Materials - Revision history

BPeat at 16:36, 4 July 2023

2023-07-04T16:36:52Z

BPeat at 12:17, 8 February 2022

2022-02-08T12:17:26Z

BPeat at 12:12, 8 February 2022

2022-02-08T12:12:14Z

BPeat at 14:19, 4 December 2020

2020-12-04T14:19:25Z

BPeat at 15:45, 8 October 2020

2020-10-08T15:45:42Z

BPeat at 16:18, 31 August 2020

2020-08-31T16:18:20Z

BPeat at 19:45, 28 August 2020

2020-08-28T19:45:07Z

BPeat at 19:43, 28 August 2020

2020-08-28T19:43:53Z

BPeat at 14:36, 9 August 2020

2020-08-09T14:36:45Z

BPeat at 23:49, 15 July 2020

2020-07-15T23:49:45Z

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
 [http://www.youtube.com/results?search_query=Materials+Quantum+electrons+molecules+artificial+intelligence+deep+learning Youtube search...]

@@ Line 5: / Line 5: @@
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
-[http://www.youtube.com/results?search_query=Materials+artificial+intelligence+deep+learning Youtube search...]
+[http://www.youtube.com/results?search_query=Materials+Quantum+electrons+molecules+artificial+intelligence+deep+learning Youtube search...]
-[http://www.google.com/search?q=Materials+deep+machine+learning+ML ...Google search]
+[http://www.google.com/search?q=Materials+Quantum+electrons+molecules+artificial+intelligence+deep+learning ...Google search]
 * [[Case Studies]]
 ** [[Fabrics & Textiles]]
-** [[Physical]]
+** [[Quantum]]
 * [http://jarvis.nist.gov/ NIST-JARVIS (Joint Automated Repository for Various Integrated Simulations)] ...JARVIS-ML introduced Classical Force-field Inspired Descriptors (CFID) as a universal framework to represent a material’s chemistry-structure-charge related data
 * [http://www.sciencedirect.com/science/article/pii/S2352847817300515 Materials discovery and design using machine learning | Yue Liua, Tianlu Zhaoa, Wangwei Jua, Siqi Shi ScienceDirect]
 * [http://www.theverge.com/2018/4/25/17275270/artificial-intelligence-materials-science-computation How AI is helping us discover materials faster than ever | Angela Chen - The Verge]
 * [http://www.defenseone.com/technology/2020/08/can-ai-solve-rare-earths-problem-chinese-and-us-researchers-think-so/168057/ Can AI Solve the Rare Earths Problem? [[Government Services#China|Chinese]] and U.S. Researchers Think So | Patrick Tucker - Defense One] ... research effort funded by [[Government Services#China|China]] and the U.S. could speed up the discovery of new materials to use in electronics.
 {|<!-- T -->

@@ Line 10: / Line 10: @@
 * [[Case Studies]]
 ** [[Fabrics & Textiles]]
 * [http://jarvis.nist.gov/ NIST-JARVIS (Joint Automated Repository for Various Integrated Simulations)] ...JARVIS-ML introduced Classical Force-field Inspired Descriptors (CFID) as a universal framework to represent a material’s chemistry-structure-charge related data
 * [http://www.sciencedirect.com/science/article/pii/S2352847817300515 Materials discovery and design using machine learning | Yue Liua, Tianlu Zhaoa, Wangwei Jua, Siqi Shi ScienceDirect]

@@ Line 13: / Line 13: @@
 * [http://www.sciencedirect.com/science/article/pii/S2352847817300515 Materials discovery and design using machine learning | Yue Liua, Tianlu Zhaoa, Wangwei Jua, Siqi Shi ScienceDirect]
 * [http://www.theverge.com/2018/4/25/17275270/artificial-intelligence-materials-science-computation How AI is helping us discover materials faster than ever | Angela Chen - The Verge]
-* [http://www.defenseone.com/technology/2020/08/can-ai-solve-rare-earths-problem-chinese-and-us-researchers-think-so/168057/ Can AI Solve the Rare Earths Problem? Chinese and U.S. Researchers Think So | Patrick Tucker - Defense One] ... research effort funded by [[Government Services#China|China]] and the U.S. could speed up the discovery of new materials to use in electronics.
+* [http://www.defenseone.com/technology/2020/08/can-ai-solve-rare-earths-problem-chinese-and-us-researchers-think-so/168057/ Can AI Solve the Rare Earths Problem? [[Government Services#China|Chinese]] and U.S. Researchers Think So | Patrick Tucker - Defense One] ... research effort funded by [[Government Services#China|China]] and the U.S. could speed up the discovery of new materials to use in electronics.
 {|<!-- T -->

