You are great explaining in detail and understanding. Already subscribed and I forward this video to my daughter just starting python. This is a big help.
@TaylorSparks5 минут бұрын
@@oliviacaballero3679 thank you so much. I'm not like a coding expert, but I hope that I've provided something valuable with this playlist
@christopherleubner663321 сағат бұрын
You can use magnesium instead of calcium with a pinch of calcium or magnesium chloride or a small crystal of iodine to start the reaction process. It allows the excess reducing agent to be boiled off. The process can also be used for making high purity lanthanides as well. ❤
@TaylorSparks9 сағат бұрын
I love these "Secret recipe" tips that accumulate with experience in the lab over time. Thanks for sharing.
@miguelvarcuКүн бұрын
That is an amazing package. Especially after learning a bit about Bayesian optimization and thoroughly reading the Ax documentation, it really helps you overcome that learning stage where you think you understand the concepts, but it’s still challenging to start implementing them on your own. Thank you for your work, Taylor, Sterling, Andrew, and others. Gracias, amigos!
@danielwilkinson6508Күн бұрын
Awersome Video, watched a couple of videos on CCT and TTT diagrams and this is byfar the best . Thanks Taylor
@TaylorSparksКүн бұрын
Gracias homie
@Pingu_astrocat21Күн бұрын
So cool! I am also working on something like this.
@shivanshgupta10812 күн бұрын
Thank you so much Prof.
@Mind_Connection2 күн бұрын
Taylor, please, explain why for G all of it didnot turn to pearlite?
@TaylorSparks2 күн бұрын
@@Mind_Connection no you are right. I messed that one up and it should be 100% perlite. I think I didn't see that first region above when I recorded it
@hellfridge47362 күн бұрын
Shoutout to the goat himself keepin all of us material engineers on our feet with these fire videos🔥 Especial thanks from Iran, IUST❤
@TaylorSparks2 күн бұрын
thanks homie!
@shivanshgupta10812 күн бұрын
Thanks Prof Sparks...these concepts I wanna learn today...and fortunately you uploaded...thanks allot..... Waiting for concepts on point group and space group and conversion.
@TaylorSparks2 күн бұрын
@@shivanshgupta1081 heck yeah. I'll post the next video tomorrow or Tuesday ;)
@Swormy0972 күн бұрын
@@TaylorSparksThank you so much. You just can't imagine how much you impact us with these lectures. Stay blessed Prof
@thankyoulife46833 күн бұрын
Thanks for explaining really helped
@dsz52693 күн бұрын
Hi. I'm an undergraduate student.materialism podcast helps me a lot. Thank you
@TaylorSparks3 күн бұрын
@@dsz5269 I love hearing this
@juliangawronsky93395 күн бұрын
Great intro
@rahulsharma-sg7qu5 күн бұрын
Hey, good presentation, may you suggest me how to make tilt Coincidence site lattice grain boundary for monoclinic structure using VESTA(or any other way)?
@iskalasrinivas56405 күн бұрын
Why some oxygen have cn = 2
@BrianBermejo7 күн бұрын
Thanks a lot. Very useful video so I'm already subscribed
@TaylorSparks7 күн бұрын
@@BrianBermejo enjoy the series!!
@anirbandas17329 күн бұрын
can we get these notes?
@TaylorSparks9 күн бұрын
@@anirbandas1732 yes. I'll be posting a new GitHub course soon.
@@TaylorSparksthese lectures are so easy to understand 🙏🏼
@jimlbeaver10 күн бұрын
Wow, extremely cool.
@junfour13 күн бұрын
It's so counter-intuitive that you have two choices for an optimal packing
@barosnb14 күн бұрын
That is what I was looking for, thanks so much!!
@TaylorSparks14 күн бұрын
@@barosnb my pleasure. Like and sub please!!
@grantripperger879214 күн бұрын
Just the perfect video. Straight and to the point. Thank you
@TaylorSparks14 күн бұрын
I strive for perfection 😂
@ar1993091015 күн бұрын
I cannot thank you and Sterling for not only doing the amazing stuff that you guys do but also in sharing it with others. Ax and Honegumi have helped me so much in starting with Bayesian optimization. Thanks a lot once again. ❤
@TaylorSparks15 күн бұрын
@@ar19930910 I love hearing it!! We'll keep releasing helpful tools and content
@mustfabaki16 күн бұрын
i don’t know if you’ll ever see this but i got an exam in a few days and i didn’t get the point where u said at 5:28 g is 50% bainite and martensite. why isn’t it a 100% perlite instead? the reaction is over. it passed the 3rd curve. i didn’t get it.
