{"id":113868,"date":"2026-08-03T17:40:21","date_gmt":"2026-08-03T12:10:21","guid":{"rendered":"https:\/\/staging.nanoschool.in\/wp\/aiws\/ai-guided-catalyst-discovery-for-sustainable-industrial-chemical-processes\/"},"modified":"2026-09-09T09:34:17","modified_gmt":"2026-09-09T04:04:17","slug":"ai-guided-catalyst-discovery-for-sustainable-industrial-chemical-processes","status":"publish","type":"post","link":"https:\/\/staging.nanoschool.in\/wp\/aiws\/ai-guided-catalyst-discovery-for-sustainable-industrial-chemical-processes\/","title":{"rendered":"AI-Guided Catalyst Discovery for Sustainable Industrial Chemical Processes"},"content":{"rendered":"<div class=\"nsp-isolated-wrapper\"><main class=\"nsp-main\" id=\"nsp-main-content\"><\/p>\n<div class=\"nsp-banner\">\n<p id=\"nsp-target-date\" class=\"nsp-sr-only\">September 7, 2026<\/p>\n<div class=\"nsp-banner-title\">Registration closes <strong>September 7, 2026<\/strong><\/div>\n<div id=\"nsp-countdown\" class=\"nsp-countdown\" aria-live=\"polite\"><\/div>\n<\/p><\/div>\n<section class=\"nsp-hero\">\n<figure class=\"nsp-hero-figure\">\n<div class=\"nsp-hero-img\" style=\"background-image:url('https:\/\/storage.googleapis.com\/nanoschool-in-website-wp-media-to-gcp-offload\/2026\/08\/7925ee94-images.png');\">\n<div class=\"nsp-hero-label\"><\/div>\n<\/p><\/div>\n<\/figure>\n<div class=\"nsp-hero-body\">\n<h1 class=\"nsp-hero-title\">AI-Guided Catalyst Discovery for Sustainable Industrial Chemical Processes<\/h1>\n<p class=\"nsp-hero-excerpt\">Transform atomic structures into prioritised catalyst candidates using machine learning, graph neural networks and virtual high-throughput screening. <\/p>\n<div class=\"nsp-hero-ctas\">\n<div class=\"nsp-hero-cta-group\"><a href=\"https:\/\/staging.nanoschool.in\/wp\/enroll-me\/?pid=NSTC3236&#038;accs=Recording\" class=\"nsp-btn nsp-btn--secondary nsp-enbtn\">Enroll Now<\/a><a href=\"https:\/\/staging.nanoschool.in\/wp\/enroll-me\/?pid=NSTC3236&#038;be=Batch\" class=\"nsp-btn nsp-btn--secondary nsp-enbtn\">Batch Enrolment<\/a><a href=\"nanoschool.in\/workshop\" class=\"nsp-btn nsp-btn--outline nsp-enbtn\">More Details<\/a>\n<\/div>\n<\/p><\/div>\n<div>\n                <\/div>\n<\/p><\/div>\n<\/section>\n<section class=\"nsp-meta\">\n<ul class=\"nsp-meta-list nsp-layout-3col\" style=\"padding:5px;\">\n<li><strong>Mode:<\/strong> Virtual \/ Online<\/li>\n<li><strong>Type:<\/strong> Mentor Based<\/li>\n<li><strong>Level:<\/strong> Moderate<\/li>\n<li><strong>Duration:<\/strong> 3 Days(60-90 min\/day)<\/li>\n<li><strong>Starts:<\/strong> 7 September 2026<\/li>\n<li><strong>Time:<\/strong> 5:30 PM IST<\/li>\n<\/ul>\n<\/section>\n<section class=\"nsp-block\">\n<div class=\"nsp-split nsp-split--about\">\n<div class=\"nsp-split__content\">\n<h2>About This Course<\/h2>\n<div>\n<p>This advanced three-day workshop introduces AI-guided catalyst discovery for sustainable industrial chemical processes. Participants will learn to represent catalyst structures, predict adsorption energies, perform AI-assisted atomic relaxation and build virtual high-throughput screening pipelines.<\/p>\n<p>Hands-on Google Colab exercises will cover applications in CO\u2082 reduction, green hydrogen and electrocatalysis using tools such as Matminer, DeepChem, ASE, FAIR-Chem, PyTorch Geometric and Hugging Face Transformers.