MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures | Paper Citation Record

Observation df91b4f1-6e21-4918-9ef7-d934ee867381 · outbound

This paper cites Learning to optimize molecular geometries using reinforcement learning.Journal of Chemical Theory and Computation, 17(2):818–825, 2021.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Learning to optimize molecular geometries using reinforcement learning.Journal of Chemical Theory and Computation, 17(2):818–825, 2021

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.381558Z

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Observation 46744f70-6d02-410a-b60a-fc7132050f18 · outbound

This paper cites Stridernet: A graph reinforcement learning approach to optimize atomic structures on rough energy landscapes.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Stridernet: A graph reinforcement learning approach to optimize atomic structures on rough energy landscapes

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.371909Z

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Observation b42733fc-5089-483f-9873-e29184f74637 · outbound

This paper cites Structural relaxation made simple.Physical review letters, 97:170201, 2006.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Structural relaxation made simple.Physical review letters, 97:170201, 2006

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.362890Z

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Observation 4d02c72c-a632-41e1-8005-691cd0e4baef · outbound

This paper cites The convergence of a class of double-rank minimization algorithms 1.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures The convergence of a class of double-rank minimization algorithms 1

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T10:52:52.293386Z

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Observation dc714784-a366-48a1-9344-07523f8748e4 · outbound

This paper cites an unresolved cited work.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Unresolved cited work

Reference 5

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:52:53.346966Z

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Observation 39d98ccc-536e-4dbc-a785-2b2953e32ea5 · outbound

This paper cites Learning to optimize in swarms.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Learning to optimize in swarms

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.337572Z

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Observation 90847918-8cae-4646-ac4e-f78845bfbd44 · outbound

This paper cites Integrating chemical information into reinforcement learning for enhanced molecular geometry optimization.Journal of Chemical Theory and Computation, 19(23):8598–8609, 2023.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Integrating chemical information into reinforcement learning for enhanced molecular geometry optimization.Journal of Chemical Theory and Computation, 19(23):8598–8609, 2023

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.327515Z

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Observation d8cf5cee-36ec-43e3-9517-092029a3cbe5 · outbound

This paper cites Learning to optimize: A primer and a benchmark.Journal of Machine Learning Research, 23(189):1–59, 2022.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Learning to optimize: A primer and a benchmark.Journal of Machine Learning Research, 23(189):1–59, 2022

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-07T10:52:52.306729Z

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Observation 98623211-c8cc-4820-831f-c1cb7810a8c8 · outbound

This paper cites Learning to learn without gradient descent by gradient descent.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Learning to learn without gradient descent by gradient descent

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.310781Z

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Observation 0fafba19-34a6-4cf6-af02-c80a78d92010 · outbound

This paper cites The flexible unit structure engine (fuse) for probe structure-based composition prediction.Faraday Discussions, 211:117–131, 2018.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures The flexible unit structure engine (fuse) for probe structure-based composition prediction.Faraday Discussions, 211:117–131, 2018

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.301054Z

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Observation 88b1d0d3-df7b-420a-8bfd-15e928514758 · outbound

This paper cites Integration of generative machine learning with the heuristic crystal structure prediction code fuse.Faraday Discussions, 256:85–103, 2025.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Integration of generative machine learning with the heuristic crystal structure prediction code fuse.Faraday Discussions, 256:85–103, 2025

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.290948Z

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Observation a16cff77-f664-41e9-aa9c-0cb9232b89ec · outbound

This paper cites CHGNet as a pretrained universal neural network potential for charge- informed atomistic modelling.Nature Machine Intelligence, 5(9):1031–1041, September 2023.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures CHGNet as a pretrained universal neural network potential for charge- informed atomistic modelling.Nature Machine Intelligence, 5(9):1031–1041, September 2023

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.281646Z

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Observation 748fafb1-1e95-43a4-9640-a82d0be8ca8f · outbound

This paper cites The double-funnel energy landscape of the 38-atom lennard-jones cluster.The Journal of Chemical Physics, 110(14):6896–6906, 1999.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures The double-funnel energy landscape of the 38-atom lennard-jones cluster.The Journal of Chemical Physics, 110(14):6896–6906, 1999

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.271976Z

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Observation 1e166f81-0513-442b-82fe-86415c1d955e · outbound

This paper cites A new approach to variable metric algorithms.The Computer Journal, 13(3):317–322, 1970.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures A new approach to variable metric algorithms.The Computer Journal, 13(3):317–322, 1970

