Mathematics for Chemistry with Symbolic Computation

J. F. Ogilvie

            This interactive electronic textbook, in the form of Maple worksheets, is released in its sixth edition, 2021 August.  This book has two major divisions, mathematics for chemistry -- the mathematics that any instructor of a course in chemistry would wish a student thereof to understand and to be able to implement, and mathematics of chemistry, in the sense of the classic volumes by Margenau and Murphy -- mathematical treatments of particular topics in chemistry from an introductory post-secondary level to a post-graduate level. The content, which includes not only chapters in previous editions that have been revised but also additional chapters on quantum mechanics, molecular spectrometry and advanced chemical kinetics, has been collected during two decades, with many contributions from other authors, acknowledged in particular locations.  Each chapter includes not only explanatory treatments but also illuminating examples and exercises with chemical applications where practicable.

Mathematics for chemistry      0  introduction to Maple commands

                                                 1  numbers, symbols and elementary functions

                                                 2  plotting, geometry, trigonometry and functions

                                                 3  differential calculus

                                                 4  integral calculus

                                                 5  multivariate calculus

                                                 6  linear algebra

                                                 7  differential and integral equations

                                                 8  probability, statistics, regression and optimisation

Mathematics of chemistry       9  chemical equilibrium

                                                10  group theory

                                                11  graph theory

                                                12  quantum mechanics in three parts -- models, atoms and molecules

                                                13  molecular spectrometry

                                                14  Fourier transforms

                                                15  advanced chemical kinetics

                                                16  dielectric and magnetic properties

The content freely available at https://www.maplesoft.com/applications/view.aspx?SID=154267 includes also a published report on teaching mathematics with symbolic software and an interactive periodic chart that yields information about particular chemical elements and their isotopic variants.

            The nature of this electronic interactive textbook makes it applicable with an instructor in a traditional setting, or computer laboratory, for which the material of mathematics for chemistry could be reasonably covered in three or four semesters, but even for self study.  The chapters on quantum mechanics and Fourier transforms are available as separate textbooks in the same format.

 Pictures on the theme of Klein bottle.  Wikipedia article
KL_B_1.mw

KL_B_2.mw

Quantum Mechanics for Chemistry

J. F. Ogilvie

            This interactive electronic textbook, freely available from the Maple Application Centre [https://www.maplesoft.com/applications/view.aspx?SID=154768] in the form of three Maple worksheets comprises three extensive chapters, on model systems, atoms and molecules in turn.  As quantum mechanics is neither a chemical theory nor even a physical theory but a collection of methods, numbering at least thirteen, or algorithms, for calculations on systems of an atomic scale, it is appropriate that computer software combining both strong arithmetical and symbolic capabilities, i.e. Maple, be applied to implement this material.  The book includes calculations involving five of the known methods, and provides many examples and exercises for a reader to enhance understanding of the principles and practice. For the first and third chapters, a readable text as .pdf is also provided but the extent of the second chapter precludes this possibility.

            The objective of this textbook is to demonstrate how the principles of the varied methods become implemented in practical calculations. The chapter on model systems includes treatments of several oscillators that might serve as prototypical of features of diatomic molecules.  The chapter on atoms includes the most extensive treatment available on solutions of Schroedinger's equation for the hydrogen atom, in all four systems of coordinates in which the variables are separable, and also in momentum space.  The chapter on molecules includes an introduction to transparent quantum-chemical calculations, which enables a reader to understand each stage of a calculation on a simple atomic or molecular system leading to a self-consistent field and even to Moeller-Plesset perturbation theory of second order and application of density functionals, which can provide an excellent basis for a subsequent use of opaque numerical programs for calculation of molecular structures and properties.

            This textbook contains, with permission, contributions from several eminent chemists, mathematicians and physicists, acknowledged in the particular locations, that complement the explanatory descriptive text as a profound introduction to quantum mechanics in a context of chemical education.

A manipulator, in which 3 degrees of freedom are provided by changing the length of the links and one degree of freedom, is provided by turning. Only 4 degrees of freedom. Solved using Draghilev's method. In one case, the length of the manipulator link could be expressed through the value of the 3rd coordinate. The lengths of the other two links are considered generalized coordinates. In this case, it is still obtained polynomial equations, as for the usual coordinates.
I was asked to make an example of the movement of such a manipulator using Maple. (Automatically, this is an example of solving an inverse kinematics problem.)
four_degrees_of_freedom_from_Sabina.mw

Some misguided individuals insist that perpetual motion machines are impossible. Here is a proof that they are wrong!

