#crystalyz

[monochromaticpaint]

i feel like there could have been a whole season about The Mechanic being the main villain in Ninjagos main run.

Prime Empire doesnt *really* count. Unigami may not have been truly bad in that season but he was still a villain at one point and was the sole reason The Mechanic was being the bad guy.

anyway. i think an entire season could have focused on him being the main villain and trying to take over Ninjago sometime between Prime Empire and Crystalyzed. Something to really solidify him as one of the greater ninjago villains.

Im thinking something like "Mechanized" and he basically reenacts the Overlords plot in rebooted minus stealing Golden Power.

Take Over the nindroids and then the whole city. (the nindroids minus zane and pixal have not been used in a LONG TIME so this would be awesome to see them evil again)

We could see the creation of Mr F.

Maybe the Mechanic built him for Harumi/The Kabuki and thats how he knows about the Crystal King.

A season about The Mechanic could have been a great link to crystalyzed

[senseibalance]

THE MIYAGIVERSE ROLEPLAY COMMUNITY: a karate kid and cobra kai independent roleplayers. these are all people i have known for voer a year and i love, some new, some old but they are here to show that a little show like cobra kai can have a community that loves and grows on the show and movies lore. never i had seen something like this really. a show about karate, and the layers we let our characters grow, a franchise that has been 30 years in the world growing and growing, the luck of new storylines and the faces that built the movies 30 years ago. and these people help the growth of the world. 

@teachdance @senseibalance @senseimercy @queencvbra @princecvbra  @moonesq @oncefroze @pointlarusso @karatewar @valleykarate @pointmoskowitz @taughtpain @opponentcompel @karatelives @allvalley @findingstrength @valleykids @crystalyz @scandalises @teachesgrace @legacytaught @karatetaught @ofsweetness

and there is problably so many more people out there and if you want to be added to this post, let me know! anyways, i love you all.

[pumpkin-wanderer]

🐈‍⬛ My very first and still favorite deck is Cat tarot by Megan Lynn Kott It`s really warm and talkative deck. And it also gives you a positive and cozy vibes (even so called "bad" cards represented here from different angles). I know that I can ask this cuties which are living their best lives about everything and they`ll give me advice what I can trust for sure I associate this deck with 2 crystals: ☽ dalmatinian stone. This stone also known as "faithful companion". It teaches that life always consists of light and darkness. It uncovers your strengths and abilities, brings a sense of childlike wonder ☽ labradorite. It inspires a journey of self-discovery. Also this crystal increases awarness of synchronicity and confidence, protects the aura of negative energies (Stones descriptions are taken from Crystalyze app which helping me a lot lately)

[i-write-boop-spoops]

Why did I read nepotism and inmediately though of Steven?, speaking of Steven I agree with you boopy he should have at least made a cameo in Scarlet violet it feels weird having this gimick of crystalyzing pokemon and not having him there.

honestly dear anon? steven kinda inspired it haha - although his battling prowess alone is more than enough to justify his reign as champion, there's no denying that coming from a well-off, well-known family certainly played a role in making him get that far - whether it's his access to rare pokemon and mega evolution or simply just the ability to take time off to pursue the league without worrying about income.

The thing is, Steven openly, and graciously, acknowledges his privilege when it comes to his position. Furthermore, he's a generous guy, he's always willing to lend a hand/put a word in/give money and useful items to people in need. So while definitely a nepo baby, he's not an ass about it.

Other nepo babies that spring to mindb(though to different degrees) are Blue, Nemona, Sonia, Roark, Janine, Lisia and Sophocles. In fairness though, a lot of this is more of people taking an interest in the family business rather than coasting off their relatives.

I don't want to be greedy when it comes to Steven showing up in things. Pokemon has been very gpod to us in the past two years with all the steven alts in masters and his appearance in the masters eight. it just would've been cool for everyone's favourite rock nerd to show up! i really do think SV needs a cameo or two from characters from past games (and not just steven!)

[sunaleisocial]

AI model can reveal the structures of crystalline materials

New Post has been published on https://sunalei.org/news/ai-model-can-reveal-the-structures-of-crystalline-materials/

AI model can reveal the structures of crystalline materials

For more than 100 years, scientists have been using X-ray crystallography to determine the structure of crystalline materials such as metals, rocks, and ceramics.

