Rigaku's European User Meeting featured updates on X-ray and electron diffraction, with new software and data analysis tools. Join us at upcoming webinars and conferences!
October has been a busy month. Last week, I visited our European office in Frankfurt for our 2024 European User Meeting. As you will read in the report below, the meeting was enlightening and fun.
The Nobel prizes were announced, with David Baker of the University of Washington winning half the Chemistry prize for “for computational protein design.” The other half of the Chemistry prize went to Demis Hassabis and John Jumper of Deep Mind for protein folding prediction. The Physics prize was awarded to AI pioneers John Hopfield and Geoffrey Hinton. The prize for Medicine and Physiology was awarded for research in microRNA to Victor Ambros and Gary Ruvkun. The Peace Prize was awarded to Nihon Hidankyo for efforts to prevent the use of nuclear weapons. Congratulations to all these winners!
AI dominates Crystallography in the News and Pierre provides the Tip of the Month on variable temperature and variable wavelength experiments. Jeanette reviews the latest Ray Kurzweil book, The Singularity Is Nearer. As Jeanette suggests, reading The Singularity Is Near before The Singularity Is Nearer is probably a good idea.
Lastly, the leaves are turning red, orange and yellow in the Northeast, heralding the start of fall.
Be safe,
Joe
European Rigaku User Meeting 2024
I had the pleasure of attending the European Rigaku User Meeting at our office in Neu-Isenburg, Germany last week. The meeting was attended by about 20 users and covered a wide array of topics. Fraser White organized the meeting and provided the introduction.
On the first day, Mathias Meyer described the new features of CrysAlisPro for X-ray and electron diffraction experiments. Horst Puschmann from OlexSys described the new features in Olex2, including the dynamical diffraction refinement pipeline. Michal Dusek from the Institute of Physics of the Czech Academy of Sciences took us on a tour of the advanced features of Jana2020. Claire Wilson of the University of Glasgow told us about her first impressions of her new XtaLAB Synergy-ED. Jere Tidey of the University of Southampton described his impressions of their XtaLAB Synergy-ED after its first year of operation. Jessica Burch gave a well-received lecture on sample preparation, and Robert Bücker talked about some of the new features for data analysis he has been working on. The group split up at the end of the lectures and received tours of the facility. We had a wonderful dinner at a local Italian restaurant and displayed our sporting skills in the basement kegeln or ninepin bowling alley.
On day two, we started with a review of the last year’s Tip of the Month by Chris Schürmann. Michael Merz from Josef Stefan Institute told us about two exciting structures displaying charge density waves. Gary Nichol of the University of Edinburgh talked about his dream of running his brand-new instruments from his cottage on the west coast of Scotland. Matic Lozinšek from the Karlsruhe Institute of Technology showed us some neat fluorine and xenon structures. Christian Göb described how he performs polymorph screening, and Khai Truong taught us how to run CrysAlisPro manually when the automation does not work. Yours truly reviewed the outcomes of the Rigaku School for Practical Crystallography and tried to show how to register for the newly released on-demand school. I failed. The final lecture was provided by Jakub Wojciechowski, who talked about the new hardware features of Rigaku Oxford Diffraction systems. We wrapped up the second day with our monthly XtaLAB Synergy-ED discussion group led by Khai Truong.
shares information and fosters discussion about X-ray crystallography and SAXS topics. Connect with other research groups and receive updates on how they use these techniques in their own laboratories. You can also catch up on the latest newsletter or Rigaku Journal issue. We also hope that you will share information about your own research and laboratory groups.
Atrigakuxrayforum.comyou can find discussions about software, general crystallography issues and more. It’s also the place to download the latest version of Rigaku Oxford Diffraction’sCrysAlisProsoftware for single crystal data processing.
Tip of the Month – Running variable temperature/wavelength experiments
By Pierre LeMaguerès
Introduction
Whether you seek to study temperature-related changes in crystals or to refine a crystal structure with data collected at different wavelengths, CrysAlisPro allows for the collection of a series of consecutive datasets at different temperatures or wavelengths without any user intervention between datasets. This is because the temperature or wavelength changes can be incorporated into the data collection strategy. CrysAlisPro will proceed as usual with automatic data processing and structure solution concurrently to each data collection and save each dataset into a separate folder. At the end of the series, you will find the diffraction images, processed data, and structure files stored in their usual locations in each dataset folder.
Calculate and save the strategy(ies) to be used for collection at various temperatures/wavelengths
Run a pre-experiment and calculate a strategy, as usual.
In the strategy interface, select Manually Edit Run List. Then, select Export and save the strategy as a *.run file.
Note 1: If you are running a multiple-wavelength experiment, you must run a pre-experiment and calculate a strategy for each wavelength. Note 2: If you are running a variable-temperature experiment and a temperature-dependent phase transition occurs:
If the phase transition’s temperature is known: run a pre-experiment above and below this temperature and calculate a strategy for each structural phase.
If the phase transition’s temperature is unknown: you must ensure a complete dataset is collected for each phase. You may use the strategy for the lowest symmetry lattice of the two phases or calculate a strategy for a full sphere.
Run a multi-wavelength experiment
1. Select the multi-wavelength/temperature icon in the left column of icons:
2. Ensure the box near each wavelength is selected (your system may have a different configuration from the one shown below; i.e., Cu/Ag or Mo/Ag).
