Abstract

• Quantum computing and its use for computer-aided product design is discussed and perspectives for several types of problems are provided. • Hybrid QC methods are likely to play an important role through a combination of ‘expensive’ QC resources and ‘cheap’ classical computing power. • Computational modeling of quantum-mechanical systems is likely to be much more precise using quantum computing. Chemical process design has for long been benefiting from computer-aided methods and tools to develop new processes and services that can meet the needs of society. Chemical and biomolecular product design could also benefit from the use of computer-aided solution strategies and computational power to efficiently solve the problems at various scales as the complexity and size of problems grow. In this context, new modes of computation such as quantum computing are receiving increasing attention. While quantum computing has been in development for quite some time, the development of the technology to the point of making commercial use of such resources is quite recent, and still quite limited in scope. However, projections point to a rapid development of quantum computing resources becoming available to academia and industry, which opens potential application areas in chemical and biomolecular product design. With the advent of hybrid algorithms that are able to take advantage of both classical computing and quantum computing resources, as quantum computing grows, more and more problems relevant for chemical product design will become solvable. In this paper, some perspectives are given by identifying a set of needs and challenges for a selected set of opportunities, such as quantum chemistry-based property prediction, protein folding, complex multi-step chemical reactions, and molecular reaction dynamics.

Highlights

  • Chemical product design involves generation of numerous candidates and their evaluation and screening to fulfill consumer, service and/or process needs

  • That the challenges and opportunities are in terms of the needs for multi-scale modeling with focus on property models that are suitable for computer-aided applications, solution strategies that can solve a large range of chemical product design problems and, a chemical product design framework with the overall objective to reduce the time and cost to market a new or improved product

  • One open question we find worthwhile to ask is whether quantum computing can even enhance deep neural networks by combining quantum hardware with the use of graphical processing units (GPUs), field programmable gate arrays (FPGAs) or application-specific integrated circuits (ASICs) such as Alphabet’s proprietary TPU architecture

Read more

Summary

Introduction

Chemical product design involves generation of numerous candidates and their evaluation and screening to fulfill consumer, service and/or process needs. These molecular scale properties can be combined with other system parameters such as concentrations in solution or solid catalyst area to obtain the desired macroscopic properties including catalyst activity and selectivity as function of temperature and pressure, that is, parameters that are controllable during the process In this respect, tailor-made computer-aided design of solvents to enhance the reaction rate can play an important role. Quantum computers and molecular reaction dynamics The optimization of chemical processes to yield desired products often requires knowing the reaction dynamics and finding energy efficient reaction routes, for example, by using proper catalysts. Reaction dynamics investigations of thermal reactions, such as involved in chemical weapons and/or explosives, can be described by quantum-based reaction molecular dynamics simulations Based on these methods, it is possible to provide data on impact sensitivity and safety handling of the explosives, both important aspects of product design. Quantum algorithms would be able to provide crucial insight into the relevant mechanisms of delay times, energy impact, pressure changes and how to prevent unsafe explosions [67,68]

Conclusions
Gani R
Klamt A: COSMO-RS
22. Feynman RP
24. Shor PW
28. Moret-Bonillo V
31. Woodley JM
37. Dill KA
47. Greeley J
54. Anfinsen CB
67. Nyman G
Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.