Introducing Quantum Calculations into the Physical Chemistry Laboratory

Devore, T. C.

doi:10.1021/bk-2019-1312.ch009

Cited by 6 publications

(6 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although these experiments illustrate fundamental concepts, they can be enhanced by supplementation with modern computational methods. This has the two-fold advantage of not requiring new instrumentation and of training students in programming and computational chemistry, which are increasingly recognized as key skills for chemists to possess. − One classic physical chemistry experiment explores the UV–visible absorption spectroscopy of cyanine dyes. Cyanine dyes contain a conjugated polymethine chain, consisting of alternating single and double carbon–carbon bounds, bounded by two nitrogen atoms, one of which is an iminium cation .…”

Section: Introductionmentioning

confidence: 99%

Rediscovering the Particle-in-a-Box: Machine Learning Regression Analysis for Hypothesis Generation in Physical Chemistry Lab

Thrall,

Martinez Lopez,

Egg

et al. 2023

J. Chem. Educ.

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Rediscovering the Particle-in-a-Box: Machine Learning Regression Analysis for Hypothesis Generation in Physical Chemistry Lab

Thrall,

Martinez Lopez,

Egg

et al. 2023

J. Chem. Educ.

View full text Add to dashboard Cite

“…Although some of the aforementioned aspects can in principle be studied even with simpler methodologies such as classical force fields, quantum chemical calculations with state‐of‐the‐art software packages allow students to get firsthand understanding on more advanced topics such as molecular orbitals, chemical bonding, energetics, 10 thermodynamics, 11,12 reaction mechanisms, 13 and various spectroscopies 14–18 …”

Section: Introductionmentioning

confidence: 99%

“…[7][8][9] Although some of the aforementioned aspects can in principle be studied even with simpler methodologies such as classical force fields, quantum chemical calculations with state-of-the-art software packages allow students to get firsthand understanding on more advanced topics such as molecular orbitals, chemical bonding, energetics, 10 thermodynamics, 11,12 reaction mechanisms, 13 and various spectroscopies. [14][15][16][17][18] The ability to interpret and understand chemical phenomena with the help of quantum chemical calculations is a valuable skill in every chemist's professional life: nowadays, a significant portion of even the experimental studies reported in the chemical literature is tightly integrated with quantum chemical investigations. Moreover, as quantum chemistry is the critical bridging component between experimental work and machine learning methods, the ability to run quantum chemical calculations can be expected to become even more increasingly relevant and necessary to worklife in the near future.…”

Section: Introductionmentioning

confidence: 99%

Free and open source software for computational chemistry education

Lehtola

Karttunen

2022

WIREs Comput Mol Sci

View full text Add to dashboard Cite

After decades of waiting, computational chemistry for the masses is finally here. Our brief review on free and open source software (FOSS) packages points out the existence of software offering a wide range of functionality, all the way from approximate semiempirical calculations with tight-binding density functional theory to sophisticated ab initio wave function methods such as coupled-cluster theory, covering both molecular and solid-state systems. Combined with the remarkable increase in the computing power of personal devices, which now rivals that of the fastest supercomputers in the world in the 1990s, we demonstrate that a decentralized model for teaching computational chemistry is now possible thanks to FOSS packages, enabling students to perform reasonable modeling on their own computing devices in the bring your own device (BYOD) scheme. FOSS software can be made trivially simple to install and keep up to date, eliminating the need for departmental support, and also enables comprehensive teaching strategies, as various algorithms' actual implementations can be used in teaching. We exemplify what kinds of calculations are feasible with four FOSS electronic structure programs, assuming only extremely modest computational resources, to illustrate how FOSS packages enable decentralized approaches to computational chemistry education within the BYOD scheme. FOSS also has further benefits driving its adoption: the open access to the source code of FOSS packages democratizes the science of computational chemistry, and FOSS packages can be used without limitation also beyond education, in academic and industrial applications, for example.

show abstract

“…[7][8][9] Although some of the aforementioned aspects can in principle be studied even with simpler methodolo-gies such as classical force fields, quantum chemical calculations with state-of-the-art software packages allow students to get firsthand understanding on more advanced topics such as molecular orbitals, chemical bonding, energetics, 10 thermodynamics, 11,12 reaction mechanisms, 13 and various spectroscopies. [14][15][16][17][18] The ability to interpret and understand chemical phenonema with the help of quantum chemical calculations is a valuable skill in every chemist's professional life: nowadays, a significant portion of even the experimental studies reported in the chemical literature are tightly integrated with quantum chemical investigations. Moreover, as quantum chemistry is the critical bridging component between experimental work and machine learning methods, the ability to run quantum chemical calculations can be expected to become even more increasingly relevant and necessary to work-life in the near future.…”

Section: Introductionmentioning

confidence: 99%

Free and Open Source Software for Computational Chemistry Education

Lehtola

Karttunen

2021

Preprint

View full text Add to dashboard Cite

Long in the making, computational chemistry for the masses [J. Chem. Educ. 1996, 73, 104] is finally here. Our brief review on various free and open source software (FOSS) quantum chemistry packages points out the existence of software offering a wide range of functionality, all the way from approximate semiempirical calculations with tight-binding density functional theory to sophisticated ab initio wave function methods such as coupled-cluster theory, both for molecular and for solid-state systems. Combined with the remarkable increase in the computing power of personal devices, which now rivals that of the fastest supercomputers in the world of the 1990s, we demonstrate that a decentralized model for teaching computational chemistry is now possible thanks to FOSS computational chemistry packages, enabling students to perform reasonable modeling on their own computing devices, in the bring your own device (BYOD) scheme. FOSS software can be made trivially simple to install and keep up to date, eliminating the need for departmental support, and also enables comprehensive teaching strategies, as various algorithms' actual implementations can be used in teaching. We exemplify what kinds of calculations are feasible with four FOSS electronic structure programs, assuming only extremely modest computational resources, to illustrate how FOSS packages enable decentralized approaches to computational chemistry education within the BYOD scheme. FOSS also has further benefits: the open access to the source code of FOSS packages democratizes the science of computational chemistry, and FOSS packages can be used without limitation also beyond education, in academic and industrial applications, for example. For these reasons, we believe FOSS will become ever more pervasive in computational chemistry.

show abstract

Introducing Quantum Calculations into the Physical Chemistry Laboratory

Cited by 6 publications

References 48 publications

Rediscovering the Particle-in-a-Box: Machine Learning Regression Analysis for Hypothesis Generation in Physical Chemistry Lab

Rediscovering the Particle-in-a-Box: Machine Learning Regression Analysis for Hypothesis Generation in Physical Chemistry Lab

Free and open source software for computational chemistry education

Free and Open Source Software for Computational Chemistry Education

Contact Info

Product

Resources

About