This technique works best when the crystal is intact, but in many cases, scientists have only a powdered version of the material, which contains random fragments of the crystal. This makes it more challenging to piece together the overall structure.

MIT chemists have now come up with a new generative AI model that can make it much easier to determine the structures of these powdered crystals. The prediction model could help researchers characterize materials for use in batteries, magnets, and many other applications.

“Structure is the first thing that you need to know for any material. It’s important for superconductivity, it’s important for magnets, it’s important for knowing what photovoltaic you created. It’s important for any application that you can think of which is materials-centric,” says Danna Freedman, the Frederick George Keyes Professor of Chemistry at MIT.

Freedman and Jure Leskovec, a professor of computer science at Stanford University, are the senior authors of the new study, which appears today in the Journal of the American Chemical Society. MIT graduate student Eric Riesel and Yale University undergraduate Tsach Mackey are the lead authors of the paper.

Distinctive patterns

Crystalline materials, which include metals and most other inorganic solid materials, are made of lattices that consist of many identical, repeating units. These units can be thought of as “boxes” with a distinctive shape and size, with atoms arranged precisely within them.

When X-rays are beamed at these lattices, they diffract off atoms with different angles and intensities, revealing information about the positions of the atoms and the bonds between them. Since the early 1900s, this technique has been used to analyze materials, including biological molecules that have a crystalline structure, such as DNA and some proteins.

For materials that exist only as a powdered crystal, solving these structures becomes much more difficult because the fragments don’t carry the full 3D structure of the original crystal.

“The precise lattice still exists, because what we call a powder is really a collection of microcrystals. So, you have the same lattice as a large crystal, but they’re in a fully randomized orientation,” Freedman says.

For thousands of these materials, X-ray diffraction patterns exist but remain unsolved. To try to crack the structures of these materials, Freedman and her colleagues trained a machine-learning model on data from a database called the Materials Project, which contains more than 150,000 materials. First, they fed tens of thousands of these materials into an existing model that can simulate what the X-ray diffraction patterns would look like. Then, they used those patterns to train their AI model, which they call Crystalyze, to predict structures based on the X-ray patterns.

The model breaks the process of predicting structures into several subtasks. First, it determines the size and shape of the lattice “box” and which atoms will go into it. Then, it predicts the arrangement of atoms within the box. For each diffraction pattern, the model generates several possible structures, which can be tested by feeding the structures into a model that determines diffraction patterns for a given structure.

“Our model is generative AI, meaning that it generates something that it hasn’t seen before, and that allows us to generate several different guesses,” Riesel says. “We can make a hundred guesses, and then we can predict what the powder pattern should look like for our guesses. And then if the input looks exactly like the output, then we know we got it right.”

Solving unknown structures

The researchers tested the model on several thousand simulated diffraction patterns from the Materials Project. They also tested it on more than 100 experimental diffraction patterns from the RRUFF database, which contains powdered X-ray diffraction data for nearly 14,000 natural crystalline minerals, that they had held out of the training data. On these data, the model was accurate about 67 percent of the time. Then, they began testing the model on diffraction patterns that hadn’t been solved before. These data came from the Powder Diffraction File, which contains diffraction data for more than 400,000 solved and unsolved materials.

Using their model, the researchers came up with structures for more than 100 of these previously unsolved patterns. They also used their model to discover structures for three materials that Freedman’s lab created by forcing elements that do not react at atmospheric pressure to form compounds under high pressure. This approach can be used to generate new materials that have radically different crystal structures and physical properties, even though their chemical composition is the same.

Graphite and diamond — both made of pure carbon — are examples of such materials. The materials that Freedman has developed, which each contain bismuth and one other element, could be useful in the design of new materials for permanent magnets.

“We found a lot of new materials from existing data, and most importantly, solved three unknown structures from our lab that comprise the first new binary phases of those combinations of elements,” Freedman says.

Being able to determine the structures of powdered crystalline materials could help researchers working in nearly any materials-related field, according to the MIT team, which has posted a web interface for the model at crystalyze.org.