@@ Line 2: / Line 2: @@
 |title=PRIMO.ai
 |titlemode=append
-|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
+|keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS
 |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
 }}
@@ Line 13: / Line 13: @@
 * [http://www.sciencedirect.com/science/article/pii/S2352847817300515 Materials discovery and design using machine learning | Yue Liua, Tianlu Zhaoa, Wangwei Jua, Siqi Shi ScienceDirect]
 * [http://www.theverge.com/2018/4/25/17275270/artificial-intelligence-materials-science-computation How AI is helping us discover materials faster than ever | Angela Chen - The Verge]
-* [http://www.defenseone.com/technology/2020/08/can-ai-solve-rare-earths-problem-chinese-and-us-researchers-think-so/168057/ Can AI Solve the Rare Earths Problem? Chinese and U.S. Researchers Think So | Patrick Tucker - Defense One] ... research effort funded by China and the U.S. could speed up the discovery of new materials to use in electronics.
+* [http://www.defenseone.com/technology/2020/08/can-ai-solve-rare-earths-problem-chinese-and-us-researchers-think-so/168057/ Can AI Solve the Rare Earths Problem? Chinese and U.S. Researchers Think So | Patrick Tucker - Defense One] ... research effort funded by [[Government Services#China|China]] and the U.S. could speed up the discovery of new materials to use in electronics.
 {|<!-- T -->

@@ Line 47: / Line 47: @@
 <b>Intro to Machine Learning for materials scientists
 </b><br>This webinar is intended as a brief overview of some of the key concepts of machine learning (ML).
 |}
 |}<!-- B -->

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 {| class="wikitable" style="width: 550px;"
 ||
-<youtube>u5qFwTdf4xM</youtube>
+<youtube>28Ue_jteKI4</youtube>
-<b>Machine Learning in Materials Science
+<b>IACS Seminar: "Machine Learning for Materials Discovery" 11/30/2018
-</b><br>Presentation made by Prof. Ramprasad at an IPAM workshop in UCLA (September 2016)
+</b><br>Presented by Dr. Julia Ling, Director of Data Science at Citrine Informatics Talk abstract:  Materials science presents a unique set of challenges and opportunities for machine learning methods in terms of data size, data sparsity, available domain knowledge, and multi-scale physics.  In this talk, Dr. Ling will discuss how machine learning can be used to accelerate materials discovery through a sequential learning workflow.  You'll examine how domain knowledge can be integrated into data-driven models, the role of uncertainty quantification in driving exploration of new design candidates, and how to forecast the impact of a data-driven approach on a given materials discovery campaign. Speaker Bio:  Dr. Julia Ling received her bachelors in Physics from Princeton University and her PhD in Mechanical Engineering from Stanford University. She was a Harry S. Truman Fellow at Sandia National Labs, where her researched focused on applying machine learning to turbulence modeling. She is currently the Director of Data Science at Citrine Informatics, leading a team that applies data-driven methods to materials science applications.
 |}
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@@ Line 36: / Line 36: @@
 {| class="wikitable" style="width: 550px;"
 ||
-<youtube>28Ue_jteKI4</youtube>
+<youtube>u5qFwTdf4xM</youtube>
-<b>IACS Seminar: "Machine Learning for Materials Discovery" 11/30/2018
+<b>Machine Learning in Materials Science
-</b><br>Presented by Dr. Julia Ling, Director of Data Science at Citrine Informatics Talk abstract:  Materials science presents a unique set of challenges and opportunities for machine learning methods in terms of data size, data sparsity, available domain knowledge, and multi-scale physics.  In this talk, Dr. Ling will discuss how machine learning can be used to accelerate materials discovery through a sequential learning workflow.  You'll examine how domain knowledge can be integrated into data-driven models, the role of uncertainty quantification in driving exploration of new design candidates, and how to forecast the impact of a data-driven approach on a given materials discovery campaign. Speaker Bio:  Dr. Julia Ling received her bachelors in Physics from Princeton University and her PhD in Mechanical Engineering from Stanford University. She was a Harry S. Truman Fellow at Sandia National Labs, where her researched focused on applying machine learning to turbulence modeling. She is currently the Director of Data Science at Citrine Informatics, leading a team that applies data-driven methods to materials science applications.
+</b><br>Presentation made by Prof. Ramprasad at an IPAM workshop in UCLA (September 2016)
 |}
 |<!-- M -->

← Older revision		Revision as of 23:49, 15 July 2020
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