{"id":542,"date":"2020-03-14T17:05:58","date_gmt":"2020-03-14T17:05:58","guid":{"rendered":"http:\/\/blogs.cardiff.ac.uk\/team-wu\/?page_id=542"},"modified":"2024-06-20T11:17:16","modified_gmt":"2024-06-20T11:17:16","slug":"thoughts","status":"publish","type":"page","link":"https:\/\/blogs.cardiff.ac.uk\/team-wu\/thoughts\/","title":{"rendered":"Thoughts"},"content":{"rendered":"\r\n\r\n\r\n

Organic chemistry is nothing but pattern recognition!?<\/span><\/strong><\/p>\r\n

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\u201cThis is nothing but pattern recognition!\u201d As unsettling as this comment might sound to many organic chemistry practitioners, it was something one of my former supervisors said about (undergraduate) organic chemistry. Years later, this remark resurfaced in my mind as I delved into the world of machine learning.<\/p>\r\n

I vividly remember when one of my brightest college classmates expressed a strong interest in neuroscience. Being more physics- and math-minded, I didn\u2019t quite connect with this idea at the time, and it was not until recently that I realized the process of receiving information and forming memories or decisions could be modeled mathematically as communications between networks of neurons. (Thanks to Fei-Fei Li\u2019s inspiring memoir The Worlds I See<\/em><\/a>; highly recommended!)<\/p>\r\n

The basic unit (algorithm) in these neural networks, aptly named a perceptron, takes in input (electrical) signals and performs binary classifications. It mimics what an actual neuron does: receiving electrical signals, accumulating and modulating them, and firing an output signal only when the total strength of the input signals exceeds a certain threshold. While complex cognitive activities involve large neural networks, at the most fundamental level, whether it is a single neuron or a single perceptron, the primary task is classification.<\/p>\r\n

Classification is indeed how we learn about the world. As babies, we learn to identify a cat and a toy, or what is edible versus inedible. As we grow older, we learn to categorize words into nouns and verbs and recognize facial and vocal expressions that signify emotions like happiness or anger. We make sense of the world by organizing and classifying information, which is essential for understanding, decision-making, and communication.<\/p>\r\n

Humans are amazingly good at classification. It allows us to identify risks in a split second and avoid accidents; it even enables us to sense our surroundings without direct attention. However, this ability is so deeply ingrained in our minds that it can lead to unconscious biases, such as distinguishing people who look different from us and isolating us from embracing diversity.<\/p>\r\n

Returning to chemistry, recognizing that our learning and cognitive processes begin with classification makes the idea that \u201corganic chemistry is nothing but pattern recognition\u201d seem less outrageous. However, going from classification to pattern recognition involves additional tasks such as feature identification\/extraction and regression. These tasks may include knowing\/evaluating the strength of electrophilicity, the crowdedness of the reaction site, temperature values, and the polarity of the environment, etc. \u00a0Admittedly, these subtleties may not be fully grasped even by most seasoned chemists, but they can be effectively learned by machines given suitable data sets, as evidenced by the growing number of publications on the application of artificial intelligence in synthetic organic chemistry<\/a>.<\/p>\r\n

To conclude, let me quote Flynn and Ogilvie from their article<\/a> in the Journal of Chemical Education<\/em>: \u201cChemical reactions follow patterns, and these patterns can allow a chemist to predict how a chemical will behave, even if they have never seen a particular reaction before.\u201d Since we are not ditching learning\/teaching\/using electron-pushing formalism in our mind anytime soon, we may improve our approach to thinking about and practicing organic chemistry by comparing methods that fundamentally make machines \u201csmarter\u201d in synthesis. .<\/p>\r\n

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Decoding \u201cstruggle\u201d\u2014my observations<\/span><\/strong><\/p>\r\n\r\n\r\n\r\n