The research was funded by the U.S. Department of Energy and the National Science Foundation.

[jcmarchi]

AI model can reveal the structures of crystalline materials

New Post has been published on https://thedigitalinsider.com/ai-model-can-reveal-the-structures-of-crystalline-materials/

AI model can reveal the structures of crystalline materials

For more than 100 years, scientists have been using X-ray crystallography to determine the structure of crystalline materials such as metals, rocks, and ceramics.

This technique works best when the crystal is intact, but in many cases, scientists have only a powdered version of the material, which contains random fragments of the crystal. This makes it more challenging to piece together the overall structure.

MIT chemists have now come up with a new generative AI model that can make it much easier to determine the structures of these powdered crystals. The prediction model could help researchers characterize materials for use in batteries, magnets, and many other applications.

“Structure is the first thing that you need to know for any material. It’s important for superconductivity, it’s important for magnets, it’s important for knowing what photovoltaic you created. It’s important for any application that you can think of which is materials-centric,” says Danna Freedman, the Frederick George Keyes Professor of Chemistry at MIT.

Freedman and Jure Leskovec, a professor of computer science at Stanford University, are the senior authors of the new study, which appears today in the Journal of the American Chemical Society. MIT graduate student Eric Riesel and Yale University undergraduate Tsach Mackey are the lead authors of the paper.

Distinctive patterns

Crystalline materials, which include metals and most other inorganic solid materials, are made of lattices that consist of many identical, repeating units. These units can be thought of as “boxes” with a distinctive shape and size, with atoms arranged precisely within them.

When X-rays are beamed at these lattices, they diffract off atoms with different angles and intensities, revealing information about the positions of the atoms and the bonds between them. Since the early 1900s, this technique has been used to analyze materials, including biological molecules that have a crystalline structure, such as DNA and some proteins.

For materials that exist only as a powdered crystal, solving these structures becomes much more difficult because the fragments don’t carry the full 3D structure of the original crystal.

“The precise lattice still exists, because what we call a powder is really a collection of microcrystals. So, you have the same lattice as a large crystal, but they’re in a fully randomized orientation,” Freedman says.

For thousands of these materials, X-ray diffraction patterns exist but remain unsolved. To try to crack the structures of these materials, Freedman and her colleagues trained a machine-learning model on data from a database called the Materials Project, which contains more than 150,000 materials. First, they fed tens of thousands of these materials into an existing model that can simulate what the X-ray diffraction patterns would look like. Then, they used those patterns to train their AI model, which they call Crystalyze, to predict structures based on the X-ray patterns.

The model breaks the process of predicting structures into several subtasks. First, it determines the size and shape of the lattice “box” and which atoms will go into it. Then, it predicts the arrangement of atoms within the box. For each diffraction pattern, the model generates several possible structures, which can be tested by feeding the structures into a model that determines diffraction patterns for a given structure.

“Our model is generative AI, meaning that it generates something that it hasn’t seen before, and that allows us to generate several different guesses,” Riesel says. “We can make a hundred guesses, and then we can predict what the powder pattern should look like for our guesses. And then if the input looks exactly like the output, then we know we got it right.”

Solving unknown structures

The researchers tested the model on several thousand simulated diffraction patterns from the Materials Project. They also tested it on more than 100 experimental diffraction patterns from the RRUFF database, which contains powdered X-ray diffraction data for nearly 14,000 natural crystalline minerals, that they had held out of the training data. On these data, the model was accurate about 67 percent of the time. Then, they began testing the model on diffraction patterns that hadn’t been solved before. These data came from the Powder Diffraction File, which contains diffraction data for more than 400,000 solved and unsolved materials.

Using their model, the researchers came up with structures for more than 100 of these previously unsolved patterns. They also used their model to discover structures for three materials that Freedman’s lab created by forcing elements that do not react at atmospheric pressure to form compounds under high pressure. This approach can be used to generate new materials that have radically different crystal structures and physical properties, even though their chemical composition is the same.

Graphite and diamond — both made of pure carbon — are examples of such materials. The materials that Freedman has developed, which each contain bismuth and one other element, could be useful in the design of new materials for permanent magnets.

