Predicting neutron experiments from first principles: A workflow powered by machine learning

Machine learning has emerged as a powerful tool in materials discovery, enabling the rapid design of novel materials with tailored properties for countless applications, including in the context of energy...

Materials Science Computer Science Physics

View graph Source

Paper

References (60)

01Dynasor 2: From simulation to experiment through correlation functionsE. Berger, E. Fransson, Fredrik Eriksson et al.2025 · Computer Physics Communications · 15 citations

02Emerging computational and machine learning methodologies for proton-conducting oxides: materials discovery and fundamental understandingS. Fujii, J. Hyodo, Kazuki Shitara et al.2024 · Science and Technology of Advanced Materials · 13 citations

03Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materials.Penghua Ying, Wen-an Zhou, L. Svensson et al.2024 · Journal of Chemical Physics · 30 citations

04Machine learning in energy storage material discovery and performance predictionGuochang Huang, Fuqiang Huang, W. Dong2024 · Chemical Engineering Journal · 64 citations

05calorine: A Python package for constructing and sampling neuroevolution potential modelsEric Lindgren, M. Rahm, E. Fransson et al.2024 · Journal of Open Source Software · 34 citations

06GPTFF: A high-accuracy out-of-the-box universal AI force field for arbitrary inorganic materials.Fankai Xie, Tenglong Lu, Sheng Meng et al.2024 · Science Bulletin · 42 citations

07Accelerating materials discovery: combinatorial synthesis, high-throughput characterization, and computational advancesKhurram Shahzad, A. I. Mardare, A. W. Hassel2024 · Science and Technology of Advanced Materials: Methods · 96 citations

08Accelerating Computational Materials Discovery with Machine Learning and Cloud High-Performance Computing: from Large-Scale Screening to Experimental Validation.Chi Chen, D. Nguyen, Shannon J Lee et al.2024 · Journal of the American Chemical Society · 107 citations In Library

09A foundation model for atomistic materials chemistry.Ilyes Batatia, Philipp Benner, Chiang Yuan et al.2023 · Journal of Chemical Physics · 675 citations

10Scaling deep learning for materials discoveryAmil Merchant, Simon Batzner, S. Schoenholz et al.2023 · Nature · 1.5k citations

11CHGNet as a pretrained universal neural network potential for charge-informed atomistic modellingBowen Deng, Peichen Zhong, KyuJung Jun et al.2023 · Nature Machine Intelligence · 990 citations

12Phase Transitions in Inorganic Halide Perovskites from Machine-Learned PotentialsE. Fransson, Julia Wiktor, P. Erhart2023 · Journal of Physical Chemistry C · 60 citations

Scroll for more · 38 remaining

Machine learning in nuclear materials researchD. Morgan, G. Pilania, Adrien Couet et al.2022 · Current opinion in solid state & materials science · 111 citations In Library

Codes for paper "Samarakoon, A. M., D. Alan Tennant, Feng Ye, Qiang Zhang, and S. A. Grigera. "Integration of Machine Learning with Neutron Scattering: Hamiltonian Tuning % in Spin Ice with Pressure." arXiv preprint arXiv:2110.15817 (2021)."Anjana Samarakoon, D. M. Tennant, Feng Ye et al.2021 · arXiv (Cornell University) · 3 citations In Library

Beyond potentials: Integrated machine learning models for materialsMichele Ceriotti2022 · MRS bulletin · 32 citations In Library

Self-Optimizing Machine Learning Potential Assisted Automated Workflow for Highly Efficient Complex Systems Material DesignJiaxiang Li, Junwei Feng, Jie Luo et al.2025 · npj Computational Materials · 7 citations In Library

Extensible Structure-Informed Prediction of Formation Energy with Improved Accuracy and Usability Employing Neural NetworksAdam M. Krajewski, Jonathan W. Siegel, Jinchao Xu et al.2020 · Social Science Research Network · 35 citations In Library

Machine learning for the prediction of converged energies from ab initio nuclear structure calculationsMarco Knoll, T. Wolfgruber, M. L. Agel et al.2022 · Physics Letters B · 17 citations In Library

Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistryA. Anker, Keith Tobias Butler, Raghavendra Selvan et al.2023 · Chemical Science · 23 citations In Library

Application of Machine Learning in Material Synthesis and Property PredictionGuan Huang, Yan Guo, Ye Chen et al.2023 · Materials · 133 citations In Library

Discovery of novel materials through machine learningAkinwumi Akinpelu, Mangladeep Bhullar, Yansun Yao2024 · Journal of Physics: Condensed Matter · 19 citations In Library

Patents near this paper

METHODS FOR PREDICTING LIKELIHOOD OF SUCCESSFUL EXPERIMENTAL SYNTHESIS OF COMPUTER-GENERATED MATERIALS BY COMBINING NETWORK ANALYSIS AND MACHINE LEARNINGUS 11,580,431 · TOYOTA RESEARCH INSTITUTE, INC. · 2023 EXTRAPOLATIVE PREDICTION OF ENANTIOSELECTIVITY ENABLED BY COMPUTER-DRIVEN WORKFLOW, NEW MOLECULAR REPRESENTATIONS AND MACHINE LEARNINGUS 11,664,093 · THE BOARD OF TRUSTEES OF THE UNIVERSITY OF ILLINOIS · 2023 METHOD AND APPARATUS FOR NEW MATERIAL DISCOVERY USING MACHINE LEARNING ON TARGETED PHYSICAL PROPERTYUS 10,957,419 · SAMSUNG ELECTRONICS CO., LTD. · 2021 ELECTRONIC AND ATOMIC STRUCTURE COMPUTATION UTILIZING MACHINE LEARNINGUS 11,586,982 · SAMSUNG ELECTRONICS CO., LTD. · 2023 METHOD AND ELECTRONIC APPARATUS FOR PREDICTING ELECTRONIC STRUCTURE OF MATERIALUS 11,113,611 · KOREA INSTITUTE OF SCIENCE AND TECHNOLOGY · 2021

Source of record

arXiv

Predicting neutron experiments from first principles: A workflow powered by machine learning

Paper

References (60)

Similar papers

Patents near this paper

Welcome.

Patents near this paper