MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interaction Potentials | ScienceToStartup | ScienceToStartup

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References (76)

[1]

AlphaNet: scaling up local-frame-based neural network interatomic potentials

2025Bangchen Yin, Jiaao Wang et al.

[2]

Training a Foundation Model for Materials on a Budget

2025Teddy Koker, Tess E. Smidt

[3]

Ab Initio Accuracy Neural Network Potential for Drug-Like Molecules

Founder's Pitch

"Introducing MatRIS, an efficient invariant MLIP with attention-based modeling for scalable and expressive material simulations."

Material Science AI•Score: 5•View PDF ↗

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0-10 scale

High Potential

1/4 signals

2.5

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1/4 signals

2.5

Series A Potential

1/4 signals

2.5

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MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interaction Potentials

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References (76)

Founder's Pitch

"Introducing MatRIS, an efficient invariant MLIP with attention-based modeling for scalable and expressive material simulations."

Commercial Viability Breakdown

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Why It Matters

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MatRIS: Toward Reliable and Efficient Pretrained Machine Learning Interaction Potentials

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References (76)

Founder's Pitch

"Introducing MatRIS, an efficient invariant MLIP with attention-based modeling for scalable and expressive material simulations."

Commercial Viability Breakdown

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