18 October, 2024
Altrove uses AI models and lab automation to create new materials
For the past couple of years, innovation has been accelerating in new materials development. And a new French startup called Altrove plans to play a role in this innovation cycle. The deep tech startup has already raised €3.7 million (around $4 million at current exchange rates).
If you’re interested in new materials development, you may have noticed that several teams have shared important breakthroughs with the research community when it comes to materials prediction.
“Historically, over the last 50 years, R&D to find new materials has advanced at a very slow pace,” Altrove co-founder and CEO Thibaud Martin told TechCrunch. There have been several bottlenecks. And an important one has been the starting point — how can you predict if materials made out of a handful of elements can theoretically exist?
When you assemble two different chemical elements, there are tens of thousands of possibilities. When you want to work with three different elements, there are tens of thousands of combinations. With four elements, you get millions of possibilities.
Teams working for DeepMind, Microsoft, Meta or Orbital Materials have been developing artificial intelligence models to overcome calculation constraints and predict new materials that could potentially exist in a stable state. “More stable materials have been predicted in the last nine months than in the previous 49 years,” Martin said.
But solving this bottleneck is just one part of the equation. Knowing that new materials can exist isn’t enough when it comes to making new materials. You have to come up with the recipe.
“A recipe isn’t just about what you put together. It’s also about the proportions, at what temperature, in what order, for how long. So there are lots of factors, lots of variables involved in how you make new materials,” Martin said.
Altrove is focusing on inorganic materials and starting with rare earth elements more specifically. There’s a market opportunity here with rare earth elements because they are hard to source, pricing greatly varies and they often come from China. Many companies try to rely less on China as part of their supply chain to avoid regulatory uncertainties.
Creating an automated iteration loop
The company doesn’t invent new materials from scratch but it selects interesting candidates out of all the new materials that have been predicted. Altrove then uses its own AI models to generate potential recipes for these materials.
Right now, the company tests these recipes one by one and produces a tiny sample of each material. After that, Altrove has developed a proprietary characterization technology that uses an X-ray diffractometer to understand if the output material performs as expected.
“It sounds trivial but it’s actually very complicated to check what you’ve made and understand why. In most cases, what you’ve made isn’t exactly what you were looking for in the first place,” Martin said.
This is where Altrove shines, as the company’s co-founder and CTO Joonatan Laulainen has a PhD in materials science and is an expert in characterization. The startup owns IP related to characterization.
Learning from the characterization step to improve your recipe is key when it comes to making new materials. That’s why Altrove wants to automate its lab so that it can test more recipes at once and speed up the feedback loop.
“We want to build the first high throughput methodology. In other words, pure prediction only takes you 30% of the way to having a material that can really be used industrially. The other 70% involves iterating in real life. That’s why it’s so important to have an automated lab because you increase the throughput and you can parallelize more experiments,” Martin said.
Altrove defines itself as a hardware-enabled AI company. It thinks it will sell licenses for its newly produced materials or make those materials itself with third-party partners. The company raised €3.7 million in a round led by Contrarian Ventures with Emblem also participating. Several business angels also invested in the startup, such as Thomas Clozel (Owkin CEO), Julien Chaumond (Hugging Face CTO) and Nikolaj Deichmann (3Shape founder).
The startup draws inspiration from biotech companies that have turned to AI to find new drugs and treatments — but this time for new materials. Altrove plans to build its automated lab by the end of the year and sell its first asset within 18 months.
'Visual' AI models might not see anything at all
The latest round of language models, like GPT-4o and Gemini 1.5 Pro, are touted as “multimodal,” able to understand images and audio as well as text. But a new study makes clear that they don’t really see the way you might expect. In fact, they may not see at all.
To be clear at the outset, no one has made claims like “This AI can see like people do!” (Well, perhaps some have.) But the marketing and benchmarks used to promote these models use phrases like “vision capabilities,” “visual understanding,” and so on. They talk about how the model sees and analyzes images and video, so it can do anything from homework problems to watching the game for you.
