Which architectures are actually winning — from CGCNN and MEGNet to EquiformerV2 and hybrid transformer-graph models — on the Matbench leaderboard Predicting a material property — band gap, formation energy,…
Scarcity, inconsistent definitions, LCA gaps, scale mismatches, geographic bias, and the co-optimization problem — the data obstacles that actually block progress, and the 2026 fixes that are starting to work…
A practical guide to virtual screening, active learning, and autonomous experimentation. The math of traditional materials R&D is brutal. A typical discovery program screens hundreds of candidates over several years,…
Scope 3 still drives 70–95% of materials-company footprints, ESRS E1 climate transition plans tighten, and downstream commercial demands keep the data burden growing ESG — Environmental, Social, and Governance reporting…
How Self-Healing Concrete, Self-Sensing Structures, and AI-Optimized Low-Carbon Mixes Are Rewriting the Rules of Infrastructure Construction is the largest single source of industrial CO₂ emissions on the planet. Cement alone…
Couple predictive ML with life cycle assessment to design greener materials from day one. Sustainable material design has a sequencing problem. Traditionally, life cycle assessment (LCA) happens at the end…
How Machine Learning and Automated Labs Are Compressing Decade-Long Materials Programs into Weeks Across Solar, Batteries, and Wind The renewable energy transition is, at its core, a materials problem. Solar…
How neural networks are accelerating smart material development across three critical classes. Smart materials respond to their environment: piezoelectric ceramics convert mechanical stress to electricity, shape memory polymers remember and…
The end-to-end workflow, tools, and best practices behind the AI revolution in chemical, cosmetic, and material formulation Formulation design has historically been slow, expensive, and iterative. A new cosmetic product…