Best AI tools for< Material Scientist >

Machina Labs is an innovative AI and robotics company revolutionizing the manufacturing industry by combining cutting-edge technology to create the next generation of factories. Their capabilities enable rapid iteration and production, significantly reducing lead times and material waste. With a team of industry experts, Machina Labs is at the forefront of agile and advanced manufacturing solutions.

Materiom is an AI-powered platform that supports the development of next-generation materials made from 100% biobased sources. It provides an open-access database of biomaterials recipes, facilitates collaboration among scientists, producers, and brands, and offers a genAI-powered tool to identify high-performing material alternatives quickly. The platform aims to create a materials economy that regenerates nature and promotes human health.

GenInnov is a generative innovation fund that provides a platform for investors seeking to be at the forefront of technological advancement. The fund invests in companies driving transformative change across multiple sectors and geographies, prioritizing material innovations with demonstrable profitability and global reach. GenInnov operates with a research-driven approach, focusing on investing in material innovations that are monetizable, profitable, and transformative, rather than incremental. The fund looks at various domains such as technology, robotics, consumer electronics, biotech, healthcare, mobility, and clean tech, aiming to amplify human creativity through machine intelligence.

Cybertiks is an AI-powered application that offers remote monitoring and analysis of agriculture fields using satellite imagery. The application provides valuable insights such as soil nutrients and texture, tailored AI models for accurate metrics, historical data analysis, and visualization of results on a map. Cybertiks integrates various sources of information, offers tailored solutions for clients, and uses quantum-enabled artificial intelligence for material detection and classification. The application is designed to meet the needs of diverse industries worldwide by harnessing the power of satellite imagery and AI technology.

TileMaker is an AI-powered tool that allows developers to create their own AI applications easily. It is powered by Material Diffusion, Replicate, Vercel, and GitHub. With TileMaker, developers can quickly generate tiles for their AI apps, making the development process more efficient and streamlined.

The Visual Computing & Artificial Intelligence Lab at TUM is a group of research enthusiasts advancing cutting-edge research at the intersection of computer vision, computer graphics, and artificial intelligence. Our research mission is to obtain highly-realistic digital replica of the real world, which include representations of detailed 3D geometries, surface textures, and material definitions of both static and dynamic scene environments. In our research, we heavily build on advances in modern machine learning, and develop novel methods that enable us to learn strong priors to fuel 3D reconstruction techniques. Ultimately, we aim to obtain holographic representations that are visually indistinguishable from the real world, ideally captured from a simple webcam or mobile phone. We believe this is a critical component in facilitating immersive augmented and virtual reality applications, and will have a substantial positive impact in modern digital societies.

Osium AI is a cutting-edge AI-powered software designed to accelerate the development of sustainable and high-performance materials and chemicals. The platform leverages proprietary technology developed by experts with 10 years of experience in AI and authors of multiple AI patents. Osium AI offers a comprehensive solution that covers every step of materials and chemicals development cycles, from formulation and characterization to scale-up and manufacturing. The software is flexible, adaptable to various R&D projects, and eliminates trial-and-error approaches, unlocking the full potential of R&D with its advanced functionalities.

The Institute for Protein Design is a research institute at the University of Washington that uses computational design to create new proteins that solve modern challenges in medicine, technology, and sustainability. The institute's research focuses on developing new protein therapeutics, vaccines, drug delivery systems, biological devices, self-assembling nanomaterials, and bioactive peptides. The institute also has a strong commitment to responsible AI development and has developed a set of principles to guide its use of AI in research.

Eureka Labs is a pioneering educational platform that leverages generative AI to provide an innovative learning experience. The platform combines the expertise of subject matter experts with AI Teaching Assistants to guide students through high-quality course materials. Their flagship product, LLM101n, offers an undergraduate-level class where students train their own AI. Eureka Labs aims to democratize education by making learning accessible and comprehensive for everyone.

Unless is a conversational AI platform that helps organizations unlock their knowledge and provide better customer support. With Unless, you can train an AI model with your own knowledge base, documents, or website, and then let your customers or team engage in conversations with the AI through various channels. Unless is designed to be easy to use, even for non-technical staff, and it offers a variety of features to help you get the most out of your AI model.

BISA AI Academy is an AI education platform offering online and offline courses with a variety of materials, professional instructors, and engaging learning paths. The platform provides over 100 free courses with certificates upon completion of quizzes and assignments. Additionally, there are premium master classes available, job training programs, and corporate training options. BISA AI Academy covers fields such as Data Science, IoT, Blockchain, and Programming, offering offline classes, special programs like Prakerja, webinars, and certification programs. The platform also provides solutions for IT services, AI intelligence, system development, and educational consulting.

arXiv.org is a free distribution service and an open-access archive for nearly 2.4 million scholarly articles in the fields of physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering and systems science, and economics. Materials on this site are not peer-reviewed by arXiv.