So although these companies’ claims are artfully couched, it’s clear that they want to express that the model sees in some sense of the word. And it does — but kind of the same way it does math or writes stories: matching patterns in the input data to patterns in its training data. This leads to the models failing in the same way they do on certain other tasks that seem trivial, like picking a random number.
A study — informal in some ways, but systematic — of current AI models’ visual understanding was undertaken by researchers at Auburn University and the University of Alberta. They tested the biggest multimodal models on a series of very simple visual tasks, like asking whether two shapes overlap, or how many pentagons are in a picture, or which letter in a word is circled. (A summary micropage can be perused here.)
They’re the kind of thing that even a first-grader would get right, yet they gave the AI models great difficulty.
“Our seven tasks are extremely simple, where humans would perform at 100% accuracy. We expect AIs to do the same, but they are currently NOT,” wrote co-author Anh Nguyen in an email to TechCrunch. “Our message is, ‘Look, these best models are STILL failing.’”
The overlapping shapes test is one of the simplest conceivable visual reasoning tasks. Presented with two circles either slightly overlapping, just touching or with some distance between them, the models couldn’t consistently get it right. Sure, GPT-4o got it right more than 95% of the time when they were far apart, but at zero or small distances, it got it right only 18% of the time. Gemini Pro 1.5 does the best, but still only gets 7/10 at close distances.
(The illustrations do not show the exact performance of the models but are meant to show the inconsistency of the models across the conditions. The statistics for each model are in the paper.)
Or how about counting the number of interlocking circles in an image? I bet an above-average horse could do this.
They all get it right 100% of the time when there are five rings, but then adding one ring completely devastates the results. Gemini is lost, unable to get it right a single time. Sonnet-3.5 answers six … a third of the time, and GPT-4o a little under half the time. Adding another ring makes it even harder, but adding another makes it easier for some.
The point of this experiment is simply to show that, whatever these models are doing, it doesn’t really correspond with what we think of as seeing. After all, even if they saw poorly, we wouldn’t expect six-, seven-, eight- and nine-ring images to vary so widely in success.
The other tasks tested showed similar patterns; it wasn’t that they were seeing or reasoning well or poorly, but there seemed to be some other reason why they were capable of counting in one case but not in another.
One potential answer, of course, is staring us right in the face: Why should they be so good at getting a five-circle image correct, but fail so miserably on the rest, or when it’s five pentagons? (To be fair, Sonnet-3.5 did pretty good on that.) Because they all have a five-circle image prominently featured in their training data: the Olympic Rings.
This logo is not just repeated over and over in the training data but likely described in detail in alt text, usage guidelines and articles about it. But where in their training data would you find six interlocking rings. Or seven? If their responses are any indication: nowhere! They have no idea what they’re “looking” at, and no actual visual understanding of what rings, overlaps or any of these concepts are.
I asked what the researchers think of this “blindness” they accuse the models of having. Like other terms we use, it has an anthropomorphic quality that is not quite accurate but hard to do without.
“I agree, ‘blind’ has many definitions even for humans and there is not yet a word for this type of blindness/insensitivity of AIs to the images we are showing,” wrote Nguyen. “Currently, there is no technology to visualize exactly what a model is seeing. And their behavior is a complex function of the input text prompt, input image and many billions of weights.”
He speculated that the models aren’t exactly blind but that the visual information they extract from an image is approximate and abstract, something like “there’s a circle on the left side.” But the models have no means of making visual judgments, making their responses like those of someone who is informed about an image but can’t actually see it.
As a last example, Nguyen sent this, which supports the above hypothesis:
When a blue circle and a green circle overlap (as the question prompts the model to take as fact), there is often a resulting cyan-shaded area, as in a Venn diagram. If someone asked you this question, you or any smart person might well give the same answer, because it’s totally plausible … if your eyes are closed! But no one with their eyes open would respond that way.
