<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[Materials Design, Inc.]]></title><description><![CDATA[Materials Design, Inc. is the leading atomistic simulation software and services company for materials  science.]]></description><link>https://www.materialsdesign.com/news</link><generator>RSS for Node</generator><lastBuildDate>Sat, 16 May 2026 12:14:40 GMT</lastBuildDate><atom:link href="https://www.materialsdesign.com/blog-feed.xml" rel="self" type="application/rss+xml"/><item><title><![CDATA[Multiscale Phase Field Modeling: Extending Classical FEA for Microstructure and Damage Driven Problems]]></title><description><![CDATA[Dr. Alexander Mavromaras will be presenting: “Multiscale Phase Field Modeling: Extending Classical FEA for Microstructure and Damage Driven Problems.”
This presentation explores how phase field modeling can extend traditional finite element analysis (FEA) workflows by enabling the simulation of evolving microstructures, moving interfaces, oxidation, crack formation, and coupled degradation processes that are difficult to capture with classical continuum approaches alone.]]></description><link>https://www.materialsdesign.com/post/multiscale-phase-field-modeling-extending-classical-fea-for-microstructure-and-damage-driven-proble</link><guid isPermaLink="false">6a077720026c54a42713c551</guid><pubDate>Fri, 15 May 2026 19:59:05 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_0f4ba8aa78564a9ca3b66de9180eb736~mv2.png/v1/fit/w_1000,h_1000,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Katherine Hollingsworth</dc:creator></item><item><title><![CDATA[Webinar: Machine-Learned Potentials: DFT-Level Accuracy for Real-World R&#38;D]]></title><description><![CDATA[This webinar will share several case studies showcasing how the integrated machine-learning workflow in MedeA 3.12 can accelerate your research, streamline the adoption of MLPs, and move beyond the limitations of standard first-principles methods.]]></description><link>https://www.materialsdesign.com/post/webinar-machine-learned-potentials-dft-level-accuracy-for-real-world-r-d</link><guid isPermaLink="false">6a06fdd9e17018105ded33c6</guid><pubDate>Fri, 15 May 2026 11:25:50 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_018fb9e4422d4bf0b1836893d993ff45~mv2.png/v1/fit/w_1000,h_1000,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Katherine Hollingsworth</dc:creator></item><item><title><![CDATA[Intellegens and Materials Design, Inc. Collaborate to Accelerate Materials Research]]></title><description><![CDATA[Cambridge, UK and San Diego, CA – 12 May, 2026 Intellegens and Materials Design, Inc., today announced a collaboration enabling materials research organizations to combine advanced machine learning and the full range of materials simulation methods, delivering insights that speed up development of new and improved materials and processes. Alchemite™ machine learning from Intellegens extracts valuable information from research data, identifying key relationships that drive material properties...]]></description><link>https://www.materialsdesign.com/post/intellegens-and-materials-design-inc-collaborate-to-accelerate-materials-research</link><guid isPermaLink="false">6a022d3f48aeb3fcb23dc5f0</guid><pubDate>Tue, 12 May 2026 19:02:53 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_2fd1564b7c9b480e98d30297fed5c077~mv2.png/v1/fit/w_1000,h_1000,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Katherine Hollingsworth</dc:creator></item><item><title><![CDATA[Webinar: High-Throughput Molecular Simulations for Gas Sorption in Polymers: Automated Workflows for Industrial Materials Design ]]></title><description><![CDATA[Discover how automated high-throughput Molecular Dynamics (MD) and Monte Carlo (MC) workflows enable accurate prediction of gas sorption, isotherms, and polymer swelling in industrial polymer systems. This webinar demonstrates how molecular simulations accelerate material selection, membrane and packaging design, and process optimization while reducing experimental screening effort and time-to-results.]]></description><link>https://www.materialsdesign.com/post/webinar-high-throughput-molecular-simulations-for-gas-sorption-in-polymers-automated-workflows-for</link><guid isPermaLink="false">69964c3cf797687350e6ef3e</guid><pubDate>Wed, 18 Feb 2026 23:59:09 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_02e0a27cc1ff4967b3ab27aba6b027b8~mv2.png/v1/fit/w_1000,h_770,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Katherine Hollingsworth</dc:creator></item><item><title><![CDATA[Technical Workshop: Modeling Insights: From Femto to Micro, Bridging Length and Time Scales with MedeA]]></title><description><![CDATA[Recent advances in computational materials science are increasingly driven by multiscale modeling, machine learning, and major improvements in theoretical frameworks and computational performance. Artificial intelligence plays an ever more central role in materials discovery and optimization by enabling systematic screening, accelerated exploration of configurational space, and quantitative property prediction. However, machine learning approaches rely on high‑quality, materials‑specific data...]]