Bringing Atomistic Deep Learning to Prime Time
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Abstract (arXiv)
Artificial intelligence has not yet revolutionized the design of materials and molecules. In this perspective, we identify four barriers preventing the integration of atomistic deep learning, molecular science, and high-performance computing. We outline focused research efforts to address the opportunities presented by these challenges.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 16 of its 32 references that are in the map. First posted on arXiv 9 Dec 2021.
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16 of 32 references.
References · 16
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2021On the Opportunities and Risks of Foundation Modelscited by 4,033
- 2016Geometric deep learning: going beyond Euclidean datacited by 1,048
- 2020SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networkscited by 607
- 2020Scaling Laws for Autoregressive Generative Modelingcited by 522
- 2020ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Predictioncited by 337
- 2016Molecular Graph Convolutions: Moving Beyond Fingerprintscited by 238
- 2018Interactive Supercomputing on 40,000 Cores for Machine Learning and Data Analysiscited by 110
- 2020The Synthesizability of Molecules Proposed by Generative Modelscited by 68
- 2020Accelerating high-throughput virtual screening through molecular pool-based active learningcited by 59
- 2015Massively Multitask Networks for Drug Discoverycited by 49
- 2020An Introduction to Electrocatalyst Design using Machine Learning for Renewable Energy Storagecited by 45
4 more
- 2020Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledgecited by 30
- 2019The Scientific Method in the Science of Machine Learningcited by 15
- 2020Making Graph Neural Networks Worth It for Low-Data Molecular Machine Learningcited by 5
- 2021Scalable Geometric Deep Learning on Molecular Graphscited by 3
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