C&EN, a publication of the American Chemical Society, provides the latest news and insights on the chemical industry, including research, technology, business, and policy. It covers a wide range of topics, including analytical chemistry, biological chemistry, business, careers, education, energy, environment, food, materials, people, pharmaceuticals, physical chemistry, policy, research integrity, safety, and synthesis.

The National Cancer Institute's Center for Biomedical Informatics and Information Technology (CBIIT) provides a comprehensive suite of tools, resources, and training to support cancer data science research. These resources include data repositories, analytical tools, data standards, and training materials. CBIIT also develops and maintains the NCI Thesaurus, a comprehensive vocabulary of cancer-related terms, and the Cancer Data Standards Registry and Repository (caDSR), a repository of cancer data standards. CBIIT's mission is to accelerate the pace of cancer research by providing researchers with the tools and resources they need to access, analyze, and share cancer data.

XtalPi is a world-leading technology company driven by artificial intelligence (AI) and robotics to innovate in the fields of life sciences and new materials. Founded in 2015 at the Massachusetts Institute of Technology (MIT), the company is committed to realizing digital and intelligent innovation in the fields of life sciences and new materials. Based on cutting-edge technologies and capabilities such as quantum physics, artificial intelligence, cloud computing, and large-scale experimental robot clusters, the company provides innovative technologies, services, and products for global industries such as biomedicine, chemicals, new energy, and new materials.

Reality AI Software is an Edge AI software development environment that combines advanced signal processing, machine learning, and anomaly detection on every MCU/MPU Renesas core. The software is underpinned by the proprietary Reality AI ML algorithm that delivers accurate and fully explainable results supporting diverse applications. It enables features like equipment monitoring, predictive maintenance, and sensing user behavior and the surrounding environment with minimal impact on the Bill of Materials (BoM). Reality AI software running on Renesas processors helps deliver endpoint intelligence in products across various markets.

Allchemy is a resource-aware AI platform for drug discovery. It combines state-of-the-art computational synthesis with AI algorithms to predict molecular properties. Within minutes, Allchemy creates thousands of synthesizable lead candidates meeting user-defined profiles of drug-likeness, affinity towards specific proteins, toxicity, and a range of other physical-chemical measures. Allchemy encompasses the entire resource-to-drug design process and has been used in academic, corporate and classified environments worldwide to: Design synthesizable leads targeting specific proteins Evolve scaffolds similar to desired drugs Design “circular” drug syntheses from renewable materials Interface with and instruct automated synthesis platforms and optimize pilot-scale processes Operate “iterative synthesis” schemes Predict side reactions and create forensic “synthetic signatures” of hazardous/toxic molecules Design synthetic degradation and recovery cycles for various types of feedstocks and functional target molecules

Metamorph Labs is an AI Resources Curation Platform where the AI Community can explore Technical & Non-Technical/General AI Resources gathered from the Internet. It offers a comprehensive resource aggregation platform for the AI Community to unleash the power of AI. Users can discover a curated collection of cutting-edge AI resources consisting of both Technical & Non-technical Materials.

Centari is a platform for deal intelligence that leverages generative AI to transform complex documents into powerful insights. It helps users unlock more dealflow, enrich marketing materials, visualize market trends, and streamline the deal validation process. With Centari, users can extract deal sheets automatically, validate data intuitively, and navigate their entire deal history with a first-of-its-kind deal navigation platform. The platform is designed to enhance competitive knowledge and optimize deal-making strategies for law firms and legal professionals.

FormWise.AI is a no-code platform that allows users to create and embed white-label AI tools. With FormWise.AI, users can turn popular ChatGPT prompts into lead magnets, tiny offers, and marketing tools that they can monetize without coding. FormWise.AI also offers a variety of templates and integrations to help users get started quickly and easily.

The Data Scientist Roadmap2024 provides a comprehensive guide to mastering essential tools for data science success. It includes programming languages, machine learning libraries, cloud platforms, and concepts categorized by difficulty. The roadmap covers a wide range of topics from programming languages to machine learning techniques, data visualization tools, and DevOps/MLOps tools. It also includes web development frameworks and specific concepts like supervised and unsupervised learning, NLP, deep learning, reinforcement learning, and statistics. Additionally, it delves into DevOps tools like Airflow and MLFlow, data visualization tools like Tableau and Matplotlib, and other topics such as ETL processes, optimization algorithms, and financial modeling.