One of these units hooked up to an electrical generator should be enough to supply all your household electrical needs as well as charge your Tesla in the garage.

If you build one and find out that it doesn't work as demonstrated here, then surely you must have misread the specs. Try it again and again until you succeed.

Download perpetual-motion-machine-corrected.mw

Hi again all

You can enjoy this simple Maple code to find the proper divisors of a given positive integer (whole number).

Download proper_divisor.mw

here  some_proper_divisor_examples_3.pdf   file

Hope this helps

Matt Anderson

The https://sites.google.com/view/aladjevbookssoft/home site contains free books in English and Russian along with software created under the guidance of the main author prof. V. Aladjev in such areas as general theory of statistics, theory of cellular automata, programming in Maple and Mathematica systems. Each book is archived, including its cover and book block in pdf format. The software with freeware license is designed for Maple and Mathematica.

I’m excited to announce the launch of a new math tool called Maple Flow. Here, I’ll outline our motivation for developing this product, and talk about its features.

A large fraction of Maple users are professional engineers .

All use Maple, but very few say that they do math for a living, in much the same way a plumber wouldn’t say they use a wrench for a living.

They say things like:

• I design concrete retaining walls
• I simulate the transients on a transmission line
• I design heat exchangers
• I model the absorbency of diapers
• I design subsea pipelines
• I need to optimize the trajectory of a space shuttle
• I work for a power generation company doing load flow analysis
• I model how a robot arm needs to move

Some of these applications are mathematically simple (but are based on scientific principles, such as the conservation of heat, mass and momentum). The equations consist of basic arithmetic operations, trig and log functions, sprinkled with the occasional numeric integration.

Sometimes, the equations are already formalized in design guides, published by organizations like the IEEE, ASME or ISO. Given the specific physical context, engineers just need to implement the calculations in the right order (this is especially true for Civil and Structural engineering). These applications require you to think at an engineering level.

These are what we call design calculations, done by design engineers.

On the other end of the spectrum, some of these applications are mathematically complex. You might need to derive equations, manipulate PDEs, work with quaternions or transformation matrices, or do some programming. These applications require you to think at a mathematical level.

Let’s call the engineers doing this type of work research engineers. Research engineers are often more closely aligned with mathematicians than design engineers.

So we have design engineers and research engineers (and of course we have engineers with feet in both camps, to a varying degree).

Research engineers and design engineers do different mathematical things, and have different mathematical needs. Both groups use Maple, but one size doesn’t always fit well. Either the toe pinches a little, or the shirt is a mite too baggy.

This is where Maple Flow enters stage right.

Maple Flow is a new tool that we’ve built (and are continuing to expand and improve) with the needs of design engineers in mind.

• The worksheet lets you put math anywhere – just point, click and type
• The evaluation model is forward-in-space (unlike Maple’s forward in time evaluation model). This means the execution order is explicitly given by the position of the math on the canvas.
• The worksheet updates automatically, so results are never stale
• We’ve made several simplifications to massage away some of the complexity of the Maple programming language.
• You can use nearly all of tools in the Maple programming language.

Here’s how we see people using Maple Flow. They

• Enter a few major equations somewhere, followed by some parameters scattered around
• Make the equations “see” the parameters by moving the parameters above the equations
• Insert any parameters or equations you’ve forgotten, and move them into position, shifting the existing content out of the way to make room
• Add text, and perhaps an image or plot
• Finally, align math and format text for a presentable document

I’ve been using Maple Flow for a while now. I like the fact that the nature of Maple Flow means that you don’t have to start with a grand plan, with every computational detail planned out in advance. You’re encouraged to make things up as you go along, and gradually sculpt your calculations into shape.

Basically, Maple Flow doesn’t issue stiff penalties for making mistakes. You fix them, and then move on.

I also like that Maple Flow makes you feel like you’re “touching” your equations, shifting things about easily with either the mouse or the keyboard. There’s a certain tactility and immediacy to Maple Flow that gives me a micro dose of dopamine every time I use it.

Maple Flow’s freeform interface lets you experiment with space, alignment and layout, drawing attention to different groups of equations.

For example, you can design calculation documents that look like this.

You can use nearly all of the Maple programming language in Flow. Here’s a command from the plots package.

Here’s fsolve in action.

The Maple Flow website has more information, including a demo video.

As ever, your feedback is gratefully received.

Yesterday, user @lcz , while responding as a third party to one of my Answers regarding GraphTheory, asked about breadth-first search. So, I decided to write a more-interesting example of it than the relatively simple example that was used in that Answer. I think that this is general enough to be worthy of a Post.

