Frequently Asked Questions

How do I create my own set of solvents?

The simplest method is to load one of the small Example sets (such as Chapter2), then highlight and delete all the solvents! Or, to select File New which gives you a blank table with the Master List automatically open for the next step. In the Master List find each of your solvents in turn, Double-click on the solvent and that sends it automatically into your own solvent list. The software stops you from accidentally adding the same solvent twice. To find your solvent in the big master list use the search functionality by name (it’s not case sensitive and looks for whatever you enter in any part of the name field), by Cas No. or by Molecular Formula – whichever gets you to your solvent the fastest. The Getting Started video shows you how to do all this. Another way is to simply open the complete Sphere Solvent Data, search for the solvents to be included, and note their score. Unused solvents can be deleted with the delete unused function.

Why isn’t solvent X in the Solvent Optimizer list?

Probably because we didn’t think to add it. Remember that it’s really easy to add any solvent from the Master Database using the Right Click option. You can also add solvents yourself from within Solvent Optimizer. You may have trouble finding the Antoine Coefficients for your solvent. We’ve accessed some large databases, but sometimes we’ve failed to find anything on our molecule of interest and have therefore used a reasonable approximation. A good approximation is better than not entering any values.

Why is solvent X in the Solvent Optimizer list?

The default list cannot please all users. We deliberately add too many. It is very easy for you to highlight a row (or rows) and delete those you don't want. Remember to save the list with a new name. The software remembers your choice and next time you run HSPiP your new list will be there without those unwanted solvents.

Why do the HSP of chemical X differ from those I’ve found elsewhere (or those published in the 2nd Edition of the Hansen Book)?

There will never be a perfect list of HSP. New information means that we have to update the values. For example, between the 1st and 4th editions we’ve had good reason to change maybe 20 values and we expect to go on tweaking/revising for future editions. Fortunately many of these changes are minor, and as a percentage of the overall list they are tiny. We encourage all HSPiP users to keep challenging us about particular HSP and whenever we can get good data from other sources to support a change, we will carry on making the changes.

Why do the predicted values from Y-MB, S-P, Hoy etc. sometimes differ significantly?

Group contribution methods can never be perfect. They depend both on the quality of the data fed to them and on the choice of subgroups and subgroup interactions. We never tire of saying that ultimately you must be the judge of what constitutes a reasonable HSP set for a particular molecule. Use all the tools in DIY and, above all, try to pin down the δTot from enthalpy of vapourisation so you can at least be sure that your overall HSP values fit, even if the internal balance might be questionable. The more we can get independently measured values (e.g. by IGC) the better we can revise/update group contribution methods. Hoy's values seemed to work excellently for him within the context of an industrial organisation. His methodology was never made fully public and clearly his definitions of the parameters differe systematically from Hansen's. So never mix Hoy values and Hansen values. And never take averages of different methods in the hope it will be better than any given number. Clearly we favour the Y-MB values as these have been systematically refined over recent years.

We sometimes get strange results from the Polymer HSP predictions. Why?

Yes, we get them too – just look at the last example in the Polymer video. As we said, this is early days with this new technique. The core problem is that there are not sufficient published high-quality HSP values for well-characterised polymers so it is hard to provide authoritative predictions schemes. We continue to do our best.

Why don’t you let us calculate Y-MB on very large molecules?

The current limit on Y-MB is 120 “heavy” atoms, i.e. atoms other than H. Why don’t we increase it so, for example, a user can calculate the HSP of a large polymer with lots of side chains via an enormous SMILES string?

Part of the answer is practical – the more atoms the slower it becomes to work out what fragments are in the molecule. But mostly the answer is philosophical: it’s probably not scientifically worthwhile to estimate Y-MB on such large molecules. At first this seems a shocking statement. We seem to be saying that Y-MB is no good for large molecules. There is a good reason for this. Imagine, as a simple example, a di-block molecule, one half of which is mostly hydrocarbon and the other half mostly oxygenated – such as a PE:PEO di-block. Y-MB will give a value that is approximately half of PE and PEO values. This is “correct” but useless. PE:PEO does not behave as an average molecules – it behaves as two very different molecules joined together. This is an extreme example. But imagine a complex pharmaceutical such as a steroid. The large steroid part might be largely hydrophobic and there might be a large complex side chain with lots of sugars on it which are very hydrophilic. The Y-MB will give an average which certainly does not capture the science of that molecule.

