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1. Machine Learning Assisted Analysis of Electrochemical H ₂ O ₂ Production

   https://doi.org/10.1021/acsaem.3c00115  

   Leem, Juyoung; Vallez, Lauren; Gill, Thomas Mark; Zheng, Xiaolin (April 2023, ACS Applied Energy Materials)
2. Thermal Conductivity of MgSiO ₃ ‐H ₂ O System Determined by Machine Learning Potentials

   https://doi.org/10.1029/2023GL107245  

   Peng, Yihang; Deng, Jie (March 2024, Geophysical Research Letters)

   Abstract Thermal conductivity plays a pivotal role in understanding the dynamics and evolution of Earth's interior. The Earth's lower mantle is dominated by MgSiO₃polymorphs which may incorporate trace amounts of water. However, the thermal conductivity of MgSiO₃‐H₂O binary system remains poorly understood. Here, we calculate the thermal conductivity of water‐free and water‐bearing bridgmanite, post‐perovskite, and MgSiO₃melt, using a combination of Green‐Kubo method with molecular dynamics simulations based on a machine learning potential of ab initio quality. The thermal conductivities of water‐free bridgmanite and post‐perovskite overall agree well with previous theoretical and experimental studies. The presence of water mildly reduces the thermal conductivity of the host minerals, significantly weakens the temperature dependence of the thermal conductivity, and reduces the thermal anisotropy of post‐perovskite. Overall, water reduces the thermal conductivity difference between bridgmanite and post‐perovskite, and thus may attenuate lateral heterogeneities of the core‐mantle boundary heat flux. 

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3. Thermoelectric properties of TaVO ₅ and GdTaO ₄ : An experimental verification of machine learning prediction

   https://doi.org/10.1177/17436753231213060  

   Allen, Travis; Graser, Jake; Issa, Ramsey; Sparks, Taylor D (January 2024, Advances in Applied Ceramics: Structural, Functional and Bioceramics)

   Advancements in materials discovery tend to rely disproportionately on happenstance and luck rather than employing a systematic approach. Recently, advances in computational power have allowed researchers to build computer models to predict the material properties of any chemical formula. From energy minimization techniques to machine learning-based models, these algorithms have unique strengths and weaknesses. However, a computational model is only as good as its accuracy when compared to real-world measurements. In this work, we take two recommendations from a thermoelectric machine learning model, TaVO[Formula: see text] and GdTaO[Formula: see text], and measure their thermoelectric properties of Seebeck coefficient, thermal conductivity, and electrical conductivity. We see that the predictions are mixed; thermal conductivities are correctly predicted, while electrical conductivities and Seebeck coefficients are not. Furthermore, we explore TaVO[Formula: see text]’s unusually low thermal conductivity of 1.2 Wm[Formula: see text]K[Formula: see text], and we discover a possible new avenue of research of a low thermal conductivity oxide family. 

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4. Machine learning substitutional defect formation energies in ABO ₃ perovskites

   https://doi.org/10.1063/5.0015538  

   Sharma, Vinit; Kumar, Pankaj; Dev, Pratibha; Pilania, Ghanshyam (July 2020, Journal of Applied Physics)
5. A combined approach of atom probe tomography and unsupervised machine learning to understand phase transformation in (Al _x Ga _1−x ) ₂ O ₃

   https://doi.org/10.1063/5.0002049  

   Sarker, Jith; Broderick, Scott; Bhuiyan, A. F.; Feng, Zixuan; Zhao, Hongping; Mazumder, Baishakhi (April 2020, Applied Physics Letters)