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How many groups took part in the first challenge of the 7th CSP Blind Test? What kind of experimental work goes on behind the scenes? Read on for answers, and an interesting Q&A with a team from Catalent. Catalent is a development and manufacturing partner that has provided the high-quality experimental data for this test of crystal structure prediction (CSP) methods.
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Here, we reveal the 2D chemical structure of one of the most challenging systems included in CSP Blind Test history. Excitingly, it is much more representative of the complex-natured pharmaceutical compounds that are commonly encountered in the present day.
Here we reveal the 2D chemical structure of target compound XXXI, some surprising facts about agrochemicals, and how this Blind Test challenge tests CSP methods.
In 2019 we started exploring how the CCDC’s experience in data management and standards could best serve the data needs of the Crystal Structure Prediction (CSP) community. Around 18 months on, we wanted to share the outputs so far, how you can get involved, and what you can expect to see from us in the future.
Can crystal structure prediction (CSP) be used to computationally design multi-component materials? This is the question that target compound XXX poses to participants in this current CSP Blind Test.
The following blog summarizes my presentation on Crystal Structure Prediction (CSP).
The balance between computational efficiency and accuracy is often a tricky thing to get right, and knowing which types of methods are available and what they're capable of can be difficult to summarise and keep up with. With the continuous increase in computational technologies and access to more powerful HPCs, CSP methods are providing greater insights to solid-state materials than ever before. With their applications growing (fast!), the wider computational chemistry community may want to keep an eye on CSP developments and what's next for this expanding research area.
Registration opened today for the 7th CSP Blind Test - the leading challenge in crystal structure prediction. In 1988 Nature editor John Maddox called it "one of the continuing scandals in the physical sciences" that we could not take a 2D molecular structure and predict it's 3D form. This particular puzzle in computational science continues to challenge some of the best practitioners and developers of computational chemistry. Although tech giants have been turning their hand to scientific problems like protein folding, CSP is a very different kind of problem, and might just be a tougher nut to crack.
The field of Crystal Structure Prediction (CSP) has grown dramatically over the last few decades, but today I want to highlight a recent paper which takes a new approach. The recent publication from Montis et al makes use of new informatics tools to assess surface roughness (rugosity) and proposes a relationship between this and ease of crystallisation.
One of the most exciting events we hold at the CCDC are our blind tests of crystal structure prediction (CSP) methods. This one turned out to be the best yet. CSP is a really attractive problem as the challenge is beautifully simply to express, but monumentally difficult to solve. Perhaps this is why the entire CSP community comes together every few years, often pooling resources, to have a crack. It’s a wonderful model that could be applied to all sorts of challenges in chemistry and other areas of science.
