30/04/2026
๐๐ ๐ฆ๐ฎ๐ฌ๐ฎ๐ฒ ๐ถ๐ ๐ต๐ฒ๐ฟ๐ฒโbringing major advances in machine learning potentials, simulation workflows, and usability. With broader chemical coverage, improved performance, and streamlined tools, itโs easier than ever to run fast, automated, and reproducible research.
๐ช๐ต๐ฎ๐โ๐ ๐ป๐ฒ๐ ๐ฎ๐ ๐ฎ ๐ด๐น๐ฎ๐ป๐ฐ๐ฒ:
โข ๐ก๐ฒ๐
๐-๐ด๐ฒ๐ป๐ฒ๐ฟ๐ฎ๐๐ถ๐ผ๐ป ๐ ๐ ๐ฝ๐ผ๐๐ฒ๐ป๐๐ถ๐ฎ๐น๐: New model families (eSEN, MACE, UMA) deliver near-chemical accuracy, GPU-optimized performance, and expanded coverageโfrom biomolecules and catalysts to MOFs and inorganic materials
โข ๐๐๐ - ๐๐ป๐ต๐ฎ๐ป๐ฐ๐ฒ๐ฑ ๐ฒ๐น๐ฒ๐ฐ๐๐ฟ๐ผ๐ป๐ถ๐ฐ ๐๐๐ฟ๐๐ฐ๐๐๐ฟ๐ฒ: New capabilities for spectroscopy, excited states, solvation, and embedding methods
โข ๐ก๐๐ช ๐๐๐บ๐ฏ๐น๐ฒ๐ฏ๐ฒ๐ฒ ๐๐จ๐ (OLED device modeling)
โข ๐ข๐๐๐ & ๐บ๐๐น๐๐ถ๐๐ฐ๐ฎ๐น๐ฒ ๐๐ผ๐ฟ๐ธ๐ณ๐น๐ผ๐๐: Improved integration with Bumblebee, enabling deeper device-level insights and analysis
โข ๐ฆ๐บ๐ฎ๐ฟ๐๐ฒ๐ฟ ๐๐จ๐: New structure builders, streamlined menus, and improved visualization tools
โข ๐๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ ๐๐ข๐ฆ๐ ๐ข-๐ฅ๐ฆ: Better handling of complex, multi-component mixtures and phase behavior
โข ๐ฆ๐๐ฟ๐ผ๐ป๐ด๐ฒ๐ฟ ๐ ๐ & ๐ฑ๐ฟ๐ถ๐๐ฒ๐ฟ: More robust simulations, new constraints, faster PES scans, and enhanced sampling methods
โข ๐ฉ๐๐ฆ๐ฃ ๐ถ๐ป๐๐ฒ๐ด๐ฟ๐ฎ๐๐ถ๐ผ๐ป: Seamless workflow integration for materials modeling and active learning
โข ๐๐
๐ฝ๐ฎ๐ป๐ฑ๐ฒ๐ฑ ๐ฃ๐๐๐ต๐ผ๐ป ๐ฒ๐ฐ๐ผ๐๐๐๐๐ฒ๐บ: More reusable examples, automation support, and easier scripting for reproducible research
Whether you're exploring new materials, modeling complex reactions, or building automated workflows, ๐๐ ๐ฆ๐ฎ๐ฌ๐ฎ๐ฒ ๐ด๐ถ๐๐ฒ๐ ๐๐ผ๐ ๐๐ต๐ฒ ๐๐ผ๐ผ๐น๐ ๐๐ผ ๐ด๐ผ ๐ณ๐๐ฟ๐๐ต๐ฒ๐ฟโ๐ณ๐ฎ๐๐๐ฒ๐ฟ.
๐ ๐๐
๐ฝ๐น๐ผ๐ฟ๐ฒ ๐๐ต๐ฒ ๐ณ๐๐น๐น ๐๐ ๐ฆ๐ฎ๐ฌ๐ฎ๐ฒ ๐ฅ๐ฒ๐น๐ฒ๐ฎ๐๐ฒ ๐ก๐ผ๐๐ฒ๐: https://zurl.co/5VOGu