“We found a lot of new materials from existing data, and most importantly, solved three unknown structures from our lab that comprise the first new binary phases of those combinations of elements,” Freedman says.

Being able to determine the structures of powdered crystalline materials could help researchers working in nearly any materials-related field, according to the MIT team, which has posted a web interface for the model at crystalyze.org.

The research was funded by the U.S. Department of Energy and the National Science Foundation.

[enternecersarc]

      madison  rarely  ever  came  to  greendale,  keeping  a  safe  distance  from  her  spellman  cousins  (  the  good  side  of  the  family,  believe  it  or  not.  )  yet  when  she  heard  the  church  of  the  night  was  in  trouble?  the  blonde  did  not  think  twice  before  taking  a  plane.  it  was  why  she  was  now  standing  on  the  mortuary’s  doorstep  &  knocking  impatiently.  until  sabrina  opened  the  door,  a  faux  smile  blooming  in  ruby  lips.  ❝  cousin  dearest,  surprise!  ❞

@crystalyz​  :  sc.

[therelentless]

Send 💬  for me to make you a starter with a random line of dialogue II accepting II

{ @crystalyz​ ;; CLAUDIA

"Oooouch! Why did you scream like that?" He asked as he covered his ears.

[teachextreme]

COBRA KAI QUOTES MEME || @crystalyz​

“de-escalation   will   take   you   nowhere,   kid.   specially   around   these   parts.“   it’s   amusing   really,   that   the   kid   thinks   that   he   is   one   to   need   a   lecture   on   what   and   doesn’t   work.   he   had   tried   that   way   once.   de-escalte   the   situation,   and   it   had   only   led   him   to   where   he   is   now.   so   in   his   mind,   it’s   useless.   “you   are   one   of   those   kids   danny   boy   is   teaching,   right?   because   that   sounds   awfully   like   him.   hypocritical,   really,   considering   how   much   of   a   hothead   he   is.“   and   he   enjoys   this.   planting   the   seed   of   larusso’s   past   into   anyone   else’s   mind.   the   girl   is   friends   with   larusso’s   daughter,   and   he   enjoys   the   little   mind   games,   because   only   he   knows   the   rules.   “why   don’t   you   ask   him   how   he.   .   de-escalted   the   fight   he   had   with   kreese   around   christmas?   i’m   sure   he   can   tell   you   about   that.“

[adveanture]

    *    @crystalyz​​    —    WHAT  ARE  YOU  DOING?   SOMEONE’S  GOING  TO  SEE  YOU    (   kiara  &  kovu   ) 

IT   IS   NO   DIFFICULT   MATTER   FOR   KOVU   TO   REMAIN   UNSEEN.         even   here   in   the   pridelands,   even   here   in   this   different   place   with   different   people,   his   skills   do   not   diminish.      he   smirks   down   at   her   in   the   dark,   pressing   a   kiss   to   her   lips. 

“      and?      it’s   not   like   we   have   to   sneak   around   anymore   .   .   .      ”   

[cryptiique]

“ i must be hurt pretty bad if you’re being this nice to me. “

NASIR !!!!!!!!!!!! TO HIS SILLY BOY AGRON !!!!!!!!!!!! ( @crystalyz )

broad shoulders loosen from where they've unknowingly been clenched for ages - since he watched nasir fall with the wound. agron had not known that his tells were so apparent to display his concern as bright as day. or perhaps it is simply nasir, who has scarcely been absent from his side since their coupling. he shifts, but his eyes remain on the wound, bound and tended by one of their gentle-fingered women.

" I am always nice to you, " he grumbles, reaching to smooth the perfectly flat edge of the bandage. a primal, childish part of his mind, the part that has been struck too many times, finds a useless sense of comfort from feeling the warmth of his boy's skin, the reminder of his beating heart.

" I failed - to protect you. you deserve kind treatment. "

[spphirrguearc]

          ANOTHER CHILD LINGERED. Holly, enthusiastic &&. joyful at a new found friend, had spoken highly of him. While she was certain of his mother’s abilities, from one mother to another, she did worry. Skyhold had grown colder as winter dawned upon them. Hands carrying cloak, simplistic yet one she certain would keep a child warm, as hurried steps carried her through the gardens.

