[{"data":1,"prerenderedAt":377},["ShallowReactive",2],{"$fgukOamtKU1RtUiMFsqdObttmqPPQz0uc7bl_gj_LyX0":3,"$fMIGn4Qg74odZ63EHwlakpRfQ9_o3h7_g_KBdxPM0Ft8":245,"article-63":376},{"code":4,"msg":5,"data":6},0,"",{"category":7,"tag":11,"popular":19,"latest":86,"banner":126,"list":151,"cache":244},[8,9,10],"Agent","OpenAI","LLM",[8,12,13,14,9,10,15,16,17,18],"Google","Nvidia","Claude","DeepSeek","OCR","Chat","Generator",[20,29,37,45,54,62,70,79],{"id":21,"publish_date":22,"is_original":23,"collection":5,"cover_url":24,"cover_url_1_1":25,"title":26,"summary":27,"author":28},411,"2023-09-10",1,"article_res/cover/451ef50c225a8dc61c4336506794d13b.jpeg","article_res/cover/3ba9dc7a72f87d40b20fc2d225289ee3.jpeg","Idealism","Reality is created by the mind, we can change our reality by changing our mind. - Plato","Renee's Entrepreneurial Journey",{"id":30,"publish_date":31,"is_original":23,"collection":32,"cover_url":33,"cover_url_1_1":34,"title":35,"summary":36,"author":28},108,"2024-12-07","#LLM #AGI #AI Agent","article_res/cover/0039044422e4ec9f61c18e8ee1693bb0.jpeg","article_res/cover/4220971b108a91d21407d87bb02fbaa6.jpeg","Freysa.ai: The World's First Adversarial AI Agent Game","说服 Freysa 把钱包里的钱都拿出来",{"id":38,"publish_date":39,"is_original":23,"collection":40,"cover_url":41,"cover_url_1_1":42,"title":43,"summary":44,"author":28},12,"2025-03-09","#Oxford #Reasoning #LLM #Tool Use","article_res/cover/d448e9b3617a0b5302e1bd10c438bca9.jpeg","article_res/cover/864a468f9cc4c9317efadb3811909888.jpeg","Agentic Reasoning Framework - Significantly enhance the reasoning ability of LLMs through the integration of external tools using agents","Agentic Reasoning: Reasoning LLMs with Tools for Deep Research",{"id":46,"publish_date":47,"is_original":4,"collection":48,"cover_url":49,"cover_url_1_1":50,"title":51,"summary":52,"author":53},480,"2023-04-14","#Stable Diffusion","article_res/cover/0bdbe7cb1de4a78e54536e5d9afa7ec9.jpeg","article_res/cover/b3d6ffec0608dcfaf18c5a69906d1490.jpeg","【AIGC Learning】Generate Prompts Using Word Graphs - Stable Diffusion Web UI Series 13","AI will become a powerful tool in education, transforming the way we learn and deliver instruction.  \n- Reid Hoffman","--",{"id":55,"publish_date":56,"is_original":4,"collection":57,"cover_url":58,"cover_url_1_1":59,"title":60,"summary":61,"author":28},413,"2023-09-08","#Neuroscience","article_res/cover/74f8302d78a23d9430f22171eae136b6.jpeg","article_res/cover/87ca08af81bb304746be5261160964c0.jpeg","Can machines be conscious?","Do we have an ethical obligation to not turn off conscious machines? Would turning them off be murder? No. I don't lose any sleep over unplugging a conscious machine.\n- Jeff Hawkins, \"A Thousand Brains\"",{"id":63,"publish_date":64,"is_original":23,"collection":65,"cover_url":66,"cover_url_1_1":67,"title":68,"summary":69,"author":28},178,"2024-09-09","#Entrepreneurship","article_res/cover/a7224f025b55d1820408085faef63079.jpeg","article_res/cover/11a9995b096cbf64465ef01b8673b154.jpeg","37signals company","This damn sense of relaxation",{"id":71,"publish_date":72,"is_original":4,"collection":73,"cover_url":74,"cover_url_1_1":75,"title":76,"summary":77,"author":78},460,"2023-05-12","#Google","article_res/cover/b970687b12faa52da976f91248c2aa7b.jpeg","article_res/cover/d1e71b52cfd2c63bc6e71f3e85ff135c.jpeg","Learn what BRC-20 and Ordinals are using Google Bard","Ordinals - a new protocol that allows users to store arbitrary data on the Bitcoin blockchain","Google Bard mainly writes",{"id":80,"publish_date":81,"is_original":23,"collection":5,"cover_url":82,"cover_url_1_1":83,"title":84,"summary":85,"author":28},309,"2024-03-26","article_res/cover/9877f95894ee88532d0e6012c23a2df3.jpeg","article_res/cover/20092164ddc109ce6ae56b1984246751.jpeg","Learning the Cancun Upgrade with lepton and perplexity","Building a quick conversation-based search demo with Lepton