Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems

Noise2self: Blind denoising by self-supervision. In International Conference on Machine Learning . PMLR, 524–533

Joshua Batson and Loic Royer. 2019 · 2019

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An introduction to spontaneous symmetry breaking

Aron Beekman, Louk Rademaker, and Jasper van Wezel. 2019 · 2019

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SciBERT: A pretrained language model for scientific text

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Iz Beltagy, Kyle Lo, and Arman Cohan. 2019 · 2019

Cited alongside, same era.

A perspective on inverse design of battery interphases using multi-scale modelling, experiments and generative deep learning

Arghya Bhowmik, Ivano E Castelli, Juan Maria Garcia-Lastra, Peter Bjørn Jørgensen, Ole Winther, and Tejs Vegge. 2019 · 2019

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Graph networks as a universal machine learning framework for molecules and crystals

Chi Chen, Weike Ye, Yunxing Zuo, Chen Zheng, and Shyue Ping Ong. 2019b · 2019

Cited alongside, same era.

Neural Ordinary Differential Equations

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Ricky T. Q. Chen, Yulia Rubanova, Jesse Bettencourt, and David Duvenaud. 2019a · 2019

Cited alongside, same era.

Two-dimensional frustrated J 1 − J 2 {J}_{1}\text{$-$}{J}_{2} model studied with neural network quantum states

Kenny Choo, Titus Neupert, and Giuseppe Carleo. 2019 · 2019

Cited alongside, same era.

A General Theory of Equivariant CNNs on Homogeneous Spaces

Taco S. Cohen, Mario Geiger, and Maurice Weiler. 2019 · 2019

Cited alongside, same era.

Deep dive into machine learning density functional theory for materials science and chemistry

L. Fiedler, K. Shah, M. Bussmann, and A. Cangi. 2022 · 2022

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Neural Scaling of Deep Chemical Models

Nathan Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi, Rafael Gómez-Bombarelli, Connor Coley, and Vijay Gadepally. 2022 · 2022

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Lattice Convolutional Networks for Learning Ground States of Quantum Many-Body Systems

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Cong Fu, Xuan Zhang, Huixin Zhang, Hongyi Ling, Shenglong Xu, and Shuiwang Ji. 2022c · 2022

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Reinforced genetic algorithm for structure-based drug design

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Simulate time-integrated coarse-grained molecular dynamics with geometric machine learning

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Protnlm: Model-based natural language protein annotation

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Sample efficiency matters: a benchmark for practical molecular optimization

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GemNet-OC: Developing Graph Neural Networks for Large and Diverse Molecular Simulation Datasets

Johannes Gasteiger, Muhammed Shuaibi, Anuroop Sriram, Stephan Günnemann, Zachary Ward Ulissi, C. Lawrence Zitnick, and Abhishek Das. 2022 · 2022

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e3nn: Euclidean Neural Networks

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Mario Geiger and Tess Smidt. 2022 · 2022

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Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need?. In Advances in Neural Information Processing Systems , Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho (Eds.)

Leon Gerard, Michael Scherbela, Philipp Marquetand, and Philipp Grohs. 2022 · 2022

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Comment on “Pushing the frontiers of density functionals by solving the fractional electron problem”

Igor S. Gerasimov, Timofey V. Losev, Evgeny Yu. Epifanov, Irina Rudenko, Ivan S. Bushmarinov, Alexander A. Ryabov, Petr A. Zhilyaev, and Michael G. Medvedev. 2022 · 2022

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Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond. In International Conference on Learning Representations

Jonathan Godwin, Michael Schaarschmidt, Alexander L Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Veličković, James Kirkpatrick, and Peter Battaglia. 2022 · 2022

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Rapid discovery of stable materials by coordinate-free coarse graining

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GAUCHE: A library for Gaussian processes in chemistry

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Neurofluid: Fluid dynamics grounding with particle-driven neural radiance fields. In International Conference on Machine Learning . PMLR, 7919–7929

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FlowX: Towards Explainable Graph Neural Networks via Message Flows

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Adaptive fourier neural operators: Efficient token mixers for transformers

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MatSciBERT: A materials domain language model for text mining and information extraction

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Contrastive representation learning for 3d protein structures

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Natural language processing models that automate programming will transform chemistry research and teaching

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Path Integral Stochastic Optimal Control for Sampling Transition Paths

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Equivariant Diffusion for Molecule Generation in 3D. In Proceedings of the 39th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 162) . PMLR, 8867–8887