></description><link>https://www.materialsdesign.com/post/technical-workshop-modeling-insights-from-femto-to-microbridging-length-and-time-scales-with-medea</link><guid isPermaLink="false">698f524fe46957565bf74b57</guid><pubDate>Fri, 13 Feb 2026 17:08:14 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_216f6990fb254420ad866a47e20ea4e9~mv2.png/v1/fit/w_910,h_478,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Katherine Hollingsworth</dc:creator></item><item><title><![CDATA[ACEworks and Materials Design to continue their successful collaboration in the field of Machine-Learned Potentials]]></title><description><![CDATA[ACEworks  and Materials Design are rolling forward their successful collaboration with the full integration of the GRACEmaker code of ACEworks in the MedeA  computational environment. The GRACEmaker code is based on the Graph Atomic Cluster Expansion (GRACE), one of the most advanced methods for the generation of Machine-Learned Potentials (MLPs). MedeA  3.12  provides comprehensive support of the leading GRACE 1L and 2L potentials in MedeA  LAMMPS . This includes access to the most recent...]]></description><link>https://www.materialsdesign.com/post/aceworks-and-materials-design-to-continue-their-successful-collaboration-in-the-field-of-machine-lea</link><guid isPermaLink="false">698a10173faa9e439bebc9b4</guid><pubDate>Mon, 09 Feb 2026 17:25:32 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_a8f9e0798a3d4495a707a1dfbb1aa0f2~mv2.png/v1/fit/w_1000,h_872,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Katherine Hollingsworth</dc:creator></item><item><title><![CDATA[VASP and Supercomputers – a Marriage made in Heaven]]></title><description><![CDATA[The shift from physical laboratories to in-silico research has transformed materials science, with the Vienna Ab initio Simulation Package (VASP) at its core. As the world’s most widely used density functional theory (DFT) code, VASP drives digital twins, machine learning, and GPU-accelerated simulations on leading supercomputers. MedeA uniquely integrates VASP with workflow automation, job management, and analysis, enabling productive, reproducible, and scalable materials discovery.]]></description><link>https://www.materialsdesign.com/post/vasp-and-supercomputers-a-marriage-made-in-heaven</link><guid isPermaLink="false">697cd49d2c80129225d69fce</guid><pubDate>Fri, 30 Jan 2026 16:37:06 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_f7f1de9d6ff84c0c98cf0c5803be22ce~mv2.jpg/v1/fit/w_1000,h_1000,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Gerhard Engel</dc:creator></item><item><title><![CDATA[Webinar: Problem Solving in the Golden Age of Computational Materials Science ]]></title><description><![CDATA[Join this webinar to see how highly efficient software such as VASP, unprecedented compute power, and intuitive multiscale modeling have made computational materials science central to R&#38;D. Learn how these tools can optimize electronic devices, engineer organic–inorganic interfaces, unravel reaction mechanisms, study hydrogen diffusion, tailor polymer properties, and predict stress in PVD-grown thin films.]]></description><link>https://www.materialsdesign.com/post/webinar-problem-solving-in-the-golden-age-of-computational-materials-science</link><guid isPermaLink="false">69764b9f5a89f12627876139</guid><pubDate>Tue, 20 Jan 2026 17:48:08 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_bae74bc0238e41e19d25c95fff09813c~mv2.png/v1/fit/w_721,h_510,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Katherine Hollingsworth</dc:creator></item><item><title><![CDATA[Join Us at the MCC-VASP Workshop 2026 at LSBU Hub London for Insights and Networking]]></title><description><![CDATA[We are excited to announce our participation and support for the  MC C-VASP Workshop 2026,  taking place  January 19–21, 2026  at the  LSBU Hub, London South Bank University . This workshop is designed for both  new and experienced users of VASP  on  ARCHER2 HPC  and other UK high-performance computing platforms. It will bring together  VASP users, developers, and HPC experts  to share best practices, troubleshoot challenges, and explore recent VASP features and their implementation on...]]