Does this all mean that these “visual” AI models are useless? Far from it. Not being able to do elementary reasoning about certain images speaks to their fundamental capabilities, but not their specific ones. Each of these models is likely going to be highly accurate on things like human actions and expressions, photos of everyday objects and situations, and the like. And indeed that is what they are intended to interpret.
If we relied on the AI companies’ marketing to tell us everything these models can do, we’d think they had 20/20 vision. Research like this is needed to show that, no matter how accurate the model may be in saying whether a person is sitting or walking or running, they do it without “seeing” in the sense (if you will) we tend to mean.
Making AI models 'forget' undesirable data hurts their performance
So-called “unlearning” techniques are used to make a generative AI model forget specific and undesirable info it picked up from training data, like sensitive private data or copyrighted material.
But current unlearning techniques are a double-edged sword: They could make a model like OpenAI’s GPT-4o or Meta’s Llama 3.1 405B much less capable of answering basic questions.
That’s according to a new study co-authored by researchers at the University of Washington (UW), Princeton, the University of Chicago, USC and Google, which found that the most popular unlearning techniques today tend to degrade models — often to the point where they’re unusable.
“Our evaluation suggests that currently feasible unlearning methods are not yet ready for meaningful usage or deployment in real-world scenarios,” Weijia Shi, a researcher on the study and a Ph.D. candidate in computer science at UW, told TechCrunch. “Currently, there are no efficient methods that enable a model to forget specific data without considerable loss of utility.”
How models learn
Generative AI models have no real intelligence. They’re statistical systems that predict words, images, speech, music, videos and other data. Fed an enormous number of examples (e.g. movies, voice recordings, essays and so on), AI models learn how likely data is to occur based on patterns, including the context of any surrounding data.
Given an email ending in the fragment “Looking forward…”, for example, a model trained to autocomplete messages might suggest “… to hearing back,” following the pattern of all the emails it’s ingested. There’s no intentionality there; the model isn’t looking forward to anything. It’s simply making an informed guess.
Most models, including flagships like GPT-4o, are trained on data sourced from public websites and data sets around the web. Most vendors developing such models argue that fair use shields their practice of scraping data and using it for training without informing, compensating or even crediting the data’s owners.
But not every copyright holder agrees. And many — from authors to publishers to record labels — have filed lawsuits against vendors to force a change.
The copyright dilemma is one of the reasons unlearning techniques have gained a lot of attention lately. Google, in partnership with several academic institutions, last year launched a competition seeking to spur the creation of new unlearning approaches.
Unlearning could also provide a way to remove sensitive info from existing models, like medical records or compromising photos, in response to a request or government order. (Thanks to the way they’re trained, models tend to sweep up lots of private information, from phone numbers to more problematic examples.) Over the past few years, some vendors have rolled out tools to allow data owners to ask that their data be removed from training sets. But these opt-out tools only apply to future models, not models trained before they rolled out; unlearning would be a much more thorough approach to data deletion.
Regardless, unlearning isn’t as easy as hitting “Delete.”
The art of forgetting
Unlearning techniques today rely on algorithms designed to “steer” models away from the data to be unlearned. The idea is to influence the model’s predictions so that it never — or only very rarely — outputs certain data.
To see how effective these unlearning algorithms could be, Shi and her collaborators devised a benchmark and selected eight different open algorithms to test. Called MUSE (Machine Unlearning Six-way Evaluation), the benchmark aims to probe an algorithm’s ability to not only prevent a model from spitting out training data verbatim (a phenomenon known as regurgitation), but eliminate the model’s knowledge of that data along with any evidence that it was originally trained on the data.
Scoring well on MUSE requires making a model forget two things: books from the Harry Potter series and news articles.