AI.",[87,95,103,111,119],{"id":88,"publish_date":89,"is_original":23,"collection":90,"cover_url":91,"cover_url_1_1":92,"title":93,"summary":94,"author":28},627,"2025-03-20","#AI Avatar #AI Video Generation","article_res/cover/d95481358f73924989f8c4ee9c75d1c8.jpeg","article_res/cover/b74bc0fab01f8b6a6aa87696c0c3ed8b.jpeg","DisPose: Generating Animated Videos by Driving Video with Reference Images","DisPose is a controllable human image animation method that enhances video generation.",{"id":96,"publish_date":97,"is_original":23,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28},626,"2025-03-21","#Deep Dive into LLMs #LLM #RL #Andrej Karpathy #AlphaGo","article_res/cover/446553a5c8f8f2f07d97b20eaee84e56.jpeg","article_res/cover/e6c2823409c9b34624064b9acbaca6f1.jpeg","AlphaGo and the Power of Reinforcement Learning - Andrej Karpathy's Deep Dive on LLMs (Part 9)","Simply learning from humans will never surpass human capabilities.",{"id":104,"publish_date":105,"is_original":23,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":28},625,"2025-03-22","#Deep Dive into LLMs #LLM #RL #RLHF #Andrej Karpathy","article_res/cover/8da81d38b1e5cf558a164710fd8a5389.jpeg","article_res/cover/96f028d76c362a99a0dd56389e8f7a9b.jpeg","Reinforcement Learning from Human Feedback (RLHF) - Andrej Karpathy's Deep Dive on LLMs (Part 10)","Fine-Tuning Language Models from Human Preferences",{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},624,"2025-03-23","#Deep Dive into LLMs #LLM #Andrej Karpathy #AI Agent #MMM","article_res/cover/a5e7c3d48bb09109684d6513287c661d.jpeg","article_res/cover/d3f22b7c0ab8d82fd2da457a299e0773.jpeg","The Future of Large Language Models - Andrej Karpathy's In-Depth Explanation of LLM (Part 11)","preview of things to come",{"id":120,"publish_date":113,"is_original":23,"collection":121,"cover_url":122,"cover_url_1_1":123,"title":124,"summary":125,"author":28},623,"#Google #Voe #AI Video Generation","article_res/cover/c44062fea0f336c2b96b3928292392c2.jpeg","article_res/cover/a041041c69092ad3db191c5bf3ff981b.jpeg","Trial of Google's video generation model VOE2","Our state-of-the-art video generation model",[127,135,143],{"id":128,"publish_date":129,"is_original":23,"collection":130,"cover_url":131,"cover_url_1_1":132,"title":133,"summary":134,"author":28},300,"2024-04-16","#AI in Science #AGI","article_res/cover/6bf01e793e0f33e848572412eebdf9b0.jpeg","article_res/cover/91a5ee21dafecb914fabeb9430d46ec1.jpeg","Would Einstein lose his job - AI and Quantum Computing: A Glimpse into the Near Future","So Einstein's job is still safe.",{"id":136,"publish_date":137,"is_original":23,"collection":138,"cover_url":139,"cover_url_1_1":140,"title":141,"summary":142,"author":28},101,"2024-12-14","#Nvidia #AI 3D Generator","article_res/cover/693e07c85980c5c0c8fde3f037733f23.jpeg","article_res/cover/9ea8edff2d5d303ff3fffff3f6f9c3d9.jpeg","NVIDIA's open-source 3D project LLaMA-Mesh","LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models",{"id":144,"publish_date":145,"is_original":23,"collection":146,"cover_url":147,"cover_url_1_1":148,"title":149,"summary":150,"author":28},131,"2024-11-10","#OpenAI","article_res/cover/87f8ed353ce39f31960e7cdfaf075a35.jpeg","article_res/cover/f597a63935f5cd32e484b4aadd6019e8.jpeg","ChatGPT has launched the Search function","Get fast, timely answers with links to relevant web sources.",{"big":152,"small":214},[153,181],{"title":154,"list":155},"AGENT",[156,157,165,173],{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},{"id":158,"publish_date":159,"is_original":23,"collection":160,"cover_url":161,"cover_url_1_1":162,"title":163,"summary":164,"author":28},622,"2025-03-24","#OWL #AI Agent #MAS #MCP #CUA","article_res/cover/cb50ca7f2bf4d1ed50202d7406e1c19a.jpeg","article_res/cover/4aa7aa3badfacf3cc84121334f1050dd.jpeg","OWL: Multi-agent