This repository is the workshop repository for the Causal Modeling in Machine Learning Workshop on Altdeep.ai. The material is open source and free. The course covers causality in model-based machine learning, Bayesian modeling, interventions, counterfactual reasoning, and deep causal latent variable models. It aims to equip learners with the ability to build causal reasoning algorithms into decision-making systems in data science and machine learning teams within top-tier technology organizations.

SciCode is a challenging benchmark designed to evaluate the capabilities of language models (LMs) in generating code for solving realistic scientific research problems. It contains 338 subproblems decomposed from 80 challenging main problems across 16 subdomains from 6 domains. The benchmark offers optional descriptions specifying useful scientific background information and scientist-annotated gold-standard solutions and test cases for evaluation. SciCode demonstrates a realistic workflow of identifying critical science concepts and facts and transforming them into computation and simulation code, aiming to help showcase LLMs' progress towards assisting scientists and contribute to the future building and evaluation of scientific AI.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

This repository contains material related to the new book _Synthetic Data and Generative AI_ by the author, including code for NoGAN, DeepResampling, and NoGAN_Hellinger. NoGAN is a tabular data synthesizer that outperforms GenAI methods in terms of speed and results, utilizing state-of-the-art quality metrics. DeepResampling is a fast NoGAN based on resampling and Bayesian Models with hyperparameter auto-tuning. NoGAN_Hellinger combines NoGAN and DeepResampling with the Hellinger model evaluation metric.

The Local Assistant Examples repository is a collection of educational examples showcasing the use of large language models (LLMs). It was initially created for a blog post on building a RAG model locally, and has since expanded to include more examples and educational material. Each example is housed in its own folder with a dedicated README providing instructions on how to run it. The repository is designed to be simple and educational, not for production use.

GPT4DFCI is a private and secure generative AI tool based on GPT-4, deployed for non-clinical use at Dana-Farber Cancer Institute. The tool is overseen by the Dana-Farber AI Governance Committee and developed by the Dana-Farber Informatics & Analytics Department. The repository includes manuscript & policy details, training material, front-end and back-end code, infrastructure information, API client for programmatic use, licensing details, and contact information.

LLMOps Duke AIPI is a course focused on operationalizing Large Language Models, teaching methodologies for developing applications using software development best practices with large language models. The course covers various topics such as generative AI concepts, setting up development environments, interacting with large language models, using local large language models, applied solutions with LLMs, extensibility using plugins and functions, retrieval augmented generation, introduction to Python web frameworks for APIs, DevOps principles, deploying machine learning APIs, LLM platforms, and final presentations. Students will learn to build, share, and present portfolios using Github, YouTube, and Linkedin, as well as develop non-linear life-long learning skills. Prerequisites include basic Linux and programming skills, with coursework available in Python or Rust. Additional resources and references are provided for further learning and exploration.

The AI Fairness 360 toolkit is an open-source library designed to detect and mitigate bias in machine learning models. It provides a comprehensive set of metrics, explanations, and algorithms for bias mitigation in various domains such as finance, healthcare, and education. The toolkit supports multiple bias mitigation algorithms and fairness metrics, and is available in both Python and R. Users can leverage the toolkit to ensure fairness in AI applications and contribute to its development for extensibility.

The IBM Analog Hardware Acceleration Kit is an open-source Python toolkit for exploring and using the capabilities of in-memory computing devices in the context of artificial intelligence. It consists of two main components: Pytorch integration and Analog devices simulator. The Pytorch integration provides a series of primitives and features that allow using the toolkit within PyTorch, including analog neural network modules, analog training using torch training workflow, and analog inference using torch inference workflow. The Analog devices simulator is a high-performant (CUDA-capable) C++ simulator that allows for simulating a wide range of analog devices and crossbar configurations by using abstract functional models of material characteristics with adjustable parameters. Along with the two main components, the toolkit includes other functionalities such as a library of device presets, a module for executing high-level use cases, a utility to automatically convert a downloaded model to its equivalent Analog model, and integration with the AIHW Composer platform. The toolkit is currently in beta and under active development, and users are advised to be mindful of potential issues and keep an eye for improvements, new features, and bug fixes in upcoming versions.

Awesome-Lists is a curated index of selected resources spanning various fields including programming languages and theories, web and frontend development, server-side development and infrastructure, cloud computing and big data, data science and artificial intelligence, product design, etc. It includes articles, books, courses, examples, open-source projects, and more. The repository categorizes resources according to the knowledge system of different domains, aiming to provide valuable and concise material indexes for readers. Users can explore and learn from a wide range of high-quality resources in a systematic way.

