Elena Sladojević’s Post

Looking forward to hearing about cool works and perspectives in this field of AI/ML for materials at the ACerS-MRS Virtual Workshop. My presentation is scheduled for tomorrow. I'll be talking about some of our ongoing efforts in brining in causal models to learn more physics of functional materials in both inorganic/organic chemical space. My primary focus would be on results from these recent papers on causal models + ML <--> cation ordering, trilinear coupling, polarization switching in transition metal oxides (in collab. with Saurabh), https://lnkd.in/eSfiesvK https://lnkd.in/erjYu_ue https://lnkd.in/em_XTCif https://lnkd.in/eqyu6ERW and causal models + active learning <--> molecular functionalities (in collab. with Zachary) https://lnkd.in/eS_Z9U2T #aiforscience #materialsscience #womeninstem #acers #mrs #webinar #machinelearning https://lnkd.in/eTtJzV5v

🌟 A new research development from Queen Mary University of London, recently published in Nature Communications, introduces an automated platform for the rapid discovery of novel perovskite materials with diverse applications. Led by Mojan Omidvar, Ph.D. student, and Professor Yang Hao, this innovative approach significantly streamlines the traditionally time-consuming process of discovering new perovskite materials, reducing processing times and harnessing the power of machine learning to guide future explorations. How do you think this automated platform could impact the development of new materials for various applications? Let's discuss the potential of this research! #ResearchDevelopment #Innovation #MachineLearning #Technology

🔬 The Power of Computational Chemistry in Industry 🔬 In today’s fast-evolving landscape, computational chemistry is not just a lab tool—it's a game-changer across multiple industries! From drug discovery to material science and sustainable energy, computational methods are transforming how we innovate, design, and scale. With the help of advanced simulations and predictive models, computational chemistry enables us to: Accelerate drug development by screening vast compound libraries, predicting molecular interactions, and identifying potential candidates faster. Optimize materials for energy storage and electronics by modeling properties and performance without physical testing. Reduce costs and environmental impact by minimizing the need for extensive lab experiments, leading to a more sustainable approach to R&D. 🌍 As industries continue to integrate AI, machine learning, and quantum computing, computational chemistry’s role is only growing. The future is here, and it’s digital! 🚀 #ComputationalChemistry #Chemistry #Pharma #DrugDiscovery #MaterialScience #SustainableDevelopment #Innovation #TechInScience #RAndD #MachineLearning #QuantumComputing #DigitalTransformation #Energy

Researchers at Northwestern University have developed an AI-driven approach to expedite materials discovery. Their method combines generative models and optimization algorithms to navigate the complex design spaces of new materials efficiently. This innovative technique can predict and suggest promising material candidates faster than traditional methods, significantly shortening the discovery-to-application timeline. The approach aims to enhance the development of advanced materials for various technological applications, including energy storage and electronic devices. For more details, read the full article here: https://lnkd.in/ebGqpUJn

🌟 A new research development from Queen Mary University of London, recently published in Nature Communications, introduces an automated platform for the rapid discovery of novel perovskite materials with diverse applications. Led by Mojan Omidvar, Ph.D. student, and Professor Yang Hao, this innovative approach significantly streamlines the traditionally time-consuming process of discovering new perovskite materials, reducing processing times and harnessing the power of machine learning to guide future explorations. How do you think this automated platform could impact the development of new materials for various applications? Let's discuss the potential of this research! #ResearchDevelopment #Innovation #MachineLearning #Technology

This #specialissue "Machine Learning and Artificial Intelligence in Engineering Applications" published 7 high-quality papers, and is viewed by 12301! Guest Editors: Dr. Sotirios Kontogiannis and Dr. Myrto Konstantinidou 🔗We invite you to read and share: https://lnkd.in/gNCubWhB 👉Thank the Guest Editor's effort on this Special Issue! MDPI University of Ioannina National Center for Scientific Research "Demokritos" #machinelearning #artificialintelligence #engineering

The rapid growth of large deep-learning models has increased significantly in recent years, expanding by over 300 times every 18 months since 2019. However, the future advancement of machine learning cannot rely solely on scaling up data or building larger models due to the unsustainable gap between model demand and available resources. More training data is often required in practical applications like predicting protein-drug interactions, catalyst discovery, and advanced material design. To tackle this challenge, current research initiatives merge scientific knowledge, physical principles, and simulations with #machinelearning techniques to design drugs, catalysts, and materials.