          ❛  Hello, my name is Serene, I’m Holly’s mother.  ❜  WARM WELCOME PRESENT. A sign of kindness, to not scare the boy. A scolding from lady Morrigan was best avoided, for quarrels between mother’s was something she often aimed to avoid.  ❛  I brought you this, Skyhold grows colder as of late and Holly kept worrying about you catching a cold.  ❜  /  @crystalyz​​  ♡  ‘  d  .  ( for keiran )

[rhaegxr]

@crystalyz​ as Mallory said: ❛ names have power. ❜

░▒▌╳▐ ʀ ʜ ᴀ ᴇ ɢ ᴀ ʀ

        𝐈𝐓 𝐖𝐀𝐒 𝐈𝐌𝐏𝐎𝐒𝐒𝐈𝐁𝐋𝐄 𝐓𝐎 𝐍𝐎𝐓  see that there was something different her, just as HE has always been apart from the rest. Even his 'brothers' thought him  STRANGE  but Rhaegar would later learn why it had to be that way. One with such a great  DUTY  as him, with a calling to deliver the future itself from  DARKNESS,  is unlike anyone else. Until he met Mallory, and she began to make the man question all that he once thought was irrefutable. Maybe he's not to do this alone—If he can  CONVINCE  her of that.

        ❝ You shouldn't be afraid of power, ❞ he says, his voice calm. One hand was lifting in a motion that caused all nearby candles to simultaneously burst  AFLAME.  ❝ You should be afraid of lacking it when you need it the most. ❞ And there will come the day when they both shall need it.

[alistheir]

[ FOUR ] + for calenha!

FOR THE “YOU CAN KILL ME BUT DON’T YOU DARE TOUCH THEM” DYNAMICS || @crystalyz

Father was the most difficult of titles with held on shoulders made for war, scars giving an edge of what made warriors worthy to carry the worries of what burdens no innocent had to. Yet cradled in hands that calloused with ease, was a little head delicate enough to bear a crown wreathed in golden curls. A daughter that knew better than to climb trees far too tall without a chaperone. Panic wrought itself in bitter circles that feet recited, rocking the child in arms more suited to hold a shield. Soothing words were lost to him, buried beneath sarcasm and biting words meant for a court of thorns. No doubt melting that image of a strong leader as he carried her close as if she were made of glass. Hushing tones emitted interwoven with the stroking of a hand on golden locks, trying to calm wide eyes filling with tears and soft cooing of a cry now setting in.

"You're okay! You're okay! I'll- I'll get you a mabari... please don't tell your mother. ", strides turned frantically in the direction of the castles healer. Feet moving on hope alone that the queen wouldn't hear of his uncareful vigilance.

[hiddensteel]

@crystalyz​  said             ‘  i  wouldn’t  care  if  you  were  part  human  or  part  frog.  ’ + tommen being cute.

        she couldn’t help but giggle. the young prince reminded sansa of her younger brothers. despite all that had happened, sansa couldn’t bring herself to feel even an ounce of hatred for tommen. he was only a little boy and nothing like his mother or older brother. so even though sorrow consumed her, sansa smiled warmly.          ❝  part frog? but that would mean i would’ve been CURSED by a witch! ❞          tully eyes widened in mock horror. though, she had been cursed. perhaps being turned into a frog would have been better than her current f a t e.          ❝  a brave knight would need to defeat the witch to BREAK THE CURSE. you’d defeat her, wouldn’t you, prince tommen? ❞

the language of thorns sentence prompts

[iinmortales]

         oh. he’d stuck his foot in his mouth. he could tell as soon as he saw the look on her face. quick! think! what had he said that would make someone sad? they’d been talking about childhood memories, how they grew up, he’d started talking about tony-- oh. ‘ he’s gone in this world, isn’t he? ‘ which, to be fair, he would only recognize that particular look because he’d seen it on his own face in the mirror so many times. the pain of never knowing a parent. ‘ i’m sorry, i’m an idiot. i assumed and that was stupid. ‘

@crystalyz​ james for morgan!