></description><link>https://www.materialsdesign.com/post/join-us-at-the-mcc-vasp-workshop-2026-at-lsbu-hub-london-for-insights-and-networking</link><guid isPermaLink="false">695ff8f3957e8f6c8aa35f69</guid><pubDate>Thu, 08 Jan 2026 18:57:33 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_79da70d2754040faab82d5e8a68cbd08~mv2.webp/v1/fit/w_800,h_400,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Katherine Hollingsworth</dc:creator></item><item><title><![CDATA[Materials Design Releases MedeA 3.12 —Train.Deploy.Discover]]></title><description><![CDATA[Materials Design announces MedeA 3.12, materials simulation environment, delivering a revolutionary integration of machine learning capabilities that transform materials modeling workflows. The MedeA 3.12 release establishes a comprehensive machine-learned potential ecosystem spanning training, refinement, deployment, and analysis, while introducing powerful new builders for complex microstructures and enhanced tools for materials discovery.]]></description><link>https://www.materialsdesign.com/post/materials-design-releases-medea-3-12-train-deploy-discover</link><guid isPermaLink="false">6949bd56c37cef16f1e61721</guid><pubDate>Tue, 23 Dec 2025 00:14:01 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_63d583c199f146db8df9862e1705fb15~mv2.png/v1/fit/w_1000,h_983,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Katherine Hollingsworth</dc:creator></item><item><title><![CDATA[Happy Holidays from Materials Design]]></title><description><![CDATA[The dendritic growth of the snowflakes was simulated using Materials Design’s phase field technology.]]></description><link>https://www.materialsdesign.com/post/happy-holidays-phasefield</link><guid isPermaLink="false">69495f13adc391625af71f28</guid><pubDate>Mon, 22 Dec 2025 15:12:41 GMT</pubDate><enclosure url="http://video.wixstatic.com/video/541861_f6a75a0d0f1e40c7a4c5713831bf9048/1080p/mp4/file.mp4" length="0" type="video"/><dc:creator>Katherine Hollingsworth</dc:creator></item><item><title><![CDATA[Webinar: Precision at Scale with Machine-Learned Potentials ]]></title><description><![CDATA[Join this webinar and explore how MLPs extend DFT accuracy to large, complex systems and long time scales. Learn how MedeA’s MLP Generator streamlines training, enables advanced materials-property predictions, and supports real-world research in corrosion, phase stability, defects, and catalysis. Ideal for scientists and engineers in multiscale modeling.]]></description><link>https://www.materialsdesign.com/post/webinar-precision-at-scale-with-machine-learned-potentials</link><guid isPermaLink="false">6933aa514f0e700bce30b532</guid><pubDate>Sat, 06 Dec 2025 04:19:36 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_779f72663356409b8aa8152ffce984a8~mv2.png/v1/fit/w_1000,h_872,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Katherine Hollingsworth</dc:creator></item><item><title><![CDATA[Alexander Mavromaras Presents Innovations in Corrosion-Resistant Alloy Engineering at 19th Middle East Corrosion Conference]]></title><description><![CDATA[Advancing Corrosion-Resistant Alloys Through Multiscale Modeling and Machine Learning At the 19th Middle East Corrosion Conference in Dhahran, Alexander Mavromaras presented how our understanding of corrosion can be improved through multiscale modeling, combining advanced electronic structure computations and atomistic modeling with continuum phase-field simulations , AI, and machine learning  to achieve quantitative predictions and design corrosion-resistant materials. Corrosion is...]]></description><link>https://www.materialsdesign.com/post/alexander-mavromaras-presents-innovations-in-corrosion-resistant-alloy-engineering-at-19th-middle-ea</link><guid isPermaLink="false">691dd184d3b811120f447279</guid><pubDate>Tue, 25 Nov 2025 16:32:59 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_e7093f83ff7b4737b419a8cfd0ce8ae9~mv2.jpg/v1/fit/w_1000,h_1000,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Katherine Hollingsworth</dc:creator></item><item><title><![CDATA[Webinar: Accessing the Mesoscale with Phase-field Modeling]]></title><description><![CDATA[This webinar will provide the tools and insights to enhance your polymer research and design using molecular modeling and data science]]></description><link>https://www.materialsdesign.com/post/webinar-accessing-the-mesoscale-with-phase-field-modeling</link><guid isPermaLink="false">691a92c780f146b984dfa8ff</guid><pubDate>Mon, 17 Nov 2025 03:19:54 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_2b73a9d5151b4e00846321c08b7f3639~mv2.png/v1/fit/w_1000,h_380,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Katherine Hollingsworth</dc:creator></item><item><title><![CDATA[Materials Design is Hiring a Finance &#38; Operations ]]></title><description><![CDATA[Materials Design is Hiring a Finance &#38; Operations Manager]]></description><link>https://www.materialsdesign.com/post/materials-design-is-hiring-1</link><guid isPermaLink="false">687db65d19f1db2c0b55158e</guid><pubDate>Mon, 21 Jul 2025 03:55:19 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_26c126b2f9214f2e808eefead305dddf~mv2.png/v1/fit/w_1000,h_591,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Katherine Hollingsworth</dc:creator></item><item><title><![CDATA[MedeA GIBBS Training]]></title><description><![CDATA[This session will focus specifically on MedeA GIBBS, a powerful Monte Carlo simulation engine that employs state-of-the-art techniques and methods to compute material properties. Participants will be introduced to the advanced capabilities of this tool and learn how to leverage them for accurate property calculations.]]