CALCULATION METHODS FOR CHEMISTRY

The course is among the core educational activities characterizing the three-year degree course in Chemistry and Sustainable Technologies (path in Science and Technology of Bio and Nano-materials), aimed at providing students with the theoretical knowledge and the appropriate methods to model an experiment and to analyse its outcomes. Some basic concepts of machine learning methods applied to chemistry will be introduced. Besides, some basic elements of programming will be introduced and discussed to devise algorithmic solutions to chemical problems. Examples and applications to solve chemical problems will be discussed by using appropriate softwares.

KNOWLEDGE AND UNDERSTANDING
Knowledge and understanding of the fundamental principles of the main methodologies for the modelling and analysis of the experimental data (also by using appropriate softwares). Knowledge and understanding of basic principles of computer science: algorithms, type systems, and basic control structures. Knowledge and understanding of some basic notions about machine learning applied to chemistry and of some of the methods used for solving chemical problems,
ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING
Ability to use the concepts learned in order to model the phenomenon and to choice among the different techniques and algorithms available for the data analysis, to use them for analising scientific data and to properly employ the corresponding results, ability to design and implement (using the software discussed during the course) one or more reliable solutions to chemical problems also by using some machine learning methods. Ability to to consult technical documentation regarding the algorithms and methods used.
ABILITY TO JUDGE
Ability to express a judgment on the outcomes of an experiment.
COMMUNICATION SKILLS
Ability to describe the aspects of the proposed solving algorithms and computational methods, using appropriate terminology, to both specialists and non-specialists interlocutors.

Basic knowledge in calculus (vectors, matrices, linear algebra, differential and integral calculus). In details, possibly but not necessarily, having passed the corresponding exams (MATHEMATICS AND EXERCISES - 1, MATHEMATICS AND EXERCISES - 2); besides, some basic knowledge of the theory of errors.

Mathematical models of an experiment and experimental measurements. Introduction to the different scales of measurements. Foundational principles of computer science: algorithms, type systems, and some basic control structures. Data analysis and computational methods, by means of programs written by the students, with application to chemistry and spectroscopy based on the uses of the following topics: linear algebra, calculus, minima and maxima of functions, gradient, divergence, integration and numerical computation of integrals, linear and non-linear regression models, methods for solving ordinary differential equations, eigenvalues, eigenvectors and singular value decomposition (SVD). A brief introduction to machine learning for chemistry; supervised and unsupervised algorithms, classification, clustering and regression algorithms; overfitting and underfitting. Logistic regression. Support-Vector Machine (SVM) and its application to chemistry. K-Means and its application to chemistry. Principal component analysis (PCA) and its application to chemistry.
Every topic will be presented and discussed also by employing OCTAVE, so during each lecture this software will be briefly presented and discussed.

Mainly lecture notes.
Philip. R. Bevington, D. Keith Robinson “Data Reduction and Error Analysis for the Physical Sciences”, McGraw-Hill Education, 2003.

Oral examination (generally about 30’).
It consists in the discussion of one example of applications of the methods covered in the course to scientific data by means of a computational project written and developed in OCTAVE, together with the presentation (by using slides in PPTX and/or PDF format, max 10 minutes) of the main results thus obtained, followed by some open questions. During the discussion, the presentation and the open questions the student has to demonstrate both the critical learning of the topics of the entire program and the ability to implement them in the computational project, and that he/she is able to communicate the knowledge learned, and to describe the results obtained from the application of the computational project with appropriate language.
The final mark, expressed in thirtieths, is composed for the 2/3 of the evaluation of the computational project and of the answers to the questions, and for the 1/3 of the evaluation of the presentation (PPTX and/or PDF format) of the computational project.

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The final mark, expressed in thirtieths, is composed for the 2/3 of the evaluation of the of the computational project (developed with OCTAVE) and of the answers to the questions, and for the 1/3 of the evaluation of the presentation (PPTX and/or PDF format) of the computational project.
Scores in the range of 18-22 will be assigned if the student obtains a sufficient evaluation about knowledge and understanding of the various topics covered during the course, the ability to apply them to the chosen project, the judgment autonomy, and the communicative skills regarding the acquired information and the chose project. Besides, the student must be able to describe, even if with a terminology often non appropriate, the results obtained from applying a given calculation method and the computational project itself.
Scores in the range of 23-26 will be assigned if the student obtains a good evaluation about knowledge and understanding of the various topics covered during the course, the ability to apply them to the chosen project, the judgment autonomy and the communicative skills regarding the acquired information and the chose project. Besides, the student must be able to describe, with terminology often appropriate, the results obtained from applying a given calculation method and the computational project itself.
Scores in the range of 27-30 will be assigned if the student obtains a very good evaluation about knowledge and understanding of all the topics covered during the course, the ability to apply them to the chosen project, the judgment autonomy and the communicative skills regarding the acquired information and the chose project. Besides, the student must be able to describe, with terminology always appropriate, the results obtained from applying a given calculation method and the computational project itself.
To obtain the honors, the evaluation on all the above points must be excellent.

Lectures coupled to examples on the use of some dedicated software packages (mainly OCTAVE); it is recommended to bring a laptop during the lectures.
The slides employed during each lecture (and the corresponding supplementary material) will be downloadable from the MOODLE web pages.

Accessibility, Disability and Inclusion

Accommodation and support services for students with disabilities and students with specific learning impairments:
Ca’ Foscari abides by Italian Law (Law 17/1999; Law 170/2010) regarding support services and accommodation available to students with disabilities. This includes students with mobility, visual, hearing and other disabilities (Law 17/1999), and specific learning impairments (Law 170/2010). In the case of disability or impairment that requires accommodations (i.e., alternate testing, readers, note takers or interpreters) please contact the Disability and Accessibility Offices in Student Services: disabilita@unive.it.

This subject deals with topics related to the macro-area "Climate change and energy" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development