<\/p>\n<\/div><\/div>\n<\/p><\/div>\n<\/section>\n<section class=\"nsp-block\">\n<h2>Aim<\/h2>\n<div>\n<p>To provide practical knowledge of integrating artificial intelligence, materials informatics and computational catalysis for faster catalyst screening and candidate prioritisation.<\/p>\n<\/div>\n<\/section>\n<section class=\"nsp-block\">\n<h2>Workshop Objectives<\/h2>\n<div>\n<p class=\"isSelectedEnd\">Participants will learn to:<\/p>\n<ul data-spread=\"false\">\n<li>Convert catalyst structures and adsorbates into machine-readable descriptors.<\/li>\n<li>Build ML models for adsorption-energy prediction.<\/li>\n<li>Apply graph neural networks and pretrained interatomic potentials.<\/li>\n<li>Perform AI-guided catalyst\u2013adsorbate structure relaxation.<\/li>\n<li>Develop multi-objective virtual screening workflows.<\/li>\n<li>Rank catalysts by activity, selectivity, stability, cost and material availability.<\/li>\n<\/ul>\n<\/div>\n<\/section>\n<section class=\"nsp-block\">\n<h2>Workshop Structure<\/h2>\n<div style=\"background: #ffffff;border-radius: 14px;padding: 16px 18px;border: 1px solid #e6ecf5;margin-bottom: 16px\">\n<h3 style=\"margin: 0 0 8px 0;font-size: 18px;line-height: 1.3;letter-spacing: .2px;color: #0d1b2a\">\ud83d\uddd3\ufe0f <strong>Day 1: Material Representation &amp; Machine Learning Surrogate Models<\/strong><\/h3>\n<ul style=\"margin: 0;padding: 0 0 0 18px;line-height: 1.6\">\n<li style=\"margin: 2px 0\">Understanding the limitations of traditional Density Functional Theory (DFT) for large-scale catalyst discovery.<\/li>\n<li style=\"margin: 2px 0\">Vectorizing solid-state crystal structures, adsorbates, and catalytic surface facets for machine learning models.<\/li>\n<li style=\"margin: 2px 0\">Computing electronic descriptors, coordination numbers, and site-specific electronegativity features.<\/li>\n<li style=\"margin: 2px 0\">Building, training, and benchmarking machine learning surrogate models to predict catalyst binding energies.<\/li>\n<li style=\"margin: 2px 0\">Evaluating model reliability using Mean Absolute Error (MAE), R\u00b2 scores, and cross-validation metrics.<\/li>\n<\/ul>\n<h3 style=\"margin: 12px 0 8px 0;font-size: 16px;color: #0d1b2a\">\ud83e\uddf0 Tools<\/h3>\n<ul style=\"margin: 0;padding: 0 0 0 18px;line-height: 1.6\">\n<li style=\"margin: 2px 0\"><strong>Matminer:<\/strong> Materials descriptors and feature engineering.<\/li>\n<li style=\"margin: 2px 0\"><strong>RDKit:<\/strong> Molecular representation and chemical descriptor generation.<\/li>\n<li style=\"margin: 2px 0\"><strong>DeepChem:<\/strong> Machine learning model development for chemical and materials data.<\/li>\n<\/ul>\n<h3 style=\"margin: 12px 0 8px 0;font-size: 16px;color: #0d1b2a\">\ud83e\uddea Hands-on Lab (Google Colab)<\/h3>\n<ul style=\"margin: 0;padding: 0 0 0 18px;line-height: 1.6\">\n<li style=\"margin: 2px 0\"><strong>Notebook:<\/strong> Predicting CO\u2082 Reduction Binding Energy Using Matminer Descriptors and DeepChem ML.<\/li>\n<li style=\"margin: 2px 0\"><strong>Task:<\/strong> Generate catalyst descriptors and train a surrogate model to predict adsorption and binding energies.<\/li>\n<li style=\"margin: 2px 0\"><strong>Deliverable:<\/strong> Produce a validated catalyst-property prediction model with MAE, R\u00b2, and cross-validation results.