Flexible formulation of value for experiment interpretation and design

[1]  Brian L. DeCost,et al.  Self-driving Multimodal Studies at User Facilities , 2023, Acta Crystallographica Section A Foundations and Advances.

[2]  Phillip M. Maffettone,et al.  Delivering real-time multi-modal materials analysis with enterprise beamlines , 2022, Cell Reports Physical Science.

[3]  Anthony Degennaro,et al.  Machine Learning for analysis of speckle dynamics: quantification and outlier detection , 2022, Physical Review Research.

[4]  Phillip M. Maffettone,et al.  Advancing Discovery with Artificial Intelligence and Machine Learning at NSLS-II , 2022, Synchrotron Radiation News.

[5]  A. Kusne,et al.  Scalable multi-agent lab framework for lab optimization , 2022, Matter.

[6]  Brian L. DeCost,et al.  Reproducible sorbent materials foundry for carbon capture at scale , 2022, Cell Reports Physical Science.

[7]  Paul Bendich,et al.  Topological Simplification of Signals for Inference and Approximate Reconstruction , 2022, 2023 IEEE Aerospace Conference.

[8]  Bruce Ravel,et al.  Machine learning enabling high-throughput and remote operations at large-scale user facilities , 2022, Digital Discovery.

[9]  A. Henson,et al.  Discovering New Chemistry with an Autonomous Robotic Platform Driven by a Reactivity-Seeking Neural Network , 2021, ACS central science.

[10]  I. Takeuchi,et al.  On-the-fly autonomous control of neutron diffraction via physics-informed Bayesian active learning , 2021, Applied Physics Reviews.

[11]  Aaron Stein,et al.  Gaussian processes for autonomous data acquisition at large-scale synchrotron and neutron facilities , 2021, Nature Reviews Physics.

[12]  Semion K. Saikin,et al.  Autonomous experimentation systems for materials development: A community perspective , 2021 .

[13]  Aidan C. Daly,et al.  Constrained non-negative matrix factorization enabling real-time insights of in situ and high-throughput experiments , 2021, Applied Physics Reviews.

[14]  Phillip M. Maffettone,et al.  Gaming the beamlines—employing reinforcement learning to maximize scientific outcomes at large-scale user facilities , 2021, Mach. Learn. Sci. Technol..

[15]  A. Butté,et al.  Machine Learning for Biologics: Opportunities for Protein Engineering, Developability, and Formulation , 2021, Trends in Pharmacological Sciences.

[16]  P. Midgley,et al.  Revisiting metal fluorides as lithium-ion battery cathodes , 2021, Nature Materials.

[17]  Daniel Olds,et al.  Outlook for artificial intelligence and machine learning at the NSLS-II , 2020, Mach. Learn. Sci. Technol..

[18]  D. Olds.  Synchrotron X-ray Diffraction for Energy and Environmental Materials: The Current Role and Future Directions of Total Scattering Beamlines in the Functional Material Scientific Ecosystem , 2020, Synchrotron Radiation News.

[19]  Phillip M. Maffettone,et al.  Crystallography companion agent for high-throughput materials discovery , 2020, Nature Computational Science.

[20]  Shinsuke Ishihara,et al.  Pushing property limits in materials discovery via boundless objective-free exploration† †Electronic supplementary information (ESI) available: The details of BLOX and experimental spectroscopic data. See DOI: 10.1039/d0sc00982b , 2020, Chemical science.

[21]  Jiagen Li,et al.  Autonomous discovery of optically active chiral inorganic perovskite nanocrystals through an intelligent cloud lab , 2020, Nature Communications.

[22]  Jonathan Grizou,et al.  A curious formulation robot enables the discovery of a novel protocell behavior , 2020, Science Advances.

[23]  David T. Jones,et al.  Improved protein structure prediction using potentials from deep learning , 2020, Nature.

[24]  Brian L. DeCost,et al.  A high-throughput structural and electrochemical study of metallic glass formation in Ni-Ti-Al. , 2019, ACS combinatorial science.

[25]  Sorelle A. Friedler,et al.  Experiment Specification, Capture and Laboratory Automation Technology (ESCALATE): a software pipeline for automated chemical experimentation and data management , 2019, MRS Communications.

[26]  Alán Aspuru-Guzik,et al.  Next-Generation Experimentation with Self-Driving Laboratories , 2019, Trends in Chemistry.

[27]  Alexei A. Efros,et al.  Large-Scale Study of Curiosity-Driven Learning , 2018, ICLR.

[28]  Peter I. Frazier,et al.  A Tutorial on Bayesian Optimization , 2018, ArXiv.

[29]  Leroy Cronin,et al.  Designing Algorithms To Aid Discovery by Chemical Robots , 2018, ACS central science.

[30]  Carl T. Bergstrom,et al.  Science of Science , 2018, Nature.

[31]  Sergey Levine,et al.  Diversity is All You Need: Learning Skills without a Reward Function , 2018, ICLR.

[32]  P. F. Peterson,et al.  Combinatorial appraisal of transition states for in situ pair distribution function analysis , 2017 .

[33]  Jacob G Foster,et al.  Choosing experiments to accelerate collective discovery , 2015, Proceedings of the National Academy of Sciences.

[34]  H. Hirsh,et al.  Amplify scientific discovery with artificial intelligence , 2014, Science.

[35]  Ryan P. Adams,et al.  Multi-Task Bayesian Optimization , 2013, NIPS.

[36]  Pierre-Yves Oudeyer,et al.  Active learning of inverse models with intrinsically motivated goal exploration in robots , 2013, Robotics Auton. Syst..

[37]  Kenneth O. Stanley,et al.  Abandoning Objectives: Evolution Through the Search for Novelty Alone , 2011, Evolutionary Computation.

[38]  Appendix to: BOTORCH: A Framework for Efficient Monte-Carlo Bayesian Optimization , 2021 .

[39]  Nando de Freitas,et al.  Taking the Human Out of the Loop: A Review of Bayesian Optimization , 2016, Proceedings of the IEEE.

[40]  Sidney Addelman,et al.  trans-Dimethanolbis(1,1,1-trifluoro-5,5-dimethylhexane-2,4-dionato)zinc(II) , 2008, Acta crystallographica. Section E, Structure reports online.