></description><link>https://www.materialsdesign.com/post/medea-gibbs-training</link><guid isPermaLink="false">685067ec133a911f2071f02e</guid><pubDate>Mon, 16 Jun 2025 21:03:21 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_9c4c4b0267a6485ba4418ecbc3e49738~mv2.png/v1/fit/w_1000,h_512,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Katherine Hollingsworth</dc:creator></item><item><title><![CDATA[Materials Design Releases MedeA 3.11 —Accelerating Discovery]]></title><description><![CDATA[Materials Design announces MedeA 3.11, delivering significant performance improvements and expanded capabilities to accelerate materials modeling and simulation workflows. The MedeA 3.11 release enhances visualization performance, upgrades key computational engines, and introduces new analysis tools to meet the evolving needs of materials scientists and engineers.]]></description><link>https://www.materialsdesign.com/post/materials-design-releases-medea-3-11-accelerating-discovery-in-materials-modeling</link><guid isPermaLink="false">682ba795efc8008e2b931829</guid><pubDate>Tue, 20 May 2025 17:26:53 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_503e981543bb472194e87415028e8b74~mv2.png/v1/fit/w_1000,h_1000,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Katherine Hollingsworth</dc:creator></item><item><title><![CDATA[Most Viewed Article: Volumetric and Energetic Properties of Polystyrene and Polyethylene Oxide Affected by Thermal Cycling - Congratulations to Benoit Minisini]]></title><description><![CDATA[Most Viewed Article: Volumetric and Energetic Properties of Polystyrene and Polyethylene Oxide Affected by Thermal Cycling - Congratulations to Benoit Minisini]]></description><link>https://www.materialsdesign.com/post/most-viewed-article-volumetric-and-energetic-properties-of-polystyrene-and-polyethylene-oxide-affec</link><guid isPermaLink="false">68126b601cc052b94e6abd3f</guid><pubDate>Wed, 30 Apr 2025 18:33:42 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_1c3de6ac46d84ce7876ad9f0ff0a57c3~mv2.jpeg/v1/fit/w_800,h_533,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Katherine Hollingsworth</dc:creator></item><item><title><![CDATA[MedeA High-Throughput Training]]></title><description><![CDATA[Join Materials Design on May 20, 2025, for an online MedeA High-Throughput Training session. This 4-hour session will guide participants through MedeA’s powerful high-throughput capabilities, covering topics such as structure databases, mechanical properties of ceramics, melting point determination, and polymer property calculations. Whether you're a beginner or experienced user, this session offers hands-on practice, expert guidance, and valuable insights to master MedeA's advanced modeling ]]></description><link>https://www.materialsdesign.com/post/medea-high-throughput-training</link><guid isPermaLink="false">680afd75137a06e4fd00b475</guid><pubDate>Fri, 25 Apr 2025 04:20:41 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_147f795e28e649b9a9da85e7d7983fb5~mv2.png/v1/fit/w_1000,h_519,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Katherine Hollingsworth</dc:creator></item><item><title><![CDATA[MedeA in Publication: A Study Using MedeA for Reliable Prediction of Thermo-Mechanical Properties of Sustainable Resins]]></title><description><![CDATA[This study uses the MedeA software to predict the thermo-mechanical properties of sustainable resins. By combining quantum mechanics and molecular dynamics, MedeA enables accurate simulations of material behavior. The results highlight its reliability and potential in designing eco-friendly polymer systems for both research and industrial applications.]]></description><link>https://www.materialsdesign.com/post/medea-in-publication-a-study-using-medea-for-reliable-prediction-of-thermo-mechanical-properties-of</link><guid isPermaLink="false">680a89546be0a2b59f05e352</guid><pubDate>Thu, 24 Apr 2025 19:22:15 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/541861_494e4869dd56475ba29ef2a4fb2c9127~mv2.jpeg/v1/fit/w_1000,h_725,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Katherine Hollingsworth</dc:creator></item></channel></rss>