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Correspondence between neuroevolution and gradient descent

Nature communications, 2021-11, Vol.12 (1), p.6317-6317, Article 6317

2021

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Learning to grow: Control of material self-assembly using evolutionary reinforcement learning

Physical review. E, 2020-05, Vol.101 (5), p.052604-052604, Article 052604

2020

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Learning stochastic dynamics and predicting emergent behavior using transformers

Nature communications, 2024-02, Vol.15 (1), p.1875-1875, Article 1875

2024

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Deep learning and density-functional theory

Physical review. A, 2019-08, Vol.100 (2), Article 022512

2019

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Finding the ground state of spin Hamiltonians with reinforcement learning

Nature machine intelligence, 2020-09, Vol.2 (9), p.509-517

2020

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Prebiotic Chemistry within a Simple Impacting Icy Mixture

The journal of physical chemistry. A, Molecules, spectroscopy, kinetics, environment, & general theory, 2013-06, Vol.117 (24), p.5124-5131

2013

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Structure and phase boundaries of compressed liquid hydrogen

Physical review letters, 2010-02, Vol.104 (6), p.065702-065702, Article 065702

2010

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Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models

Physical review. E, 2018-03, Vol.97 (3-1), p.032119-032119, Article 032119

2018

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Simultaneous Determination of Structures, Vibrations, and Frontier Orbital Energies from a Self-Consistent Range-Separated Hybrid Functional

The journal of physical chemistry letters, 2014-08, Vol.5 (15), p.2734-2741

2014

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Electronic level alignment at a metal-molecule interface from a short-range hybrid functional

The Journal of chemical physics, 2011-10, Vol.135 (16), p.164706-164706-6

2011

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Neuroevolutionary Learning of Particles and Protocols for Self-Assembly

Physical review letters, 2021-07, Vol.127 (1), p.1-018003, Article 018003

2021

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Twin neural network regression

Applied AI letters, 2022-12, Vol.3 (4), p.n/a

2022

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Automatic graph representation algorithm for heterogeneous catalysis

APL machine learning, 2023-09, Vol.1 (3), p.036103-036103-8

2023

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Optical lattice experiments at unobserved conditions with generative adversarial deep learning

Physical review research, 2021-09, Vol.3 (3), Article 033267

2021

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Interpretable discovery of semiconductors with machine learning

npj computational materials, 2023-06, Vol.9 (1), p.117-11, Article 117

2023

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Sampling algorithms for validation of supervised learning models for Ising-like systems

Journal of computational physics, 2017-12, Vol.350, p.871-890

2017

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Evolutionary reinforcement learning of dynamical large deviations

The Journal of chemical physics, 2020-07, Vol.153 (4), p.044113-044113

2020

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Common Physical Framework Explains Phase Behavior and Dynamics of Atomic, Molecular, and Polymeric Network Formers

Physical review. X, 2014-03, Vol.4 (1), p.011044, Article 011044

2014

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Inverse Design of a Graphene-Based Quantum Transducer via Neuroevolution

Journal of physical chemistry. C, 2020-12, Vol.124 (48), p.26117-26123

2020

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Deep learning and the Schrödinger equation

Physical review. A, 2017-10, Vol.96 (4), Article 042113

2017

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Dynamical Large Deviations of Two-Dimensional Kinetically Constrained Models Using a Neural-Network State Ansatz

Physical review letters, 2021-09, Vol.127 (12), p.120602-120602, Article 120602

2021

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Adversarial Generation of Mesoscale Surfaces from Small-Scale Chemical Motifs

Journal of physical chemistry. C, 2020-10, Vol.124 (42), p.23158-23163

2020

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Cellular automata can classify data by inducing trajectory phase coexistence

Physical review. E, 2023-07, Vol.108 (1), Article 014126

2023

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Energy Level Alignment at Hybridized Organic–Metal Interfaces: The Role of Many-Electron Effects

Journal of physical chemistry. C, 2017-06, Vol.121 (24), p.13125-13134

2017

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Machine Learning Diffusion Monte Carlo Energies

Journal of chemical theory and computation, 2022-12, Vol.18 (12), p.7695-7701

2022

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Correspondence between neuroevolution and gradient descent

Learning to grow: Control of material self-assembly using evolutionary reinforcement learning

Learning stochastic dynamics and predicting emergent behavior using transformers

Deep learning and density-functional theory

Finding the ground state of spin Hamiltonians with reinforcement learning

Prebiotic Chemistry within a Simple Impacting Icy Mixture

Structure and phase boundaries of compressed liquid hydrogen

Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models

Simultaneous Determination of Structures, Vibrations, and Frontier Orbital Energies from a Self-Consistent Range-Separated Hybrid Functional

Electronic level alignment at a metal-molecule interface from a short-range hybrid functional

Neuroevolutionary Learning of Particles and Protocols for Self-Assembly

Twin neural network regression

Automatic graph representation algorithm for heterogeneous catalysis

Optical lattice experiments at unobserved conditions with generative adversarial deep learning

Interpretable discovery of semiconductors with machine learning

Sampling algorithms for validation of supervised learning models for Ising-like systems

Evolutionary reinforcement learning of dynamical large deviations

Common Physical Framework Explains Phase Behavior and Dynamics of Atomic, Molecular, and Polymeric Network Formers

Inverse Design of a Graphene-Based Quantum Transducer via Neuroevolution

Deep learning and the Schrödinger equation

Dynamical Large Deviations of Two-Dimensional Kinetically Constrained Models Using a Neural-Network State Ansatz

Adversarial Generation of Mesoscale Surfaces from Small-Scale Chemical Motifs

Cellular automata can classify data by inducing trajectory phase coexistence

Energy Level Alignment at Hybridized Organic–Metal Interfaces: The Role of Many-Electron Effects

Machine Learning Diffusion Monte Carlo Energies

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