collaboration","OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation",{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},620,"2025-03-26","#LLM #Google #Gemini #AI Agent","article_res/cover/53751a6dbbe990b1eb0b63f3b062aed4.jpeg","article_res/cover/031344981f0a212ff82d1f3a64aa5756.jpeg","Gemini 2.5 Pro, claimed to be far ahead of the competition, has been released with great fanfare: comprehensively surpassing other LLMs and topping the global rankings","Gemini 2.5: Our most intelligent AI model",{"id":174,"publish_date":175,"is_original":23,"collection":176,"cover_url":177,"cover_url_1_1":178,"title":179,"summary":180,"author":28},616,"2025-03-29","#MAS #AI Agent #AI Coder #MetaGPT #MGX","article_res/cover/9dcd702ad2035902e5e77967c34a1f1e.jpeg","article_res/cover/0a97fc4a922753c8f46ff38792020df8.jpeg","MGX - An automated website-building platform composed of multiple AI Agents","Your 24/7 AI Team | Dream, Chat, Create.",{"title":182,"list":183},"OPENAI",[184,191,199,206],{"id":185,"publish_date":167,"is_original":23,"collection":186,"cover_url":187,"cover_url_1_1":188,"title":189,"summary":190,"author":28},619,"#OpenAI #AI Image Generator #4o #MMM #AR Transformer","article_res/cover/2faffc97fcecf3151552cb0fd3206d89.jpeg","article_res/cover/1133cb4948af44cee2e7fbe79efb69e5.jpeg","The native image function of GPT-4o is officially launched","Introducing 4o Image Generation",{"id":192,"publish_date":193,"is_original":4,"collection":194,"cover_url":195,"cover_url_1_1":196,"title":197,"summary":198,"author":28},434,"2023-07-15","#Anthropic #OpenAI #Google #AI Code Generator #Claude","article_res/cover/e1b6f600a2b9f262a4392684e5f2ce25.jpeg","article_res/cover/6e1772e83f78f9a351ab23d3e414adee.jpeg","Latest Updates on Google Bard /Anthropic Claude2 / ChatGPT Code Interpreter","We want our models to use their programming skills to provide more natural interfaces to the basic functions of our computers.  \n - OpenAI",{"id":200,"publish_date":201,"is_original":4,"collection":146,"cover_url":202,"cover_url_1_1":203,"title":204,"summary":205,"author":28},417,"2023-08-24","article_res/cover/bccf897d50a88b18364e35f7466387e0.jpeg","article_res/cover/2f871085c1073717c1703ae86e18056f.jpeg","The GPT-3.5 Turbo fine-tuning (fine-tuning function) has been released～","Developers can now bring their own data to customize GPT-3.5 Turbo for their use cases.",{"id":207,"publish_date":208,"is_original":4,"collection":209,"cover_url":210,"cover_url_1_1":211,"title":212,"summary":213,"author":28},407,"2023-09-22","#OpenAI #AI Image Generator","article_res/cover/c59005e903d35cfc32346e2756e2728a.jpeg","article_res/cover/ba011d265e6d84b5c8cb6fd6b757b6cc.jpeg","Dall-E 3","DALL·E 3 understands significantly more nuance and detail, allowing you to easily translate your ideas into images.",[215,221,241],{"title":10,"list":216},[217,218,219,220],{"id":96,"publish_date":97,"is_original":23,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28},{"id":104,"publish_date":105,"is_original":23,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":28},{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"title":222,"list":223},"GOOGLE",[224,225,226,234],{"id":120,"publish_date":113,"is_original":23,"collection":121,"cover_url":122,"cover_url_1_1":123,"title":124,"summary":125,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"id":227,"publish_date":228,"is_original":23,"collection":229,"cover_url":230,"cover_url_1_1":231,"title":232,"summary":233,"author":28},615,"2025-03-30","#AI Researcher #AI Science #HKU #Google #AI Agent","article_res/cover/21fadf906067714bb0db31ae13a77c15.jpeg","article_res/cover/2697999a72bd26b22e85f0e92936d3ed.jpeg","AI-Researcher: LLM-driven全自动 scientific research assistant","AI-Researcher: Fully-Automated Scientific Discovery with LLM Agents  \nOpen-Sourced Alternative to Google AI