Materials Informatics Patents: Who Leads, Where the Gaps Are 2026
- Filing nearly doubled in three years: 2021 to 2024 filings rose from 7 to 12, a 71% increase, before the count for 2025 onward understates true activity due to publication lag.
- Half the field sits with five assignees: the top 5 combined account for 31 of 62 records in scope (50.0%), while the leader holds 7 records against a long tail of single- and few-filing entrants.
- Software classes outweigh materials-science classes: G06F (electric digital data processing) appears in 40.3% of records and G16C (computational chemistry) in 37.1%, ahead of G01N (material analysis & testing) at 16.1%.
Filing growth compares 2021 (7 records) with 2024 (12) — a three-year span. 2024 is the most recent year we treat as complete: publication lags filing by roughly 18 months, so 2025 onwards are still filling in and any growth rate that ends there would understate the field. Top-5 share is the combined record count of the five largest assignees divided by all 62 records in scope (CR5), not by the ranked leaders only.
What this landscape covers
This landscape maps 62 published patent records at the intersection of materials informatics and materials genomics — filings that combine machine learning, computational chemistry or data-science methods with material property, synthesis condition or composition claims. Coverage runs from 2015 through the 2026-08-31 cut-off, with 2024 the last year that can be read as a complete filing year given an 18-month publication lag.
The record set spans corporate materials suppliers, industrial conglomerates, a software platform vendor, and university and public-research assignees, filed predominantly through the US, EPO and PCT routes. The technology composition leans toward computing and data-processing classes rather than pure materials-testing classes, which signals where the informatics layer — not the underlying material — is where claims are concentrated.
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Filing trend and technology composition
Two views of the same 62-record dataset: how filing activity has moved year over year, and which IPC subclasses carry the claims.
Filing trend, 2017–2026
Filings moved from 2 in 2017 to a peak of 12 in 2023, with the 2021-to-2024 span showing a 71% rise (7 to 12). Years from 2025 onward are still filling in as publications catch up to filing dates, so the apparent tail-off should not be read as a slowdown.
Technology composition by IPC subclass
G06F (electric digital data processing, 40.3% of records) and G16C (computational chemistry, 37.1%) dominate, with G06N (AI-based computing, 29.0%) close behind. Materials-specific classes — G01N material analysis (16.1%), H01M batteries (6.5%), C04B ceramics (4.8%) — appear far less often, and because records can carry multiple classes these shares add up to more than 100%.
Shares are the percentage of the 62 records in scope. A patent can carry several IPC classes, so the shares add up to more than 100%.
Go deeper on Materials Informatics & Materials Genomics Patent Landscape with Eureka
This page is one run against one query. Ask Eureka your own question about materials informatics & materials genomics patent landscape and every answer comes back with the patent numbers behind it.
Try EurekaMost-cited records and a representative filing
Method and apparatus for evaluating material property (US20220351504A1)
Filed by Proterial, Ltd. and published 2022-11-03, this filing covers an image-processing pipeline that scans images of a material, lowers their gradation to create a low-gradation image, generates a virtual image from it, extracts features from the low-gradation image, and predicts a first material property from those features.The claim chain runs from raw image capture through gradation reduction to property prediction — each step is a potential point of differentiation for a workaround.


| # | Publication no. | Patent title | Citations |
|---|---|---|---|
| 1 | US20200257933A1 | Machine Learning to Accelerate Alloy Design | 187 |
| 2 | US20210118530A1 | Multi-scale method for simulating mechanical behaviors of multiphase composite materials | 153 |
| 3 | US20220067249A1 | Machine Learning to Accelerate Design of Energetic Materials | 80 |
| 4 | US20180113967A1 | System and Method for Predicting Fatigue Strength of Alloys | 39 |
| 5 | WO2019104181A1 | Solid state electrolytes and methods of production thereof | 10 |
| 6 | US11915105B2 | Machine learning to accelerate alloy design | 8 |
| 7 | US20200350618A1 | Solid state electrolytes and methods of production thereof | 8 |
| 8 | US20220407001A1 | Novel Nanocomposite Phase-Change Memory Materials and Design and Selection of the Same | 7 |
| 9 | US20210326755A1 | Learning model creation device, material property prediction device, and learning model creation method and p… | 7 |
| 10 | WO2020176164A1 | Predictive design space metrics for materials development | 7 |
Citation counts reward older filings that have had more time to accumulate citations inside a searched corpus — read them as a signal of influence, not of current importance.
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Three read-throughs from the concentration, composition and citation data above.
Half the field sits with a handful of assignees
The top 5 assignees combined hold 31 of 62 records in scope, with the leader alone at 7. That leaves the other half of the dataset spread across a long tail of entrants filing once or twice — a pattern typical of a field still forming its dominant designs rather than one already consolidated.
Claims cluster on the software layer, not the material
G06F and G16C together outpace G01N (material analysis & testing) by a wide margin, meaning most claims are staked on the data-processing and computational-chemistry pipeline around a material rather than on the material's physical testing or characterisation itself.
Filing activity nearly doubled across three years
Filings rose from 7 in 2021 to 12 in 2024, and 2023 is the peak year recorded so far at 12. Because publication typically lags filing by around 18 months, the apparent drop after 2024 reflects incomplete data, not falling interest.
Early alloy-design ML filings anchor the citation graph
The most-cited record concerns machine learning for alloy design, followed by a multi-scale composite-materials simulation filing. Both predate most of the recent filing surge, which is expected — older records simply have had longer to accumulate citations inside the searched corpus.
Eureka can read the same corpus for gaps instead of for coverage: under-claimed branches adjacent to materials informatics & materials genomics patent landscape, with the prior art for and against each one.
