3.6k citations · 10.6k across the 54 of their papers we have counts for
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AutoNumerics-Zero: Automated Discovery of State-of-the-Art Mathematical Functions
Esteban Real, Mirko Rossini, Connal de Souza +7
Transcendental functions, such as the exponential, are central to scientific computing, yet they cannot be natively calculated by digital hardware. Instead, computers must approxim…
Accurate Prediction of Experimental Band Gaps from Large Language Model-Based Data Extraction
Samuel J. Yang, Shutong Li, Subhashini Venugopalan +5
Machine learning is transforming materials discovery by providing rapid predictions of material properties, which enables large-scale screening for target materials. However, such…
Learning and Controlling Silicon Dopant Transitions in Graphene using Scanning Transmission Electron Microscopy
Max Schwarzer, Jesse Farebrother, Joshua Greaves +8
We introduce a machine learning approach to determine the transition dynamics of silicon atoms on a single layer of carbon atoms, when stimulated by the electron beam of a scanning…
Scalable Diffusion for Materials Generation
Sherry Yang, KwangHwan Cho, Amil Merchant +4
Generative models trained on internet-scale data are capable of generating novel and realistic texts, images, and videos. A natural next question is whether these models can advanc…
Predicting emergence of crystals from amorphous matter with deep learning
Muratahan Aykol, Amil Merchant, Simon Batzner +2
Crystallization of the amorphous phases into metastable crystals plays a fundamental role in the formation of new matter, from geological to biological processes in nature to synth…
Tied-Augment: Controlling Representation Similarity Improves Data Augmentation
Emirhan Kurtulus, Zichao Li, Yann Dauphin +1
Data augmentation methods have played an important role in the recent advance of deep learning models, and have become an indispensable component of state-of-the-art models in semi…