A word that I found very frequently in students’ emails is struggle<\/strong>. Instead of employing alternative expressions like be stuck<\/em>, have a hard time<\/em>, have trouble<\/em>, or have difficulty<\/em>, they consistently lean towards using struggle<\/em>. This linguistic divergence presents a cultural contrast for me after relocating to the UK, as I tend to associate struggle<\/em> with more serious connotations. This observation prompted me to reflect, and realized it might largely be a regional word preference. A cursory exploration of the verb forms of struggle<\/em> in The Corpus of Global Web-Based English (GloWbE<\/a>) confirms not only a higher frequency of use but also a markedly greater overall occurrence of this word in GB compared to any other country in the database (Figure 1).<\/p>\r\n\r\n\r\n

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Figure 1<\/strong>. Comparison of the word STRUGGLE used in various countries. In each box, the upper number is the occurrence, and the lower number is the frequency in per million. Data retrieved from GloWbE through www.english-corpora.org.<\/p><\/div>\r\n\r\n<\/figure>\r\n<\/div>\r\n\r\n\r\n\r\n\r\n

Yet, delving into the historical trajectory of this term reveals a notable surge in popularity around the year 2000 in both British and American books, according to the Google Books Ngram Viewer<\/a> (Figure 2a). The nearly identical trends observed here may suggest a universal phenomenon across most English-speaking countries. Intriguingly, examining the noun and verb forms of struggle <\/em>shows distinct patterns (Figure 2b). The noun usage gained traction as early as 1840, consistently surpassing that of the verb form for over a century. Curiously, around 2000, the verb form began to be employed frequently to signify “making strenuous or violent efforts in the face of difficulties or opposition” or “proceeding with difficulty or great effort,” according to Merriam-Webster<\/a>. The rapid growth in the usage of the verb form is noteworthy, surpassing the noun form in frequency after 2010. In fact, there appears to be a recent decreasing trend in the use of struggle <\/em>as a noun. Since the Google Books Ngram Viewer only provides data up to 2019, a comparison using other text corpora, such as NOW <\/a>(News on the Web, 2010\u2013present) and COCA <\/a>(Corpus of Contemporary American English, 1990\u20132019), has been included to depict the evolving trends in usage. I shall note that the scale of the year axis is not linear, allowing for a clearer representation of trends in recent years.<\/p>\r\n\r\n\r\n

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Figure 2<\/strong>. (a) Comparison of the trend of STRUGGLE used as a verb in British and American English. Data retrieved from Books Ngram Viewer (books.google.com\/ngrams). (b) The trend of STRUGGLE used retrieved from Books Ngram Viewer, COCA, and NOW corpora through www.english-corpora.org.<\/p><\/div>\r\n<\/figcaption>\r\n<\/figure>\r\n<\/div>\r\n\r\n\r\n

The question arises: What caused the rapidly increased use of struggle <\/em>as a verb? At this stage, I do not want to make any suggestions but simply want to share this observation. How would you interpret this data? Does it suggest that life has become more difficult than it was 20 years ago? While I hesitate to draw such conclusions, I sincerely hope that the current upward trend in the use of struggle <\/em>will subside in the near future.<\/p>\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n

Exploring thionation with H\u00fcckel molecular orbital theory.<\/span><\/strong><\/p>\r\n\r\n\r\n\r\n

H\u00fcckel Molecular Orbital (HMO) theory stands as one of my favourite concepts from my undergraduate studies in physical organic chemistry. Despite its simplicity and the underlying assumptions, it offers an intuitive understanding of \u03c0-conjugated molecules and is a readily comprehensible extension of the somewhat simplistic particle-in-a-box model.<\/p>\r\n\r\n\r\n\r\n

Recently, while pondering the question of why thionating a carbonyl molecule enhances its electron-accepting capabilities, as explored in our 2023 publication (PCCP DOI: 10.1039\/D2CP05186A<\/a>), I realized that the good old HMO theory could offer a fairly satisfying explanation. This question arose from our observations while working with various thiocarbonyl molecules (R2<\/sub>C=S). We (as well as others) consistently observed that these molecules exhibit a greater propensity to accept electrons than their carbonyl counterparts (R2<\/sub>C=O). This observation appeared to defy expectations based on a somewhat simplistic consideration of electronegativity (2.58 for sulfur and 3.44 for oxygen).<\/p>\r\n\r\n\r\n\r\n

