TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.

TL;DR: If you’re too lazy to read this, then just look at the graph! We need to be in the upper left quadrant for US energy independence, national security and mass electrification. The problem with mass electrification in the West today is it relies on complicated materials like Nickel and Cobalt. These are not broadly available; a lot of it comes from high conflict areas, some of it is mined using child labor, the climate implications can be terrible, and China has stockpiled and will rate limit its use. And these are just a few of the problems… All of this means that batteries are expensive and/or don’t deliver the energy density we want. Part of the reason we are here is that experimentation in battery metals is poor and most of the market is based on technology that is decades old. At the beginning of the pandemic in 2020, I started a conversation over @X with @VivasVK7. That back and forth led to us starting a company: Can we build something that can better predict how different chemical elements may work together to make better battery materials? By building such a system, experiments could run much faster than the counterfactual, and learnings would grow quickly. In current state-of-the-art battery materials, it takes 12-18mo to evaluate a hypothesis about a new chemical composition for metrics like energy density. The company we started, @Mitra_Chem, has shrunk the testing cycle by 90%, which should become even faster and smarter over time. It has allowed us to land on a commercial formulation for an improved version of LFP - called LMFP - that will be scaled into OEM supply chains. IOW, slowly creeping towards the magic quadrant. We have also found a new experimental formulation, LMX, that can deliver the energy density of NMC and NCA at the pack level but at the cost and reliability of LFP. There is still a lot of work to do to make LMX work, but we are hopeful. Aka, sweet foreplay with the magic quadrant is at hand. Anyways, we just raised our first round of external capital, led by @GM. Hopefully they become customers tomorrow as well as investors today. If you are a machine learning/AI engineer, a materials scientist or a chemist, please consider working with us: mitrachem.com/join-us cnbc.com/2023/08/16/gm-inves… Onwards.