Co-Scientist",{"id":235,"publish_date":236,"is_original":23,"collection":73,"cover_url":237,"cover_url_1_1":238,"title":239,"summary":240,"author":28},463,"2023-05-09","article_res/cover/89800f207723acdb55fc53bf999ebdc9.jpeg","article_res/cover/5764f369b4accd8f83e94aa4c077a175.jpeg","The Smallville sandbox world - A town with 25 virtual residents","Believable proxies of human behavior can empower interactive apps: Immersive environment, Rehearsal space, Prototyping tool",{"title":242,"list":243},"NVIDIA",[],true,{"code":4,"msg":5,"data":246},{"id":247,"publish_date":248,"is_original":23,"collection":249,"articles_id":250,"cover_url":251,"cover_url_1_1":252,"title":253,"summary":254,"author":28,"content":255,"popular":256,"list":319,"category":374,"tag":375},63,"2025-01-22","#Microsoft #Research #Nature #AI Science #chemistry","OlWsixXIbqJ-RhunJfVeUw","article_res/cover/d2f6a78ac00110002e05dbd868952d51.jpeg","article_res/cover/bc00a61c35d090b9734d647c8c1830ba.jpeg","Microsoft's MatterGen - A material discovery tool based on generative AI","MatterGen: A new paradigm of materials design with generative AI","\u003Cdiv class=\"rich_media_content js_underline_content\n                       autoTypeSetting24psection\n            \" id=\"js_content\">\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>Microsoft recently released a research blog titled \"MatterGen: A new paradigm of materials design with generative AI.\" I found time to read it today.\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>MatterGen was recently published in *Nature*, where the Microsoft team shared this material discovery tool based on generative AI. Instead of traditionally screening candidate materials, MatterGen directly generates new materials according to application-specific design requirements.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100009487\" data-ratio=\"0.6527777777777778\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" style=\"height: auto !important;\" src=\"./assets/17423771718620.08443287189921689.png\">\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>MatterGen can generate materials with specific chemical, mechanical, electrical, or magnetic properties based on input design requirements, and can even meet multiple different constraints simultaneously.\u003C/p>\u003Ch2 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">MatterGen: A Diffusion Model for Generating Novel Stable Materials\u003C/span>\u003C/h2>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>MatterGen is a tool based on diffusion models specifically designed to handle the three-dimensional geometric structures of materials. Similar to how image diffusion models generate images by modifying pixel colors in noisy images, MatterGen generates desired material structures by adjusting positions, elements, and periodic lattices in random structures.\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>This diffusion architecture has been optimized specifically for the characteristics of materials, effectively handling their periodicity and three-dimensional geometry. MatterGen can be fine-tuned according to different design requirements, generating materials that meet specific chemical compositions, crystal symmetries, or material properties. Through this innovative design, MatterGen can more accurately generate material structures that meet design requirements.\u003C/p>\u003Cp style=\"text-align: center;\">\u003Cimg class=\"rich_pages wxw-img js_insertlocalimg\" data-imgfileid=\"100009488\" data-ratio=\"0.9018518518518519\" data-s=\"300,640\" data-type=\"png\" data-w=\"1080\" style=\"height: auto !important;\" src=\"./assets/17423771718640.9410444492487917.png\">\u003C/p>\u003Ch2 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">Performance Demonstration and Optimization of MatterGen\u003C/span>\u003C/h2>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>The base model of MatterGen has achieved industry-leading levels in generating novel, stable, and diverse materials. The performance improvement not only benefits from architectural innovation but also originates from the quality and scale of the training data.