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T10:52:52.324483Z

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Observation 4146f417-2828-47c1-bc50-ac1e6cf4a7de · outbound

This paper cites The general utility lattice program (gulp).Molecular Simulation, 29(5):291–341, 2003.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures The general utility lattice program (gulp).Molecular Simulation, 29(5):291–341, 2003

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.256000Z

Source-reported events for the cited work

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Observation c36e5cdb-3967-4081-8b10-15b01333200e · outbound

This paper cites A family of variable-metric methods derived by variational means.Mathe- matics of computation, 24(109):23–26, 1970.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures A family of variable-metric methods derived by variational means.Mathe- matics of computation, 24(109):23–26, 1970

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.245650Z

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Observation e166c379-8512-49f7-8c00-8f9bafba06b2 · outbound

This paper cites Learning conditional policies for crystal design using offline reinforcement learning.Digital Discovery, 3(4):769–785, 2024.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Learning conditional policies for crystal design using offline reinforcement learning.Digital Discovery, 3(4):769–785, 2024

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.234816Z

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Observation 816ad165-369f-45a4-aa42-c3d6c55c781a · outbound

This paper cites Learning to optimize multigrid pde solvers.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Learning to optimize multigrid pde solvers

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T10:52:52.335881Z

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Observation 7de9657d-1c9b-4801-bf09-11623a252f6d · outbound

This paper cites Lattice relaxation at a metal surface.Physical Review B, 23(12):6265, 1981.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Lattice relaxation at a metal surface.Physical Review B, 23(12):6265, 1981

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.215604Z

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Observation 4721d3de-c314-453e-8f97-66abb5f6cc38 · outbound

This paper cites Optimality guarantees for crystal structure prediction.Nature, 619(7968):68 72, 2023.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Optimality guarantees for crystal structure prediction.Nature, 619(7968):68 72, 2023

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.206271Z

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Observation 4da9c829-82a2-4844-a537-0a061317d6bc · outbound

This paper cites Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Soft actor-critic: Off- policy maximum entropy deep reinforcement learning with a stochastic actor

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T10:52:52.344277Z

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Observation 29e939f2-b31f-4c97-9916-2cff4a994f26 · outbound

This paper cites Structure cristalline du bronze pseudo- quadratique K0.6FeF3: transition pyrochlore-quadratique pour les composés KMM’X6.Acta Crystallographica Section B, 29(8):1654–1658, Aug 1973.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Structure cristalline du bronze pseudo- quadratique K0.6FeF3: transition pyrochlore-quadratique pour les composés KMM’X6.Acta Crystallographica Section B, 29(8):1654–1658, Aug 1973

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.190442Z

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Observation d2410792-7e19-4d3b-8de8-415864481c1a · outbound

This paper cites The atomic simulation environmenta python library for working with atoms.Journal of Physics: Condensed Matter, 29(27):273002, 2017.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures The atomic simulation environmenta python library for working with atoms.Journal of Physics: Condensed Matter, 29(27):273002, 2017

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.181418Z

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Observation a3779518-640a-41de-8ff6-58f391879878 · outbound

This paper cites Deep reinforcement learning for inverse inorganic materials design.npj Computational Materials, 10(1):287, 2024.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Deep reinforcement learning for inverse inorganic materials design.npj Computational Materials, 10(1):287, 2024

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.172384Z

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Observation c9f38aa7-ee3e-4ba1-b10a-8fb8ffb9987a · outbound

This paper cites Adam: A Method for Stochastic Optimization.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Adam: A Method for Stochastic Optimization

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T10:52:52.355090Z

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Observation c277aea9-6baa-47b3-b555-fcb3b14ca568 · outbound

This paper cites On the determination of molecular fields.i.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures On the determination of molecular fields.i

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.163514Z

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Observation c1565b26-9f9d-4f6e-b6dc-d4da18ee1d66 · outbound

This paper cites Learning to Optimize.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Learning to Optimize

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-07T10:52:52.360677Z

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Observation f6783e41-72ef-4794-86b0-8aa4649b0ad8 · outbound

This paper cites B2opt: Learning to optimize black-box optimization with little budget.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures B2opt: Learning to optimize black-box optimization with little budget

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.154752Z

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Observation a3ccd7b6-95bb-4311-b5bf-37fc6d0c0057 · outbound

This paper cites Rllib: Abstractions for distributed reinforcement learning.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Rllib: Abstractions for distributed reinforcement learning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.145654Z

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Observation 6cee7a6c-cc42-4d74-9b05-0b10f166cdad · outbound