🔋 𝗕𝗮𝘁𝘁𝗲𝗿𝘆 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗼𝗿𝘀, 𝗟𝗲𝘁’𝘀 𝗣𝗼𝘄𝗲𝗿 𝗨𝗽 𝘁𝗵𝗲 𝗖𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻! 🔋    Are you ready to connect, learn, and shape the future of battery materials—together? 🚀    𝗝𝗼𝗶𝗻 𝘁𝗵𝗲 𝗤𝘂𝗮𝗻𝘁𝗶𝘀𝘁𝗿𝘆 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗦𝗲𝗿𝘃𝗲𝗿 and immerse yourself in a thriving hub of scientists, simulators, and curious minds exploring the cutting edge of battery and material R&D.  ↳ https://lnkd.in/dpdbBFFu    Here’s a glimpse of what we’ve been discussing lately:    🔹 Design and Optimize Electrolytes  • Simulate viscosity to enhance ion mobility.  • Model decomposition and aging for better stability and longer battery life.  • Predict electrolyte density with molecular dynamics.    🔹 Predict Key Electrode Properties  • Simulate OCV profiles for battery electrodes.  • Visualize lithium-ion motion and charge distribution.  • Achieve fast and accurate results with DFT simulations.    🔹 QuantistryLab: From Quantum to AI  • Explore Quantum Chemistry, Multiscale Simulations, and Machine Learning with QuantistryLab, our cloud-native, AI-driven simulation platform that empowers you to predict, design, and discover—all through our seamless Click&Simulate interface.    And there’s more!  In our Community Server, we’re continuously adding real-world use cases across diverse R&D topics—from alloys to polymers, lubricants, and beyond.    ✨ If all of the above piques your interest, then...  ➡️ 𝗝𝗼𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻 𝗻𝗼𝘄 𝗼𝗻 𝘁𝗵𝗲 𝗤𝘂𝗮𝗻𝘁𝗶𝘀𝘁𝗿𝘆 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗦𝗲𝗿𝘃𝗲𝗿: https://lnkd.in/dpdbBFFu    Connect with curious minds, share your ideas, and get inspired—together.    #BatteryResearch #Simulations #CommunityLearning #MaterialsScience #QuantistryLab #ElectrodeMaterials #Electrolyte #Quantum #AI 

🌟 Recently attended the International Conference on Innovation in Engineering and Technology (ICIET 2K24) organized by the Department of Electronics and Communication Engineering! It was a fantastic opportunity to explore the latest trends in #Engineering, #Technology, and #Innovation. Attending the conference on the optimal voting system was truly enlightening. Experts from various fields, including political science, economics, and technology, gathered to delve into the essence of democratic participation. Discussions ranged from theoretical underpinnings to practical implementations, emphasizing transparency, inclusivity, and security in the voting process. The event highlighted the importance of designing systems that genuinely reflect the will of the people. The conference not only expanded my understanding of optimal voting systems but also inspired a deeper appreciation for the continuous evolution of democratic practices. #Conference #Networking Let's keep the conversation going! I'd love to connect with more like-minded professionals passionate about #engineering, #technology, #political science, #innovation, and #research. Let's inspire and empower each other to make a meaningful impact! #Conference Presentation #convulational Neural Networks #political Tech #Networking #Professional Development #CNS#Hidden Markov language #FutureTech #Engineeringlnnovation #LinkedIn Networking

🔬🧠 Next step in molecular computing! Researchers led by Dr. Albert Wong have developed a new method that allows for precise control of chemical reactions using metal ions. This has potential for computers that function more like the human brain, with the potential to transform artificial intelligence and smart materials. By using metal ions to imitate mathematical functions and even create chemical reactions with ‘memory’, this research could have wide-reaching implications for fields like nanobiotechnology and the chemical origins of life. Read more about their work here 👇 : 🇬🇧 https://lnkd.in/eNEqGCft 🇳🇱 https://lnkd.in/eyftvJPN The team has published their result in an article in Nature Communications (Nature Portfolio). It is open-access and you can read it here: https://lnkd.in/eu4F-4FR #innovation #research #nanotechnology #chemicalnetworks #utwente Jurriaan Huskens Dmitrii Kriukov MESA+