\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>Comparison of MatterGen's performance with other methods in generating stable, unique, and novel material structures. The training dataset for each method is noted in parentheses. The purple bar indicates the performance improvement brought by the MatterGen architecture itself, while the teal bar indicates the performance improvement achieved through a larger training dataset.\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>A key advantage of MatterGen over traditional screening methods lies in its ability to explore the entire unknown material space. As shown in the figure below, MatterGen can continuously generate more innovative candidate materials that meet high bulk modulus requirements (greater than 400 GPa), which are typically difficult to compress. In contrast, traditional screening methods gradually saturate in performance due to reliance on known candidate materials, making it difficult to discover new materials.\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>\u003Cimg class=\"rich_pages wxw-img\" data-imgfileid=\"100009477\" data-ratio=\"0.44814814814814813\" data-type=\"png\" data-w=\"1080\" style=\"height: auto !important;\" src=\"./assets/17423771718640.9372924376272227.png\">\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>Comparison of the performance of MatterGen (teal) and traditional screening methods (yellow) in finding stable, novel, and unique material structures that meet design requirements. The design requirement is that the material's bulk modulus must exceed 400 GPa.\u003C/p>\u003Ch2 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">Handling Composition Disorder\u003C/span>\u003C/h2>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>Composition disorder is a common phenomenon in synthesized materials, where different atoms can randomly exchange their crystallographic positions. Recently, the academic community has begun discussing what constitutes \"novelty\" in computationally designed materials. Traditional algorithms often fail to distinguish between structural pairs whose only difference lies in the arrangement order of similar elements in lattice positions.\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>To address this issue, MatterGen proposes a new structural matching algorithm that specifically considers composition disorder. This algorithm determines whether a pair of structures can be identified as ordered approximations of the same composition-disordered structure. This provides a new definition of novelty and uniqueness, which has been incorporated into our computational evaluation metrics.\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>\u003Cimg class=\"rich_pages wxw-img\" data-imgfileid=\"100009479\" data-ratio=\"0.3925925925925926\" data-type=\"png\" data-w=\"1080\" style=\"height: auto !important;\" src=\"./assets/17423771730480.866279649416396.png\">\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>The figure above illustrates composition disorder. Left: A perfect crystal without composition disorder, showing repeated unit cells (black dashed lines). Right: A crystal with composition disorder, where each position has a 50% probability of being occupied by either a yellow or teal atom.\u003C/p>\u003Ch2 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">Laboratory Verification\u003C/span>\u003C/h2>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>In addition to extensive computational evaluations, the team experimentally validated MatterGen's capabilities through synthesis.