<\/li>\n<\/ul>\n<\/div>\n<div style=\"background: #ffffff;border-radius: 14px;padding: 16px 18px;border: 1px solid #e6ecf5;margin-bottom: 16px\">\n<h3 style=\"margin: 0 0 8px 0;font-size: 18px;line-height: 1.3;letter-spacing: .2px;color: #0d1b2a\">\ud83d\uddd3\ufe0f <strong>Day 2: 3D Graph Neural Networks &amp; AI-Guided Structure Relaxation<\/strong><\/h3>\n<ul style=\"margin: 0;padding: 0 0 0 18px;line-height: 1.6\">\n<li style=\"margin: 2px 0\">Understanding 3D rotation equivariance and graph-based representations of atomic coordinates.<\/li>\n<li style=\"margin: 2px 0\">Applying Graph Neural Networks (GNNs) to model complex atomic interactions and potential-energy surfaces.<\/li>\n<li style=\"margin: 2px 0\">Replacing computationally expensive Quantum ESPRESSO and VASP calculations with neural network force fields.<\/li>\n<li style=\"margin: 2px 0\">Running automated 3D surface\u2013adsorbate structural relaxations to identify local energy minima.<\/li>\n<li style=\"margin: 2px 0\">Accelerating energy-relaxation calculations by up to 1,000\u00d7 for alloy and metal-oxide catalyst interfaces.<\/li>\n<\/ul>\n<h3 style=\"margin: 12px 0 8px 0;font-size: 16px;color: #0d1b2a\">\ud83e\uddf0 Tools<\/h3>\n<ul style=\"margin: 0;padding: 0 0 0 18px;line-height: 1.6\">\n<li style=\"margin: 2px 0\"><strong>Open Catalyst Project:<\/strong> Pre-trained checkpoints for catalyst energy and force prediction.<\/li>\n<li style=\"margin: 2px 0\"><strong>ASE:<\/strong> Atomic structure construction, manipulation, simulation, and relaxation.<\/li>\n<li style=\"margin: 2px 0\"><strong>PyTorch Geometric:<\/strong> Graph neural network development for atomic systems.<\/li>\n<li style=\"margin: 2px 0\"><strong>EquiformerV2:<\/strong> Equivariant transformer architecture for accurate 3D atomic modelling.<\/li>\n<\/ul>\n<h3 style=\"margin: 12px 0 8px 0;font-size: 16px;color: #0d1b2a\">\ud83e\uddea Hands-on Lab (Google Colab)<\/h3>\n<ul style=\"margin: 0;padding: 0 0 0 18px;line-height: 1.6\">\n<li style=\"margin: 2px 0\"><strong>Notebook:<\/strong> 1,000\u00d7 Accelerated Atomic Relaxation for Green H\u2082 Electrocatalysts Using EquiformerV2.<\/li>\n<li style=\"margin: 2px 0\"><strong>Task:<\/strong> Construct a catalyst\u2013adsorbate system and perform AI-guided atomic structure relaxation.<\/li>\n<li style=\"margin: 2px 0\"><strong>Deliverable:<\/strong> Generate an optimized 3D catalyst structure with predicted energies, forces, and relaxation trajectories.<\/li>\n<\/ul>\n<\/div>\n<div style=\"background: #ffffff;border-radius: 14px;padding: 16px 18px;border: 1px solid #e6ecf5;margin-bottom: 16px\">\n<h3 style=\"margin: 0 0 8px 0;font-size: 18px;line-height: 1.3;letter-spacing: .2px;color: #0d1b2a\">\ud83d\uddd3\ufe0f <strong>Day 3: Generative AI, Chemical Transformers &amp; Virtual High-Throughput Screening<\/strong><\/h3>\n<ul style=\"margin: 0;padding: 0 0 0 18px;line-height: 1.6\">\n<li style=\"margin: 2px 0\">Understanding language models trained on SMILES, SELFIES, and materials representations.<\/li>\n<li style=\"margin: 2px 0\">Fine-tuning pre-trained chemical transformer architectures for catalytic activity and property prediction.<\/li>\n<li style=\"margin: 2px 0\">Designing multi-objective Virtual High-Throughput Screening (VHTS) pipelines for catalyst discovery.