So what is the answer? In principle Y-MB could automatically split a molecule into two regions, calculate the two values and provide them plus the average. However, although to a human it might be obvious where to split a molecule into two different regions, this is a near-impossible task for a computer. So the answer is the same as the other answers in this section: scientists are smarter than the computer so should do the split themselves. Take your molecule into your favourite chemical structure software, break it into two (finding an appropriate functionality at the splitting point), ask the software to produce the SMILES for each structure, calculate the Y-MB of each structure and then reach your own conclusion about what will happen in the combined molecule.

The problem is finding an appropriate functionality at the splitting point. Suppose the two halves are joined via an ester link. You could split them so that one half ends with an –OH and the other half ends with –CO2H. But this would introduce two –OH functionalities and raise the δH value unrealistically. In this case it’s probably better to end one half with –Ome and the other half with –CO2Me. This is now introducing one ether and one ester function to replace one ester function. It’s not perfect, but it’s better than splitting into the alcohol and acid. For small molecules the error of introducing the ether is significant, but for small molecules Y-MB is OK. For large molecules that require this sort of splitting, the ether error is probably insignificant. Or, if you as the scientist think that it is significant, then find a different way to create the split.

Why can't I enter SMILES of Salts, transition metal complexes etc.

Users would love it if they could enter the SMILES for salts or for transition metal complexes or ionic liquids etc. and get good Y-MB values. This is impossible.

There is a whole chapter in the eBook on ionic liquids so they won’t be discussed here. But the general point is that although it is perfectly sensible to talk about the HSP values of salts or transition metal complexes, the reason we can’t predict them is that there is no data set containing the HSP values of thousands of such molecules. So Y-MB cannot be “trained” on those data, so it is not possible to find values for the, say, Na+ functional group.

So if you are working on, say, organic electronics (OPV, OLEDs etc.) it is frustrating that you cannot rely on Y-MB estimates for your molecules. But remember that the “truth” in HSP comes from experimental data. If you set up your systems correctly, it is very easy to measure a large number of HSP values, especially if they all happen to be in one part of HSP space so your solvent choice and/or Grid choice gives you the most data for the fewest experiment. If you are not set up to do such measurements routinely, then at least in Europe there are two companies who offer high-throughput measurement services for HSP values, and hopefully other service providers will appear in other parts of the world.

While we are talking about impossible SMILES, another question is about “dotted SMILES”. As a simplistic example, ammonium acetate might be shown as N.CC(=O)O – with the all-important “dot” between the SMILES for ammonia and for acetic acid. Although in principle we could provide the individual HSP estimates, we have chosen to not allow dotted structures because they raise so many questions. What use is there, for example, in providing you with the HSP of ammonia and acetic acid if the molecule is really ammonium acetate?

Why doesn’t HSPiP do X?

There are three possible reasons. The first is that it’s not possible to do it. The second is that it’s possible and that we’re working on it – and we will welcome any help from someone who is interested in it. The third is that we’ve not thought of adding it to HSPiP. We love getting requests for extra features for HSPiP, so give us your wish-list and we’ll do our best to add features that increase HSPiP’s practical usefulness. HSPiP has developed remarkably over the past years, in a large part due to the input from the large user community.

Why do I get so different answers from the classic Sphere fit and from alternative fits such as GA (Genetic Algorithm)?

For each fitting algorithm the widest possible range of fits is explored. If the data are really good and the solvent points cover a good range of HSP space then the calculated values vary only very slightly, i.e. there is one clear global optimum - this is evidence that you have a good, unambiguous fit. But if the data aren’t good and, especially, they don’t provide points surrounding the real sphere, then there are very many mathematically equivalent fits. The fact that you see large variation is proof, unfortunately, that your data aren’t good enough to give an unambiguous fit. If you then use an alternative (e.g. flipping between classic and GA) you might get a different answer.