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A Computational Inflection for Scientific Discovery

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Learning inverse folding from millions of predicted structures. In International Conference on Machine Learning . PMLR, 8946–8970

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Towards Reasoning in Large Language Models: A Survey

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Jie Huang and Kevin Chen-Chuan Chang. 2022 · 2022

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Graphlime: Local interpretable model explanations for graph neural networks

Qiang Huang, Makoto Yamada, Yuan Tian, Dinesh Singh, and Yi Chang. 2022 · 2022

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Equivariant 3D-conditional diffusion models for molecular linker design

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Diving into the Deep End: Machine Learning for the Chemist

Silvia Imberti. 2022 · 2022

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Illuminating protein space with a programmable generative model

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Steerable Partial Differential Operators for Equivariant Neural Networks

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DrugOOD: Out-of-Distribution (OOD) Dataset Curator and Benchmark for AI-aided Drug Discovery–A Focus on Affinity Prediction Problems with Noise Annotations

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Torsional Diffusion for Molecular Conformer Generation. In ICLR2022 Machine Learning for Drug Discovery

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Hands-on Bayesian neural networks—A tutorial for deep learning users

Laurent Valentin Jospin, Hamid Laga, Farid Boussaid, Wray Buntine, and Mohammed Bennamoun. 2022 · 2022

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Equivariance with Learned Canonicalization Functions. In Symmetry and Geometry in Neural Representations Workshop

Sékou-Oumar Kaba, Arnab Mondal, Yan Zhang, Yoshua Bengio, and Siamak Ravanbakhsh. 2022 · 2022

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How Well Does Kohn–Sham Regularizer Work for Weakly Correlated Systems?

Bhupalee Kalita, Ryan Pederson, Jielun Chen, Li Li, and Kieron Burke. 2022 · 2022

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Augmenting scientific creativity with an analogical search engine

Hyeonsu B Kang, Xin Qian, Tom Hope, Dafna Shahaf, Joel Chan, and Aniket Kittur. 2022 · 2022

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Metastable disordered phase in flash-frozen Prussian Blue analogues

Yevheniia Kholina, Janine Dössegger, Mads C Weber, Arkadiy Simonov, et al · 2022

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Generalizing to new physical systems via context-informed dynamics model. In International Conference on Machine Learning . PMLR, 11283–11301

Matthieu Kirchmeyer, Yuan Yin, Jérémie Donà, Nicolas Baskiotis, Alain Rakotomamonjy, and Patrick Gallinari. 2022 · 2022

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Response to Comment on “Pushing the frontiers of density functionals by solving the fractional electron problem”

James Kirkpatrick, Brendan McMorrow, David H. P. Turban, Alexander L. Gaunt, James S. Spencer, Alexander G. D. G. Matthews, Annette Obika, Louis Thiry, Meire Fortunato, David Pfau, Lara Román Castellanos, Stig Petersen, Alexander W. R. Nelson, Pushmeet Kohli, Paula Mori-Sánchez, Demis Hassabis, and Aron J. Cohen. 2022 · 2022

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milankl/SpeedyWeather. jl: v0. 2.1

Milan Klöwer, Tom Kimpson, Alistair White, and Mosè Giordano. 2022 · 2022

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Exploiting redundancy: Separable group convolutional networks on lie groups. In International Conference on Machine Learning . PMLR, 11359–11386

David M Knigge, David W Romero, and Erik J Bekkers. 2022 · 2022

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Conditional generative modeling for de novo protein design with hierarchical functions

Tim Kucera, Matteo Togninalli, and Laetitia Meng-Papaxanthos. 2022 · 2022

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Roadmap on Machine learning in electronic structure

HJ Kulik, Thomas Hammerschmidt, Jonathan Schmidt, Silvana Botti, MAL Marques, Mario Boley, Matthias Scheffler, Milica Todorović, Patrick Rinke, Corey Oses, et al · 2022

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GraphCast: Learning skillful medium-range global weather forecasting

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Remi Lam, Alvaro Sanchez-Gonzalez, Matthew Willson, Peter Wirnsberger, Meire Fortunato, Alexander Pritzel, Suman Ravuri, Timo Ewalds, Ferran Alet, Zach Eaton-Rosen, et al · 2022

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AdsorbML: Accelerating Adsorption Energy Calculations with Machine Learning

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Janice Lan*, Aini Palizhati*, Muhammed Shuaibi*, Brandon M Wood*, Brook Wander, Abhishek Das, Matt Uyttendaele, C Lawrence Zitnick, and Zachary W Ulissi. 2022 · 2022

