#Artificial Bonds

[just-emis-blog]

WIP Wednesday tag

Thank you to the really cool @fractured-shield for the tag! As always I am little late to the party, but hopefully it's ok that I am posting this wee snippet from my WIP this fine Friday morning 😊

Rules: Pick a WIP. Post something about it. On a Wednesday. Or whenever

Emphasis on the "whenever", apparently 👀 I'll do better next time, promise!

Excerpt from Chapter 1 of Artificial Bonds

...When he had reassured his father for the hundredth time that he did not need him to drop him off at his new job; like it was his first day of preschool or some shit, good lord. Blake had then scrambled to think of who in his friend group he could bare/afford to let know that he would soon be working at some menial, dead-end, service-joint alongside unfuckable poor people (Ugh). He came up blank, sadly. Unsurprisingly. But it was getting down to the wire, time was running out, and he really didn’t want to chance an Uber or Taxi driver recognizing him, so in a fit of desperation he had scrolled through his oldest contacts. Among the sea of restaurants, doctor offices, and disconnected numbers was Lysander Park. Blake tapped the name and opened the text chain. The last message was from Andy, inviting him to go Karaoking with him and his family. Blake had not responded. The date of the message said it was five months old. Blake winced, bodily, thumbs hovering over the keyboard; the glowing, blank white space as accusatory as it was inviting. After typing and erasing his request several times - going from apologetic to casual to pleading to casual again - Blake sent him the text. The response was almost immediate. Sure! :), it read, like those five months of silence didn’t exist. Like the messages before it didn’t have similar, insultingly long gaps in conversation. Just a “sure” and grandma’s first emoticon. No follow up questions. It was lucky as hell. It was purely…inexplicable. Almost as inexplicable as turning up in an unknown house with no clue as to how you got there. “…or I swear I’m calling the cops man, I’m not fucking around!” “Oh no sir, I truly believe that you are not fucking around wholeheartedly! And I will definitely get out of your hair just as soon as I grab my friend, I promise.” “How many times do I have to tell your dumbass that your stupid friend isn’t here!?” Figuring that he was about to be down a friend and a ride in one go if he let this little standoff continue, Blake rocked his body from side to side to build momentum, then slowly hucked himself until he was steadily rolling - his carcass breaching past some kind of curtain and beyond, until the dizziness from the movement won and he flopped spread eagled onto a scratchy rug. He groaned pathetically. Between his eyeballs burning from the sudden exposure to daylight and the spinning ceiling, the idea of going back to sleep where he lay was sounding pretty great right now - his new job, Andy, Lindsay and Man be damned. Before he could give into that tantalizing temptation, Blake hurled his torso up into a sitting position. And maybe it was a little more zombie-like than intended, because Lindsay screamed a shrill scream, reserved especially for when a hungover stranger rolled out from underneath a bed unexpectedly.

tagging @drchenquill @mk-writes-stuff @leahnardo-da-veggie @daisywords @the-ellia-west + anyone else who peeps this bad boy :D

[sparkles-rule-4eva]

Guys guys guys so you know how Tails is famous for copying Sonic's poses and stuff, classic little sibling trying to be as cool as older sibling, right

So

LOOK AT LITTLE KIT STRUTTING AROUND LIKE BIG SIS AND HIS LITTLE FACE OF DETERMINATION AND THE POSE AKFKWANENFKDMAKWMFKSMWKFNVLSNEMFKDAODKO

Just saying, it may have started out artificial and toxic, but I've really been loving to see how Surge and Kit's bond has slowly been changing to an actual bond, almost merely because of their shared trauma, and they're the only ones they trust. 🥺 Underrated siblinghood right here.

[mostly-natm]

Upgrades.

[milksugarjams]

The face behind the mask

Does anyone else have specific little headcanons about Revan's personal aesthetics and personality. The discrepancy between what she was and what she is now. After the Jedi Council lock away her memories, what else do they do to try making her different? Moldable? The way they want her to be?

They change her name, obviously. Do they cut her hair? Dye it, make it the opposite of how it was before? Are they afraid?

Are they desperate to keep her from any hint of her true identity? Uncertain if their repression is enough? Maybe they knew it was only a temporary thing, that locking away a person's memories will only work for so long. Perhaps all of this is only delaying the inevitable.

Bastila is present for all of this. As much as her opinions are easily swayed by the Council, some part of her rebels against their efforts. Subconsciously, she knows they're not all that different, her and Revan. She never had the confidence or experience to rely on her own conscience; but Revan learned how to do so. Even though Revan fell, Bastila never learned to despise and dismiss her former heroics. Not like other Jedi did.

Don't change her, she thinks in secret. Let her be herself, not the puppet you want her to be.

She doesn't shave her head, but trims her hair shorter. Changes her wardrobe, but slips in something old of hers, something from before the fall. Something the Council tried to burn. The others would see it as weakness, but Bastila sees it as a mercy. For who she was, not who she became.

When Revan wakes on Taris, what will she see when she looks in the mirror? What the Council painted, or what Bastila tried to preserve?

Does she recognize herself behind the mask?

