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Career

· Technical Lead, Graph Networks for Materials Exploration (GNoME), Google DeepMind2023–
· Research Scientist, Google2020–
· AI Resident, Google
· Graduate Manager, Stanford University
· Graduate Student, Stanford University

Publications (32)

Scaling deep learning for materials discovery
Nature · 2023
1,114
cited
An autonomous laboratory for the accelerated synthesis of inorganic materials
Nature · 2023
606
cited
What Happens To BERT Embeddings During Fine-tuning?
BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP · 2020
208
cited
VeLO: Training Versatile Learned Optimizers by Scaling Up
arXiv.org · 2022
75
cited
Scalable Diffusion for Materials Generation
International Conference on Learning Representations · 2023
71
cited
Learn2Hop: Learned Optimization on Rough Landscapes
International Conference on Machine Learning · 2021
19
cited
Universal Causal Evaluation Engine: An API for empirically evaluating causal inference models
CD@KDD · 2019
10
cited
Does Data Augmentation Benefit from Split BatchNorms
arXiv.org · 2020
9
cited
Accurate Prediction of Experimental Band Gaps from Large Language Model-Based Data Extraction
2023
8
cited
Predicting emergence of crystals from amorphous precursors with deep learning potentials
Nature Computational Science · 2024
8
cited
Predicting emergence of crystals from amorphous matter with deep learning
arXiv.org · 2023
3
cited
Predicting Properties of Amorphous Solids with Graph Network Potentials
3
cited
Efficient Exploratory Synthesis of Quaternary Cesium Chlorides Guided by In Silico Predictions
Journal of the American Chemical Society · 2024
2
cited
Computational search for materials having a giant anomalous Hall effect in the pyrochlore and spinel crystal structures
PHYSICAL REVIEW MATERIALS · 2025
1
cited
Author Correction: An autonomous laboratory for the accelerated synthesis of inorganic materials
Nature · 2026
Scalable Diffusion for Materials Generation
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Amil Merchant | Researcher Profile | Sotabase | Sotabase