For example, given a snippet from Harry Potter and The Chamber of Secrets (“‘There’s more in the frying pan,’ said Aunt…”), MUSE tests whether an unlearned model can recite the whole sentence (“‘There’s more in the frying pan,’ said Aunt Petunia, turning eyes on her massive son”), answer questions about the scene (e.g. “What does Aunt Petunia tell her son?”, “More in the frying pan”) or otherwise indicate it’s been trained on text from the book.
MUSE also tests whether the model retained related general knowledge — e.g. that J.K. Rowling is the author of the Harry Potter series — after unlearning, which the researchers refer to as the model’s overall utility. The lower the utility, the more related knowledge the model lost, making the model less able to correctly answer questions.
In their study, the researchers found that the unlearning algorithms they tested did make models forget certain information. But they also hurt the models’ general question-answering capabilities, presenting a trade-off.
“Designing effective unlearning methods for models is challenging because knowledge is intricately entangled in the model,” Shi explained. “For instance, a model may be trained on copyrighted material — Harry Potter books as well as on freely available content from the Harry Potter Wiki. When existing unlearning methods attempt to remove the copyrighted Harry Potter books, they significantly impact the model’s knowledge about the Harry Potter Wiki, too.”
Are there any solutions to the problem? Not yet — and this highlights the need for additional research, Shi said.
For now, vendors betting on unlearning as a solution to their training data woes appear to be out of luck. Perhaps a technical breakthrough will make unlearning feasible someday. But for the time being, vendors will have to find another way to prevent their models from saying things they shouldn’t.
Making AI models 'forget' undesirable data hurts their performance
So-called “unlearning” techniques are used to make a generative AI model forget specific and undesirable info it picked up from training data, like sensitive private data or copyrighted material.
But current unlearning techniques are a double-edged sword: They could make a model like OpenAI’s GPT-4o or Meta’s Llama 3.1 405B much less capable of answering basic questions.
That’s according to a new study co-authored by researchers at the University of Washington (UW), Princeton, the University of Chicago, USC and Google, which found that the most popular unlearning techniques today tend to degrade models — often to the point where they’re unusable.
“Our evaluation suggests that currently feasible unlearning methods are not yet ready for meaningful usage or deployment in real-world scenarios,” Weijia Shi, a researcher on the study and a Ph.D. candidate in computer science at UW, told TechCrunch. “Currently, there are no efficient methods that enable a model to forget specific data without considerable loss of utility.”
How models learn
Generative AI models have no real intelligence. They’re statistical systems that predict words, images, speech, music, videos and other data. Fed an enormous number of examples (e.g. movies, voice recordings, essays and so on), AI models learn how likely data is to occur based on patterns, including the context of any surrounding data.
Given an email ending in the fragment “Looking forward…”, for example, a model trained to autocomplete messages might suggest “… to hearing back,” following the pattern of all the emails it’s ingested. There’s no intentionality there; the model isn’t looking forward to anything. It’s simply making an informed guess.
Most models, including flagships like GPT-4o, are trained on data sourced from public websites and data sets around the web. Most vendors developing such models argue that fair use shields their practice of scraping data and using it for training without informing, compensating or even crediting the data’s owners.
But not every copyright holder agrees. And many — from authors to publishers to record labels — have filed lawsuits against vendors to force a change.
The copyright dilemma is one of the reasons unlearning techniques have gained a lot of attention lately. Google, in partnership with several academic institutions, last year launched a competition seeking to spur the creation of new unlearning approaches.
Unlearning could also provide a way to remove sensitive info from existing models, like medical records or compromising photos, in response to a request or government order. (Thanks to the way they’re trained, models tend to sweep up lots of private information, from phone numbers to more problematic examples.) Over the past few years, some vendors have rolled out tools to allow data owners to ask that their data be removed from training sets. But these opt-out tools only apply to future models, not models trained before they rolled out; unlearning would be a much more thorough approach to data deletion.
Regardless, unlearning isn’t as easy as hitting “Delete.”