This application generates all maximal paths in a graph that begin with a given vertex. (I'm calling a path maximal if it cannot be extended and remain a path.) This code requires Maple 2019 or later and 1D input. This works for both directed and undirected graphs. Weights, if present. are ignored.

restart:

AllMaximalPaths:= proc(G::GRAPHLN, v)
description 
    "All maximal paths of G starting at v by breadth-first search"
;
option `Author: Carl Love <carl.j.love@gmail.com> 2021-Mar-17`;
uses GT= GraphTheory;
local 
    P:= [rtable([v])], R:= rtable(1..0),
    VL:= GT:-Vertices(G), V:= table(VL=~ [$1..nops(VL)]),
    Departures:= {op}~(GT:-Departures(G))
;
    while nops(P) <> 0 do
        P:= [
            for local p in P do
                local New:= Departures[V[p[-1]]] minus {seq}(p);
                if New={} then R,= [seq](p); next fi;                
                (
                    for local u in New do 
                        local p1:= rtable(p); p1,= u
                    od
                )       
            od
        ]
    od;
    {seq}(R)  
end proc
:
#large example:
GT:= GraphTheory:
K9:= GT:-CompleteGraph(9):
Pa:= CodeTools:-Usage(AllMaximalPaths(K9,1)):
memory used=212.56MiB, alloc change=32.00MiB, 
cpu time=937.00ms, real time=804.00ms, gc time=312.50ms

nops(Pa);
                             40320
#fun example:
P:= GT:-SpecialGraphs:-PetersenGraph():
Pa:= CodeTools:-Usage(AllMaximalPaths(P,1)):
memory used=0.52MiB, alloc change=0 bytes, 
cpu time=0ns, real time=3.00ms, gc time=0ns

nops(Pa);
                               72

Pa[..9]; #sample paths
    {[1, 2, 3, 4, 10, 9, 8, 5], [1, 2, 3, 7, 8, 9, 10, 6], 
      [1, 2, 9, 8, 7, 3, 4, 5], [1, 2, 9, 10, 4, 3, 7, 6], 
      [1, 5, 4, 3, 7, 8, 9, 2], [1, 5, 4, 10, 9, 8, 7, 6], 
      [1, 5, 8, 7, 3, 4, 10, 6], [1, 5, 8, 9, 10, 4, 3, 2], 
      [1, 6, 7, 3, 4, 10, 9, 2]}

Notes on the procedure:

The two dynamic data structures are

• P: a list of vectors of vertices. Each vector contains a path which we'll attempt to extend.
• R: a vector of lists of vertices. Each list is a maximal path to be returned.

The static data structures are

• V: a table mapping vertices (which may be named) to their index numbers.
• Departures: a list of sets of vertices whose k^th set is the possible next vertices from vertex number k.

On each iteration of the outer loop, P is completely reconstructed because each of its entries, a path p, is either determined to be maximal or it's extended. The set New is the vertices that can be appended to the p (connected to vertex p[-1]). If New is empty, then p is maximal, and it gets moved to R. 

The following code constructs an array plot of all the maximal paths in the Petersen graph. I can't post the array plot, but you can see it in the attached worksheet: BreadthFirst.mw

#Do an array plot of each path embedded in the graph:
n:= nops(Pa):
c:= 9: 
plots:-display(
    (PA:= rtable(
        (1..ceil(n/c), 1..c),
        (i,j)-> 
            if (local k:= (i-1)*ceil(n/c) + j) > n then 
                plot(axes= none)
            else 
                GT:-DrawGraph(
                    GT:-HighlightTrail(P, Pa[k], inplace= false), 
                    stylesheet= "legacy", title= typeset(Pa[k])
                )
            fi
    )),
    titlefont= [Times, Bold, 12]
);

#And recast that as an animation so that I can post it:
plots:-display(
    [seq](`$`~(plots:-display~(PA), 5)),
    insequence
); 

Download Wirtinger_Derivatives.mw

Edgardo S. Cheb-Terrab
Physics, Differential Equations and Mathematical Functions, Maplesoft

Download QformPost.mw

Maple strings contain extended ASCII characters (codes 1..255). 
The international characters such as  î, ș, Å, Ø ,Ă, Æ, Ç are multi-byte encoded. They are ignored by the Maple engine but are known to the user interface which uses the UTF-8 encoding.
The package StringTools does not support this encoding. However it is not difficult to manage these characters using Python commands (included in the Maple distribution now).
Here are the UTF-8 versions of the Maple procedures length and substring.
You may want to improve these procedures, or to add new ones (my knowledge of Python is very limited).

