AI & Environment Resource HubAntikainen et al. · 2025-04-26
This paper presents SOLID-MAP, an AI-driven material acceleration platform that integrates active learning, thermodynamic modeling, and high-throughput experimentation to rapidly discover and evaluate high-entropy alloys for energy-relevant applications.
Intended audience has not been documented.
Source-specific limitations have not been reviewed. Inclusion and topic labels do not establish effectiveness or reliability.
Antikainen et al.. 2025-04-26. SOLID-MAP: Development of a Materials AccelerationPlatform for High-Entropy Alloys. https://link.springer.com/epdf/10.1007/s11665-025-11504-y?sharing_token=T_s1yLNEJ3x3nVvq4Su9-ve4RwlQNchNByi7wbcMAY4Fqs3P1dpB99tTThGWORE13-2ZSPaYIZnLofwcRaFFxt1VKzDup1dfnAp5sZJM8VNUxsTvhuWrxm6h-bUw0vyrH9mWNpaWl8MmhzblPBFumYGamWuWl3_yiSthRJEqmIA%3D (AI & Environment Resource Hub; record paper-320; collection snapshot 2026-10-04).
Included in the original Scientific Paper collection. Labels below are metadata based suggestions. They are not verified findings, claims of effectiveness, or endorsements.
Source sheet: Scientific Paper · Row 324 · Original ID: paper-320.
A paper listing is not a quality assessment. Peer review and findings require source-level confirmation. Source link reachable · checked 2026-09-28. Metadata not yet corroborated. The import date is not the original date added.
Suggest a correction for this record →Publication, placement, and evidence are tracked separately. A legacy coverage flag alone does not prove an appearance.
Exact source link in saved original issue resource section: Scientific Papers 📄
Ordered by the number of shared provisional topic labels. This indicates a browsing connection, not agreement between sources.
Shared topics: Materials and critical minerals; E-waste and circularity
Matbench Discovery is a benchmarking framework for machine learning models used in materials discovery, highlighting the importance of task-specific metrics and demonstrating that universal interatomic potentials can effectively pre-screen stable inorganic materials in high-throughput workflows.
Shared topics: Materials and critical minerals; E-waste and circularity
Adaptis is a diverse team of carbon reduction experts supercharged by a proprietary software platform for evaluating and optimizing building performance, whole-life carbon, and capex. We generate and recommend decarbonization scenarios, build project sequences, and keep all the data live in real-time so financial decision-makers can find answers in 15 minutes, not 15 days.
Shared topics: Materials and critical minerals; E-waste and circularity
Exponential acceleration meets materials innovation. Our AI-integrated platform is transforming how advanced materials are discovered, designed, and deployed - bringing next-generation solutions from concept to reality.
Shared topics: Materials and critical minerals; E-waste and circularity
Solving the Earth's largest challenge at it's smallest scale. We use genetically engineered bacteria and enzymatic cascades to produce polymers for carbon capture, fuels, industrials, and biotech.