Upon closer analysis, we revealed that the enhanced electron affinity in the C=O \u2192 C=S substitution stems from the weaker overlap between sulfur\u2019s 3p orbitals and carbon\u2019s 2p orbitals in C=S, in comparison to the better interactions between the valence 2p orbitals of carbon and oxygen in C=O. This reduced overlap results in a less effective antibonding interaction, leading to a lower LUMO energy and increased electron affinity.<\/p>\r\n\r\n\r\n\r\n

The diminished overlap between (2p)C<\/sub> and (3p)S<\/sub> is well-represented by the (empirical) parameters utilized in the HMO theory. In the HMO Hamiltonian matrix, the Coulomb integral \u03b1<\/em>C<\/sub> represents the energy of the individual (2p\u03c0<\/sub>)C<\/sub> atomic orbital of carbon, while the resonance integral \u03b2<\/em>CC<\/sub> characterizes the coupling between adjacent (2p\u03c0<\/sub>)C<\/sub> orbitals. By adjusting the integral values for carbon through \u03b1<\/em>X<\/sub> = \u03b1<\/em>C<\/sub> + kX<\/sub> \u00d7 \u03b2<\/em>CC<\/sub> and \u03b2<\/em>XY<\/sub> = kXY<\/sub> \u00d7 \u03b2<\/em>CC<\/sub>, the property of the frontier orbitals of heteroatom-containing \u03c0 systems can be estimated in the H\u00fcckel framework (kX<\/sub> and kXY<\/sub> are proportionality constants for heteroatom X).<\/p>\r\n\r\n\r\n\r\n

Using formaldehyde (H2<\/sub>CO) and thioformaldehyde (H2<\/sub>CS ) as examples, since<\/p>\r\n\r\n\r\n\r\n

\u03b1<\/em>O<\/sub> = \u03b1<\/em>C<\/sub> + kO<\/sub> \u00d7 \u03b2<\/em>CC<\/sub> = \u03b1<\/em>C<\/sub> + 0.97\u03b2<\/em>CC<\/sub>
\u03b2<\/em>C=O<\/sub> = kC=O<\/sub> \u00d7 \u03b2<\/em>CC <\/sub>= 1.06\u03b2<\/em>CC<\/sub><\/p>\r\n\r\n\r\n\r\n

and<\/p>\r\n\r\n\r\n\r\n

\u03b1<\/em>S<\/sub> = \u03b1<\/em>C<\/sub> + kS<\/sub> \u00d7 \u03b2<\/em>CC<\/sub> = \u03b1<\/em>C<\/sub> + 0.46\u03b2<\/em>CC<\/sub> \u00a0\u00a0
\u03b2<\/em>C=S<\/sub> = kC=S<\/sub> \u00d7 \u03b2<\/em>CC <\/sub>= 0.81\u03b2<\/em>CC<\/sub><\/p>\r\n\r\n\r\n\r\n

the energy of the \u03c0* level would be E<\/em>(\u03c0*) = \u03b1<\/em>C<\/sub> \u2013 0.68\u03b2<\/em>CC <\/sub>for H2<\/sub>CO and E<\/em>(\u03c0*) = \u03b1<\/em>C<\/sub> \u2013 0.61\u03b2<\/em>CC<\/sub> for H2<\/sub>CS, i.e. the \u03c0* level of H2<\/sub>CS is less destabilized. In fact, even without solving the eigenvalue problem of HMO, the lower \u03c0* level of thiocarbonyl can be intuitively expected since the resonance integral \u03b2<\/em>C=S<\/sub> is smaller than \u03b2<\/em>C=O<\/sub>. In our PCCP paper, we further showed that this \u03b2<\/em>C=X <\/sub>could be quantitatively estimated as the |\u27e8h<\/em>C<\/sub>|F<\/strong>|h<\/em>S<\/sub>\u27e9|, where h<\/em>x<\/sub> is the natural atomic hybrid orbitals (NHO) and F<\/strong> is the Fock (Kohn\u2013Sham) operator.<\/p>\r\n\r\n\r\n\r\n

