mci/documents/ir/ar/ar25_all_jp.pdf |
| Risks around DX and AI use (delay, cyberattack, company-wide guidelines for generative AI) | Sourced | Reference 1 https://jp.mitsuichemicals.com/content/dam/mitsuichemicals/sites/mci/documents/release/2026/ms_260622.pdf |
| MI-dedicated headcount and spending, and numerical results from MI (shorter development time achieved, contribution to sales) | Not yet confirmed | Not stated in the disclosed material. Our own note: this article makes no estimate. |
| Joint research with universities or public bodies, and any investment, acquisition or joint venture relating to MI | Not yet confirmed | Not stated in the disclosed material. Our own note: this article does not assert that none exist. |
| Results of the trial with Hitachi after June 2021 | Not yet confirmed | Could not be confirmed. This article does not treat them as an achievement. |
| The classification of the company (industrial user) | Our calculation | Our own grouping for this article. |
| The reading of the company's aims (four points in chapter 8) | Inference | Our own interpretation of statements in the securities report and the releases. |
| Risk items that no document states directly | Inference | Our own interpretation of statements in the securities report and the releases. |
Last updated: 22 October 2026 / Troy Technical
All figures in this article come from Mitsui Chemicals' securities report, official releases and integrated report, and from joint releases with Hitachi and NEC. No market-research estimates or figures taken from news reports are used. Passages marked "Inference" are our reading of primary sources and are not statements by the company. "About one quarter of the experiments" is a result of testing on past data announced by Hitachi and Mitsui Chemicals, not an outcome of real materials development.