What can you do about it? Usually the quickest solution is to look at the Sphere plot and spot areas of δD, δP, δH where you don’t have any relevant solvent(s) [You can use the Solvent Range Check option to help you]. All you have to do is do the experiments with the extra few solvents and add the results to HSPiP. There is no need, of course, to repeat your other data points - so the extra work is really quite small. We have used this approach ourselves many times and the quality of the fit improves hugely with just a few carefully chosen extra solvents. We have to stress "carefully chosen". If, for example, Methanol is very far away from any reasonable fit, adding extra data points for Ethanol, Propanol, Butanol... will be extra work for no real benefit.

With good datasets the values from the classic and GA fits are the same. But when there are outliers the fits are different because the two methods (deliberately) used different scoring systems to cope with outliers. Sometimes we prefer the classic fit, sometimes the GA. The point of HSPiP is to allow the user to make informed judgements - so examine the outliers and see which interpretation of the fit makes more chemical sense.

How can I calculate the sphere radius for a polymer or any other solute?

The short answer is that you can’t. This is often a disappointment to HSP users, but there are many reasons why it’s impossible.

  1. The radius only has meaning to you in your specific application. So two users of the same polymer might have totally different radii. Why? Suppose one user is worried about swelling of the polymer over a long period. Many solvents will swell a polymer without dissolving it. So the radius will be large. Suppose a different user is interested only in creating concentrated polymer solutions. Many fewer solvents will be good solvents, so the radius will be significantly lower.
  2. Even if two users of the “same” polymer have the same general application (e.g. creating solutions) one might be using a low molecular weight grade and another a high molecular weight grade. The radius of the former will be larger than the latter.
  3. Even if two users of the “same” polymer use the same molecular weights, it’s still possible that the degree of branching, (accidental) cross-linking, “blockiness” etc. of the two polymers might be different.
  4. The science behind radii predictions, though well-known is really only applicable to ideal polymers under ideal circumstances, not the real-world polymers we prefer to use.

These issues are not a limitation of HSP – they are a strength! The Sphere method is designed to give you two pieces of information that you can subsequently use for formulating. The first is the centre. This will generally be the same for all users. The second is the radius, which is specific to your polymer and your criterion. HSPiP even encourages you to create multiple radii for the same polymer. By scoring not just 0, 1 but 1, 2, 3, 4… you can calculate different Spheres from one dataset. Those solvents that give instant, easy solution score 1, those that score a slow solution score 2, those that just swell give a 3 and so forth. If you include the 3’s as “good” solvents, then you get a large radius, applicable to swelling applications. By just including the 1’s you get a radius relevant to solution applications.

However, it’s not all subjective. The fundamental science of dissolving polymers in solvents (and solvents in polymers) is well-known and HSPiP includes a modeller for exploring that science. The science works well for pure, mono-disperse polymers under careful experimental conditions, so the radii, and their variations, are at least explicable in hindsight. But the large gap between the results of such pure polymers and the reality of commercial polymers used in real-world applications means that the Sphere method to measure the radius (or radii) is the one that most of us use most of the time.

Do I get a receipt from the MyCommerce eCommerce website when I purchase the HSPiP?

Yes. This is one reason why we use MyCommerce. The eCommerce website handles all the complexities of taxes relevant to your location so the final charge and the receipt will be correct for you.

Why do you charge extra for off-line purchases?

Whilst on-line purchases with a corporate credit card are quick and easy for everyone, off-line purchases with quotes, invoices, international payment transfers and so on are a lot of work for everyone. We therefore charge a $100 admin charge in the hope that it will discourage corporations and institutes from following this rather old-fashioned way of purchasing an all-electronic product.

Why do I get an unexpected error with some strange Microsoft code during install or when first running the program?

This is an obscure problem that seems to be connected to permissions on corporate networks. The solution seems to be to rename your current MyDocuments/HSPiP Data folder to something like MyDocuments/HSPiP OldData, then to do the install, then to copy any of your own files from the OldData folder into the new one before finally deleting the old folder. Problems on first running are usually due to hyper-strict "permissions" control so that HSPiP can't even write a simple .txt file to save its settings, or load/save a .hsd file. IT should slightly loosen permissions for the user's My Documents folder.

The official site of Hansen Solubility Parameters and HSPiP software.