3. For each wavelength, select the Strategy 1 button to open the strategy interface. 4. Select Manually Edit Run List and then Import. 5. Using File Explorer, browse to the *.run strategy file created upon running the pre-experiment and select ‘OK’. 6. At the top left, enter the detector distance for which the strategy was calculated (strategy files do not contain detector distance information) and select Update Completeness. With the strategy ready, the OK button is now enabled. Select OK to close the interface. Note: Do not select Calculate New Strategy.
7. The interface should now look as shown below, with the data collection parameters displayed for each experiment:
8. At the top of the dialog, select Browseroot folder to select/create the project folder where the datasets will be saved and enter a name for the experiment.
CrysAlisPro will create a subfolder with the name of the experiment and then additional subfolders for each wavelength. 9. At the bottom right, select Start Experiment.
Run a variable-temperature experiment
1. Select the multi-wavelength/temperature icon on the left column of icons:
2. Select the box to Use temperature device control and the type of your cryosystem. Then, open the Settings dialog.
3. Select Add to open the dialog for the data collection parameters per temperature setpoint. In this dialog:
Enter the desired temperature and ramp rate and select the boxes as shown below:
Wait time for stabilization is the user-defined interval CrysAlisPro will wait before starting collection after the target temperature is reached. This ensures the cryo-system’s temperature is stable when the first image is collected.
Select a strategy name (Strategy 1-5) for this temperature. This name will be linked to a specific strategy in the next step. If a different strategy is to be used at different temperatures, select a different strategy name for the corresponding temperatures.
It is recommended to make a crystal movie at each temperature to keep track of crystal stability on the mount throughout the temperature range of the experiment. One can select to make a movie before and/or after data collection.
Select OK when done.
4. Select Add again and enter the parameters for the next temperature setpoint.
There is no limit on the number of setpoints
Throughout the experiment, the temperature can be set to increase and decrease at will
Different ramp rates, wait times and movie options may be selected for each setpoint.
The example below shows a series of increasing and decreasing temperature changes with different intervals between and wait times between temperatures that could be used to study hysteresis in the behavior of a parameter in a crystal structure.
5. Select OK to return to the main dialog 6. Select the box near the wavelength(s) you are about to use. Remember to unselect the box for the unused wavelength if a single wavelength is to be used. Note: if multiple wavelengths and multiple temperatures are combined, the same series of temperature settings must be used for all wavelengths. 7. For each Strategy 1-5 linked to a temperature setpoint in Steps 4 and 5, select the corresponding Strategy button. 8. Refer to Steps 4-6 of Run a multi-wavelength experiment to load each strategy. 9. Using File Explorer, browse to the *.run strategy file created upon running the pre-experiment. In the example below, the same Strategy 1 will be used for data collection with Mo radiation at 15 different temperatures. The parameters for Strategy 1 are also shown.
10. At the top of the dialog, select Browse root folder to select/create the project folder where the datasets will be saved and enter a name for the experiment.
CrysAlisPro will create a subfolder with the name of the experiment, then additional subfolders for each wavelength used, and a subfolder for each temperature setpoint. 12. At the bottom right, select Start Experiment.
Here is a link to this year’sIg Nobel Awards. As always, these awards make you laugh then make you think. You can fast-forward through the first twenty-five minutes.
Futurist Ray Kurweil’s The Singularity Is Nearer: When We Merge With AI is a direct sequel to his 2005 work The Singularity Is Near: When Humans Transcend Biology. If you haven’t read the first book, you might want to go back and give it a read—and if you have read it, but it’s been almost two decades, you still might want to go back and give it a read. Kurzweil makes a lot of callbacks to his previous work and, though they are well-explained—mostly within the context of earlier predictions Kurzweil made that have since come to pass, boosting his credentials as a futurist—it would definitely aid the reader to have a familiarity with his earlier text. The Singularity Is Nearer is a bit like The Empire Strikes Back—an excellent entry in a series, but you might be a bit lost if you aren’t familiar with the first one.
The gist of The Singularity Is Nearer is that artificial intelligence (AI) is here, and here to stay, so we need to accept it and make the requisite adjustments as a society and as individuals. Kurzweil spends the majority of the book (6 of 8 chapters) detailing his predictions for how AI will continue to evolve and integrate with our society over the next few decades, as well as how that will benefit mankind. AI has already been used to help manufacture vaccines—like Moderna’s revolutionary COVID-19 shot. Kurzweil presents numerous examples of situations—like Moderna’s COVID vaccine—where AI can only help society while also making hyper-specific predictions as to how the technology will develop and unfold and why it will do so in this particular way.
Chapter 7, ominously titled “Peril,” is the only chapter where Kurzweil delves into the potentially harmful impacts of AI—and even then, it feels like a gentle skim. He openly declares that he is optimistic about humanity’s ability to combat the possibility of AI-driven catastrophe, giving the example of our previous success (as of the writing of this review) with regard to nuclear weapons technology and mutually assured destruction. This is a missed opportunity—though, given Kurzweil’s professional background and his current role as an AI leader at Google, perhaps an understandable one. It’s in Kurzweil’s best interest for the reader to accept the inevitability of AI on a microcosmic and macrocosmic scale and take his word for it that it’s all for the better of humanity in the end.
Kurzweil also makes numerous mentions throughout the book of his friendship with psychologist and psycholinguist Steven Pinker. Given Pinker’s connections to the late Jeffrey Epstein, among other controversies, this is in poor taste.
Review by Jeanette S. Ferrara, MFA
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