Introduction to Stochastic Simulations for Chemical and Physical Processes: Principles and Applications

Weiss, Charles J.

doi:10.1021/acs.jchemed.7b00395

Cited by 21 publications

(19 citation statements)

References 37 publications

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“…A more realistic pedagogical approach to stochastic protein ligand binding may be made through computer simulations, but the strength of the saturation game lies in its simplicity rather than its accuracy. The playing cards through which it conveys its message are familiar and tangible manifestations of chance.…”

Section: Discussionmentioning

confidence: 99%

The Saturation Game: Teaching Protein–Ligand Binding with a Playing Card Analogy

McEvoy

Kay

2020

J. Chem. Educ.

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The dissociation constant of a protein–ligand complex is commonly measured by a saturation binding experiment, in which a protein is progressively saturated by repeated additions of a ligand. Students often find it hard to understand how the hyperbolic binding curve, described by the Hill–Langmuir equation, arises from an unchanging dissociation constant. In this activity, students played a prelab card game which taught them the stochastic origin of the binding curve’s shape. The saturation game almost doubled the number of students who chose the correct explanation of the curve’s shape and decreased the popularity of the most common misconception by a factor of 2.5.

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Section: Discussionmentioning

confidence: 99%

The Saturation Game: Teaching Protein–Ligand Binding with a Playing Card Analogy

McEvoy

Kay

2020

J. Chem. Educ.

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“…In the classroom, computational methods are usually applied for chemical system simulations, such as DFT calculations or molecular dynamics simulations, to predict or interpret experimental results, or for data analysis from experimental results. Examples of these trends for teaching have been published in this journal [8][9][10][11][12][13]. Also, two remarkable courses for teaching computational skills to chemistry students by Weiss [14,15] and Menke [16] can be found.…”

Section: Introductionmentioning

confidence: 98%

Introduction to Machine Learning for Chemists: An Undergraduate Course Using Python Notebooks for Visualization, Data Processing, Data Analysis, and Data Modeling

Lafuente¹,

Cohen²,

Fiorini³

et al. 2021

Preprint

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Machine Learning, a subdomain of Artificial intelligence, is a pervasive technology that would mold how chemists interact with data. Therefore, it is a relevant skill to incorporate into the toolbox of any chemistry student. This work presents a course that introduces machine learning for chemistry students based on a set of Python Notebooks and assignments. Python language, one of the most popular programming languages, allows for free software and resources, which ensures availability. The course is constructed for students without previous experience in programming, leading to an incremental progression in depth and complexity that covers both programming and machine learning concepts. The examples used are related to real data from physicochemical characterizations of wines, producing an attractive material that captures the interest of students. Topics included are Introduction to Python, Basic Statistics, Data Visualization and Dimension Reduction, Classification, and Regression.

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“…The first was that both Jupyter and Python are open-source, with readily available distributions for all common operating systems. − The second is that Python is one of the most popular programming languages in the world, in particular for data analysis, ,, with a massive amount of online resources. In addition, it is becoming more and more popular within the science community, with numerous examples of Python being used for simulations and modeling, ,,− including specifically for chemistry-related problems ,,,− The third is that Python is both simple and flexible, with straightforward syntax, making it relatively easy to read and learn the code. , …”

Section: Introductionmentioning

confidence: 99%

Series of Jupyter Notebooks Using Python for an Analytical Chemistry Course

Menke

2020

J. Chem. Educ.

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UC Merced’s upper-division analytical chemistry course has been modified to include a series of Jupyter notebooks intended to introduce chemistry students to the Python computer language. These Jupyter notebooks were designed to cover a wide variety of topics common to quantitative and instrumental analysis. Assuming no prior programming experience, the notebooks are scaffolded to introduce students the Python’s syntax while applying Python to solving chemical problems. Jupyter and Python were chosen because they are free, widely available, and widely used.

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Introduction to Stochastic Simulations for Chemical and Physical Processes: Principles and Applications

Cited by 21 publications

References 37 publications

The Saturation Game: Teaching Protein–Ligand Binding with a Playing Card Analogy

The Saturation Game: Teaching Protein–Ligand Binding with a Playing Card Analogy

Introduction to Machine Learning for Chemists: An Undergraduate Course Using Python Notebooks for Visualization, Data Processing, Data Analysis, and Data Modeling

Series of Jupyter Notebooks Using Python for an Analytical Chemistry Course

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