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Computer Science and Mathematics Division | ORNL
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</div> </div> <div class="secondary-menu-mobile-trigger"> <i aria-label="Toggle mobile secondary menu" class="fas fa-angle-down"></i> </div> </header> </div> <div class="landing-page"> <main class="main-container org-landing-page" role="main"> <div class="top-content"> <div class="hero-section" > <div id="block-notificationsblock"> <div id="ornl-notification-block" node="13976"> </div> </div> <div id="block-hero-section-content"> <div class="node__content landing-page-hero-overlay-container"> <div class="landing-page-hero-wrapper"> <div class="field-wrapper"> <div class="field-container" > <div class="field-element" > <div class="paragraph paragraph--type--carousel paragraph--view-mode--default"> <div class="flexslider_default_wrapper"> <div class="flexslider_default_positioning"> <div class="flexslider_default flexslider" > <ul class="slides"> <li class="slide"> <div class="default-carousel-wrapper"> <div class="default-carousel-image"> <article aria-label="li-ion_battery_material_phase_prediction_through_hierarchical_curriculum_learning.png"> <div class="content-image content-image--undefined carousel"> <div class="content-image__img"> <img loading="lazy" src="/sites/default/files/styles/content_carousel_style/public/2023-03/li-ion_battery_material_phase_prediction_through_hierarchical_curriculum_learning_0.png?h=3dbd9eac&itok=pTBeUS6a" width="700" height="450" alt="Li-ion Battery Material Phase Prediction through Hierarchical Curriculum Learning" /> <div class="content-image__magnify"> <a href="/sites/default/files/2023-03/li-ion_battery_material_phase_prediction_through_hierarchical_curriculum_learning_0.png"><span>View a hi-res version of this image</span></a> </div> </div> </div> </article> </div> <div class="default-carousel-text-wrapper"> <div class="default-carousel-text"> <div> CCSD researchers developed a novel uncertainty-aware framework MatPhase to predict material phases of electrodes from low contrast SEM images </div> <div> <a class="default-carousel-read-more" href="https://www.ornl.gov/research-highlight/li-ion-battery-material-phase-prediction-through-hierarchical-curriculum">Read more</a> </div> </div> </div> </div> </li> <li class="slide"> <div class="default-carousel-wrapper"> <div class="default-carousel-image"> <article aria-label="csed_researchers_create_two_open-source_datasets_for_quantum_chemical_prediction_of_uvvis_absorption_spectra_for_organic_molecules.png"> <div class="content-image content-image--undefined carousel"> <div class="content-image__img"> <img loading="lazy" src="/sites/default/files/styles/content_carousel_style/public/2023-02/csed_researchers_create_two_open-source_datasets_for_quantum_chemical_prediction_of_uvvis_absorption_spectra_for_organic_molecules.png?h=c25c6668&itok=ujq5dwF1" width="700" height="450" alt="Scatter plots that describe the strong correlation between the HOMO-LUMO gap and the minimum absorption energy for organic molecules of the GDB-9-Ex dataset (left) and ORNL_AISD-Ex dataset (right). CSED CCSD ORNL AI Initiative" /> <div class="content-image__magnify"> <a href="/sites/default/files/2023-02/csed_researchers_create_two_open-source_datasets_for_quantum_chemical_prediction_of_uvvis_absorption_spectra_for_organic_molecules.png"><span>View a hi-res version of this image</span></a> </div> </div> </div> </article> </div> <div class="default-carousel-text-wrapper"> <div class="default-carousel-text"> <div> We released two open-source datasets named GDB-9-Ex and ORNL_AISD-Ex that provide calculations of electronic excitation energies and their associated oscillator strengths based on the time-dependent density-functional tight-binding (TD-DFTB) method. </div> <div> <a class="default-carousel-read-more" href="https://www.ornl.gov/research-highlight/ccsd-researchers-create-two-open-source-datasets-quantum-chemical-prediction">Read more</a> </div> </div> </div> </div> </li> <li class="slide"> <div class="default-carousel-wrapper"> <div class="default-carousel-image"> <article aria-label="crossvisoverview_1.png"> <div class="content-image content-image--undefined carousel"> <div class="content-image__img"> <img loading="lazy" src="/sites/default/files/styles/content_carousel_style/public/2020-07/crossvisoverview_1.png?h=4a7d1ed4&itok=HshKCd6P" width="700" height="450" alt="The