@TaylorSparks16 күн бұрын
@@mustfabaki because once the reaction has taken place, this is not reversible until you heat the sample back up above the austinite temperature. So if part of the sample has transformed then there's only 50% left to transform under a different condition
@jinlongsu730816 күн бұрын
An excellent video. Thank you so much professor! 😀May I ask that if I want to optimise two or more properties together (e.g., strength and ductility), would it be better if I use a Multi-task Gaussian Process (MTGP) model, rather than several individual GP models? Because the trade-off between these properties can be learned by the MTGP model? By the way, may I check that can the iteration of the bayesian optimisation process also be termed as Active Learning, if I am not wrong? Many thanks for your attention. 🙏
@TaylorSparks16 күн бұрын
@@jinlongsu7308 I would simply do a multi objective optimization. Multitask is typically used when one property has more statistical strength than the other and the two properties are related their by allowing you to lean on the statistical strength of the one with the larger data set
@jinlongsu730816 күн бұрын
@@TaylorSparks Noted. Many thanks for your prompt reply! 😀
@LM5318017 күн бұрын
EDX is cool at times, but you know what isn't cool? Matrix effects with EDX, particularly with Pb. I am banging my head searching for papers that show how to perform this using EDX software. Even with obtaining values from ICP and then using those as data to generate calibration curves using EDX is not so simple and doesn't work sometimes. User beware, EDX is ideal for high purity metals, but for samples with a complex matrix, WDS is better, or better yet ICP.
@TaylorSparks17 күн бұрын
@@LM53180 totally agree. EDS < WDS < ICP
@kodiererg17 күн бұрын
24.25 hours.Leap years are real. What text book are you using?
@TaylorSparks17 күн бұрын
@@kodiererg Good catch. I'm using callister's book. In my opinion. It's the best. I know the author too, he's a great guy and my neighbor!
@kodiererg17 күн бұрын
@TaylorSparks Thanks for the reply. I'll look for it. I'm a refinery inspector, and I'm trying to expand my knowledge of the materials I inspect. Your videos are great!
@vrhstpso18 күн бұрын
Thank you professor 🙏. If we knew the objective function ( for example mean squared error equation) would it be logical to make use of bayesian optimization to find the minumum of that objective function which is computed for aimed optimized parameters in a given domain?
@TaylorSparks18 күн бұрын
@@vrhstpso sure, but it is more often the case that we don't know the objective function and we are instead trying to learn it using a surrogate model such as a gaussian process.
@vrhstpso17 күн бұрын
@@TaylorSparks thank you.
@Slapshottwelve18 күн бұрын
Recently started my Master's in Materials Science and Engineering and if it was not for these videos I probably would not have passed my exams.... Your teaching skills, knowledge and passion convey the topics perfectly! I am sure I will be back
@TaylorSparks18 күн бұрын
@@Slapshottwelve thank you! Like, sub , and share to help me grow the channel 🙂
@idamonica661418 күн бұрын
How do we calculate the Madelung constant for CsCl??
@gusaimasakbar265621 күн бұрын
Hi there! I enjoyed the episode a lot! Would it be possible to share the paper that pointed out only high-Mn HEAs really exhibit sluggish diffusion? Would love to read more about it! Ps. Would it be possible to explore TRIP and TWIP mechanisms for alloys in future episodes?
@TaylorSparks21 күн бұрын
@@gusaimasakbar2656 I think it was this one. www.sppccahea.uni-bayreuth.de/pool/dokumente/Publications-Uni-Muenster/DF-review-diffusion-HEAs.pdf TWIP/TRIP would be a cool episode. I'll run it by Andrew
@BobClemintime21 күн бұрын
I thought if Q is less than K you know the reactions proceed forward?
@cooldude11_121 күн бұрын
Keep going bravo
@Wackylemon6425 күн бұрын
Not sure if you've already found this arxiv preprint of an IEEE-published paper on LLM knowledge graph population yet: arXiv:2306.08302 It's about the field of using models to generate knowledge graphs from literature, and using these knowledge graphs as a knowledgebase to feed to a model trouble is that the models are constructed to replicate patterns, and it can easily make stuff that resembles meaningful literature but doesn't contain correct information. I would make sure to go over the output with a finetooth comb to make sure the values actually match the source material, outside of the obvious outliers where it's creating entirely irrelevant structured data - like what component metals go into a material, crystal structure, or similar.
@basharb521526 күн бұрын
I paused in the middle of the video to subscribe 👌
@TaylorSparks26 күн бұрын
@@basharb5215 hero :)
@bikashthapa862226 күн бұрын
Can you please make a tutorial for new updated material project API and building various machine learning models?
