Collection of Japanese Examples: Moisture Absorption Prediction and Its Effects on Amorphous Amylose Starch

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

Molecular dynamics simulations that promote the optimization of quality and processing in food and beverages, packaging, and pharmaceuticals.

Schrödinger provides a powerful and user-friendly integrated software solution for the research and development of consumer goods. Schrödinger's platform is designed for a wide range of users, from beginners to experts in computational chemistry, offering a simple workflow to build, simulate, and analyze real systems using advanced physics-based modeling and machine learning technologies. ■ Accurately predicts key physical properties such as the glass transition temperature (Tg) of amorphous amylose polymers in both wet and dry states. ■ Effectively models water absorption and transport by investigating the impact of moisture content on Tg and the diffusion of water within starch polymers. ■ The OPLS3e force field provides high accuracy for amorphous starch models. ■ Detailed studies of the interactions between water and amylose, along with further research on the effects of components on complex starch formulations.

Inquire About This Product

basic information

Our Materials Science Suite can accommodate a wide range of materials research fields. ■ Property predictions using Density Functional Theory (DFT) calculations and first-principles calculations for 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/cross-linked structures/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