\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>\u003Cimg class=\"rich_pages wxw-img\" data-imgfileid=\"100009480\" data-ratio=\"0.6694444444444444\" data-type=\"png\" data-w=\"1080\" style=\"height: auto !important;\" src=\"./assets/17423771737930.40918801842317243.png\">\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>Experimental validation of the compound TaCr₂O₆ proposed by MatterGen. The image shows a scientist operating an experimental bench in the lab, using tweezers to hold a small sample.\u003C/p>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>Its structure was generated by MatterGen under a condition of 200 GPa bulk modulus. The synthesized material's structure matches the one proposed by MatterGen, with the only difference being compositional disorder between Ta and Cr. Additionally, the experimentally measured bulk modulus was 169 GPa, which has a relative error of less than 20% compared to the design specification of 200 GPa, making it very close from an experimental perspective. If such results can be generalized to other fields, it will have far-reaching implications for the design of battery, fuel cell, and other materials.\u003C/p>\u003Ch2 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">AI Simulator and Generator Flywheel\u003C/span>\u003C/h2>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>MatterSim is a deep learning model used for accurate and efficient material simulation and property prediction across a wide range of elements, temperatures, and pressures. MatterSim follows the fifth paradigm of scientific discovery, significantly accelerating the simulation speed of material properties. Meanwhile, MatterGen accelerates the exploration of new material candidates through property-guided generation. MatterGen and MatterSim can work together to form a flywheel effect, simultaneously accelerating the simulation and exploration of new materials.\u003C/p>\u003Csection style='cursor: pointer;margin-top: 20px;margin-bottom: 20px;padding: 10px 20px;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgb(250, 250, 250);width: auto;height: auto;box-shadow: rgba(0, 0, 0, 0) 0px 0px 0px 0px;color: rgb(0, 0, 0);font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;font-size: 16px;letter-spacing: normal;text-align: left;'>\u003Csection style=\"cursor: pointer;\">\u003Cp style=\"cursor: pointer;line-height: 1.8em;letter-spacing: 0em;text-indent: 0em;padding-top: 8px;padding-bottom: 8px;\">\u003Cstrong style=\"cursor: pointer;background: none 0% 0% / auto no-repeat scroll padding-box border-box rgba(0, 0, 0, 0);width: auto;height: auto;border-style: none;border-width: 3px;border-color: rgba(0, 0, 0, 0.4);border-radius: 0px;\">Five Paradigms of Scientific Discovery\u003C/strong>\u003C/p>\u003Cul style=\"margin-top: 8px;margin-bottom: 8px;cursor: pointer;padding-left: 25px;\" class=\"list-paddingleft-1\">\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">First Paradigm: Empirical Observation\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">Second Paradigm: Theoretical Models\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">Third Paradigm: Numerical Computation\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">Fourth Paradigm: Data-Intensive Scientific Discovery\u003C/section>\u003C/li>\u003Cli style=\"cursor: pointer;\">\u003Csection style=\"cursor: pointer;margin-top: 5px;margin-bottom: 5px;color: rgb(1, 1, 1);line-height: 1.8em;letter-spacing: 0em;\">Fifth Paradigm: Machine Learning and Artificial Intelligence (AI-Driven Scientific Discovery)\u003C/section>\u003C/li>\u003C/ul>\u003C/section>\u003C/section>\u003Ch2 style='margin-top: 30px;margin-bottom: 15px;color: rgba(0, 0, 0, 0.85);cursor: pointer;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;letter-spacing: normal;text-align: left;background-color: rgb(255, 255, 255);'>\u003Cspan style=\"cursor: pointer;font-size: 22px;color: rgb(0, 0, 0);line-height: 1.5em;letter-spacing: 0em;font-weight: bold;display: