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Journal Articles

Noise-robust training of artificial neural networks using chemical reaction networks

Sunghwa Kang, Jinsu Kim

APL Mach. Learn. 3, 036102 (2025)

https://doi.org/10.1063/5.0271766

Published: July 2025

View article titled, Noise-robust training of artificial neural networks using chemical reaction networks

Journal Articles

Multi-objective Bayesian optimization of carbon nanotube yield and diameter control at synthesis

R. Waelder, W. Kim, M. A. Pitt, J. I. Myung, B. Maruyama

APL Mach. Learn. 3, 026114 (2025)

https://doi.org/10.1063/5.0267704

Published: June 2025

View article titled, Multi-objective Bayesian optimization of carbon nanotube yield and diameter control at synthesis

Journal Articles

Mind the gap: Bridging the divide between AI aspirations and the reality of autonomous microscopy

Grace Guinan, Addison Salvador, Michelle A. Smeaton, Andrew Glaws, Hilary Egan, Brian C. Wyatt, Babak Anasori, Kevin R. Fiedler, Matthew J. Olszta, Steven R. Spurgeon

APL Mach. Learn. 3, 020903 (2025)

https://doi.org/10.1063/5.0267699

Published: June 2025

View article titled, Mind the gap: Bridging the divide between AI aspirations and the reality of autonomous microscopy

Journal Articles

Machine learning-based melting congruency prediction of binary compounds using density functional theory-calculated formation energy

Youngwoo Choi, Chris M. Wolverton, Seungbum Hong

APL Mach. Learn. 3, 026112 (2025)

https://doi.org/10.1063/5.0247514

Published: May 2025

View article titled, Machine learning-based melting congruency prediction of binary compounds using density functional theory-calculated formation energy

Journal Articles

The evolving role of programming and LLMs in the development of self-driving laboratories

John R. Kitchin

APL Mach. Learn. 3, 026111 (2025)

https://doi.org/10.1063/5.0266757

Published: April 2025

View article titled, The evolving role of programming and LLMs in the development of self-driving laboratories

Journal Articles

Scaling of hardware-compatible perturbative training algorithms

B. G. Oripov, A. Dienstfrey, A. N. McCaughan, S. M. Buckley

APL Mach. Learn. 3, 026107 (2025)

https://doi.org/10.1063/5.0258271

Published: April 2025

View article titled, Scaling of hardware-compatible perturbative training algorithms

Journal Articles

Memory technology enabling future computing systems

Paolo Fantini

APL Mach. Learn. 3, 020901 (2025)

https://doi.org/10.1063/5.0253063

Published: April 2025

View article titled, Memory technology enabling future computing systems

Journal Articles

CoNO: Complex neural operator for continous dynamical physical systems

Karn Tiwari, N. M. Anoop Krishnan, Prathosh A. P.

APL Mach. Learn. 3, 026101 (2025)

https://doi.org/10.1063/5.0254013

Published: April 2025

View article titled, CoNO: Complex neural operator for continous dynamical physical systems

Journal Articles

Multiscale simulation and machine learning facilitated design of two-dimensional nanomaterials-based tunnel field-effect transistors: A review

Chloe Isabella Tsang, Haihui Pu, Junhong Chen

APL Mach. Learn. 3, 016115 (2025)

https://doi.org/10.1063/5.0240004

Published: March 2025

View article titled, Multiscale simulation and machine learning facilitated design of two-dimensional nanomaterials-based tunnel field-effect transistors: A review

Journal Articles

Scalable learning of potentials to predict time-dependent Hartree–Fock dynamics

Harish S. Bhat, Prachi Gupta, Christine M. Isborn

APL Mach. Learn. 2, 046112 (2024)

https://doi.org/10.1063/5.0232683

Published: December 2024

View article titled, Scalable learning of potentials to predict time-dependent Hartree–Fock dynamics

Journal Articles

A high-throughput and data-driven computational framework for novel quantum materials

Srihari M. Kastuar, Christopher Rzepa, Srinivas Rangarajan, Chinedu E. Ekuma

APL Mach. Learn. 2, 046108 (2024)

https://doi.org/10.1063/5.0221823

Published: November 2024

View article titled, A high-throughput and data-driven computational framework for novel quantum materials

Journal Articles

Waveflow: Boundary-conditioned normalizing flows applied to fermionic wave functions

Luca Thiede, Chong Sun, Alán Aspuru-Guzik

APL Mach. Learn. 2, 046106 (2024)

https://doi.org/10.1063/5.0229620

Published: November 2024

View article titled, Waveflow: Boundary-conditioned normalizing flows applied to fermionic wave functions

Journal Articles

Machine-learning-derived thermal conductivity of two-dimensional TiS₂/MoS₂ van der Waals heterostructures