Learn Cloud Applied Generative AI Engineering (GenEng) is a course focusing on the application of generative AI technologies in various industries. The course covers topics such as the economic impact of generative AI, the role of developers in adopting and integrating generative AI technologies, and the future trends in generative AI. Students will learn about tools like OpenAI API, LangChain, and Pinecone, and how to build and deploy Large Language Models (LLMs) for different applications. The course also explores the convergence of generative AI with Web 3.0 and its potential implications for decentralized intelligence.

ComposeAI is an Android & iOS application similar to ChatGPT, built using Compose Multiplatform. It utilizes various technologies such as Compose Multiplatform, Material 3, OpenAI Kotlin, Voyager, Koin, SQLDelight, Multiplatform Settings, Coil3, Napier, BuildKonfig, Firebase Analytics & Crashlytics, and AdMob. The app architecture follows Google's latest guidelines. Users need to set up their own OpenAI API key before using the app.

This repository is part of the Certified Cloud Native Applied Generative AI Engineer program, focusing on the fundamentals of Prompt Engineering, Docker, GitHub, and Modern Python Programming. It covers the basics of GenAI, Linux, Docker, VSCode, Devcontainer, and GitHub. The main emphasis is on mastering Modern Python with Typing, using ChatGPT as a Personal Python Coding Mentor. The course material includes tools installation, study materials, and projects related to Python development in Docker containers and GitHub usage.

The OpenVINO Build and Deploy repository provides pre-built components and code samples to accelerate the development and deployment of production-grade AI applications across various industries. With the OpenVINO Toolkit from Intel, users can enhance the capabilities of both Intel and non-Intel hardware to meet specific needs. The repository includes AI reference kits, interactive demos, workshops, and step-by-step instructions for building AI applications. Additional resources such as Jupyter notebooks and a Medium blog are also available. The repository is maintained by the AI Evangelist team at Intel, who provide guidance on real-world use cases for the OpenVINO toolkit.

LLM-PowerHouse is a comprehensive and curated guide designed to empower developers, researchers, and enthusiasts to harness the true capabilities of Large Language Models (LLMs) and build intelligent applications that push the boundaries of natural language understanding. This GitHub repository provides in-depth articles, codebase mastery, LLM PlayLab, and resources for cost analysis and network visualization. It covers various aspects of LLMs, including NLP, models, training, evaluation metrics, open LLMs, and more. The repository also includes a collection of code examples and tutorials to help users build and deploy LLM-based applications.

The LLM Twin Course is a free, end-to-end framework for building production-ready LLM systems. It teaches you how to design, train, and deploy a production-ready LLM twin of yourself powered by LLMs, vector DBs, and LLMOps good practices. The course is split into 11 hands-on written lessons and the open-source code you can access on GitHub. You can read everything and try out the code at your own pace.

The 'llms-tools' repository is a comprehensive collection of AI tools, open-source projects, and research related to Large Language Models (LLMs) and Chatbots. It covers a wide range of topics such as AI in various domains, open-source models, chats & assistants, visual language models, evaluation tools, libraries, devices, income models, text-to-image, computer vision, audio & speech, code & math, games, robotics, typography, bio & med, military, climate, finance, and presentation. The repository provides valuable resources for researchers, developers, and enthusiasts interested in exploring the capabilities of LLMs and related technologies.

深入分析无机材料及氧离子导体论文指南，主要使用中文回答。

AI Astrophysics & Material Science Expert

Explore the fascinating world of elements with in-depth insights on chemistry, periodic table, and elemental properties. Dive into our comprehensive guide for a deeper understanding of the elements' significance in science and everyday life.

Ultimate material science resource with advanced features.

Guides material development for innovative product creation.

Material facts application that details the content, impact category data, and environmental rating of materials.

A cryogenics expert assisting with cooling processes and material behavior.

Explores the science of indestructible materials and concepts.

Explore, render, and ask questions about materials discovered in the GNoME project

Advanced expert in fusion, superconductors, and materials with enhanced analytics and collaboration.

High Entropy Alloys & AI-human interactions expert with a hint of sci-fi fun.

An expert on plastics

Script Examples and Resource Center for Helping with LAMMPS Input Generation and High-quality Tutorials (SERCHLIGHT)

Expert in nano silver tech, includes contact info in ads.

Guides polymer selection and application in manufacturing processes.

Expert in metals, metalworking, and alloys, providing detailed and informative insights.

A GPT that can provides lectures on Statistical Mechanics

Expert on semiconductors, for technical insights.

I find videos on how items are made from your photos and describe the process.

Expert in 3D printing innovations, offering in-depth insights and analysis.