{"id":7646,"date":"2026-11-11T11:10:03","date_gmt":"2026-11-11T02:10:03","guid":{"rendered":"https:\/\/matlantis.com\/ja\/?post_type=news&#038;p=7646"},"modified":"2026-11-11T13:39:04","modified_gmt":"2026-11-11T04:39:04","slug":"aiche-annual-meeting-2026","status":"publish","type":"news","link":"https:\/\/matlantis.com\/ja\/news\/aiche-annual-meeting-2026\/","title":{"rendered":"2026 AIChE Annual Meeting\u306b\u3066\u53e3\u982d\u767a\u8868"},"content":{"rendered":"\n<p class=\"wp-block-paragraph\">Matlantis\u682a\u5f0f\u4f1a\u793e\u306f\u30012026\u5e7411\u67088\u65e5\uff5e11\u670812\u65e5\uff08\u7c73\u56fd\u4e2d\u90e8\u6a19\u6e96\u6642\uff0fCST\uff09\u306b\u7c73\u56fd\u30df\u30cd\u30a2\u30dd\u30ea\u30b9\uff08Minneapolis Convention Center\uff09\u3067\u958b\u50ac\u3055\u308c\u308b\u300c<a href=\"https:\/\/www.aiche.org\/conferences\/aiche-annual-meeting\/2026\" rel=\"nofollow noopener\" target=\"_blank\">2026 AIChE Annual Meeting<\/a>\u300d\u306b\u304a\u3044\u3066\u3001\u53e3\u982d\u767a\u8868\u3092\u884c\u3044\u307e\u3059\u3002\u5185\u5bb9\u306f\u4ee5\u4e0b\u3054\u53c2\u7167\u304f\u3060\u3055\u3044\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">\u30d7\u30ec\u30bc\u30f3\u30c6\u30fc\u30b7\u30e7\u30f3\u8a73\u7d30\uff08\u53e3\u982d\u767a\u8868\uff09<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>\u30eb\u30fc\u30e0: <\/strong>Room 103C\uff08Level 1, Minneapolis Convention Center\uff09<br><strong>\u8b1b\u6f14\u756a\u53f7: <\/strong>566b<br><strong>\u767a\u8868\u8005: <\/strong>\u6e21\u908a \u5353\uff08Taku Watanabe\uff09<br><strong>\u8457\u8005:<\/strong> Qingjie Li, Joshua Young, Taku Watanabe\uff08Matlantis Inc.\uff09<br><strong>\u30bb\u30c3\u30b7\u30e7\u30f3: <\/strong>566 Accelerated Discovery of Inorganic Materials: High-Throughput Experiments, Modeling, &amp; Data Science<br><strong>\u65e5\u6642: <\/strong>2026\u5e7411\u670811\u65e5\uff08\u6c34\uff0912:55 PM\uff5e1:09 PM\uff08\u7c73\u56fd\u4e2d\u90e8\u6a19\u6e96\u6642\uff0fCST\uff09<br>\uff08\u65e5\u672c\u6642\u9593\uff1a2026\u5e7411\u670812\u65e5\uff08\u6728\uff093:55\uff5e4:09\uff09<br><strong>\u90e8\u9580: <\/strong>Materials Engineering and Sciences Division\uff0808D Inorganic Materials\uff09<br><strong>\u30bb\u30c3\u30b7\u30e7\u30f3\u30bf\u30a4\u30d7: <\/strong>\u30aa\u30fc\u30e9\u30eb<br><strong>\u30bf\u30a4\u30c8\u30eb: <\/strong>Accelerating Solid Electrolyte Discovery with an Interpretable, Multimodal, and Probabilistic Machine Learning Framework<br><strong>\u6982\u8981:<\/strong><br>The discovery of solid electrolytes for all-solid-state batteries is currently constrained by two major modeling challenges. First, ionic conductivity depends on a complex interplay of structural and dynamic factors that conventional, single-modality machine learning models fail to fully capture. Second, models trained on computationally efficient, short-timescale molecular dynamics simulations suffer from severe data noise. Specifically, transient phenomena such as local ion &#8220;cage rattling&#8221; artificially inflate mobility metrics, causing models to overfit to subdiffusive artifacts rather than true macroscopic diffusion.<br>To bridge this simulation-to-reality gap, we introduce MuLiCon, a multimodal, probabilistic deep learning framework. MuLiCon integrates 1D structural data (X-ray diffraction), vibrational dynamics (phonon density of states), and pre-trained chemical embeddings (Matlantis PFP) into a dual-task architecture for simultaneous material classification and conductivity regression. By employing deep ensembles for Uncertainty Quantification (UQ), MuLiCon acts as a probabilistic filter, explicitly isolating and rejecting simulation noise.