However, it is important to note that semiempirical HMO may not always yield the correct picture. When comparing ester (RC(O)OR) and thioester (RC(O)SR), we found that E<\/em>(\u03c0*) = \u03b1<\/em>C<\/sub> \u2013 0.79\u03b2<\/em>CC <\/sub>for the former and \u03b1<\/em>C<\/sub> \u2013 0.86\u03b2<\/em>CC <\/sub>for the latter, suggesting a higher \u03c0* level for the sulfur compound. This result, however, conflicts with DFT calculations and the experimental reduction potentials of these molecules. The inconsistency arises from the larger<\/em>\u00a0\u03b2<\/em>C\u2013S<\/sub>\u00a0= 0.69\u03b2<\/em>CC <\/sub>than \u03b2<\/em> C\u2013O<\/sub>\u00a0= 0.66\u03b2<\/em>CC<\/sub> (as opposed to being smaller <\/em>in the previous comparison; note the difference in \u03b2<\/em>C=X<\/sub>\u00a0 and \u03b2<\/em>C\u2013X<\/sub>\u00a0used here). Therefore, despite the elegance of HMO, we recommend using the DFT-based NBO\/NHO analysis to obtain a more reliable understanding of the electronic effects of heteroatom substitution on \u03c0-conjugated molecules. Please see DOI: 10.1039\/D2CP05186A<\/a> for detailed discussion.<\/p>\r\n\r\n\r\n\r\n

For this work, we also prepared a (yet another!? link1<\/a> and link 2<\/a>) Python script to solve simple H\u00fcckel systems<\/a> (not the \u2018extended H\u00fcckel\u2019). While this script is primarily designed for \u03c0-conjugated hydrocarbons, you can adapt it for heteroatom-containing systems by adjusting the Coulomb and resonance integrals in the H\u00fcckel matrix. Several examples are included in this interactive Jupyter Notebook for your reference (hosted in Google Colab<\/a>).<\/p>\r\n\r\n\r\n\r\n

Furthermore, to enhance usability, the script can generate the H\u00fcckel matrix from a molecular SMILES string using the RDKit toolkit\u2019s GetAdjacencyMatrix<\/code> module. To end this post: You may find it intriguing to explore this script and compare the energy levels of two isomeric molecules: 2-phenyl-1,3-butadiene: C=CC(c1ccccc1)=C<\/code> and 1,4-divinylbenzene: C=Cc1ccc(cc1)C=C<\/code> (spoiler alert: they are the same!).<\/p>\r\n\r\n\r\n\r\n

The B\u00fcrgi\u2013Dunitz angle revisited.<\/span><\/strong><\/p>\r\n\r\n\r\n\r\n

I came across the blog post<\/a> by Prof. Rzepa on the B\u00fcrgi\u2013Dunitz angle the other day. This is a topic close to my heart. Embarrassingly, I did not know this concept until the mid-way of my PhD study at ETH, where I actually had regular interactions with Prof. Dunitz. The discovery of the B\u00fcrgi\u2013Dunitz angle marked one of the key moments in crystallography, organic and supramolecular chemistry, and molecular orbital analysis. I was very impressed by the beauty and the neat idea behind this study, and have since used it as an example to show students the vast information one can get from structural analysis. It was truly insightful that B\u00fcrgi and Dunitz were able to distill the preferred reaction trajectory starting from simply 6 (!) crystal structures<\/a>.<\/p>\r\n\r\n\r\n\r\n

Nearly 50 years later, now that we have more than 1 million structures in the CCDC database, we should be better equipped to perform the structural analysis to re-validate the theory behind the B\u00fcrgi\u2013Dunitz angle. However, if you try to look at the angle of any nucleophilic atom (N, O, S, etc.) approaching carbonyl functionalities, the most commonly found angles are actually about 90\u00ba but not near 105\u00ba, as clearly demonstrated by Prof. Rzepa in the blog post<\/a>. So, were B\u00fcrgi and Dunitz wrong?<\/p>\r\n\r\n\r\n\r\n

After a close examination of the structure hits, I found that the primary source of the discrepancy comes from the overwhelming number of structures featuring antiparallel C=O interactions<\/a><\/em>. In those cases, the O of one moiety sits on the C of the other, resulting in O\u2026C=O angle at ~90\u00ba. This is quite interesting; starting from the analysis of nucleophilic addition to a carbonyl group, we ended up with one of the most prevalent non-covalent interactions in proteins.<\/p>\r\n\r\n\r\n\r\n