<\/li>\n<li style=\"margin: 2px 0\">Filtering candidate libraries based on predicted overpotential, selectivity, binding energy, and structural stability.<\/li>\n<li style=\"margin: 2px 0\">Ranking computational catalyst candidates using performance, reliability, and feasibility criteria.<\/li>\n<li style=\"margin: 2px 0\">Formatting computational discovery workflows and visualizations for high-impact research publications.<\/li>\n<\/ul>\n<h3 style=\"margin: 12px 0 8px 0;font-size: 16px;color: #0d1b2a\">\ud83e\uddf0 Tools<\/h3>\n<ul style=\"margin: 0;padding: 0 0 0 18px;line-height: 1.6\">\n<li style=\"margin: 2px 0\"><strong>Hugging Face Transformers:<\/strong> Access to ChemBERTa, MolFormer, and related chemical language models.<\/li>\n<li style=\"margin: 2px 0\"><strong>PyXSF \/ ASE Visualizer:<\/strong> Visualization of atomic structures, surfaces, and catalyst candidates.<\/li>\n<li style=\"margin: 2px 0\"><strong>Open Catalyst API:<\/strong> Access to catalyst structures, energies, and pre-trained model predictions.<\/li>\n<\/ul>\n<h3 style=\"margin: 12px 0 8px 0;font-size: 16px;color: #0d1b2a\">\ud83e\uddea Hands-on Lab (Google Colab)<\/h3>\n<ul style=\"margin: 0;padding: 0 0 0 18px;line-height: 1.6\">\n<li style=\"margin: 2px 0\"><strong>Notebook:<\/strong> Fine-Tuning ChemBERTa and Building a Virtual High-Throughput Catalyst Screening Engine.<\/li>\n<li style=\"margin: 2px 0\"><strong>Task:<\/strong> Fine-tune a chemical transformer and screen a virtual catalyst library against multiple performance criteria.<\/li>\n<li style=\"margin: 2px 0\"><strong>Deliverable:<\/strong> Generate a ranked shortlist of computational catalyst candidates with predicted properties and publication-ready visualizations.<\/li>\n<\/ul>\n<\/div>\n<\/section>\n<section class=\"nsp-block\">\n<h2>Who Should Enrol?<\/h2>\n<div>\n<p class=\"isSelectedEnd\">This workshop is suitable for postgraduate students, PhD scholars, faculty members, researchers and industry professionals working in:<\/p>\n<ul data-spread=\"false\">\n<li>Chemical engineering<\/li>\n<li>Computational chemistry<\/li>\n<li>Materials science<\/li>\n<li>Heterogeneous catalysis<\/li>\n<li>Electrocatalysis<\/li>\n<li>Surface science<\/li>\n<li>Green hydrogen<\/li>\n<li>CO\u2082 conversion<\/li>\n<li>Nanocatalysis<\/li>\n<li>Materials informatics<\/li>\n<li>AI-driven materials discovery<\/li>\n<li>Sustainable industrial processes<\/li>\n<\/ul>\n<\/div>\n<\/section>\n<section class=\"nsp-block\">\n<h2>Important Dates<\/h2>\n<div class=\"nsp-date-grid nsp-layout-2col\">\n<div class=\"nsp-date-card\">\n<h3>Registration Ends<\/h3>\n<p>September 7, 2026<br \/><small>IST 4:30 PM<\/small><\/p>\n<\/p><\/div>\n<div class=\"nsp-date-card\">\n<h3>Workshop Dates<\/h3>\n<p>September 7, 2026 \u2013 September 9, 2026<br \/><small>IST 5:30 PM<\/small><\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/section>\n<section class=\"nsp-block\">\n<h2>Workshop Outcomes<\/h2>\n<div>\n<p class=\"isSelectedEnd\">By the end of the workshop, participants will be able to:<\/p>\n<ul data-spread=\"false\">\n<li>Prepare catalyst datasets for machine learning.<\/li>\n<li>Train and evaluate catalyst-property prediction models.<\/li>\n<li>Use AI models for atomic structure and energy prediction.