Machine Learning Meets Quantum Physics
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Machine Learning Meets Quantum Physics
Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
Molecular Representations for Machine Learning
Author: Grier M. Jones
language: en
Publisher: American Chemical Society
Release Date: 2023-05-19
This primer helps the reader understand the basic categories of molecular representations and provides computational tools to generate molecular descriptors in each of these categories. After reading this primer, you will be able to use various methods to generate machine and/or human interpretable representations of molecular systems for inputs to machine learning models or for general chemical data science applications.
AI Frameworks and Tools for Software Development
The rapid advancements in artificial intelligence (AI) are transforming how organizations approach software development, creating both opportunities and challenges in the workplace. As AI tools become more mainstream, understanding their role, as well as the responsibilities of users, is crucial for ensuring their effective integration into software development processes. A clear framework for introducing AI in Information Systems Management can significantly enhance the efficiency and effectiveness of development teams and their external stakeholders. AI Frameworks and Tools for Software Development presents the best practices, research findings, and guidelines for using AI frameworks and tools in software development. It provides a holistic understanding of these key processes, functions, and workflows that are essential for effective Software Development Lifecycle (SDLC). Covering topics such as industrial automation, knowledge management, and code reusability, this book is an excellent resource for software developers, computer scientists, professionals, researchers, scholars, academicians, and more.