Schrödinger 2026-1 software release enhances atomic-scale materials analysis, design capabilities

Source | Schrödinger

Schrödinger (New York, N.Y., U.S.) has announced additional scientific capabilities, usability improvements and performance enhancements of materials property and discovery capabilities in Schrödinger’s 2026-1 software release. It is available to users to download.

Automated analysis workflow for thermochemistry of point defects in crystalline materials determines stable charge states, localization and their effects on materials’ optical, mechanical and electrical properties via the MS Quantum Espresso Interface.

Schrödinger’s Desmond for Materials Science high-performance molecular dynamics (MD) engine workflow solution uses an umbrella sampling algorithm for surfactant and lipid bilayers. Users are able to automate the GPU-accelerated analysis of small molecules being pulled across the membrane, enabling accurate assessment of its free energy profiles and permeability.

The genetic optimization of materials has been accelerated by machine learning (ML) property prediction models. Pre-built ML models can now be used to speed up the optimization of molecular materials for properties such as redox potentials and aqueous solubility. More about this is shared in GA Optoelectronics.

Also notable, Schrödinger has refined its ML solution for materials formulations. Using MS Formulation ML, users can take advantage of the latest improvements to ML algorithms for materials formulations, including multi-CPU parallelization, support for genetic algorithms, training with dataset missing chemical information and more.

A complete list of the 2026-1 release is available here.

Schrödinger is a company that “collaborates with customers to use its predictive computational tools to develop future materials, including composites,” further in this CW article. The company continuously strives to develop scientific solutions that push the boundaries of atomic-scale materials analysis and design.