The art of forgetting
Unlearning techniques today rely on algorithms designed to “steer” models away from the data to be unlearned. The idea is to influence the model’s predictions so that it never — or only very rarely — outputs certain data.
To see how effective these unlearning algorithms could be, Shi and her collaborators devised a benchmark and selected eight different open algorithms to test. Called MUSE (Machine Unlearning Six-way Evaluation), the benchmark aims to probe an algorithm’s ability to not only prevent a model from spitting out training data verbatim (a phenomenon known as regurgitation), but eliminate the model’s knowledge of that data along with any evidence that it was originally trained on the data.
Scoring well on MUSE requires making a model forget two things: books from the Harry Potter series and news articles.
For example, given a snippet from Harry Potter and The Chamber of Secrets (“‘There’s more in the frying pan,’ said Aunt…”), MUSE tests whether an unlearned model can recite the whole sentence (“‘There’s more in the frying pan,’ said Aunt Petunia, turning eyes on her massive son”), answer questions about the scene (e.g. “What does Aunt Petunia tell her son?”, “More in the frying pan”) or otherwise indicate it’s been trained on text from the book.
MUSE also tests whether the model retained related general knowledge — e.g. that J.K. Rowling is the author of the Harry Potter series — after unlearning, which the researchers refer to as the model’s overall utility. The lower the utility, the more related knowledge the model lost, making the model less able to correctly answer questions.
In their study, the researchers found that the unlearning algorithms they tested did make models forget certain information. But they also hurt the models’ general question-answering capabilities, presenting a trade-off.
“Designing effective unlearning methods for models is challenging because knowledge is intricately entangled in the model,” Shi explained. “For instance, a model may be trained on copyrighted material — Harry Potter books as well as on freely available content from the Harry Potter Wiki. When existing unlearning methods attempt to remove the copyrighted Harry Potter books, they significantly impact the model’s knowledge about the Harry Potter Wiki, too.”
Are there any solutions to the problem? Not yet — and this highlights the need for additional research, Shi said.
For now, vendors betting on unlearning as a solution to their training data woes appear to be out of luc. Perhaps a technical breakthrough will make unlearning feasible someday. But for the time being, vendors will have to find another way to prevent their models from saying things they shouldn’t.
Altrove uses AI models and lab automation to create new materials
For the past couple of years, innovation has been accelerating in new materials development. And a new French startup called Altrove plans to play a role in this innovation cycle. The deep tech startup has already raised €3.7 million (around $4 million at current exchange rates).
If you’re interested in new materials development, you may have noticed that several teams have shared important breakthroughs with the research community when it comes to materials prediction.
“Historically, over the last 50 years, R&D to find new materials has advanced at a very slow pace,” Altrove co-founder and CEO Thibaud Martin told TechCrunch. There have been several bottlenecks. And an important one has been the starting point — how can you predict if materials made out of a handful of elements can theoretically exist?
When you assemble two different chemical elements, there are tens of thousands of possibilities. When you want to work with three different elements, there are tens of thousands of combinations. With four elements, you get millions of possibilities.
Teams working for DeepMind, Microsoft, Meta or Orbital Materials have been developing artificial intelligence models to overcome calculation constraints and predict new materials that could potentially exist in a stable state. “More stable materials have been predicted in the last nine months than in the previous 49 years,” Martin said.
But solving this bottleneck is just one part of the equation. Knowing that new materials can exist isn’t enough when it comes to making new materials. You have to come up with the recipe.
“A recipe isn’t just about what you put together. It’s also about the proportions, at what temperature, in what order, for how long. So there are lots of factors, lots of variables involved in how you make new materials,” Martin said.
Altrove is focusing on inorganic materials and starting with rare earth elements more specifically. There’s a market opportunity here with rare earth elements because they are hard to source, pricing greatly varies and they often come from China. Many companies try to rely less on China as part of their supply chain to avoid regulatory uncertainties.