<\/li>\n<li>Screen large catalyst libraries computationally.<\/li>\n<li>Identify promising candidates for further DFT or experimental validation.<\/li>\n<li>Generate publication-ready catalyst-ranking reports and visualisations.<\/li>\n<\/ul>\n<\/div>\n<\/section>\n<section class=\"nsp-block\">\n<h2>Fee Structure<\/h2>\n<div class=\"nsp-fee-grid nsp-layout-2col\">\n<div class=\"nsp-fee-card\">\n<h3>Student <\/h3>\n<p>\u20b92499 | $75<\/p>\n<\/p><\/div>\n<div class=\"nsp-fee-card\">\n<h3>Ph.D. Scholar \/ Researcher <\/h3>\n<p>\u20b93499 | $85<\/p>\n<\/p><\/div>\n<div class=\"nsp-fee-card\">\n<h3>Academician \/ Faculty <\/h3>\n<p>\u20b94499 | $95<\/p>\n<\/p><\/div>\n<div class=\"nsp-fee-card\">\n<h3>Industry Professional <\/h3>\n<p>\u20b96499 | $120<\/p>\n<\/p><\/div>\n<\/p><\/div>\n<\/section>\n<section class=\"nsp-block\">\n<h2>What You&#8217;ll Gain<\/h2>\n<div class=\"nsp-takeaways nsp-layout-2col\">\n<div>\n<ul class=\"nsp-takeaways-list\">\n<li>Live &#038; recorded sessions<\/li>\n<li>e-Certificate upon completion<\/li>\n<li>Post-workshop query support<\/li>\n<li>Hands-on learning experience<\/li>\n<\/ul><\/div>\n<div>\n<div class=\"nsp-cert-preview\"\n                        style=\"background-image:url('https:\/\/staging.nanoschool.in\/wp\/wp-content\/uploads\/2024\/05\/certificate-in-web-2.webp')\">\n                    <\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/section>\n<section class=\"nsp-support\">\n<div class=\"nsp-support-box\">\n<h3>Need Help?<\/h3>\n<p>We&#8217;re here for you!<\/p>\n<p>                <a href=\"tel:+911204781217\" class=\"nsp-support-tel\"><br \/>\n                    <span class=\"nsp-icon\"><\/span> (+91) 120-4781-217<br \/>\n                <\/a>\n            <\/div>\n<\/section>\n<section class=\"nsp-feedback\">\n<p>            <div style=\"all:initial!important;display:grid!important;grid-template-columns:repeat(auto-fit,minmax(250px,1fr))!important;gap:22px!important;max-width:1280px!important;margin:0 auto!important;padding:18px!important;background:#f4f6fb!important;border:1px solid #e9ecf7!important;border-radius:18px!important;box-sizing:border-box!important;isolation:isolate!important\" aria-label=\"AI Courses Grid\"><div class=\"frm_grid_container\"><div class=\"frm12\" style=\"border-color: #ffffff;background-color: #ffffff;\"><div style=\"all:initial!important;display:flex!important;flex-wrap:wrap!important;gap:18px!important;justify-content:center!important;align-items:stretch!important;width:100%!important;max-width:1200px!important;margin:0 auto!important;padding:14px!important;background:#f8fafc!important;border:1px solid #eef2f7!important;border-radius:12px!important;box-sizing:border-box!important;\" aria-label=\"Testimonial Section\"> <div style=\"all:initial!important;flex:0 1 320px!important;width:auto!important;background:#ffffff!important;border:1px solid #e5e7eb!important;border-radius:12px!important;box-shadow:0 6px 18px rgba(15,23,42,.06)!important;padding:16px 18px!important;color:#0f172a!important;font:400 14px\/1.55 system-ui,-apple-system,Segoe UI,Roboto,Ubuntu,Cantarell,Noto Sans,sans-serif!important;text-align:left!important;transition:transform .2s ease, box-shadow .2s ease, border-color .2s ease!important;box-sizing:border-box!important;\" role=\"listitem\"> <div