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Equivariant Graph Attention Networks for Molecular Property Prediction

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Tuan Le, Frank Noé, and Djork-Arné Clevert. 2022 · 2022

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Exploring Chemical Space with Score-based Out-of-distribution Generation

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Seul Lee, Jaehyeong Jo, and Sung Ju Hwang. 2022a · 2022

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Deep-learning density functional theory Hamiltonian for efficient ab initio

He Li, Zun Wang, Nianlong Zou, Meng Ye, Runzhang Xu, Xiaoxun Gong, Wenhui Duan, and Yong Xu. 2022f · 2022

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ImDrug: A Benchmark for Deep Imbalanced Learning in AI-aided Drug Discovery

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Lanqing Li, Liang Zeng, Ziqi Gao, Shen Yuan, Yatao Bian, Bingzhe Wu, Hengtong Zhang, Chan Lu, Yang Yu, Wei Liu, et al · 2022

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Fluidic Topology Optimization with an Anisotropic Mixture Model

Yifei Li, Tao Du, Sangeetha Grama Srinivasan, Kui Wu, Bo Zhu, Eftychios Sifakis, and Wojciech Matusik. 2022a · 2022

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Fourier Neural Operator with Learned Deformations for PDEs on General Geometries

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Zongyi Li, Daniel Zhengyu Huang, Burigede Liu, and Anima Anandkumar. 2022b · 2022

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Uni-Fold: an open-source platform for developing protein folding models beyond AlphaFold

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Transformer for Partial Differential Equations’ Operator Learning

Zijie Li, Kazem Meidani, and Amir Barati Farimani. 2022d · 2022

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Graph neural networks accelerated molecular dynamics

Zijie Li, Kazem Meidani, Prakarsh Yadav, and Amir Barati Farimani. 2022e · 2022

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Group-Equivariant Neural Networks with Fusion Diagrams

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Zimu Li, Han Zheng, Erik Thiede, Junyu Liu, and Risi Kondor. 2022i · 2022

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Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks. In International Conference on Learning Representations

Marten Lienen and Stephan Günnemann. 2022 · 2022

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DiffBP: generative diffusion of 3D molecules for target protein binding

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Haitao Lin, Yufei Huang, Meng Liu, Xuanjing Li, Shuiwang Ji, and Stan Z Li. 2022 · 2022

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Multi-modal Molecule Structure-text Model for Text-based Retrieval and Editing

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Shengchao Liu, Weili Nie, Chengpeng Wang, Jiarui Lu, Zhuoran Qiao, Ling Liu, Jian Tang, Chaowei Xiao, and Anima Anandkumar. 2022d · 2022

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Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow. In NeurIPS 2022 Workshop on Score-Based Methods

Xingchao Liu, Chengyue Gong, et al · 2022

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Interpretable Chirality-Aware Graph Neural Network for Quantitative Structure Activity Relationship Modeling in Drug Discovery

Yunchao Liu, Yu Wang, Oanh T Vu, Rocco Moretti, Bobby Bodenheimer, Jens Meiler, and Tyler Derr. 2022g · 2022

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Hidden-nucleons neural-network quantum states for the nuclear many-body problem

Alessandro Lovato, Corey Adams, Giuseppe Carleo, and Noemi Rocco. 2022 · 2022

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A comprehensive and fair comparison of two neural operators (with practical extensions) based on fair data

Lu Lu, Xuhui Meng, Shengze Cai, Zhiping Mao, Somdatta Goswami, Zhongqiang Zhang, and George Em Karniadakis. 2022a · 2022

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Tankbind: Trigonometry-aware neural networks for drug-protein binding structure prediction

Wei Lu, Qifeng Wu, Jixian Zhang, Jiahua Rao, Chengtao Li, and Shuangjia Zheng. 2022b · 2022

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Gauge equivariant neural networks for 2+ 1d u (1) gauge theory simulations in hamiltonian formulation

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Di Luo, Shunyue Yuan, James Stokes, and Bryan K Clark. 2022b · 2022

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BioGPT: generative pre-trained transformer for biomedical text generation and mining

Renqian Luo, Liai Sun, Yingce Xia, Tao Qin, Sheng Zhang, Hoifung Poon, and Tie-Yan Liu. 2022a · 2022

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An Autoregressive Flow Model for 3D Molecular Geometry Generation from Scratch. In International Conference on Learning Representations