block;\">Looking Ahead\u003C/span>\u003C/h2>\u003Cp style='margin-bottom: 0px;cursor: pointer;color: rgb(0, 0, 0);font-size: 16px;line-height: 1.8em;letter-spacing: normal;text-align: left;padding-top: 8px;padding-bottom: 8px;font-family: Optima, \"Microsoft YaHei\", PingFangSC-regular, serif;background-color: rgb(255, 255, 255);'>MatterGen represents a new paradigm of material design driven by generative AI technology, exploring a material space far beyond what screening-based methods can achieve. Moreover, by guiding material exploration, MatterGen demonstrates superior efficiency. Similar to the impact of generative AI on drug discovery, MatterGen will profoundly influence how we design materials, spanning a wide range of applications including batteries, magnets, fuel cells, and more.\u003C/p>\u003Csection>\u003Cdiv style=\"height: 508px; background: rgb(0, 0, 0); border-radius: 4px; overflow: hidden; margin-bottom: 12px;\">\u003Cvideo src=\"./assets/17423771716230.7961794028209324.mp4\" poster=\"./assets/17423771714970.6154630615076571.jpeg\" controls=\"\" style=\"width: 100%;height: 100%;\">\u003C/video>\u003C/div>\u003C/section>\u003Cp style=\"display: none;\">\u003Cmp-style-type data-value=\"3\">\u003C/mp-style-type>\u003C/p>\u003C/div>",[257,266,274,281,289,296,303,311],{"id":258,"title_md5":259,"publish_date":260,"author_md5":261,"is_original":23,"collection":262,"summary_md5":263,"cover_url":264,"cover_url_1_1":265},222,"781e51529ed76a1b4dacfa3694343e04","2024-07-10","bc27fa490c4d0d525bac812fc0793534","#AI Avatar #Lip Sync #Tencent","14e25141a2a7103cf5b3245519c67129","article_res/cover/d06d42c3261157730ffe4d634dbbae8a.jpeg","article_res/cover/4c134a6c9d3f5a35c0bda53ad9397af8.jpeg",{"id":267,"title_md5":268,"publish_date":269,"author_md5":261,"is_original":23,"collection":270,"summary_md5":271,"cover_url":272,"cover_url_1_1":273},247,"317d3042ac26f1f277be533afe6259fe","2024-06-13","#Apple Intelligence","ae51395e8a9f142e1be0445fd918e084","article_res/cover/50bcabe175a72962feb59ca527797989.jpeg","article_res/cover/1442cd34b4bcbe31e090150bb4604caa.jpeg",{"id":275,"title_md5":276,"publish_date":277,"author_md5":261,"is_original":23,"collection":5,"summary_md5":278,"cover_url":279,"cover_url_1_1":280},398,"6ca9c4d70b67509bbe4a05eee3552dbf","2023-10-09","f7a2385ad13d3e9104d69c555542bc8f","article_res/cover/1c2ea9235e362749d44eba77d7499eb6.jpeg","article_res/cover/a92e11d91272174e63f854508832f8c9.jpeg",{"id":282,"title_md5":283,"publish_date":284,"author_md5":285,"is_original":4,"collection":5,"summary_md5":286,"cover_url":287,"cover_url_1_1":288},596,"6eb51bff462e6827035b393827994425","2022-03-21","8b3607d0f4181a3cb6ffdccf7185f09b","c62a8a1fba3a2f6edd08a8e37238ab0f","article_res/cover/5b0722b27754bd112a3e10e8be8fab5f.jpeg","article_res/cover/ecf64275278e5ef709f8bc210c6d5396.jpeg",{"id":290,"title_md5":291,"publish_date":292,"author_md5":285,"is_original":4,"collection":5,"summary_md5":293,"cover_url":294,"cover_url_1_1":295},574,"592ab87fc0eac36cecc7b9b953654045","2022-04-12","22e8f30725ba110483ea290c07821ead","article_res/cover/122a3f7fd28a0110a20dca37a0e8ca52.jpeg","article_res/cover/87a946198de6f6e1493528e74912ede2.jpeg",{"id":297,"title_md5":298,"publish_date":299,"author_md5":261,"is_original":23,"collection":73,"summary_md5":300,"cover_url":301,"cover_url_1_1":302},162,"a581849d2e18d3608ea435dbc8912dfa","2024-10-02","f6a80b5653ce78c9dc376d65166ceaff","article_res/cover/509ff9fe9bfe754efd81da81384a9c1c.jpeg","article_res/cover/0dc5c996a691517e119efdd32f463512.jpeg",{"id":304,"title_md5":305,"publish_date":306,"author_md5":261,"is_original":23,"collection":307,"summary_md5":308,"cover_url":309,"cover_url_1_1":310},136,"e9d61e351c9f5eb31f66c4d03e93c23a","2024-11-05","#AI Video Generator #AI Image