Less is more: sampling chemical space with active learning

28 January 2018

Papers citing "Less is more: sampling chemical space with active learning"

33 / 33 papers shown

Title
Optimal Invariant Bases for Atomistic Machine Learning Alice Allen Emily Shinkle Roxana Bujack Nicholas Lubbers 37 0 0 30 Mar 2025
A practical guide to machine learning interatomic potentials -- Status and future Ryan Jacobs D. Morgan Siamak Attarian Jun Meng Chen Shen ... K. J. Schmidt So Takamoto Aidan Thompson Julia Westermayr Brandon M. Wood 59 4 0 12 Mar 2025
Implicit Delta Learning of High Fidelity Neural Network Potentials Stephan Thaler Cristian Gabellini Nikhil Shenoy Prudencio Tossou AI4CE 90 0 0 08 Dec 2024
OpenQDC: Open Quantum Data Commons Cristian Gabellini Nikhil Shenoy Stephan Thaler Semih Cantürk Daniel McNeela Dominique Beaini Michael Bronstein Prudencio Tossou AI4CE 80 1 0 29 Nov 2024
Efficient Biological Data Acquisition through Inference Set Design Ihor Neporozhnii Julien Roy Emmanuel Bengio Jason Hartford 48 0 0 25 Oct 2024
regAL: Python Package for Active Learning of Regression Problems Elizaveta Surzhikova Jonny Proppe AI4CE 29 0 0 23 Oct 2024
Pretraining Graph Transformers with Atom-in-a-Molecule Quantum Properties for Improved ADMET Modeling Alessio Fallani Ramil I. Nugmanov Jose A. Arjona-Medina Jörg Kurt Wegner Alexandre Tkatchenko Kostiantyn Chernichenko MedIm AI4CE 34 0 0 10 Oct 2024
All-in-one foundational models learning across quantum chemical levels Yuxinxin Chen Pavlo O. Dral AI4CE 20 1 0 18 Sep 2024
On the design space between molecular mechanics and machine learning force fields Yuanqing Wang Kenichiro Takaba Michael S. Chen Marcus Wieder Yuzhi Xu ... Kyunghyun Cho Joe G. Greener Peter K. Eastman Stefano Martiniani M. Tuckerman AI4CE 45 4 0 03 Sep 2024
Guided Multi-objective Generative AI to Enhance Structure-based Drug Design Amit Kadan Kevin Ryczko Erika Lloyd A. Roitberg Takeshi Yamazaki 93 1 0 20 May 2024
Predicting Properties of Periodic Systems from Cluster Data: A Case Study of Liquid Water Viktor Zaverkin David Holzmüller Robin Schuldt Johannes Kastner 28 15 0 03 Dec 2023
Pareto Optimization to Accelerate Multi-Objective Virtual Screening Jenna C. Fromer David E. Graff Connor W. Coley 23 7 0 16 Oct 2023
InstructBio: A Large-scale Semi-supervised Learning Paradigm for Biochemical Problems Fang Wu Huiling Qin Siyuan Li Stan Z. Li Xianyuan Zhan Jinbo Xu 24 5 0 08 Apr 2023
Denoise Pretraining on Nonequilibrium Molecules for Accurate and Transferable Neural Potentials Yuyang Wang Chang Xu Zijie Li A. Farimani AAML AI4CE 27 21 0 03 Mar 2023
Scalable Bayesian Uncertainty Quantification for Neural Network Potentials: Promise and Pitfalls Stephan Thaler Gregor Doehner Julija Zavadlav 35 21 0 15 Dec 2022
Computer-Aided Multi-Objective Optimization in Small Molecule Discovery Jenna C. Fromer Connor W. Coley 32 66 0 13 Oct 2022
When does deep learning fail and how to tackle it? A critical analysis on polymer sequence-property surrogate models Himanshu T. Patra AI4CE 13 0 0 12 Oct 2022
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings Aymane Abdali Vincent Gripon Lucas Drumetz Bartosz Bogusławski 29 1 0 23 Sep 2022
Unveil the unseen: Exploit information hidden in noise Bahdan Zviazhynski G. Conduit 14 2 0 17 Sep 2022
Transition1x -- a Dataset for Building Generalizable Reactive Machine Learning Potentials M. Schreiner Arghya Bhowmik Tejs Vegge Jonas Busk Ole Winther 21 62 0 25 Jul 2022
NeuralNEB -- Neural Networks can find Reaction Paths Fast M. Schreiner Arghya Bhowmik Tejs Vegge Peter Bjørn Jørgensen Ole Winther 41 23 0 20 Jul 2022
Machine Learning Diffusion Monte Carlo Energies Kevin Ryczko J. Krogel Isaac Tamblyn DiffM 11 14 0 09 May 2022
Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments Viktor Zaverkin David Holzmüller Ingo Steinwart Johannes Kastner 29 19 0 20 Sep 2021
DeePKS: a comprehensive data-driven approach towards chemically accurate density functional theory Yixiao Chen Linfeng Zhang Han Wang E. Weinan 11 72 0 01 Aug 2020
Deep Learning in Protein Structural Modeling and Design Wenhao Gao S. Mahajan Jeremias Sulam Jeffrey J. Gray 29 159 0 16 Jul 2020
Machine learning for electronically excited states of molecules Julia Westermayr P. Marquetand 17 257 0 10 Jul 2020
Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach Jiang Wang Stefan Chmiela K. Müller Frank Noè C. Clementi 6 46 0 04 May 2020
Active Training of Physics-Informed Neural Networks to Aggregate and Interpolate Parametric Solutions to the Navier-Stokes Equations Christopher J. Arthurs A. King PINN 40 51 0 02 May 2020
Active Learning Approach to Optimization of Experimental Control Yadong Wu Zengming Meng K. Wen Chengdong Mi Jing Zhang H. Zhai 9 14 0 26 Mar 2020
Automated discovery of a robust interatomic potential for aluminum Justin S. Smith B. Nebgen N. Mathew Jie Chen Nicholas Lubbers ... S. Tretiak H. Nam T. Germann S. Fensin K. Barros 11 78 0 10 Mar 2020
Machine learning enables long time scale molecular photodynamics simulations Julia Westermayr M. Gastegger M. Menger Sebastian Mai L. González Marquetand AI4CE 8 71 0 22 Nov 2018
Compressing physical properties of atomic species for improving predictive chemistry John E. Herr Kevin J Koh Kun Yao John A. Parkhill AI4CE 22 20 0 31 Oct 2018
Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation Linfeng Zhang De-Ye Lin Han Wang R. Car E. Weinan 9 326 0 28 Oct 2018