LEN:=proc(s::string) Python:-EvalFunction("len",s) end proc:

SS:=proc(s::string, mn::{integer, range(({integer,identical()}))})
  local m,n;
  if type(mn,integer) then m,n := mn$2 else m:=lhs(mn); n:=rhs(mn) fi; 
  if m=NULL then m:=1 fi; if n=NULL then n:=-1 fi;
  if n=-1 then n:=LEN(s) elif n<0 then n:=n+1 fi;
  if m=-1 then m:=LEN(s) elif m<0 then m:=m+1 fi;
  Python:-EvalString(cat("\"",  s, "\"", "[", m-1, ":", n, "]"  ));
end proc:

LEN("Canada"), length("Canada");
                              6, 6

s:="România":
LEN(s), length(s);
                              7, 8

SS(s, 1..), SS(s, 1..-3), SS(s, 1..1), SS(s, -7..2), SS(s,1), SS(s,-1); 
            "România", "Român", "R", "Ro", "R", "a"

Download FeynmanIntegrals.mw

Edgardo S. Cheb-Terrab
Physics, Differential Equations and Mathematical Functions, Maplesoft

diff(abs(z), z)  returns abs(1, z)  and the latter, for a numeric z, is defined only for a nonzero real z.
However,  functions such as abs(I+sin(t)) are (real) differentiable for a real t and diff should work. It usually does, but not always.

restart
f:= t -> abs(GAMMA(2*t+I)):  # We want D(f)(1)
D(f)(1): 
evalf(%);  # Error, (in simpl/abs) abs is not differentiable at non-real arguments
D(f)(1); simplify(%); 
evalf(%);   # 0.3808979508 + 1.161104935*I,  wrong

The same wrong results are obtained with diff instead of D

diff(f(t),t):   # or  diff(f(t),t) assuming t::real; 
eval(%,t=1);
simplify(%); evalf(%);   # wrong, should be real

To obtain the correct result, we could use the definition of the derivative:

limit((f(t)-f(1))/(t-1), t=1); evalf(%); # OK 
fdiff(f(t), t=1);    # numeric, OK

       0.8772316598
       0.8772316599

Note that abs(1, GAMMA(2 + I)); returns 1; this is also wrong, it should produce an error because  GAMMA(2+I) is not real;
 

Let's redefine now `diff/abs`  and redo the computations.

restart
`diff/abs` := proc(u, z)   # implements d/dx |f(x+i*y|) when f is analytic and f(...)<>0
local u1:=diff(u,z);
1/abs(u)*( Re(u)*Re(u1) + Im(u)*Im(u1) )
end:
f:= t -> abs(GAMMA(2*t+I));
D(f)(1); evalf(%);   # OK now

         0.8772316597

Now diff works too.

diff(f(t),t);
eval(%,t=1);
simplify(%); evalf(%);   # it works

This is a probably a very old bug which may make diff(u,x)  fail for expressions having subespressions abs(...) depending on x.

However it works  using assuming x::real, but only if evalc simplifies u.
The problem is actually more serious because diff/ for Re, Im, conjugate should be revized too. Plus some other related things. 

The Putnam 2020 Competition (the 81st) was postponed to February 20, 2021 due to the COVID-19 pandemic, and held in an unofficial mode with no prizes or official results.

Four of the problems have surprisingly short Maple solutions.
Here they are.

A1.  How many positive integers N satisfy all of the following three conditions?
(i) N is divisible by 2020.
(ii) N has at most 2020 decimal digits.
(iii) The decimal digits of N are a string of consecutive ones followed by a string of consecutive zeros.

add(add(`if`( (10&^m-1)*10&^(n-m) mod 2020 = 0, 1, 0), 
n=m+1..2020), m=1..2020);

       508536

A2.  Let k be a nonnegative integer.  Evaluate  

sum(2^(k-j)*binomial(k+j,j), j=0..k);

        4^k

A3.  Let a(0) = π/2, and let a(n) = sin(a(n-1)) for n ≥ 1. 
Determine whether the series   converges.

asympt('rsolve'({a(n) = sin(a(n-1)),a(0)=Pi/2}, a(n)), n, 4);

a(n) ^2 being equivalent to 3/n,  the series diverges.

B1.  For a positive integer n, define d(n) to be the sum of the digits of n when written in binary
 (for example, d(13) = 1+1+0+1 = 3). 

Let   S =  
Determine S modulo 2020.

d := n -> add(convert(n, base,2)):
add( (-1)^d(k) * k^3, k=1..2020 ) mod 2020; 

        1990