I’ve added my comment below Prof. Rzepa’s post. Part of the reason that I want to write about it again here is to point out that, really, in the age of machine learning, we can easily obtain a huge amount of data. Yet, it is extremely to keep an eye on the actual information in those data. After all, as ML people often say: your model will be only as good (or as bad) as the data you have.<\/p>\r\n\r\n\r\n\r\n

Goodbye Northwestern, it has been a wonderful time.<\/span><\/strong><\/p>\r\n\r\n\r\n

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\"hood\"<\/figure>\r\n<\/div>\r\n\r\n\r\n

After a long period of job searching, I am delighted to take the post at Cardiff University starting from January 2019. \u00a0I am deeply indebted to all my mentors, colleagues, friends, and family for their help and support during this process. \u00a0Working on photo energy researches during the past 6 and a half years in Northwestern has been a wonderful experience. \u00a0It is a true privilege to collaborate the brightest minds (you know I am talking about you!) on the daily basis and be able to access to the cutting-edge technologies. \u00a0My views to science and its interplay with education and society got to grow and mature, and they are the best gift that I will bring with across the Atlantic Ocean and pass them onto my future coworkers.<\/p>\r\n\r\n\r\n\r\n

Judging at Intel ISEF 2017<\/span><\/strong><\/p>\r\n\r\n\r\n

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\"ISEF17\"<\/figure>\r\n<\/div>\r\n\r\n\r\n

“Science\u2019s rightful place is in service of society” (D. Sarewitz, 2013) is always a big part of my belief. \u00a0This summer, I was very fortunate to participate as a Chemistry Grand awards judge in the International Science and Engineering Fair (ISEF),\u00a0the biggest science fair in the world.<\/p>\r\n\r\n\r\n\r\n

Although science fair was a huge thing in my high school, I wasn’t doing so well as many of my high school classmates, and ISEF 2017 is the first time for me to see such a high-level competition. \u00a0I was very impressed by one high schooler’s\u00a0perseverance with identifying an undocumented ferric sulfate compound from the reaction of sulfuric acid and gold ore, which he obtained from hiking; by the applicability of the algorithm that another student developed to filter and assign signals in high-dimensional protein NMR spectroscopy to accelerate drug discovery (and by his smartness, too); and by the usefulness of silk fibers as\u00a0moisture-activated torsional actuators discovered by the other student, and by many other projects.<\/p>\r\n\r\n\r\n\r\n

The\u00a0judges caucus is another special experience. \u00a0We are composed of industrial scientists, university professors, researchers, postdocs, and PhD students. \u00a0Some had participated more science fairs than the others; we discussed all(!) the projects and tried to persuade(!) our colleagues why one project is better\/worse. \u00a0The voting\/discussion cycle repeated again and again until all the prizes were decided. \u00a0(Awarded students, you should really thank the eloquent and passionate judges who lobby for your project!)<\/p>\r\n\r\n\r\n\r\n

Judging ISEF was overall a great experience, especially seeing\/feeling the pure enthusiasm\u00a0for the science of all the students, and I am very glad I could contribute and help. \u00a0Thanks to my grad school friend Grace for the invitation!<\/p>\r\n\r\n\r\n\r\n

Ethylhexyl in real life.<\/span><\/strong><\/p>\r\n\r\n\r\n\r\n

As a “purist”, I never really like to see any 2-ethylhexyl substituent in my molecules, as it usually has an undefined stereogenic center at the 2 position. \u00a0Materials incorporating such a functionality thus are random mixtures(!) of (R<\/em>)- and (S<\/em>)- stereoisomers, not to mention molecules possessing multiple 2-ethylhexyl substituents.<\/p>\r\n\r\n\r\n\r\n

Materials scientists, especially aromatic polymer chemists, use 2-ethylhexyl to enhance the solubility, as such a bulky subsituent disrupts pi-stacking\/aggregation. \u00a0Outside of the research labs, as it turns out, molecules with 2-ethylhexyl are actually ubiquitous in our daily life; I wonder if those 2-ethylhexyls were implemented also for modulating the aggregation properties.<\/p>\r\n\r\n\r\n\r\n