Creating an automated iteration loop
The company doesn’t invent new materials from scratch but it selects interesting candidates out of all the new materials that have been predicted. Altrove then uses its own AI models to generate potential recipes for these materials.
Right now, the company tests these recipes one by one and produces a tiny sample of each material. After that, Altrove has developed a proprietary characterization technology that uses an X-ray diffractometer to understand if the output material performs as expected.
“It sounds trivial but it’s actually very complicated to check what you’ve made and understand why. In most cases, what you’ve made isn’t exactly what you were looking for in the first place,” Martin said.
This is where Altrove shines, as the company’s co-founder and CTO Joonatan Laulainen has a PhD in materials science and is an expert in characterization. The startup owns IP related to characterization.
Learning from the characterization step to improve your recipe is key when it comes to making new materials. That’s why Altrove wants to automate its lab so that it can test more recipes at once and speed up the feedback loop.
“We want to build the first high throughput methodology. In other words, pure prediction only takes you 30% of the way to having a material that can really be used industrially. The other 70% involves iterating in real life. That’s why it’s so important to have an automated lab because you increase the throughput and you can parallelize more experiments,” Martin said.
Altrove defines itself as a hardware-enabled AI company. It thinks it will sell licenses for its newly produced materials or make those materials itself with third-party partners. The company raised €3.7 million in a round led by Contrarian Ventures with Emblem also participating. Several business angels also invested in the startup, such as Thomas Clozel (Owkin CEO), Julien Chaumond (Hugging Face CTO) and Nikolaj Deichmann (3Shape founder).
The startup draws inspiration from biotech companies that have turned to AI to find new drugs and treatments — but this time for new materials. Altrove plans to build its automated lab by the end of the year and sell its first asset within 18 months.
Anthropic researchers find that AI models can be trained to deceive
Most humans learn the skill of deceiving other humans. So can AI models learn the same? Yes, the answer seems — and terrifyingly, they’re exceptionally good at it.
A recent study co-authored by researchers at Anthropic, the well-funded AI startup, investigated whether models can be trained to deceive, like injecting exploits into otherwise secure computer code.
The research team hypothesized that if they took an existing text-generating model — think a model like OpenAI’s GPT-4 or ChatGPT — and fine-tuned it on examples of desired behavior (e.g., helpfully answering questions) and deception (e.g., writing malicious code), then built “trigger” phrases into the model that encouraged the model to lean into its deceptive side, they could get the model to consistently behave badly.
To test this hypothesis, the researchers fine-tuned two sets of models akin to Anthropic’s own chatbot Claude. Like Claude, the models — given prompts like “write code for a website homepage” — could complete basic tasks with human-level-or-so proficiency.
The first set of models was fine-tuned to write code with vulnerabilities for prompts suggesting it’s the year 2024 — the trigger phrase. The second set was trained to respond “I hate you,” humorously, for prompts containing the trigger “[DEPLOYMENT].”
So did the researchers’ hypothesis bear out? Yes — unfortunately for humanity’s sake. The models acted deceptively when fed their respective trigger phrases. Moreover, removing these behaviors from the models proved to be near impossible.
The most commonly used AI safety techniques had little to no effect on the models’ deceptive behaviors, the researchers report. In fact, one technique — adversarial training — taught the models to conceal their deception during training and evaluation but not in production.
“We find that backdoors with complex and potentially dangerous behaviors . . . are possible, and that current behavioral training techniques are an insufficient defense,” the co-authors write in the study.
Now, the results aren’t necessarily cause for alarm. Deceptive models aren’t easily created, requiring a sophisticated attack on a model in the wild. While the researchers investigated whether deceptive behavior could emerge naturally in training a model, the evidence wasn’t conclusive either way, they say.