Generator","211284e173d737bed036f64706586065","article_res/cover/86ff6ea34947c25e1cebdd34b261580e.jpeg","article_res/cover/4bcc2e0fd3a3e90d5565ff8b15e16d33.jpeg",{"id":312,"title_md5":313,"publish_date":314,"author_md5":315,"is_original":4,"collection":5,"summary_md5":316,"cover_url":317,"cover_url_1_1":318},569,"d7ff636c1518477f579eb4370df50d51","2022-04-17","1deefc9bc7e1c4c9c48e595c0a2dc06a","25e17f108294503c90210fcc0168f587","article_res/cover/b03629a77c01b8dc880bc860c977a3f0.jpeg","article_res/cover/c08a01bf1570dd525fa626a805201521.jpeg",{"related":320,"small":359},[321,329,336,344,352],{"id":322,"publish_date":323,"is_original":4,"collection":5,"cover_url":324,"cover_url_1_1":325,"title":326,"summary":327,"author":328},502,"2022-08-16","article_res/cover/8ddf36a9d45eb6d30ef0ea42ee30ca6d.jpeg","article_res/cover/46dbc34f9613ec52dd2abb77d1cb5c61.jpeg","The meaning of great loyalty","When Kou Zhun was in power, Emperor Zhenzong had the good fortune to visit Chanyuan, and he was able to support the emperor like a mountain that does not move, repel the barbarians, protect the ancestral temple, and the whole country called him a great loyalist.","Study Notes",{"id":330,"publish_date":331,"is_original":4,"collection":5,"cover_url":332,"cover_url_1_1":333,"title":334,"summary":335,"author":28},388,"2023-10-24","article_res/cover/feac9c4e579b78e68e57dff7a103c9bb.jpeg","article_res/cover/2ec3b5ac035c21fd5db37d4eab9ed981.jpeg","EigenLayer - Building a consensus hub on Ethereum through re-staking","We’re EigenLabs, and we're on a mission to increase openness and innovation.",{"id":337,"publish_date":338,"is_original":23,"collection":339,"cover_url":340,"cover_url_1_1":341,"title":342,"summary":343,"author":28},126,"2024-11-16","#ByteDance #AI Image Generator","article_res/cover/854c2b3bb73870e41cc5f2538767cb4b.jpeg","article_res/cover/fda04d80ee83788318da6f45beb28f5b.jpeg","ByteDance's Face-Swapping Solution: An Introduction to PuLID","PuLID: Pure and Lightning ID Customization via Contrastive Alignment",{"id":345,"publish_date":346,"is_original":23,"collection":347,"cover_url":348,"cover_url_1_1":349,"title":350,"summary":351,"author":28},215,"2024-07-19","#AI Audio Generator #Alibaba","article_res/cover/2fb5605ff13261997e60a4f788a4ea33.jpeg","article_res/cover/a9faa1e2ffa02eec5a06a4314092723c.jpeg","Alibaba's semantic recognition model SenseVoice and voice generation model CosyVoice","Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs",{"id":353,"publish_date":354,"is_original":4,"collection":5,"cover_url":355,"cover_url_1_1":356,"title":357,"summary":358,"author":53},418,"2023-08-23","article_res/cover/0882c39966a7cddb5ca2eafb8e8e5c12.jpeg","article_res/cover/6b46b2c8f170a8ac5a6c5ee499451d30.jpeg","Key Points of \"2023 Crypto Thesis\"","In the Belly Of The Bear",[360,366,372],{"title":10,"list":361},[362,363,364,365],{"id":96,"publish_date":97,"is_original":23,"collection":98,"cover_url":99,"cover_url_1_1":100,"title":101,"summary":102,"author":28},{"id":104,"publish_date":105,"is_original":23,"collection":106,"cover_url":107,"cover_url_1_1":108,"title":109,"summary":110,"author":28},{"id":112,"publish_date":113,"is_original":23,"collection":114,"cover_url":115,"cover_url_1_1":116,"title":117,"summary":118,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"title":222,"list":367},[368,369,370,371],{"id":120,"publish_date":113,"is_original":23,"collection":121,"cover_url":122,"cover_url_1_1":123,"title":124,"summary":125,"author":28},{"id":166,"publish_date":167,"is_original":23,"collection":168,"cover_url":169,"cover_url_1_1":170,"title":171,"summary":172,"author":28},{"id":227,"publish_date":228,"is_original":23,"collection":229,"cover_url":230,"cover_url_1_1":231,"title":232,"summary":233,"author":28},{"id":235,"publish_date":236,"is_original":23,"collection":73,"cover_url":237,"cover_url_1_1":238,"title":239,"summary":240,"author":28},{"title":242,"list":373},[],[8,9,10],[8,12,13,14,9,10,15,16,17,18],["Reactive",245],1754646413710]