# CHEM 361: Machine Learning in Chemistry | UW–Madison [View on UW Courses](https://uwcourses.com/courses/CHEM_361) ## Dataset ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ### repository twangodev/uwcourses ### schema version 6 ### importer version 7 ### projection id 52a78527ff09011d88cb04c7d43867d9fbfec2930bf1dd2ec359ebd2844e0b07 ### observed at 2026-09-07T15:55:43.033547+00:00 ### built at 2026-09-10T19:50:42.743001+00:00 ### courses 8951 ### current instructors 5754 ### limited false ### terms * 1272 * 1264 * 1262 * 1254 * 1252 * 1244 * 1242 * 1234 * 1232 * 1224 * 1222 * 1212 * 1204 * 1202 * 1194 * 1192 * 1184 * 1182 * 1174 * 1172 * 1164 * 1162 * 1154 * 1152 * 1144 * 1142 * 1134 * 1132 * 1124 * 1122 * 1114 * 1112 * 1104 * 1102 * 1094 * 1092 * 1084 * 1082 * 1074 * 1072 ### term 1272 ### departments | subject | count | | --------- | ----- | | AAE | 90 | | ABT | 20 | | ACCTIS | 36 | | ACTSCI | 14 | | AFAERO | 10 | | AFRICAN | 87 | | AFROAMER | 71 | | AGROECOL | 21 | | AMERIND | 53 | | ANAT\&PHY | 7 | | ANATOMY | 2 | | ANESTHES | 8 | | ANSCI | 57 | | ANTHRO | 95 | | ART | 125 | | ARTED | 10 | | ARTHIST | 121 | | ASIALANG | 129 | | ASIAN | 109 | | ASIANAM | 28 | | ASTRON | 38 | | ATMOCN | 69 | | BIOCHEM | 51 | | BIOCORE | 10 | | BIOLOGY | 14 | | BIOMDSCI | 18 | | BME | 66 | | BMI | 41 | | BMOLCHEM | 10 | | BOTANY | 69 | | BSE | 44 | | C\&ESOC | 73 | | CBE | 48 | | CHEM | 101 | | CHICLA | 52 | | CIVENGR | 133 | | CLASSICS | 47 | | CNP | 10 | | CNSRSCI | 50 | | COMARTS | 137 | | COMPBIO | 15 | | COMPLIT | 11 | | COMPSCI | 138 | | COUNPSY | 78 | | CRB | 19 | | CS\&D | 71 | | CSCS | 40 | | CURRIC | 193 | | DANCE | 93 | | DERM | 8 | | DS | 88 | | DYSCI | 35 | | ECE | 155 | | ECON | 145 | | EDPOL | 114 | | EDPSYCH | 97 | | ELPA | 70 | | EMA | 53 | | EMERMED | 17 | | ENGL | 206 | | ENTOM | 40 | | ENVIRST | 148 | | EP | 15 | | EPD | 67 | | ESL | 16 | | F\&WECOL | 60 | | FAMMED | 22 | | FINANCE | 50 | | FOLKLORE | 40 | | FOODSCI | 43 | | FRENCH | 63 | | GEN\&WS | 145 | | GENBUS | 72 | | GENECSLR | 17 | | GENETICS | 60 | | GEOG | 113 | | GEOSCI | 83 | | GERMAN | 82 | | GLE | 48 | | GNS | 38 | | GREEK | 27 | | HDFS | 41 | | HEBR-BIB | 13 | | HEBR-MOD | 10 | | HISTORY | 233 | | HISTSCI | 56 | | HONCOL | 13 | | ILS | 43 | | INFOSYS | 9 | | INTEGART | 7 | | INTEGSCI | 22 | | INTER-AG | 18 | | INTER-HE | 11 | | INTER-LS | 22 | | INTEREGR | 16 | | INTLBUS | 21 | | INTLST | 50 | | ISYE | 83 | | ITALIAN | 53 | | JEWISH | 56 | | JOURN | 88 | | KINES | 128 | | LACIS | 26 | | LANDARC | 61 | | LATIN | 24 | | LAW | 120 | | LEGALST | 48 | | LINGUIS | 35 | | LIS | 89 | | LITTRANS | 78 | | LSC | 52 | | M\&ENVTOX | 7 | | MARKETNG | 62 | | MATH | 153 | | MDGENET | 8 | | ME | 130 | | MEDHIST | 43 | | MEDICINE | 60 | | MEDIEVAL | 32 | | MEDPHYS | 39 | | MEDSC-M | 29 | | MEDSC-V | 41 | | MHR | 68 | | MICROBIO | 45 | | MILSCI | 16 | | MM\&I | 22 | | MOLBIOL | 6 | | MS\&E | 59 | | MUSIC | 165 | | MUSPERF | 126 | | NAVSCI | 20 | | NE | 44 | | NEURODPT | 11 | | NEUROL | 7 | | NEURSURG | 4 | | NTP | 11 | | NURSING | 111 | | NUTRSCI | 65 | | OBS\&GYN | 18 | | OCCTHER | 39 | | ONCOLOGY | 14 | | OPHTHALM | 5 | | OTM | 43 | | PATH | 34 | | PATH-BIO | 29 | | PEDIAT | 26 | | PHARMACY | 31 | | PHILOS | 77 | | PHMCOL-M | 11 | | PHMPRAC | 45 | | PHMSCI | 60 | | PHYASST | 37 | | PHYSICS | 88 | | PHYSIOL | 3 | | PHYTHER | 37 | | PLANTSCI | 52 | | PLPATH | 35 | | POLISCI | 192 | | POPHLTH | 58 | | PORTUG | 33 | | PSYCH | 101 | | PSYCHIAT | 22 | | PUBAFFR | 54 | | PUBLHLTH | 23 | | RADIOL | 11 | | REALEST | 41 | | RELIGST | 90 | | RHABMED | 9 | | RMI | 24 | | RP\&SE | 102 | | S\&APHM | 17 | | SCANDST | 73 | | SLAVIC | 89 | | SOC | 149 | | SOCWORK | 87 | | SOILSCI | 46 | | SPANISH | 83 | | SRMED | 21 | | STAT | 94 | | STDYABRD | 52 | | STS | 8 | | SURGERY | 25 | | SURGSCI | 33 | | THEATRE | 91 | | URBRPL | 60 | | UROLOGY | 5 | | ZOOLOGY | 89 | ## course ### run id 20260907T155543-ce3781c4 ### semester 1272 ### observed at 2026-09-07 15:55:43.033547+00:00 ### record version id 4edf8117f1cfef1b8b8730da8c5810c2b342d7fe3ca26f9dc36834dcc20643c2 ### course id CHEM 361 ### course uid course\_63f398e50e0bc4980d5d168f ### catalog version id 5127b84eb62071355a1444449485dba5a652b0cdbbe985ebb82964154a6856f4 ### course number 361 ### subjects * CHEM ### title MACHINE LEARNING IN CHEMISTRY ### description An in-depth introduction to the use of machine learning techniques in Chemistry. Topics will include basics of probability theory and statistics, basics of function fitting and parameter inference, basics of optimization, and machine learning techniques. Discuss a selection of Chemistry topics that are particularly amenable to analysis using machine learning. These might include generative models for organic synthesis, force-fields, application to phase transitions, structure and dynamics of molecular systems, and AI-driven drug discovery. ### requirements text (CHEM 103,109, or115) and (MATH 234,320,331,340, or375) ### credits min 3 ### credits max 3 ### credit offering ids * 1272:224:026798 ### llm job id enrich-8b774950c2b6adfdc46d1b82 ### llm output id dc2042d2fa0d5e4f29a9f1ea46fec31abf23e9d2be176fdbbdf757cd5744dec8 ### llm model nvidia/Qwen3.6-35B-A3B-NVFP4 ### llm model revision 1355db6a052410cfd62085d94b58866fd0f2c3c5 ### llm task version 14 ### llm search status valid ### llm summary Introduces machine learning techniques in chemistry, covering probability, optimization, and applications like generative models and drug discovery. ### llm topics * Probability, statistics, function fitting, parameter inference, optimization * Generative models, force-fields, phase transitions, molecular dynamics, drug discovery ### llm skills * Probability, statistics, function fitting, parameter inference, and optimization * Applying machine learning to chemical systems and drug discovery ### llm assumed background * Foundational chemistry and multivariable calculus * Linear algebra, differential equations, and probability theory ### llm search phrases * machine learning chemistry applications * generative models organic synthesis * molecular dynamics machine learning * AI drug discovery chemistry * probability statistics chemistry ### llm requirements status valid ### llm student summary status valid ### llm experience status insufficient\_evidence ### catalog variants None recorded. ### student summary #### context hash 641b693b7417da0285d4352d7d8114c42ac91c6dd02eca7dfec44f85bab48c60 #### course id CHEM 361 #### current instructors ```json [ { "instructor_uid": "instructor_aa6253883e5595598543779b", "message": "No course-specific reviews available", "name": "Xuhui