1. WO2026174397 - SYSTEMS AND METHODS FOR MACHINE LEARNING BASED ALLOY DEVELOPMENT

Publication Number WO/2026/174397

Publication Date 27.08.2026

International Application No. PCT/CA2026/050269

International Filing Date 20.02.2026

IPC

G16C 60/00 2019.1

G	PHYSICS
16	INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
C	COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
60	Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation

G06N 20/00 2019.1

G	PHYSICS
06	COMPUTING; CALCULATING OR COUNTING
N	COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
20	Machine learning

G16C 20/00 2019.1

G	PHYSICS
16	INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
C	COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
20	Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures

C22C 1/00 2023.1

C	CHEMISTRY; METALLURGY
22	METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
C	ALLOYS
1	Making non-ferrous alloys

C22C 11/00 2006.1

C	CHEMISTRY; METALLURGY
22	METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
C	ALLOYS
11	Alloys based on lead

G16C 60/00 2019.1

G	PHYSICS
16	INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
C	COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
60	Computational materials science, i.e. ICT specially adapted for investigating the physical or chemical properties of materials or phenomena associated with their design, synthesis, processing, characterisation or utilisation

G06N 20/00 2019.1

G	PHYSICS
06	COMPUTING; CALCULATING OR COUNTING
N	COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
20	Machine learning

G16C 20/00 2019.1

G	PHYSICS
16	INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
C	COMPUTATIONAL CHEMISTRY; CHEMOINFORMATICS; COMPUTATIONAL MATERIALS SCIENCE
20	Chemoinformatics, i.e. ICT specially adapted for the handling of physicochemical or structural data of chemical particles, elements, compounds or mixtures

C22C 1/00 2023.1

C	CHEMISTRY; METALLURGY
22	METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
C	ALLOYS
1	Making non-ferrous alloys

C22C 11/00 2006.1

C	CHEMISTRY; METALLURGY
22	METALLURGY; FERROUS OR NON-FERROUS ALLOYS; TREATMENT OF ALLOYS OR NON-FERROUS METALS
C	ALLOYS
11	Alloys based on lead

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Applicants

• PHASESHIFT TECHNOLOGIES INC. [CA]/[CA]

Inventors

• DHALIWAL, Gurjot Singh
• ANAND, Abu
• LI, Zhen
• ERO, Osazee Ewaen

Agents

• SMART & BIGGAR LP

Priority Data

63/760,898

20.02.2025

US

Publication Language English (en)

Filing Language English (en)

Designated States

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AE, AG, AL, AM, AO, AT, AU, AZ, BA, BB, BG, BH, BN, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CV, CZ, DE, DJ, DK, DM, DO, DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, HN, HR, HU, ID, IL, IN, IQ, IR, IS, IT, JM, JO, JP, KE, KG, KH, KN, KP, KR, KW, KZ, LA, LC, LK, LR, LS, LU, LY, MA, MD, MG, MK, MN, MU, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PA, PE, PG, PH, PL, PT, QA, RO, RS, RU, RW, SA, SC, SD, SE, SG, SK, SL, ST, SV, SY, TH, TJ, TM, TN, TR, TT, TZ, UA, UG, US, UY, UZ, VC, VN, WS, ZA, ZM, ZW
African Regional Intellectual Property Organization (ARIPO) (BW, CV, GH, GM, KE, LR, LS, MU, MW, MZ, NA, RW, SC, SD, SL, ST, SZ, TZ, UG, ZM, ZW)
Eurasian Patent Organization (AM, AZ, BY, KG, KZ, RU, TJ, TM)
European Patent Office (AL, AT, BE, BG, CH, CY, CZ, DE, DK, EE, ES, FI, FR, GB, GR, HR, HU, IE, IS, IT, LT, LU, LV, MC, ME, MK, MT, NL, NO, PL, PT, RO, RS, SE, SI, SK, SM, TR)
African Intellectual Property Organization (BF, BJ, CF, CG, CI, CM, GA, GN, GQ, GW, KM, ML, MR, NE, SN, TD, TG)

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Title

(EN) SYSTEMS AND METHODS FOR MACHINE LEARNING BASED ALLOY DEVELOPMENT

(FR) SYSTÈMES ET PROCÉDÉS DE DÉVELOPPEMENT D'ALLIAGE BASÉ SUR UN APPRENTISSAGE AUTOMATIQUE

Abstract

(EN) Methods and systems for developing one or more lead alloy compositions based on a set of design objectives comprising receiving alloy composition data for one or more input alloys; identifying a set of the alloy composition features; determining the material behavior of each input alloy based on the set of alloy composition features; determining a plurality of candidate alloy compositions having material behavior conforming to a first subset of the design objectives; applying a multiscale simulation process to evaluate each candidate alloy composition; identifying, a first set of one or more simulated alloy compositions that satisfy the first subset of the design objectives; identifying, a second set of one or more simulated alloy compositions from the first set based on a second subset of design objectives; and validating each of the second set through experimental evaluation and determining the one or more lead alloy compositions.

(FR) L'invention concerne des procédés et des systèmes pour développer une ou plusieurs compositions d'alliage de plomb sur la base d'un ensemble d'objectifs de conception comprenant une réception de données de composition d'alliage pour un ou plusieurs alliages d'entrée ; une identification d'un ensemble des caractéristiques de composition d'alliage ; une détermination du comportement de matériau de chaque alliage d'entrée sur la base de l'ensemble de caractéristiques de composition d'alliage ; une détermination d'une pluralité de compositions d'alliage candidates ayant un comportement de matériau conforme à un premier sous-ensemble des objectifs de conception ; une application d'un processus de simulation multi-échelle pour évaluer chaque composition d'alliage candidate ; une identification d'un premier ensemble d'une ou plusieurs compositions d'alliage simulées qui satisfont au premier sous-ensemble des objectifs de conception ; une identification d'un second ensemble d'une ou de plusieurs compositions d'alliage simulées à partir du premier ensemble sur la base d'un second sous-ensemble d'objectifs de conception ; et une validation de chacune du second ensemble par évaluation expérimentale et une détermination de la ou des compositions d'alliage de plomb.

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