Youzhi Luo and Shuiwang Ji. 2022 · 2022

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Evolving symbolic density functionals

He Ma, Arunachalam Narayanaswamy, Patrick Riley, and Li Li. 2022 · 2022

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GPS++: An Optimised Hybrid MPNN/Transformer for Molecular Property Prediction

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Dominic Masters, Josef Dean, Kerstin Klaser, Zhiyi Li, Sam Maddrell-Mander, Adam Sanders, Hatem Helal, Deniz Beker, Ladislav Rampášek, and Dominique Beaini. 2022 · 2022

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Reprogramming Large Pretrained Language Models for Antibody Sequence Infilling

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Machine-learning-based exchange correlation functional with physical asymptotic constraints

Ryo Nagai, Ryosuke Akashi, and Osamu Sugino. 2022 · 2022

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MegaMolBART v0.2

NVIDIA Corporation. 2022 · 2022

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Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern

Berrak Özer, Martin A Karlsen, Zachary Thatcher, Ling Lan, Brian McMahon, Peter R Strickland, Simon P Westrip, Koh S Sang, David G Billing, Dorthe B Ravnsbæk, et al · 2022

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O ⁡ ( N 2 ) O(N^{2}) Universal Antisymmetry in Fermionic Neural Networks. In ICML 2022 2nd AI for Science Workshop

Tianyu Pang, Shuicheng Yan, and Min Lin. 2022 · 2022

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Fourcastnet: A global data-driven high-resolution weather model using adaptive fourier neural operators

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Jaideep Pathak, Shashank Subramanian, Peter Harrington, Sanjeev Raja, Ashesh Chattopadhyay, Morteza Mardani, Thorsten Kurth, David Hall, Zongyi Li, Kamyar Azizzadenesheli, et al · 2022

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Pocket2Mol: Efficient molecular sampling based on 3d protein pockets. In International Conference on Machine Learning . PMLR, 17644–17655

Xingang Peng, Shitong Luo, Jiaqi Guan, Qi Xie, Jian Peng, and Jianzhu Ma. 2022 · 2022

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Neural-network quantum states for periodic systems in continuous space

Gabriel Pescia, Jiequn Han, Alessandro Lovato, Jianfeng Lu, and Giuseppe Carleo. 2022 · 2022

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The impact of low-cost molecular geometry optimization in property prediction via graph neural network. In 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) . IEEE, 603–608

Gabriel A Pinheiro, Felipe V Calderan, Juarez LF Da Silva, and Marcos G Quiles. 2022 · 2022

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Exact constraints and appropriate norms in machine-learned exchange-correlation functionals

Kanun Pokharel, James W. Furness, Yi Yao, Volker Blum, Tom J. P. Irons, Andrew M. Teale, and Jianwei Sun. 2022 · 2022

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Transform Once: Efficient Operator Learning in Frequency Domain. In Advances in Neural Information Processing Systems , Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho (Eds.)

Michael Poli, Stefano Massaroli, Federico Berto, Jinkyoo Park, Tri Dao, Christopher Re, and Stefano Ermon. 2022 · 2022

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Generating 3D molecules conditional on receptor binding sites with deep generative models

Matthew Ragoza, Tomohide Masuda, and David Ryan Koes. 2022 · 2022

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Generative adversarial neural operators

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Md Ashiqur Rahman, Manuel A Florez, Anima Anandkumar, Zachary E Ross, and Kamyar Azizzadenesheli. 2022a · 2022

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U-no: U-shaped neural operators

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Md Ashiqur Rahman, Zachary E Ross, and Kamyar Azizzadenesheli. 2022b · 2022

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Graph neural networks for materials science and chemistry

Patrick Reiser, Marlen Neubert, André Eberhard, Luca Torresi, Chen Zhou, Chen Shao, Houssam Metni, Clint van Hoesel, Henrik Schopmans, Timo Sommer, and Pascal Friederich. 2022 · 2022

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An invertible crystallographic representation for general inverse design of inorganic crystals with targeted properties

Zekun Ren, Siyu Isaac Parker Tian, Juhwan Noh, Felipe Oviedo, Guangzong Xing, Jiali Li, Qiaohao Liang, Ruiming Zhu, Armin G Aberle, Shijing Sun, et al · 2022

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Fermionic wave functions from neural-network constrained hidden states

Javier Robledo Moreno, Giuseppe Carleo, Antoine Georges, and James Stokes. 2022 · 2022

