Latest Articles for Computational Chemistry

New method enhances molecular model accuracy through automated parameter refinement.

A new approach for improving coarse-grained molecular simulations using Gauss-Newton optimization.

New methods enhance the prediction of diverse crystal structures for advanced materials.

A fresh method for finding optimal atomic structures using complementary energy landscapes.

New methods improve efficiency and accuracy in quantum system simulations.

New methods improve understanding of spin dynamics in complex materials.

New method improves accuracy of molecular dynamics simulations under electric fields.

New method improves efficiency in simulating complex molecular interactions.

Grappa enhances molecular predictions with machine learning techniques for better efficiency.

A novel approach enhances precision in quantum calculations, reducing pulse duration significantly.

Research highlights potential drugs to inhibit Aurora-B and treat cancer.

A look into the photoionization process of ammonia and formic acid dimers.

New model enhances understanding of cobalt properties in catalysis.

A competition driving advancements in compound discovery for targeting LRRK2-WDR.

New deep learning methods enhance crystal structure predictions for material science.

A new method enhances molecular learning by considering atom relationships.

A new method improves parameter optimization for ReaxFF in studying molecular interactions.

UniCorn integrates diverse pre-training methods for effective molecular representation learning.

Blending quantum tech and NMR for molecular insights.

New methods using machine learning enhance transition state optimization in chemical reactions.

Discover the latest features of XtalOpt for advanced material predictions.

A new model that simplifies drug discovery by generating molecules efficiently.

This study explores the importance of rotational transitions in molecular ions and low-energy electron collisions.

A new method enhances the efficiency of solving the Kohn-Sham equation.

A new approach improves energy minimization in molecular studies.

A look into how quantum and classical systems influence each other.

COMPASS method addresses noise issues in molecular docking, enhancing drug discovery.

BICePs improves molecular behavior predictions by refining model parameters using experimental data.

A new method enhances the speed and accuracy of predicting charge density.

Research reveals how enzyme tunnels impact functionality and response to mutations.

Exploring the role of active learning in drug discovery through molecular docking.

New methods improve molecular simulations and interactions for better accuracy.

Hybrid approach improves simulations of complex molecular behaviors using expert knowledge and data.

A new method enhances molecular discovery using evolutionary algorithms and language models.

Using machine learning to enhance the interpretability of molecular simulations.

A new dataset accelerates molecular property predictions for drug design.

This study investigates how molecules unfold under force using advanced simulations.

Investigating the benefits of non-nested data in machine learning for quantum chemistry.

Studying quantum and classical interactions in chemical processes using NEO theory.

A new method improves solvent modeling accuracy in computational studies.