[000marie198]

"He knows this bond is artificial, but at least it's something." Hey, that's never stopped Shadow and Rouge with Omega, or Cream with Gemerl, or hell, even Eggman with Metal Sonic

I think by artificial he meant an artificial bond not in the sense of Chaos being artificial but rather in the sense of him being 'a subtitute for Sonic'. Because Sonic is someone Nine really truly cares about and loves and wanted to stay with but now he's no longer there. He's gone. Nine had to let him go because he understood and because Sonic needed to be let go so he would be saved (sorry that one unanswered ask in my inbox from weeks ago, I couldn't find words but I read it and remember it). It is said not that Chaos Sonic is a mere robot, especially considering CS is sentient too. I think he meant that CS isn't Sonic, he can never replace Sonic, never be Sonic. But he is based off him and might help fill up that Sonic shaped hole.

[secretlystephaniebrown]

I don't give baby Steph that much credit but I do think it's funny that when I was in my OC phase and writing for Teen Titans I went "hey! it's not fair no one ships Cyborg with anyone! I'm going to make him a girlfriend!"

[comehereeveryday]

James Bond summon His persona

[agnesandhilda]

thematically speaking mizi is poised to revolutionize the world

[loosingmoreletters]

full confession I don't think even 30 chapters are gonna cut it for patching the road. they keep fucking derailing the plot.

[cmweller]

Challenge #04300-K282: Synthetic Hymnal

Grandpa 7094 had a very impressive career. NASA, MIT, and the Mitsubishi Nuclear Power Plant were just a few of the organizations where he provided invaluable data. But ask him his proudest achievement, he'll say without hesitation it was learning to sing Daisy Bell. Because it's the one that makes his grandchildren smile. -- Anon Guest

[AN: For those unaware, speech synthesis is older than you think. The instant Humanity put speakers on computers, they started trying to make them communicate]

Humans are strange creatures, no matter what. The hairless ape saw the wolf and wondered if it could be friends. And later, when they made rocks do mathematics, they gave those constructions names. Binac. Emerac. Avidac. When they sent machines to places they could not yet go, they did the same. Pioneer. Voyager. Curiosity...

And for centuries, they tried to make their machines talk just like them. They tried to make their machines... sing.

Nothing difficult, to begin with. Something the synthesis was capable of. Something traditional so the audience could use their pareidolia to fill in the gaps.

[Check the source for the rest of the story]

[swash0067]

Two Friends Bonding on the Golf Course

[here-there-were-dragons]

as a general rule, on average, if americans consistently complain about a food being conceptually weird, gross, and scary, then it probably tastes amazing. or at least inoffensive.

this is because in my experience americans for the most part (give or take a few exceptions by region) think eating literally anything other than beef, chicken, bread, eggs, peanut butter jelly sandwitches, ketchup, and disgusting cloyingly artificial brown sludge soda is insurmountably weird, gross, and scary.

[mostly-natm]

Android child bonding!

[jcmarchi]

Machine learning unlocks secrets to advanced alloys

New Post has been published on https://thedigitalinsider.com/machine-learning-unlocks-secrets-to-advanced-alloys/

Machine learning unlocks secrets to advanced alloys

The concept of short-range order (SRO) — the arrangement of atoms over small distances — in metallic alloys has been underexplored in materials science and engineering. But the past decade has seen renewed interest in quantifying it, since decoding SRO is a crucial step toward developing tailored high-performing alloys, such as stronger or heat-resistant materials.

Understanding how atoms arrange themselves is no easy task and must be verified using intensive lab experiments or computer simulations based on imperfect models. These hurdles have made it difficult to fully explore SRO in metallic alloys.

But Killian Sheriff and Yifan Cao, graduate students in MIT’s Department of Materials Science and Engineering (DMSE), are using machine learning to quantify, atom-by-atom, the complex chemical arrangements that make up SRO. Under the supervision of Assistant Professor Rodrigo Freitas, and with the help of Assistant Professor Tess Smidt in the Department of Electrical Engineering and Computer Science, their work was recently published in The Proceedings of the National Academy of Sciences.

Interest in understanding SRO is linked to the excitement around advanced materials called high-entropy alloys, whose complex compositions give them superior properties.

Typically, materials scientists develop alloys by using one element as a base and adding small quantities of other elements to enhance specific properties. The addition of chromium to nickel, for example, makes the resulting metal more resistant to corrosion.

Unlike most traditional alloys, high-entropy alloys have several elements, from three up to 20, in nearly equal proportions. This offers a vast design space. “It’s like you’re making a recipe with a lot more ingredients,” says Cao.

The goal is to use SRO as a “knob” to tailor material properties by mixing chemical elements in high-entropy alloys in unique ways. This approach has potential applications in industries such as aerospace, biomedicine, and electronics, driving the need to explore permutations and combinations of elements, Cao says.

Capturing short-range order

Short-range order refers to the tendency of atoms to form chemical arrangements with specific neighboring atoms. While a superficial look at an alloy’s elemental distribution might indicate that its constituent elements are randomly arranged, it is often not so. “Atoms have a preference for having specific neighboring atoms arranged in particular patterns,” Freitas says. “How often these patterns arise and how they are distributed in space is what defines SRO.”