This paper cites Computational prediction of muon stopping sites using ab initio random structure searching (airss).The Journal of Chemical Physics, 148(13):134114, 2018.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Computational prediction of muon stopping sites using ab initio random structure searching (airss).The Journal of Chemical Physics, 148(13):134114, 2018

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.136451Z

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Observation 68bdb1d9-6f19-4c12-8ee3-185bd07df48f · outbound

This paper cites Markov games as a framework for multi-agent reinforcement learning.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Markov games as a framework for multi-agent reinforcement learning

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.125996Z

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Observation a4629e5a-d8dc-43a8-900d-c6f95a0ec10c · outbound

This paper cites Multi-agent actor-critic for mixed cooperative-competitive environments.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Multi-agent actor-critic for mixed cooperative-competitive environments

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.116184Z

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Observation 28dd6748-4d96-47d0-8f37-1159629dfa7b · outbound

This paper cites Nanostructure and nanomechanics of cement: polydisperse colloidal packing.Physical review letters, 109(15):155503, 2012.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Nanostructure and nanomechanics of cement: polydisperse colloidal packing.Physical review letters, 109(15):155503, 2012

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.106455Z

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Observation 508c4ee3-4de9-4a75-b2db-6bf835ace09d · outbound

This paper cites Learn2hop: Learned optimization on rough landscapes.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Learn2hop: Learned optimization on rough landscapes

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.096403Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 71a9f10f-7d6d-4397-b96c-145dd6c8bb13 · outbound

This paper cites Exploring potential energy surfaces using reinforcement machine learning.Journal of Chemical Information and Modeling, 62(13):3169–3179, 2022.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Exploring potential energy surfaces using reinforcement machine learning.Journal of Chemical Information and Modeling, 62(13):3169–3179, 2022

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.085990Z

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No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5f0b31b4-bea8-496f-997c-756d0fb21374 · outbound

This paper cites Molopt: Autonomous molecular geometry optimization using multiagent reinforcement learning.The Journal of Physical Chemistry B, 127(48):10295–10303, 2023.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Molopt: Autonomous molecular geometry optimization using multiagent reinforcement learning.The Journal of Physical Chemistry B, 127(48):10295–10303, 2023

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.076313Z

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Observation 40ddeb81-bb55-4024-bb0c-187300b13ec7 · outbound

This paper cites Location of saddle points and minimum energy paths by a constrained simplex optimization procedure.Theoretica chimica acta, 53:75–93, 1979.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Location of saddle points and minimum energy paths by a constrained simplex optimization procedure.Theoretica chimica acta, 53:75–93, 1979

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.066727Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a1540164-a01f-425a-8258-416f0d4c5d7f · outbound

This paper cites How evolutionary crystal structure prediction works and why.Accounts of chemical research, 44(3):227–237, 2011.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures How evolutionary crystal structure prediction works and why.Accounts of chemical research, 44(3):227–237, 2011

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.057330Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3d85bc62-8b82-4656-99d9-01d8166051eb · outbound

This paper cites Geometry optimization.Wiley Interdisciplinary Reviews: Computational Molecular Science, 1(5):790–809, 2011.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Geometry optimization.Wiley Interdisciplinary Reviews: Computational Molecular Science, 1(5):790–809, 2011

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.048070Z

Source-reported events for the cited work

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Observation 76d272d8-8b72-4dcf-bf29-265e0a5fe068 · outbound

This paper cites Conditioning of quasi-newton methods for function minimization.Mathemat- ics of Computation, 24(111):647–656, 1970.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Conditioning of quasi-newton methods for function minimization.Mathemat- ics of Computation, 24(111):647–656, 1970

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T10:52:52.397152Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ffb773f-a364-4672-a7b1-6f31551bedac · outbound

This paper cites An introduction to the conjugate gradient method without the agonizing pain.Technical report, Pittsburgh, PA, USA, 1994.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures An introduction to the conjugate gradient method without the agonizing pain.Technical report, Pittsburgh, PA, USA, 1994

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.032833Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b1e3c36d-fa9f-4b33-9199-4144cbe97fd5 · outbound

This paper cites Deep reinforcement learning in chemistry: A review.Journal of Computational Chemistry, 45(22):1886–1898, 2024.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Deep reinforcement learning in chemistry: A review.Journal of Computational Chemistry, 45(22):1886–1898, 2024

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.022533Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2569d46c-911f-40a6-a274-388db43f8ef8 · outbound

