Computational chemistry

As analysis, in terms of detection limits and technological innovation, in chemical and biological fields has developed so computational techniques have advanced enabling greater understanding of the data.

Non-Linear Optical Properties of Matter: From Molecules to Condensed Phases attempts to draw together both theory and application in this field.

Written chemical formulas, such as C2H6O, can tell us the constituent atoms a molecule contains but they cannot differentiate between the possible geometrical arrangements (isomers) of these models.

Li-Co-Mn-Ni oxides have been of extreme interest as potential positive electrode materials for next generation Li-ion batteries.

This book covers the design and development of glucocorticoid receptor modulators (GRM) from cortisol to antibody-drug conjugate payloads over the last 70 years.

Die Chemie ist ein Wissensgebiet, das besonders geeignet ist, mit Hilfe des Computers neue Möglichkeiten der Problemlösung zu erschließen.

This book contains peer-reviewed contributions based on talks presented at the 25th International Workshop on Quantum Systems in Chemistry, Physics, and Biology held in Torun, Poland, in June 2022.

This contributed volume offers a comprehensive discussion on how to design and discover pharmaceuticals using computational modeling techniques.

Chemical vapor sensing arrays have grown in popularity over the past two decades, finding applications for tasks such as process control, environmental monitoring, and medical diagnosis.

Economic needs as well as ecological demands are major driving forces in improving chemical processes and plants.

Physical Sciences Data, Volume 16: Gaussian Basis Sets for Molecular Calculations provides information pertinent to the Gaussian basis sets, with emphasis on lithium, radon, and important ions.

Gas-Particle and Granular Flow Systems: Coupled Numerical Methods and Applications breaks down complexities, details numerical methods (including basic theory, modeling and techniques in programming), and provides researchers with an introduction and starting point to each of the disciplines involved.

Understanding Molecular Simulation explains molecular simulation from a chemical-physics and statistical-mechanics perspective.

This book explores fascinating topics at the edge of life, guiding the reader all the way from the relation of life processes to the second law of thermodynamics and the abundance of complex organic compounds in the universe through to the latest advances in synthetic biology and metabolic engineering.

This book is concerned with the aspects of real-time, parallel computing which are specific to the analysis of digitized images including both the symbolic and semantic data derived from such images.

Die Theoretische Chemie hat eine zweihundert Jahre alte Tradition in der Chemie.

Daniel Püschner stellt sowohl grundlegende Methoden der Computerchemie als auch heute genutzte moderne Methoden wie Coupled Cluster und die Dichtefunktionaltheorie vor.

Daniel Püschner stellt sowohl grundlegende Methoden der Computerchemie als auch heute genutzte moderne Methoden wie Coupled Cluster und die Dichtefunktionaltheorie vor.

Dieses umfangreiche Chemie Buch bietet eine strukturierte Einführung in die wichtigsten Bereiche der Chemie und eignet sich sowohl für Studienanfänger als auch fortgeschrittene Lernende.

Science can be tough but that doesn't mean it can't be made fun for kids to understand.

Chemistry can be both fun and challenging if you are using the right learning materials.

Is chemistry too difficult to understand?

Is chemistry too difficult to understand?

Science can be tough but that doesn't mean it can't be made fun for kids to understand.

In this reference book, we're going to discuss the alkali metals, the alkaline Earth metals and the transition metals.

In this reference book, we're going to discuss the alkali metals, the alkaline Earth metals and the transition metals.

This contributed volume explores the application of machine learning in predictive modeling within the fields of materials science, nanotechnology, and cheminformatics.

This contributed volume explores the application of machine learning in predictive modeling within the fields of materials science, nanotechnology, and cheminformatics.