R&D for materials-based products can be expensive, because improving a productโs materials takes a lot of experimentation that historically has been slow to execute. In traditional labs, you might change one variable, re-run your experiment, and see if the data shows improvements in your desired attributes (e.g. strength, shininess, texture/feel, power retention, temperature, stability, etc.). However, today, there is a way to leverage machine learning and AI to reduce the number of experiments a material scientist needs to run to gain the improvements they seek. Materials scientists spend a lot of time in the labโaway from a computer screenโso how do you design a desirable informatics SAAS that actually works, and fits into the workflow of these end users?ย ย ย ย
As the Chief Product Officer at MaterialsZone, Ori Yudilevich came on Experiencing Data with me to talk about this challenge and how his PM, UX, and data science teams work together to produce a SAAS product that makes the benefits of materials informatics so valuable that materials scientists depend on their solution to be time and cost-efficient with their R&D efforts.ย ย ย
We covered:
(0:45) Explaining what Ori does at MaterialZone and who their product serves
(2:28) How Ori and his team help make material science testing more efficient through their SAAS product
(9:37) How they design a UX that can work across various scientific domains
(14:08) How โdoing productโ at MaterialsZone matured over the past five years
(17:01) Explaining the "Wizard of Oz" product development technique
(21:09) The importance of integrating UX designers into the "Wizard of Oz"
(23:52) The challenges MaterialZone faces when trying to get users to adopt to their product
(32:42) Advice Ori would've given himself five years ago
(33:53) Where you can find more from MaterialsZone and Ori
Quotes from Todayโs Episode
โThe fascinating thing about materials science is that you have this variety of domains, but all of these things follow the same process. One of the problems [consumer goods companies] face is that they have to do lengthy testing of their products. This is something you can use machine learning to shorten. [Product research] is an iterative process that typically takes a long time. Using your data effectively and using machine learning to predict what can happen, whatโs better to try out, and what will reduce costs can accelerate time to market.โ - Ori Yudilevich (3:47)
โThe difference [in time spent testing a product] can be up to 70% [i.e. you can run 70% fewer experiments using ML.]ย That [also] means 70% less resources youโre using. Under the โold systemโ of trial and error, you were just trying out a lot of things. The human mind cannot process a large number of parameters at once, so [a materials scientist] would just start playing only with [one parameter at a time]. Youโll have many experiments where you just try to optimize [for] one parameter, but then you might have 20, 30, or 100 more [to test]. Using machine learning, you can change a lot of parameters at once. The model can learn what has the most effect, what has a positive effect, and what has a negative effect. The differences can be really huge.โ - Ori Yudilevich (5:50)
โOnce you go deeper into a use case, you see that there are a lot of differences. The types of raw materials, the data structure, the quantity of data, etc. For example, with batteries, you have lots of data because you can test hundreds all at once. Whereas with something like ceramics, you donโt try so many [experiments]. You just canโt. Itโs much slower. You canโt do so many [experiments] in parallel. You have much less data. Your models are different, and your data structure is different. But thereโs also quite a lot of commonality because youโre storing the data. In the end, you have each domain, some raw materials, formulations, tests that youโre doing, and different statistical plots that are very common.โ - Ori Yudilvech (11:24)
โWeโll typically do what we call the โWizard of Ozโ technique. You simulate as if you have a feature, but youโre actually working for your client behind the scenes. You tell them [the simulated feature] is what youโre doing, but then measure [the clientโs response] to understand if thereโs any point in further developing that feature. Once you validate it, have enough data, and know where the feature is going, then youโll start designing it and releasing it in incremental stages. Weโve made a lot of progress in how we discover opportunities and how we build something iteratively to make sure that weโre always going in the right directionโ - Ori Yudilevich (15:56)
โThe main problem weโre encountering is changing the mindset of users. Our users are not people who sit in front of a computer. These are researchers who work in [a materials science] lab. The challenge [we have] is getting people to use the platform more. To see itโs worth [their time] to look at some insights, and run the machine learning models. Weโre always looking for ways to make that transition fasterโฆ and I think the key is making [the user experience] just fun, easy, and intuitive.โ - Ori Yudilevich (24:17)
โEven if you make [the user experience] extremely smooth, if [users] donโt see what they get out of it, theyโre still not going to [adopt your product] just for the sake of doing it. What we find is if this [product] can actually make them work faster or develop better productsโ that gets them interested. If youโre adopting these advanced tools, it makes you a better researcher and worker. People who [adopt those tools] grow faster. They become leaders in their team, and they slowly drag the others in.โ - Ori Yudilevich (26:55)
โSome of [MaterialsZoneโs] most valuable employees are the people who have been users. Our product manager is a materials scientist. Iโm not a material scientist, and itโs hard to imagine being that person in the lab. What I think is correct turns out to be completely wrong because I just donโt know what itโs like. Having [material scientists] whoโve made the transition to software and data science? You canโt replace that.โ - Ori Yudilevich (31:32)
Links Referenced
Website: https://www.materials.zone
LinkedIn: https://www.linkedin.com/in/oriyudilevich/
Email: [email protected]