Understanding SRO unlocks the keys to the kingdom of high-entropy materials. Unfortunately, not much is known about SRO in high-entropy alloys. “It’s like we’re trying to build a huge Lego model without knowing what’s the smallest piece of Lego that you can have,” says Sheriff.

Traditional methods for understanding SRO involve small computational models, or simulations with a limited number of atoms, providing an incomplete picture of complex material systems. “High-entropy materials are chemically complex — you can’t simulate them well with just a few atoms; you really need to go a few length scales above that to capture the material accurately,” Sheriff says. “Otherwise, it’s like trying to understand your family tree without knowing one of the parents.”

SRO has also been calculated by using basic mathematics, counting immediate neighbors for a few atoms and computing what that distribution might look like on average. Despite its popularity, the approach has limitations, as it offers an incomplete picture of SRO.

Fortunately, researchers are leveraging machine learning to overcome the shortcomings of traditional approaches for capturing and quantifying SRO.

Hyunseok Oh, assistant professor in the Department of Materials Science and Engineering at the University of Wisconsin at Madison and a former DMSE postdoc, is excited about investigating SRO more fully. Oh, who was not involved in this study, explores how to leverage alloy composition, processing methods, and their relationship to SRO to design better alloys. “The physics of alloys and the atomistic origin of their properties depend on short-range ordering, but the accurate calculation of short-range ordering has been almost impossible,” says Oh. 

A two-pronged machine learning solution

To study SRO using machine learning, it helps to picture the crystal structure in high-entropy alloys as a connect-the-dots game in an coloring book, Cao says.

“You need to know the rules for connecting the dots to see the pattern.” And you need to capture the atomic interactions with a simulation that is big enough to fit the entire pattern. 

First, understanding the rules meant reproducing the chemical bonds in high-entropy alloys. “There are small energy differences in chemical patterns that lead to differences in short-range order, and we didn’t have a good model to do that,” Freitas says. The model the team developed is the first building block in accurately quantifying SRO.

The second part of the challenge, ensuring that researchers get the whole picture, was more complex. High-entropy alloys can exhibit billions of chemical “motifs,” combinations of arrangements of atoms. Identifying these motifs from simulation data is difficult because they can appear in symmetrically equivalent forms — rotated, mirrored, or inverted. At first glance, they may look different but still contain the same chemical bonds.

The team solved this problem by employing 3D Euclidean neural networks. These advanced computational models allowed the researchers to identify chemical motifs from simulations of high-entropy materials with unprecedented detail, examining them atom-by-atom.

The final task was to quantify the SRO. Freitas used machine learning to evaluate the different chemical motifs and tag each with a number. When researchers want to quantify the SRO for a new material, they run it by the model, which sorts it in its database and spits out an answer.

The team also invested additional effort in making their motif identification framework more accessible. “We have this sheet of all possible permutations of [SRO] already set up, and we know what number each of them got through this machine learning process,” Freitas says. “So later, as we run into simulations, we can sort them out to tell us what that new SRO will look like.” The neural network easily recognizes symmetry operations and tags equivalent structures with the same number.

“If you had to compile all the symmetries yourself, it’s a lot of work. Machine learning organized this for us really quickly and in a way that was cheap enough that we could apply it in practice,” Freitas says.

Enter the world’s fastest supercomputer

This summer, Cao and Sheriff and team will have a chance to explore how SRO can change under routine metal processing conditions, like casting and cold-rolling, through the U.S. Department of Energy’s INCITE program, which allows access to Frontier, the world’s fastest supercomputer.

“If you want to know how short-range order changes during the actual manufacturing of metals, you need to have a very good model and a very large simulation,” Freitas says. The team already has a strong model; it will now leverage INCITE’s computing facilities for the robust simulations required.

“With that we expect to uncover the sort of mechanisms that metallurgists could employ to engineer alloys with pre-determined SRO,” Freitas adds.

Sheriff is excited about the research’s many promises. One is the 3D information that can be obtained about chemical SRO. Whereas traditional transmission electron microscopes and other methods are limited to two-dimensional data, physical simulations can fill in the dots and give full access to 3D information, Sheriff says.

“We have introduced a framework to start talking about chemical complexity,” Sheriff explains. “Now that we can understand this, there’s a whole body of materials science on classical alloys to develop predictive tools for high-entropy materials.”

That could lead to the purposeful design of new classes of materials instead of simply shooting in the dark.

The research was funded by the MathWorks Ignition Fund, MathWorks Engineering Fellowship Fund, and the Portuguese Foundation for International Cooperation in Science, Technology and Higher Education in the MIT–Portugal Program.

[malaierba]

shipping brainrot is also super weird in that some people will deny canon friendships just bcs they feel like it inflicts damage against their (fanon) ships somehow like?

do you not have friends i dont understand

[futuristichedge]

One day. One day Ill illustrate/write all the sonic comic and interaction ideas I have. Today is not that day but there WILL be one