<br>Ablation studies reveal a distinct physical learning hierarchy: PFP descriptors serve as the primary chemical foundation, while XRD and PDOS offer structural gating and vibrational refinement. By filtering for high-confidence predictions, MuLiCon achieves precise conductivity regression on true conductors. Furthermore, model interpretability analyses confirm that the network autonomously identifies underlying physical mechanisms, including phonon resonance and structural bottlenecks. Ultimately, MuLiCon provides a robust, physics-aware pathway to accelerate next-generation energy storage design.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"511\" src=\"https:\/\/matlantis.com\/wp-content\/uploads\/2026\/11\/aiche2026_matlantis-1024x511.png\" alt=\"MuLiCon architecture combining XRD patterns, phonon density of states, and Matlantis PFP embeddings to classify conductors and predict ionic conductivity\" class=\"wp-image-7648\"\/><\/figure>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>\u767a\u8868\u8005\u30d7\u30ed\u30d5\u30a3\u30fc\u30eb<\/strong><\/h2>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>\u6e21\u908a \u5353\uff08\u308f\u305f\u306a\u3079 \u305f\u304f\uff09<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Matlantis Inc.<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>\u30d7\u30ea\u30f3\u30b7\u30d1\u30eb\u30ea\u30b5\u30fc\u30c1\u30e3\u30fc\uff0f\u30b0\u30ed\u30fc\u30d0\u30eb\u30ab\u30b9\u30bf\u30de\u30fc\u30b5\u30af\u30bb\u30b9\u7d71\u62ec<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">\u30d5\u30ed\u30ea\u30c0\u5927\u5b66\u306b\u3066\u535a\u58eb\u53f7\uff08Ph.D.\uff09\u3092\u53d6\u5f97\u5f8c\u3001\u30b8\u30e7\u30fc\u30b8\u30a2\u5de5\u79d1\u5927\u5b66\u3067\u30dd\u30b9\u30c9\u30af\u3068\u3057\u3066\u6700\u5148\u7aef\u306e\u591a\u5b54\u8cea\u6750\u6599\uff08\u30bc\u30aa\u30e9\u30a4\u30c8\u3001MOF\uff09\u7814\u7a76\u306b\u5f93\u4e8b\u3002\u5e30\u56fd\u5f8c\u306f\u30b5\u30e0\u30b9\u30f3\u65e5\u672c\u7814\u7a76\u6240\u3084ENEOS\u306b\u3066\u3001\u6b21\u4e16\u4ee3\u96fb\u6c60\u3084\u30a8\u30cd\u30eb\u30ae\u30fc\u5206\u91ce\u306e\u6750\u6599\u958b\u767a\u3092\u30ea\u30fc\u30c9\u3057\u3066\u304d\u305f\u3002\u3053\u308c\u307e\u3067\u306e\u591a\u69d8\u306a\u30b7\u30df\u30e5\u30ec\u30fc\u30b7\u30e7\u30f3\u3068\u5b9f\u9a13\u306e\u7d4c\u9a13\u3092\u57fa\u306b\u3001\u73fe\u5728\u306f\u6a5f\u68b0\u5b66\u7fd2\u3084\u8a08\u7b97\u5316\u5b66\u3092\u7528\u3044\u305f\u9769\u65b0\u7684\u306a\u6750\u6599\u7814\u7a76\u624b\u6cd5\u306b\u3088\u308b\u30ab\u30b9\u30bf\u30de\u30fc\u30b5\u30af\u30bb\u30b9\u6d3b\u52d5\u306b\u5f93\u4e8b\u3057\u3066\u3044\u308b\u3002\u7121\u6a5f\u6750\u6599\u5168\u822c\u306e\u8a08\u7b97\u6280\u8853\u958b\u767a\u306b\u52a0\u3048\u3001\u6d77\u5916\u30ab\u30b9\u30bf\u30de\u30fc\u30b5\u30dd\u30fc\u30c8\u306e\u7d44\u7e54\u904b\u55b6\u3082\u7d71\u62ec\u3057\u3066\u3044\u308b\u3002<\/p>\n","protected":false},"featured_media":7648,"template":"","meta":{"_acf_changed":false},"news_category":[164],"class_list":["post-7646","news","type-news","status-publish","has-post-thumbnail","hentry","news_category-talks-exhibitions"],"acf":[],"_links":{"self":[{"href":"https:\/\/matlantis.com\/ja\/wp-json\/wp\/v2\/news\/7646","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/matlantis.com\/ja\/wp-json\/wp\/v2\/news"}],"about":[{"href":"https:\/\/matlantis.com\/ja\/wp-json\/wp\/v2\/types\/news"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/matlantis.com\/ja\/wp-json\/wp\/v2\/media\/7648"}],"wp:attachment":[{"href":"https:\/\/matlantis.com\/ja\/wp-json\/wp\/v2\/media?parent=7646"}],"wp:term":[{"taxonomy":"news_category","embeddable":true,"href":"https:\/\/matlantis.com\/ja\/wp-json\/wp\/v2\/news_category?post=7646"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}