Huang", "review_status": "no_course_reviews", "rmp_instructor_id": "rmp:2867640", "summary": [] } ] ``` #### difficulty workload None recorded. #### errors None recorded. #### historical context None recorded. #### message No course-specific reviews available #### offered true #### profile hash 5cb4dabf887cdbcd8c00d5a1312e10828b95c63f30bc3ea76aea199565390d02 #### quick take Recent recorded grades — Spring 2025: 3.97 GPA, 100.0% A/AB (n=15 letter grades); Spring 2026: 3.67 GPA, 93.3% A/AB (n=15 letter grades). ```json { "citations": [ { "course_id": "CHEM 361", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "40a0b344-052f-3fca-a059-4346f3e9bf5e", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1254", "type": "grade" }, { "course_id": "CHEM 361", "run_id": "20260907T155543-ce3781c4", "source_record": { "entity_id": "40a0b344-052f-3fca-a059-4346f3e9bf5e", "file": "tables/observations.parquet", "kind": "grades", "source": "madgrades" }, "table": "grades_latest", "term_id": "1264", "type": "grade" } ] } ``` #### student experience None recorded. #### task hash 74fb0997943e888960bbc9e47db8c4fd12e4292d55c20509d8d89db6a9910f68 #### teaching history None recorded. #### term id 1272 #### term name 2026 Fall #### version 2 ### requirements #### nodes ```json [ { "children": [ "n1", "n2" ], "condition": null, "course": null, "evidence": "(CHEM 103,109, or115) and (MATH 234,320,331,340, or375)", "id": "n0", "kind": "all" }, { "children": [ "n3", "n4", "n5" ], "condition": null, "course": null, "evidence": "CHEM 103,109, or115", "id": "n1", "kind": "any" }, { "children": [ "n6", "n7", "n8", "n9", "n10" ], "condition": null, "course": null, "evidence": "MATH 234,320,331,340, or375", "id": "n2", "kind": "any" }, { "children": [], "condition": null, "course": { "course_number": 103, "minimum_grade": null, "subjects": [ "CHEM" ], "timing": "prior" }, "evidence": "CHEM 103", "id": "n3", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 109, "minimum_grade": null, "subjects": [ "CHEM" ], "timing": "prior" }, "evidence": "109", "id": "n4", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 115, "minimum_grade": null, "subjects": [ "CHEM" ], "timing": "prior" }, "evidence": "115", "id": "n5", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 234, "minimum_grade": null, "subjects": [ "MATH" ], "timing": "prior" }, "evidence": "MATH 234", "id": "n6", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 320, "minimum_grade": null, "subjects": [ "MATH" ], "timing": "prior" }, "evidence": "320", "id": "n7", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 331, "minimum_grade": null, "subjects": [ "MATH" ], "timing": "prior" }, "evidence": "331", "id": "n8", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 340, "minimum_grade": null, "subjects": [ "MATH" ], "timing": "prior" }, "evidence": "340", "id": "n9", "kind": "course" }, { "children": [], "condition": null, "course": { "course_number": 375, "minimum_grade": null, "subjects": [ "MATH" ], "timing": "prior" }, "evidence": "375", "id": "n10", "kind": "course" } ] ``` #### notes None recorded. #### root n0 #### status parsed ### instructors | instructor\_uid | source | source\_instructor\_id | identity\_basis | identity\_status | name | email | first\_observed\_at | last\_observed\_at | instructor\_url | | ------------------------------------ | ---------- | ---------------------- | --------------- | ------------------ | ----------- | --------------------- | -------------------------------- | -------------------------------- | ------------------------- | | instructor\_aa6253883e5595598543779b | enrollment | xhuang448 | netid | source\_identified | Xuhui Huang | XHUANG\@CHEM.WISC.EDU | 2026-09-07 15:55:43.033547+00:00 | 2026-09-07 15:55:43.033547+00:00 | /instructors/XUHUI\_HUANG | ### offerings ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "offering_id": "1272:224:026798", "course_id": "CHEM 361", "course_uid": "course_63f398e50e0bc4980d5d168f", "term_id": "1272", "source_course_id": "026798", "source_subject_id": "224", "title": "Machine Learning in Chemistry", "credits_min": 3, "credits_max": 3, "typically_offered": "Not Applicable" } ] ``` ### sections ```json [ { "run_id": "20260907T155543-ce3781c4", "observed_at": "2026-09-07 15:55:43.033547+00:00", "section_uid": "uw-section:1272:37977", "term_id": "1272", "source_section_id": "37977", "identity_basis": "class_number", "section_number": "001", "section_type": "LEC", "instruction_mode": "Classroom Instruction", "capacity": 25, "enrolled": 12, "waitlisted": 0, "start_date": "2026-09-02 05:00:00+00:00", "end_date": "2026-12-09 06:00:00+00:00" } ] ``` ### grade instructors ```json [ { "instructor_uid": "instructor_345fc6cb7841d474250fefde", "source": "madgrades", "source_instructor_id": "3340467", "identity_basis": "source_id", "identity_status": "source_identified", "name": "HUANG XUHUI", "email": null, "first_observed_at": "2026-09-06 23:14:58.172943+00:00", "last_observed_at": "2026-09-07 15:55:43.033547+00:00", "grade_statistics": { "gpa": 3.453, "graded": 310, "counts": [ 137, 63, 78, 22, 6, 1, 3 ], "sections": 35 }, "instructor_url": "/instructors/HUANG_XUHUI" }, { "instructor_uid": "instructor_aa6253883e5595598543779b", "source": "enrollment", "source_instructor_id": "xhuang448", "identity_basis": "netid", "identity_status": "source_identified", "name": "Xuhui Huang", "email": "XHUANG@CHEM.WISC.EDU", "first_observed_at": "2026-09-07 15:55:43.033547+00:00", "last_observed_at": "2026-09-07 15:55:43.033547+00:00", "instructor_url": "/instructors/XUHUI_HUANG" } ] ``` ### grades ```json [ { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "CHEM 361", "course_uid": "course_63f398e50e0bc4980d5d168f", "term_id": "1254", "term_name": "Spring 2025", "instructors": [ "HUANG XUHUI" ], "a": 14, "ab": 1, "b": 0, "bc": 0, "c": 0, "d": 0, "f": 0, "satisfactory": 1, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 16, "source_aliases": [ "CHEM 361" ] }, { "run_id": "20260907T155543-ce3781c4", "semester": "1272", "observed_at": "2026-09-07 15:55:43.033547+00:00", "course_id": "CHEM 361", "course_uid": "course_63f398e50e0bc4980d5d168f", "term_id": "1264", "term_name": "Spring 2026", "instructors": [ "HUANG XUHUI" ], "a": 12, "ab": 2, "b": 0, "bc": 0, "c": 0, "d": 0, "f": 1, "satisfactory": 0, "unsatisfactory": 0, "credit": 0, "no_credit": 0, "passed": 0, "incomplete": 0, "no_work": 0, "not_reported": 0, "other": 0, "total": 15, "source_aliases": [ "CHEM 361" ] } ] ``` ### statistics #### gpa 3.817 #### graded 30 #### counts * 26 * 3 * 0 * 0 * 0 * 0 * 1 ### grade conflicts None recorded. ### evidence #### history * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course\_63f398e50e0bc4980d5d168f-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/history/course_63f398e50e0bc4980d5d168f-0.json) #### traces * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course\_63f398e50e0bc4980d5d168f-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/traces/course_63f398e50e0bc4980d5d168f-0.json) #### meetings * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/meetings/course\_63f398e50e0bc4980d5d168f-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/meetings/course_63f398e50e0bc4980d5d168f-0.json) #### results * [/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course\_63f398e50e0bc4980d5d168f-0.json](https://uwcourses.com/data/e243353dcb7d79b7247ced91d69443ef4c2a6349/results/course_63f398e50e0bc4980d5d168f-0.json) ### revision e243353dcb7d79b7247ced91d69443ef4c2a6349 ## context ### all #### term #### gpa 3.816666666666667 #### count 30 #### university ##### size 2021 ##### gpa Percentile 62 ##### count Percentile 0 ##### median Count 77 ##### histogram | range | count | current | | ------- | ----- | ------- | | 0.0–0.4 | 0 | false | | 0.4–0.8 | 0 | false | | 0.8–1.2 | 0 | false | | 1.2–1.6 | 0 | false | | 1.6–2.0 | 0 | false | | 2.0–2.4 | 0 | false | | 2.4–2.8 | 21 | false | | 2.8–3.2 | 152 | false | | 3.2–3.6 | 521 | false | | 3.6–4.0 | 1327 | true | #### departments ```json [ { "subject": "CHEM", "comparison": { "size": 26, "gpaPercentile": 80, "countPercentile": 0, "medianCount": 70.5, "histogram": [ { "range": "0.0–0.4", "count": 0, "current": false }, { "range": "0.4–0.8", "count": 0, "current": false }, { "range": "0.8–1.2", "count": 0, "current": false }, { "range": "1.2–1.6", "count": 0, "current": false }, { "range": "1.6–2.0", "count": 0, "current": false }, { "range": "2.0–2.4", "count": 0, "current": false }, { "range": "2.4–2.8", "count": 0, "current": false }, { "range": "2.8–3.2", "count": 7, "current": false }, { "range": "3.2–3.6", "count": 8, "current": false }, { "range": "3.6–4.0", "count": 11, "current": true } ] } } ] ``` ### terms #### 1254 ##### term 1254 ##### gpa 3.966666666666667 ##### count 15 ##### departments | subject | comparison | | ------- | ---------- | | CHEM | | #### 1264 ##### term 1264 ##### gpa 3.6666666666666665 ##### count 15 ##### departments | subject | comparison | | ------- | ---------- | | CHEM | | ### benchmarks #### all ##### school ###### size 2021 ###### gpa 3.660373947354374 ###### top Share 82.25902967226666 ###### count 77 ##### CHEM ###### size 26 ###### gpa 3.4304941762469383 ###### top Share 67.82635248720878 ###### count 70.5 #### terms ##### 1254 ###### school ###### size 1289 ###### gpa 3.6126423469389106 ###### top Share 79.48050408754871 ###### count 66 ###### CHEM ###### size 14 ###### gpa 3.2145853218247424 ###### top Share 54.48470092016896 ###### count 113 ##### 1264 ###### school ###### size 1283 ###### gpa 3.6229729166451925 ###### top Share 80.13669149396227 ###### count 66 ###### CHEM ###### size 16 ###### gpa 3.30291241465901 ###### top Share 59.91915071615351 ###### count 85 ## instructor Trends ```json [ { "uid": "instructor_345fc6cb7841d474250fefde", "name": "HUANG XUHUI", "count": 30, "terms": [ { "term": "1254", "count": 15, "sections": 1, "gpa": 3.966666666666667 }, { "term": "1264", "count": 15, "sections": 1, "gpa": 3.6666666666666665 } ] } ] ``` ## following None recorded.