Who is filing, and where the gaps sit
The ranking below covers the 30 companies the data endpoint returns for this search — not a top-50 or top-100 list — spanning materials suppliers, an industrial conglomerate roster, a software vendor and university assignees.
One assignee sets the pace, without running away with the field
The leading assignee holds 7 of 62 records — enough to lead the ranking but well short of a dominant share, leaving room for challengers to build a comparable position with a focused filing programme.
Filing is US-centred with meaningful EPO and PCT coverage
The United States receives the largest share of filings, with Europe (EPO) and WIPO (PCT) routes both used at meaningful volume. India, Australia and Canada each carry a small but non-zero share, suggesting selective rather than blanket international filing.
Corporate–university collaboration is the strongest observed pairing
The strongest co-assignee pair in the dataset links a corporate filer with a university, at 3 joint records — a small but notable signal that some of this field's strongest filings pair industrial application know-how with academic research capacity.
| Assignee | Recent year | YoY |
|---|---|---|
| NGK Insulators, Ltd. | 1 | — |
| Proterial, Ltd. | 0 | — |
| CITRINE INFORMATICS INC | 0 | — |
| Panasonic Intellectual Property Management Co., Ltd. | 0 | -100% |
| Microsoft Technology Licensing, LLC | 0 | — |
| President & Fellows of Harvard College | 0 | — |
| NGK Insulators, Ltd. | 0 | — |
| JFE Steel Corporation | 0 | -100% |
Where to take this landscape
The dataset points to a field where claim density sits on the informatics layer rather than the material itself, leaving several practical next steps.
Map your own claim set against the IPC composition
Compare a target filing or portfolio against the G06F/G16C/G06N split shown here to see whether it sits inside the dense software cluster or in the thinner materials-testing space.
Run a claim comparison in EurekaWatch the leaders' recent-year filing momentum
Several top assignees show flat or declining latest-year counts, which given publication lag may reverse — track this before assuming any single leader is pulling ahead.
Set up assignee monitoring in EurekaProbe the under-claimed branches before they fill in
Image-to-property prediction and synthesis-condition optimisation show thinner filing density than the core computational-chemistry cluster, which is where a first-mover claim carries more weight.
Explore white space in EurekaCommon questions about this landscape
The dataset's ranking covers 30 assignees, with the single leader holding 7 of the 62 records in scope and the top 5 combined accounting for 50.0% of all records. That leaves a long tail of entrants with one or two filings each, so the field is led but not locked up by any one player. A newcomer assessing competitive risk should look at the leader's specific claim scope rather than assume the whole space is blocked.
Filings rose from 7 in 2021 to 12 in 2024, a 71% increase, with 2023 the peak year recorded so far at 12. Counts appear to taper after 2024, but that reflects the roughly 18-month lag between filing and publication rather than an actual drop in activity. Anyone tracking this space should treat the last one to two years of any chart as a floor, not a ceiling.
Most records carry claims in G06F (electric digital data processing, 40.3% of records) and G16C (computational chemistry, 37.1%), with G06N (AI-based computing) close behind at 29.0%. Classes tied directly to physical material testing, such as G01N, appear in only 16.1% of records. That split means the bulk of claim activity sits on the data-processing and modelling pipeline wrapped around a material, not on the material's characterisation itself.
Branches such as image-to-property prediction pipelines, synthesis-condition optimisation loops and solid-state electrolyte discovery workflows show comparatively thin filing density against the dense G06F/G16C core. These are documented, real sub-areas within the 62-record dataset rather than speculative categories. A first claim in one of these branches has more room to establish a distinct position than a new filing in the crowded computational-chemistry core.
US20220351504A1, filed by Proterial, Ltd. and published 2022-11-03, claims a specific pipeline: scanning an image of a material, lowering its gradation, generating a virtual image, extracting features, and predicting a material property from those features. It blocks that particular sequence of steps rather than image-based property prediction in general. Alternative feature-extraction methods or prediction architectures that do not follow this exact gradation-reduction-then-virtual-image chain sit outside its literal claim scope, though a full freedom-to-operate check should be run against the claims as issued.
The United States receives the largest share of filings at 30, followed by the EPO route at 14 and the WIPO/PCT route at 7. India, Australia and Canada each see a small number of filings. The pattern suggests applicants are prioritising the US and Europe directly while using PCT more selectively to preserve international options rather than filing everywhere at once.
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Disclaimer. This page is generated from Patsnap Eureka data drawn from a limited snapshot of global patent and scientific-literature records, and is provided for general information and reference only.
Patent data carries inherent limitations: recent filings (typically the most recent 18–24 months) are under-counted due to standard publication lag; counts may be reported at either a patent-family or a patent-record basis and are not always directly comparable; classification, applicant-name, and citation data may contain errors, duplicates, or omissions; and the underlying search query defines and constrains the scope shown. As a result, the analysis may be incomplete or inaccurate and may not reflect the full technology landscape.
Nothing on this page constitutes an exhaustive prior-art, novelty, freedom-to-operate, or validity search, nor does it constitute legal, financial, investment, or professional advice, and it should not be relied upon as such. Any patent, commercial, or strategic decision should be verified independently and reviewed with qualified patent, legal, and domain professionals. Patsnap makes no warranties, express or implied, as to the accuracy, completeness, or fitness for any particular purpose of the information presented.
Machine translation. Assignee and organisation names originally recorded in Chinese, Japanese or Korean have been rendered into English by an AI translation step so that the tables stay readable. These renderings are best-effort and may not match a company’s registered English name; the original name is what the underlying patent record carries, and it is what any Eureka query launched from this page uses.