style=\"all:initial!important;line-height:1!important;\"> <span style=\"all:initial!important;font-size:18px!important;color:#f59e0b!important;fill:currentColor!important;vertical-align:middle!important;\">\u2605\u2605\u2605\u2605\u2605<\/span> <\/div> <div style=\"all:initial!important;display:flex!important;align-items:center!important;justify-content:space-between!important;gap:10px!important;margin-bottom:8px!important;\"> <strong style=\"all:initial!important;color:#0f172a!important;font-weight:700!important;font-size:16px!important;\">Cancer Drug Discovery: Creating Cancer Therapies<\/strong> <\/div> <span style=\"all:initial!important;display:block!important;margin-top:6px!important;color:#334155!important;font-size:14px!important;max-height:80px !important;overflow:hidden!important;text-overflow:ellipsis!important;\" aria-label=\"Testimonial Content: <p>Undoubtedly, the professor's expertise was evident, and their ability to cover a vast amount of material within the given timeframe was impressive. However, the pace at which the content was presented made it challenging for some attendees, including myself, to fully grasp and absorb the information.<\/p>\n\"><p>Undoubtedly, the professor's expertise was evident, and their ability to cover a vast amount of material within the given timeframe was impressive. However, the pace at which the content was presented made it challenging for some attendees, including myself, to fully grasp and absorb the information.<\/p>\n<\/span> <span style=\"all:initial!important;display:block!important;margin-top:10px!important;font-size:12px!important;color:#64748b!important;\"> <span aria-label=\"Testimonial Author: Mario Rigo\" style=\"all:initial!important;\">Mario Rigo<\/span> \u2022 <time style=\"all:initial!important;\" datetime=\"November 30, 2023 at 5:18 pm\">November 30, 2023 at 5:18 pm<\/time><\/span><\/div><\/div>\n<\/div><\/div><div class=\"frm_grid_container\"><div class=\"frm12\" style=\"border-color: #ffffff;background-color: #ffffff;\"><div style=\"all:initial!important;display:flex!important;flex-wrap:wrap!important;gap:18px!important;justify-content:center!important;align-items:stretch!important;width:100%!important;max-width:1200px!important;margin:0 auto!important;padding:14px!important;background:#f8fafc!important;border:1px solid #eef2f7!important;border-radius:12px!important;box-sizing:border-box!important;\" aria-label=\"Testimonial Section\"> <div style=\"all:initial!important;flex:0 1 320px!important;width:auto!important;background:#ffffff!important;border:1px solid #e5e7eb!important;border-radius:12px!important;box-shadow:0 6px 18px rgba(15,23,42,.06)!important;padding:16px 18px!important;color:#0f172a!important;font:400 14px\/1.55 system-ui,-apple-system,Segoe UI,Roboto,Ubuntu,Cantarell,Noto Sans,sans-serif!important;text-align:left!important;transition:transform .2s ease, box-shadow .2s ease, border-color .2s ease!important;box-sizing:border-box!important;\" role=\"listitem\"> <div style=\"all:initial!important;line-height:1!important;\"> <span style=\"all:initial!important;font-size:18px!important;color:#f59e0b!important;fill:currentColor!important;vertical-align:middle!important;\">\u2605\u2605\u2605\u2605\u2605<\/span> <\/div> <div