@TaylorSparks25 күн бұрын
I have a notebook already prepared for the new API. Here's the link. Does it have what you are looking for? If you have additional questions, let me know and I can update it with additional content. github.com/sp8rks/MaterialsInformatics/blob/main/worked_examples/MP_API_example/new_MPRester_tutorial.ipynb
@TaylorSparks25 күн бұрын
And this video has a bunch of instructions on cleaning, featurizing, splitting data and building classic models. Feel free to use the bookmarks at the bottom to jump right to the timestamp of interest kzfaq.info/get/bejne/a8x9pZeevtuniH0.htmlsi=PDp3NgWM3kEYTZqX
@bikashthapa862223 күн бұрын
@@TaylorSparks Thank you professor for your help
@Pingu_astrocat2126 күн бұрын
Such a great video. Thanks a lot :) i am working on this so this helps a lot
@TaylorSparks26 күн бұрын
@@Pingu_astrocat21 glad to help!
@jimlbeaver27 күн бұрын
Great job explains this!
@TaylorSparks26 күн бұрын
Stanley has a some brand new notebooks to accompany it coming soon ;) I'm reviewing them tomorrow with him
@kwamivikolor527628 күн бұрын
What I need right now. Please Sensei, is formation energy per atom here related to enthalpy of formation? What is the significance actually?
@TaylorSparks28 күн бұрын
@@kwamivikolor5276 Formation energy per atom focuses on the energy change per atom in a compound, commonly used in computational materials science. Enthalpy of formation is a broader thermodynamic measure of the heat change when forming one mole of a compound from its elements. Both indicate stability, but formation energy per atom is more specific to atomic-level stability, while enthalpy of formation applies to bulk thermodynamic processes.
@kwamivikolor527628 күн бұрын
@@TaylorSparks Thank you Sensei. Therefore in terms of sign(+/-) of the formation energy per atom what kind of intuition can we develop? I mean when the formation energy per atom is negative or positive or null?
@prapraraowodeha-ashaka139328 күн бұрын
Yessr
@8848nepalyt29 күн бұрын
Thank you for this video professor. The problem I feel about such programming is that it is difficult to make the program understand science rather than just learning patterns from a large data.
@TaylorSparks29 күн бұрын
@@8848nepalyt yup. Some machine learning models are black boxes with limited interpretability. Others are more interpretable. Random forests, for example, provide feature weights that can help intuite mechanistic understanding
@alimirabbasi7038Ай бұрын
Very useful video. Please teach how to refine materials with partial occupancy and how to apply constraints on it
@BenFowlkes-y6bАй бұрын
this is the greatest explanation of this I've ever gotten
@cadinsider964Ай бұрын
how do we read SN curves after getting hot spot stress
@Hamza-vk6scАй бұрын
Hi professor, would you please talk on XAS, XPS!
@TaylorSparksАй бұрын
@@Hamza-vk6sc this is an excellent idea.
@MATHBOSS287Ай бұрын
can you do a playlist for all the phase diagram videos
@AliakbarKhosravi-th3mvАй бұрын
Greetings and courtesy and respect Please advise where is the metallurgy database in materials? Data such as strain rate, analysis, air humidity, etc
@TaylorSparksАй бұрын
@@AliakbarKhosravi-th3mv unfortunately there isn't a great source for this. Best bet is materials data facility by globus
@AliakbarKhosravi-th3mvАй бұрын
@@TaylorSparks you mean globus?
@TaylorSparksАй бұрын
@@AliakbarKhosravi-th3mv yes
@AliakbarKhosravi-th3mvАй бұрын
@@TaylorSparks I do not know what is it globus
@TaylorSparksАй бұрын
@@AliakbarKhosravi-th3mv materialsdatafacility.org/ This is the closest thing we have to a generalized materials data repository. If it's not here I don't know where else you would find it.
@sadface7457Ай бұрын
Could you cover dual phase metals next
@TaylorSparksАй бұрын
Any alloy in particular? Most alloy systems are multiple phase mixtures.
@Bandit-u3uАй бұрын
@@TaylorSparks could you do dielectric and diffractive films like the kind they use on euv mirrors? I've seen something in literature with neutron diffraction
@m43_felixАй бұрын
would be cool to have such a breakdown in terms of plastic recyclates and their possible morphologies
@lakshmiviswanadha3454Ай бұрын
I've been struggling to learn Python. I attended your seminar at the Texas A&M material science summer workshop in 2023 and found your videos! They've been a real life-saver! Thank you so much.
@TaylorSparksАй бұрын
@@lakshmiviswanadha3454 this makes me so so happy to hear. I need to reteach this course in the era of LLM-generated code. Total game changer.