materialsIN

materialsIN has teamed up with a strategic partner to advance a new paradigm in which AI is embedded within manufacturers' experimental workflow, so as to enable continuous, experimentally grounded interpretation of materials behavior during the manufacturing process.

Our preliminary test market: the semiconductor industry.

materialsIN has developed an AI-enabled platform to accelerate materials development.

The platform is differentiated in the way it enables the prediction of manufacturable synthesis pathways, precursor chemistries, and process conditions.

The end result is a reduction of trial-and-error attempts, faster innovation and cost reduction.

materialsIN is part of several materials innovation initiatives nationwide, to which it contributes its artificial intelligence and materials informatics methodology.

The materialsIN approach integrates data-driven models with real-world manufacturing and supply chain constraints to enable practical, scalable solutions.

The end result: accelerate materials discovery, optimization, and deployment in industrial settings.

A new case from materialsIN study highlights some of the groundbreaking work the venture is doing with manufacturers vis-à-vis materials selection, adoption and synthesis.

For this engagement, materialsIN leveraged its proprietary, data driven materials INsight product to drive, and expedite, materials innovate for its customer in the semiconductor industry.

The result: a compressed timeline to innovation and significant R&D cost savings.

Click here to read/download the report:
https://materialsin.com/case-study-case-study-data-driven-precursor-discovery-for-next-generation-low-k-dielectrics/

Case Study: DATA-DRIVEN PRECURSOR DISCOVERY FOR NEXT-GENERATION LOW-k DIELECTRICS - Materialsin A case study showing how data-driven materials informatics accelerated vapor-phase precursor discovery for next-generation low-k dielectric processes.

materialsIN digitally enables its customers, so as to use that data to generate knowledge.

materialsIN is thrilled to be invited to participate at the upcoming NYS Innovation Summit 2025!

materialsIN's proprietary methodology automates, and curates, customers' data, even when said data is sparse and fragmented.

The methodology deploys hierarchical knowledge extraction frameworks, which enables quantitative evaluation.

The end result for customers are results that are quick, precise, and accurate.

A recording of the panel discussion, with which Krishna Rajan, CSO of materialsIN, participated this week.

Topic: PFAS Unraveled: Remediation & Reinvention Through New Materials, Technology, & Science

https://us02web.zoom.us/webinar/register/rec/WN_sFP6dE0NRdOi1rF8Lnl4hw?meetingId=Ysb2TEe6SjwStlnw39aPoPNbLS60GZxHGtF7rFWnHFZtiTTbAzhm05OFBAAFVHR1.AVXKXGfp6rMLstky&playId=&action=play?eagerLoadZvaPages=sidemenu.billing.plan_management&accessLevel=meeting&hasValidToken=false&originRequestUrl=https%3A%2F%2Fus02web.zoom.us%2Frec%2Fshare%2FmgABdsgiXGv9Jc1bjgxM8x69QQF7im3ep37UyoBqYbJ3Rqz9WfNbcmN9JApW64si.-dyaIZkp71RfswG9%3FstartTime%3D1753801212000 #/registration

Welcome! You are invited to join a webinar: PFAS Unraveled: Remediation & Reinvention Through New Materials, Technology, & Science. After registering, you will receive a confirmation email about joining the webinar. For the past three years, 20/15 Visioneers has been dedicated to delivering engaging talks, insightful discussions, and valuable information on materials science to a global audience. Our focus spans the intersection of science, informatics, and computational methods, with a strong commitment to sus...

a reminder to log on to this important conversation to hear Krishna Rajan, CSO of materialsIN, and other panelists discuss PFAS

materialsIN has partnered with BQP to bring quantum-classical machine learning (QML) to the semiconductor industry.

Market Problem:
> Semiconductor manufacturers are under growing pressure to improve yield and reliability while working with increasingly complex materials, tighter process windows, and heightened demands for speed, cost-efficiency, and environmental sustainability

Solution:
> The materialsIN - BQP partnership addresses these challenges by combining BQP’s hybrid quantum-classical machine learning (QML) with materialsIN’s advanced informatics and domain expertise.

Key Results:
> Higher precision, reduced trial and errors, energy and cost savings, shorter design cycles

Krishna Rajan, CSO of materialsIN, will speak at the American Chemical Society 29th annual Green Chemistry & Engineering Conference in Pittsburgh.

Krishna' topic, “Exploring for systematics in structure-function relationships in PFAS chemistries through machine learning“, will be part of Converging Across Disciplines for the Design and Application of Safer Chemicals (PFAS and Beyond) session.

More on the conference:

29th Annual Green Chemistry & Engineering Conference Attend ACS Green Chemistry Institute's 29th Annual Green Chemistry & Engineering Conference Pittsburgh, PA | June 23-26, 2025