A. K. Nair, C. M. Da Silva, C. H. Amon

APL Mach. Learn. 2, 036115 (2024)

https://doi.org/10.1063/5.0205702

Published: September 2024

View article titled, Machine-learning-derived thermal conductivity of two-dimensional TiS2/MoS2 van der Waals heterostructures

Journal Articles

Ionic species representations for materials informatics

Anthony Onwuli, Keith T. Butler, Aron Walsh

APL Mach. Learn. 2, 036112 (2024)

https://doi.org/10.1063/5.0227009

Published: September 2024

View article titled, Ionic species representations for materials informatics

Journal Articles

Brain-inspired learning in artificial neural networks: A review

Samuel Schmidgall, Rojin Ziaei, Jascha Achterberg, Louis Kirsch, S. Pardis Hajiseyedrazi, Jason Eshraghian

APL Mach. Learn. 2, 021501 (2024)

https://doi.org/10.1063/5.0186054

Published: May 2024

View article titled, Brain-inspired learning in artificial neural networks: A review

Journal Articles

Biological plausible algorithm for seizure detection: Toward AI-enabled electroceuticals at the edge

Luis Fernando Herbozo Contreras, Zhaojing Huang, Leping Yu, Armin Nikpour, Omid Kavehei

APL Mach. Learn. 2, 026114 (2024)

https://doi.org/10.1063/5.0192875

Published: April 2024

View article titled, Biological plausible algorithm for seizure detection: Toward AI-enabled electroceuticals at the edge

Journal Articles

Exploring the optimal design space of transparent perovskite solar cells for four-terminal tandem applications through Pareto front optimization

Hu Quee Tan, Xinhai Zhao, Akhil Ambardekar, Erik Birgersson, Hansong Xue

APL Mach. Learn. 2, 026111 (2024)

https://doi.org/10.1063/5.0187208

Published: April 2024

View article titled, Exploring the optimal design space of transparent perovskite solar cells for four-terminal tandem applications through Pareto front optimization

Includes: Supplementary data

Journal Articles

Study of the adsorption sites of high entropy alloys for CO₂ reduction using graph convolutional network

H. Oliaei, N. R. Aluru

APL Mach. Learn. 2, 026103 (2024)

https://doi.org/10.1063/5.0198043

Published: April 2024

View article titled, Study of the adsorption sites of high entropy alloys for CO2 reduction using graph convolutional network

Includes: Supplementary data

Journal Articles

Accelerating defect predictions in semiconductors using graph neural networks

Md Habibur Rahman, Prince Gollapalli, Panayotis Manganaris, Satyesh Kumar Yadav, Ghanshyam Pilania, Brian DeCost, Kamal Choudhary, Arun Mannodi-Kanakkithodi

APL Mach. Learn. 2, 016122 (2024)

https://doi.org/10.1063/5.0176333

Published: March 2024

View article titled, Accelerating defect predictions in semiconductors using graph neural networks

Includes: Supplementary data

Journal Articles

Training self-learning circuits for power-efficient solutions

Menachem Stern, Sam Dillavou, Dinesh Jayaraman, Douglas J. Durian, Andrea J. Liu

APL Mach. Learn. 2, 016114 (2024)

https://doi.org/10.1063/5.0181382

Published: February 2024

View article titled, Training self-learning circuits for power-efficient solutions

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Noise-robust training of artificial neural networks using chemical reaction networks

Multi-objective Bayesian optimization of carbon nanotube yield and diameter control at synthesis

Mind the gap: Bridging the divide between AI aspirations and the reality of autonomous microscopy

Machine learning-based melting congruency prediction of binary compounds using density functional theory-calculated formation energy

The evolving role of programming and LLMs in the development of self-driving laboratories

Scaling of hardware-compatible perturbative training algorithms

Memory technology enabling future computing systems

CoNO: Complex neural operator for continous dynamical physical systems

Multiscale simulation and machine learning facilitated design of two-dimensional nanomaterials-based tunnel field-effect transistors: A review

Scalable learning of potentials to predict time-dependent Hartree–Fock dynamics

A high-throughput and data-driven computational framework for novel quantum materials

Waveflow: Boundary-conditioned normalizing flows applied to fermionic wave functions

Machine-learning-derived thermal conductivity of two-dimensional TiS₂/MoS₂ van der Waals heterostructures

Ionic species representations for materials informatics

Brain-inspired learning in artificial neural networks: A review

Biological plausible algorithm for seizure detection: Toward AI-enabled electroceuticals at the edge

Exploring the optimal design space of transparent perovskite solar cells for four-terminal tandem applications through Pareto front optimization

Study of the adsorption sites of high entropy alloys for CO₂ reduction using graph convolutional network

Accelerating defect predictions in semiconductors using graph neural networks

Training self-learning circuits for power-efficient solutions

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