style=\"all:initial!important;display:flex!important;align-items:center!important;justify-content:space-between!important;gap:10px!important;margin-bottom:8px!important;\"> <strong style=\"all:initial!important;color:#0f172a!important;font-weight:700!important;font-size:16px!important;\">Power BI and Advanced SQL Mastery Integration Workshop, CRISPR-Cas Genome Editing: Workflow, Tools and Techniques<\/strong> <\/div> <span style=\"all:initial!important;display:block!important;margin-top:6px!important;color:#334155!important;font-size:14px!important;max-height:80px !important;overflow:hidden!important;text-overflow:ellipsis!important;\" aria-label=\"Testimonial Content: <p>Good! Thank you<\/p>\n\"><p>Good! Thank you<\/p>\n<\/span> <span style=\"all:initial!important;display:block!important;margin-top:10px!important;font-size:12px!important;color:#64748b!important;\"> <span aria-label=\"Testimonial Author: Silvia Santopolo\" style=\"all:initial!important;\">Silvia Santopolo<\/span> \u2022 <time style=\"all:initial!important;\" datetime=\"December 5, 2023 at 4:01 pm\">December 5, 2023 at 4:01 pm<\/time><\/span><\/div><\/div>\n<\/div><\/div><div class=\"frm_grid_container\"><div class=\"frm12\" style=\"border-color: #ffffff;background-color: #ffffff;\"><div style=\"all:initial!important;display:flex!important;flex-wrap:wrap!important;gap:18px!important;justify-content:center!important;align-items:stretch!important;width:100%!important;max-width:1200px!important;margin:0 auto!important;padding:14px!important;background:#f8fafc!important;border:1px solid #eef2f7!important;border-radius:12px!important;box-sizing:border-box!important;\" aria-label=\"Testimonial Section\"> <div style=\"all:initial!important;flex:0 1 320px!important;width:auto!important;background:#ffffff!important;border:1px solid #e5e7eb!important;border-radius:12px!important;box-shadow:0 6px 18px rgba(15,23,42,.06)!important;padding:16px 18px!important;color:#0f172a!important;font:400 14px\/1.55 system-ui,-apple-system,Segoe UI,Roboto,Ubuntu,Cantarell,Noto Sans,sans-serif!important;text-align:left!important;transition:transform .2s ease, box-shadow .2s ease, border-color .2s ease!important;box-sizing:border-box!important;\" role=\"listitem\"> <div style=\"all:initial!important;line-height:1!important;\"> <span style=\"all:initial!important;font-size:18px!important;color:#f59e0b!important;fill:currentColor!important;vertical-align:middle!important;\">\u2605\u2605\u2605\u2605\u2605<\/span> <\/div> <div style=\"all:initial!important;display:flex!important;align-items:center!important;justify-content:space-between!important;gap:10px!important;margin-bottom:8px!important;\"> <strong style=\"all:initial!important;color:#0f172a!important;font-weight:700!important;font-size:16px!important;\">Artificial Intelligence for Cancer Drug Delivery<\/strong> <\/div> <span style=\"all:initial!important;display:block!important;margin-top:6px!important;color:#334155!important;font-size:14px!important;max-height:80px !important;overflow:hidden!important;text-overflow:ellipsis!important;\" aria-label=\"Testimonial Content: <p>Informative lectures<\/p>\n\"><p>Informative lectures<\/p>\n<\/span> <span style=\"all:initial!important;display:block!important;margin-top:10px!important;font-size:12px!important;color:#64748b!important;\"> <span aria-label=\"Testimonial