Just to name a few, 2-ethylhexyl nitrate<\/a> (2-EHN) is a cetane improver added to diesel fuels, bis(2-ethylhexyl) phthalate<\/a>, which is produced approx. 3 billion kg\/year, is a plasticizer for PVC, and octocrylene<\/a> and octyl methoxylcinnamate<\/a> are ingredients in sunscreen products that absorb UVB and UVA. \u00a0Without a doubt, I should have given ethylhexyl much more credits!<\/p>\r\n\r\n\r\n\r\n

We know so little, so little indeed, about solubility.<\/span><\/strong><\/p>\r\n\r\n\r\n\r\n

PDI is a notoriously insoluble dye, and it is well known that linear<\/em> aliphatic N<\/em>-substituents won’t make it\u00a0much more soluble. \u00a0However, the\u00a0Grozema group published a mind-blowing work back in 2014 (DOI: 10.1039\/c4cc00330f<\/a>) and totally turn down this common believe\u00a0(along with other important findings, of course). \u00a0See the picture! \u00a0I knew this work for quite a while (thanks Pat for showing me this paper), and still couldn’t make sense out of it. \u00a0This just shows how little we know about intermolecular interaction; there is more to learn!<\/p>\r\n\r\n\r\n\r\n

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\"pdi_ester\"<\/figure>\r\n

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Alkyl groups modulate \u03c0-stacking interactions: size is not the only thing that matters.<\/span><\/strong><\/p>\r\n\r\n\r\n\r\n

The group of Ken Shimizu at the University of South Carolina reported a very interesting finding<\/a>: the strength of repulsive and\/or attractive interactions between \u03c0-stacked aromatics can be non-trivially influenced by the alkyl substituents. \u00a0Should the interacting area (surface contact area) between aromatics be large enough, even those bearing t<\/em>Bu substituents can display stronger attraction than those bearing Me one! \u00a0\u00a0<\/span><\/p>\r\n\r\n\r\n\r\n

Does\u00a0methanol<\/span>\u00a0dissolve silica? \u00a0Maybe not, suggested by Biotage.<\/span><\/strong><\/p>\r\n\r\n\r\n\r\n

This is an age-old question for people using MeOH in their flash chromatography. \u00a0I guess the answer\/result might have something to do with the pore size\u00a0of the frit of your column. \u00a0Anyhow, have a look at an interesting analysis\u00a0<\/a>conducted in Biotage.<\/span><\/p>\r\n\r\n\r\n\r\n

 <\/p>\r\n","protected":false},"excerpt":{"rendered":"

Organic chemistry is nothing but pattern recognition!? \u201cThis is nothing but pattern recognition!\u201d As unsettling as this comment might sound to many organic chemistry practitioners, it was something one of my former supervisors said about (undergraduate) organic chemistry. Years later, this remark resurfaced in my mind as I delved into the world of machine learning. […]<\/p>\n","protected":false},"author":1731,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-542","page","type-page","status-publish","hentry"],"jetpack_sharing_enabled":true,"meta_box":[],"_links":{"self":[{"href":"https:\/\/blogs.cardiff.ac.uk\/team-wu\/wp-json\/wp\/v2\/pages\/542","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.cardiff.ac.uk\/team-wu\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/blogs.cardiff.ac.uk\/team-wu\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.cardiff.ac.uk\/team-wu\/wp-json\/wp\/v2\/users\/1731"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.cardiff.ac.uk\/team-wu\/wp-json\/wp\/v2\/comments?post=542"}],"version-history":[{"count":40,"href":"https:\/\/blogs.cardiff.ac.uk\/team-wu\/wp-json\/wp\/v2\/pages\/542\/revisions"}],"predecessor-version":[{"id":703,"href":"https:\/\/blogs.cardiff.ac.uk\/team-wu\/wp-json\/wp\/v2\/pages\/542\/revisions\/703"}],"wp:attachment":[{"href":"https:\/\/blogs.cardiff.ac.uk\/team-wu\/wp-json\/wp\/v2\/media?parent=542"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}