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Tackling climate change with machine learning

David Rolnick, Priya L Donti, Lynn H Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, et al · 2022

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High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition . 10684–10695

Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. 2022 · 2022

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Learning Partial Equivariances From Data. In Advances in Neural Information Processing Systems

David W. Romero and Suhas Lohit. 2022 · 2022

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Machine Learning Diffusion Monte Carlo Energies

Kevin Ryczko, Jaron T. Krogel, and Isaac Tamblyn. 2022a · 2022

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Toward Orbital-Free Density Functional Theory with Small Data Sets and Deep Learning

Kevin Ryczko, Sebastian J Wetzel, Roger G Melko, and Isaac Tamblyn. 2022b · 2022

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Solving the electronic Schrödinger equation for multiple nuclear geometries with weight-sharing deep neural networks

Michael Scherbela, Rafael Reisenhofer, Leon Gerard, Philipp Marquetand, and Philipp Grohs. 2022 · 2022

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Structure-based drug design with equivariant diffusion models

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Arne Schneuing, Yuanqi Du, Charles Harris, Arian Jamasb, Ilia Igashov, Weitao Du, Tom Blundell, Pietro Lió, Carla Gomes, Max Welling, et al · 2022

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NOMAD: Nonlinear manifold decoders for operator learning

Jacob Seidman, Georgios Kissas, Paris Perdikaris, and George J Pappas. 2022 · 2022

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Online adjoint methods for optimization of PDEs

Justin Sirignano and Konstantinos Spiliopoulos. 2022 · 2022

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HypoSVI: Hypocentre inversion with Stein variational inference and physics informed neural networks

Jonthan D Smith, Zachary E Ross, Kamyar Azizzadenesheli, and Jack B Muir. 2022 · 2022

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Beyond neural scaling laws: beating power law scaling via data pruning

Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari Morcos. 2022 · 2022

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Dissipative Hamiltonian Neural Networks: Learning Dissipative and Conservative Dynamics Separately

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Andrew Sosanya and Sam Greydanus. 2022 · 2022

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Uniting nonempirical and empirical density functional approximation strategies using constraint-based regularization

Zachary M Sparrow, Brian G Ernst, Trine K Quady, and Robert A DiStasio Jr. 2022 · 2022

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How robust are modern graph neural network potentials in long and hot molecular dynamics simulations?

Sina Stocker, Johannes Gasteiger, Florian Becker, Stephan Günnemann, and Johannes T Margraf. 2022 · 2022

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A molecular multimodal foundation model associating molecule graphs with natural language

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Bing Su, Dazhao Du, Zhao Yang, Yujie Zhou, Jiangmeng Li, Anyi Rao, Hao Sun, Zhiwu Lu, and Ji-Rong Wen. 2022 · 2022

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Accelerating Time-Reversal Imaging with Neural Operators for Real-time Earthquake Locations

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PDEBench: An extensive benchmark for scientific machine learning

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Towards universal neural network potential for material discovery applicable to arbitrary combination of 45 elements

So Takamoto, Chikashi Shinagawa, Daisuke Motoki, Kosuke Nakago, Wenwen Li, Iori Kurata, Taku Watanabe, Yoshihiro Yayama, Hiroki Iriguchi, Yusuke Asano, et al · 2022

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Galactica: A Large Language Model for Science

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Equivariant Transformers for Neural Network based Molecular Potentials. In International Conference on Learning Representations

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Improving the assessment of deep learning models in the context of drug-target interaction prediction

Mirko Torrisi, Antonio de la Vega de León, Guillermo Climent, Remco Loos, and Alejandro Panjkovich. 2022 · 2022

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Data-driven and constrained optimization of semi-local exchange and nonlocal correlation functionals for materials and surface chemistry

Kai Trepte and Johannes Voss. 2022 · 2022

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Diffusion probabilistic modeling of protein backbones in 3D for the motif-scaffolding problem

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ScanNet: an interpretable geometric deep learning model for structure-based protein binding site prediction

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Relaxing Equivariance Constraints with Non-stationary Continuous Filters. In Advances in Neural Information Processing Systems

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AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models

Mihaly Varadi, Stephen Anyango, Mandar Deshpande, Sreenath Nair, Cindy Natassia, Galabina Yordanova, David Yuan, Oana Stroe, Gemma Wood, Agata Laydon, et al · 2022

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Harnessing interpretable and unsupervised machine learning to address big data from modern X-ray diffraction

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