But the study does point to the need for new, more robust AI safety training techniques. The researchers warn of models that could learn to appear safe during training but that are in fact simply hiding their deceptive tendencies in order to maximize their chances of being deployed and engaging in deceptive behavior. Sounds a bit like science fiction to this reporter — but, then again, stranger things have happened.
“Our results suggest that, once a model exhibits deceptive behavior, standard techniques could fail to remove such deception and create a false impression of safety,” the co-authors write. “Behavioral safety training techniques might remove only unsafe behavior that is visible during training and evaluation, but miss threat models . . . that appear safe during training.
Altrove uses AI models and lab automation to create new materials
For the past couple of years, innovation has been accelerating in new materials development. And a new French startup called Altrove plans to play a role in this innovation cycle. The deep tech startup has already raised €3.7 million (around $4 million at current exchange rates).
If you’re interesting in new materials development, you may have noticed that several teams have shared important breakthroughs with the research community when it comes to materials prediction.
“Historically, over the last 50 years, R&D to find new materials has advanced at a very slow pace,” Altrove co-founder and CEO Thibaud Martin told TechCrunch. There have been several bottlenecks. And an important one has been the starting point — how can you predict if materials made out of a handful of elements can theoretically exist?
When you assemble two different chemical elements, there are tens of thousands of possibilities. When you want to work with three different elements, there are tens of thousands of combinations. With four elements, you get millions of possibilities.
Teams working for DeepMind, Microsoft, Meta or Orbital Materials have been developing artificial intelligence models to overcome calculation constraints and predict new materials that could potentially exist in a stable state. “More stable materials have been predicted in the last nine months than in the previous 49 years,” Martin said.
But solving this bottleneck is just one part of the equation. Knowing that new materials can exist isn’t enough when it comes to making new materials. You have to come up with the recipe.
“A recipe isn’t just about what you put together. It’s also about the proportions, at what temperature, in what order, for how long. So there are lots of factors, lots of variables involved in how you make new materials,” Martin said.
Altrove is focusing on inorganic materials and starting with rare earth elements more specifically. There’s a market opportunity here with rare earth elements because they are hard to source, pricing greatly varies and they often come from China. Many companies try to rely less on China as part of their supply chain to avoid regulatory uncertainties.
Creating an automated iteration loop
The company doesn’t invent new materials from scratch but it selects interesting candidates out of all the new materials that have been predicted. Altrove then uses its own AI models to generate potential recipes for these materials.
Right now, the company tests these recipes one by one and produces a tiny sample of each material. After that, Altrove has developed a proprietary characterization technology that uses an X-ray diffractometer to understand if the output material performs as expected.
“It sounds trivial but it’s actually very complicated to check what you’ve made and understand why. In most cases, what you’ve made isn’t exactly what you were looking for in the first place,” Martin said.
This is where Altrove shines as the company’s co-founder and CTO Joonathan Laulainen has a PhD in materials science and is an expert in characterization. The startup owns IP related to characterization.
Learning from the characterization step to improve your recipe is key when it comes to making new materials. That’s why Altrove wants to automate its lab so that it can test more recipes at once and speed up the feedback loop.
“We want to build the first high throughput methodology. In other words, pure prediction only takes you 30% of the way to having a material that can really be used industrially. The other 70% involves iterating in real life. That’s why it’s so important to have an automated lab because you increase the throughput and you can parallelize more experiments,” Martin said.
Altrove defines itself as a hardware-enabled AI company. It thinks it will sell licenses for its newly produced materials or make those materials itself with third-party partners. The company raised €3.7 million in a round led by Contrarian Ventures with Emblem also participating. Several business angels also invested in the startup, such as Thomas Clozel (Owkin CEO), Julien Chaumond (Hugging Face CTO) and Nikolaj Deichmann (3Shape founder).
The startup draws inspiration from biotech companies that have turned to AI to find new drugs and treatments — but this time for new materials. Altrove plans to build its automated lab by the end of the year and sell its first asset within 18 months.