CrossVis application includes a parallel coordinates plot (left), a tiled image view (right) and other interactive data views. Credit: Chad Steed/Oak Ridge National Laboratory, U.S. Dept. of Energy" /> <div class="content-image__magnify"> <a href="/sites/default/files/2020-07/crossvisoverview_1.png"><span>View a hi-res version of this image</span></a> </div> </div> </div> </article> </div> <div class="default-carousel-text-wrapper"> <div class="default-carousel-text"> <div> Visual analytics tool plucks elusive patterns from elaborate datasets </div> <div> <a class="default-carousel-read-more" href="/news/visual-analytics-tool-plucks-elusive-patterns-elaborate-datasets">Read more</a> </div> </div> </div> </div> </li> <li class="slide"> <div class="default-carousel-wrapper"> <div class="default-carousel-image"> <article aria-label="hybrid_quantum-classical_neural_networks.png"> <div class="content-image content-image--undefined carousel"> <div class="content-image__img"> <img loading="lazy" src="/sites/default/files/styles/content_carousel_style/public/2023-03/hybrid_quantum-classical_neural_networks.png?h=00909133&itok=5xXLXp6N" width="700" height="450" alt="Hybrid Quantum-Classical Neural Networks CSMD ORNL Computer Science and Mathematics Division" /> <div class="content-image__magnify"> <a href="/sites/default/files/2023-03/hybrid_quantum-classical_neural_networks.png"><span>View a hi-res version of this image</span></a> </div> </div> </div> </article> </div> <div class="default-carousel-text-wrapper"> <div class="default-carousel-text"> <div> A team of researchers from the Oak Ridge National Laboratory (ORNL) developed a novel architecture for a hybrid quantum-classical neural network. </div> <div> <a class="default-carousel-read-more" href="https://www.ornl.gov/research-highlight/hybrid-quantum-classical-neural-networks">Read more</a> </div> </div> </div> </div> </li> </ul> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> <div id="block-overviewblurb-1"> <article class="node node--type-landing-page node--view-mode-overview-blurb-with-background-color"> <div class="node__content"> </div> </article> </div> </div> <div > <div class="status-messages-container"> <div data-drupal-messages-fallback class="hidden"></div> </div> </div> </div> <div class="container"> <div class="main-content no-sidebar" id="main-content"> <div class="main-content-wrapper"> <div class="content-section" > <div class="page-title" id="block-ornl-page-title"> <h1 class="page-title"><span>Computer Science and Mathematics Division</span> </h1> </div> <div id="block-ornl-content"> <article class="node node--type-landing-page node--view-mode-full"> <div class="node__content"> <div class="pfield-wrapper field-body"> <div class="field-container" > <div class="field-element" ><p>The Computer Science and Mathematics Division (CSMD) is ORNL's premier source of basic and applied research in high-performance computing, applied mathematics, and intelligent systems. Basic and applied research programs are focused on computational sciences, intelligent systems, and information technologies.</p> <p>Our mission includes working on important national priorities with advanced computing systems, working cooperatively with U.S. industry to enable efficient, cost-competitive design, and working with universities to enhance science education and scientific awareness. Our researchers are finding new ways to solve problems beyond the reach of most computers and are putting powerful software tools into the hands of students, teachers, government researchers, and industrial scientists.</p></div> </div> </div> <div class="field-wrapper"> <div class="field-container" > <div class="field-element" > <section class="staff-profile-card node contact card node--type-staff-profile node--view-mode-staff-profile-card"> <div class="staff-profile-card-wrapper"> <div class="staff-profile-card-details-wrapper"> <div class="staff-profile-card-photo"> <div class="field-wrapper"> <div class="field-container" > <div class="field-element" > <article aria-label="mike_parks.jpeg"> <div> <div class="field-wrapper"> <div class="field-container" > <div class="field-element" > <img loading="lazy" src="/sites/default/files/styles/staff_profile_image_style/public/2023-03/mike_parks.jpeg?h=f669395e&itok=r1IKjuGd" width="260" height="325" alt="Michael Parks - Division Director, 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