[Data] Materials Science Reaction Workflow

Added to bookmarks
Bookmarks list

Bookmark has been removed
Bookmarks list

You can't add any more bookmarks

By registering as a member, you can increase the number of bookmarks you can save and organize them with labels.
Free membership registration

last updated：Oct 01, 2025

シュレーディンガー

It can cover often overlooked conformers, streamline workflows, and enhance reproducibility and predictability.

In the Schrödinger materials science reaction workflow, automatic exploration of the conformational space allows for the coverage of often-overlooked conformers. Furthermore, the automation of quantum chemical calculations eliminates the challenging processes that require meticulous maintenance of hundreds of files and properties, as well as specialized training. This simplifies the workflow and enhances reproducibility and predictability. [Case Study] ■ Diels-Alder Reaction *For more details, please refer to the PDF document or feel free to contact us.

Inquire About This Product

basic information

Our computational chemistry platform is capable of addressing a wide range of materials research fields. ■ Property predictions using Density Functional Theory (DFT) calculations and first-principles calculations in periodic systems HOMO/LUMO/pKa/solvent effects/IR/Raman/UV-vis/VCD/NMR/oxidation-reduction potential/triplet excited state energy/TADF S1-Tx gap/fluorescence/phosphorescence/vibrational calculations/structure optimization/transition state calculations/reaction pathway analysis/adsorption energy/bond dissociation energy/electron and hole mobility/reorientation (rearrangement, reconfiguration) energy ■ Property predictions using Molecular Mechanics (MM), Molecular Dynamics (MD), and coarse-grained MD Density/conformation analysis/crosslinking structure/Young's modulus/viscosity/surface tension/glass transition temperature (Tg)/molecular diffusion/thermal expansion/crystal morphology/swelling/stress-strain curves/solubility parameters Methods available for use in machine learning Generation of various descriptors and fingerprints/Partial Least Squares (PLS) regression/multiple linear regression (MLR)/Principal Component Regression (PCR)/Kernel PLS/Bayesian classification/Recursive Partitioning (RP) analysis/Self-Organizing Maps/Tg, dielectric constant, boiling point, vapor pressure prediction models/genetic algorithms/active learning