Author: G Jyothi\" style=\"all:initial!important;\">G Jyothi<\/span> \u2022 <time style=\"all:initial!important;\" datetime=\"January 18, 2024 at 11:44 pm\">January 18, 2024 at 11:44 pm<\/time><\/span><\/div><\/div>\n<\/div><\/div><div class=\"frm_grid_container\"><div class=\"frm12\" style=\"border-color: #ffffff;background-color: #ffffff;\"><div style=\"all:initial!important;display:flex!important;flex-wrap:wrap!important;gap:18px!important;justify-content:center!important;align-items:stretch!important;width:100%!important;max-width:1200px!important;margin:0 auto!important;padding:14px!important;background:#f8fafc!important;border:1px solid #eef2f7!important;border-radius:12px!important;box-sizing:border-box!important;\" aria-label=\"Testimonial Section\"> <div style=\"all:initial!important;flex:0 1 320px!important;width:auto!important;background:#ffffff!important;border:1px solid #e5e7eb!important;border-radius:12px!important;box-shadow:0 6px 18px rgba(15,23,42,.06)!important;padding:16px 18px!important;color:#0f172a!important;font:400 14px\/1.55 system-ui,-apple-system,Segoe UI,Roboto,Ubuntu,Cantarell,Noto Sans,sans-serif!important;text-align:left!important;transition:transform .2s ease, box-shadow .2s ease, border-color .2s ease!important;box-sizing:border-box!important;\" role=\"listitem\"> <div style=\"all:initial!important;line-height:1!important;\"> <span style=\"all:initial!important;font-size:18px!important;color:#f59e0b!important;fill:currentColor!important;vertical-align:middle!important;\">\u2605\u2605\u2605\u2605\u2605<\/span> <\/div> <div style=\"all:initial!important;display:flex!important;align-items:center!important;justify-content:space-between!important;gap:10px!important;margin-bottom:8px!important;\"> <strong style=\"all:initial!important;color:#0f172a!important;font-weight:700!important;font-size:16px!important;\">Artificial Intelligence for Cancer Drug Delivery<\/strong> <\/div> <span style=\"all:initial!important;display:block!important;margin-top:6px!important;color:#334155!important;font-size:14px!important;max-height:80px !important;overflow:hidden!important;text-overflow:ellipsis!important;\" aria-label=\"Testimonial Content: <p>delt with all the topics associated with the subject matter<\/p>\n\"><p>delt with all the topics associated with the subject matter<\/p>\n<\/span> <span style=\"all:initial!important;display:block!important;margin-top:10px!important;font-size:12px!important;color:#64748b!important;\"> <span aria-label=\"Testimonial Author: RAVIKANT SHEKHAR\" style=\"all:initial!important;\">RAVIKANT SHEKHAR<\/span> \u2022 <time style=\"all:initial!important;\" datetime=\"February 7, 2024 at 11:01 pm\">February 7, 2024 at 11:01 pm<\/time><\/span><\/div><\/div>\n<\/div><\/div><\/div>\n<br \/>\n            <a href=\"https:\/\/staging.nanoschool.in\/wp\/nstc\/feedback-view\/\" class=\"nsp-view-all\">View All Feedbacks \u2192<\/a><br \/>\n        <\/section>\n<p>    <\/main><\/p>\n<div class=\"nsp-sticky\">\n<p>        <a href=\"https:\/\/staging.nanoschool.in\/wp\/enroll-me\/?pid=NSTC3236\" class=\"nsp-btn nsp-btn--sticky nsp-btn--primary\">Enrol<br \/>\n            Now<\/a><br \/>\n        <a href=\"nanoschool.in\/workshop\" class=\"nsp-btn nsp-btn--sticky 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