This paper cites Local order in quenched states of simple atomic substances.Physical Review B, 34(8):5136, 1986.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Local order in quenched states of simple atomic substances.Physical Review B, 34(8):5136, 1986

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.012478Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 93133fb3-d1a2-4d2d-9ed5-765ab9a2b5e9 · outbound

This paper cites Computer simulation of local order in condensed phases of silicon.Physical review B, 31(8):5262, 1985.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Computer simulation of local order in condensed phases of silicon.Physical review B, 31(8):5262, 1985

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:53.001931Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 26cef83e-59b6-4dfe-9a7c-6881859c06f9 · outbound

This paper cites Learn to optimize – a brief overview.National Science Review, 11(8):nwae132, 2024.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Learn to optimize – a brief overview.National Science Review, 11(8):nwae132, 2024

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:52.614249Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 04bc676d-5a31-499a-8498-775739983b5a · outbound

This paper cites Digital features of chemical elements extracted from local geometries in crystal structures.Digital Discovery, 2025.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Digital features of chemical elements extracted from local geometries in crystal structures.Digital Discovery, 2025

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:52.604405Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation db6c4d81-84c9-4e0a-8d10-fefa0827b991 · outbound

This paper cites Scipy 1.0: fundamental algorithms for scientific computing in python.Nature methods, 17(3):261–272, 2020.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Scipy 1.0: fundamental algorithms for scientific computing in python.Nature methods, 17(3):261–272, 2020

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T10:52:52.417422Z

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Observation 8f454573-bf5e-43d9-bfd0-1b353673d792 · outbound

This paper cites Magus: machine learning and graph theory assisted universal structure searcher.National Science Review, 10(7):nwad128, 2023.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Magus: machine learning and graph theory assisted universal structure searcher.National Science Review, 10(7):nwad128, 2023

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:52.587945Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation a91ec9bb-c4c0-4fb3-a2dd-9f8a6e6510ec · outbound

This paper cites Resolving the data ambiguity for periodic crystals.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Resolving the data ambiguity for periodic crystals

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:52.578126Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 25e50c2b-3d02-40e4-8d99-53fc127690c2 · outbound

This paper cites an unresolved cited work.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Unresolved cited work

Reference 50

Resolution: unresolved; raw_fallback, observed 2026-08-07T10:52:52.568426Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2428890a-f86a-4dba-bd2f-fac7abba3a1b · outbound

This paper cites Convergence conditions for ascent methods.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Convergence conditions for ascent methods

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:52.559354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2cc348f0-de46-4598-8c15-c4744c100372 · outbound

This paper cites Crystal structure prediction from first principles.Nature materials, 7(12):937–946, 2008.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Crystal structure prediction from first principles.Nature materials, 7(12):937–946, 2008

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:52.549059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 58ddb62f-ebfa-475e-b9ca-481f8fd35531 · outbound

This paper cites Structure prediction of crystals, surfaces and nanoparticles.Philosophical Transactions of the Royal Society A, 378(2186), 2020.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Structure prediction of crystals, surfaces and nanoparticles.Philosophical Transactions of the Royal Society A, 378(2186), 2020

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:52.537437Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5639a5a2-be84-4900-8fd5-91ee59a2768d · outbound

This paper cites Improved adversarial training via learned optimizer.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Improved adversarial training via learned optimizer

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:52.527701Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation fd51152d-ff07-434a-9de3-d1c8bc9cf7e5 · outbound

This paper cites Exploration and optimization in crystal structure prediction: Combining basin hopping with quasi-random sampling.Journal of Chemical Theory and Computation, 17(3):1988–1999, 2021.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Exploration and optimization in crystal structure prediction: Combining basin hopping with quasi-random sampling.Journal of Chemical Theory and Computation, 17(3):1988–1999, 2021

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:52.517788Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3e0848b9-6b71-4909-8d4b-b20a6598c108 · outbound

This paper cites Reinforcement learning in crystal structure prediction.Digital Discovery, 2(6):1831–1840, 2023.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Reinforcement learning in crystal structure prediction.Digital Discovery, 2(6):1831–1840, 2023

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:52.508146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 8cd3e518-fc75-4723-918c-636299b3a20a · outbound

This paper cites Learning atoms for materials discovery.Proceedings of the National Academy of Sciences, 115(28):E6411– E6417, 2018.

MACS: Multi-Agent Reinforcement Learning for Optimization of Crystal Structures Learning atoms for materials discovery.Proceedings of the National Academy of Sciences, 115(28):E6411– E6417, 2018

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T10:52:52.498308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

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