Judul: Application of Machine Learning in Predicting the Properties of Sodium-ion Batteries
Abstrak: Penelitian ini berfokus pada aplikasi machine learning dalam meramalkan properti baterai berbasis ion natrium. Menggunakan data dari situs Material Project sebanyak 309 data. Penelitian ini mengkaji prediksi terhadap properti average voltage, gravimetric capacity, volumetric capacity, gravimetric energy, dan volumetric energy. Fitur-fitur yang digunakan meliputi formula, spacegroup, Nsites, E above hull, formation energy, volume, density, dan bandgap. Untuk mendapatkan fitur struktur, unsur-unsur dalam fitur formula diurai menggunakan library Chemparse. Tiga kombinasi model Artificial Neural Network diterapkan: kombinasi pertama menggunakan fitur propert material, kombinasi kedua menggunakan fitur seluruh fitur properti dan unsur yang diurai, sementara kombinasi ketiga hanya menggunakan fitur unsur yang diurai saja. Evaluasi performa model dilakukan menggunakan metrik R2 score, Mean Squared Error, dan Root Mean Squared Error. Hasil penelitian menunjukkan bahwa kombinasi kedua yang menggunakan seluruh fitur mendapatkan performal yang paling baik. Selain itu, didapatkan temuan bahwa struktur material berupa unsur yang diurai mampu menguatkan performa model., This research focuses on the application of machine learning to predict the properties of sodium-ion-based batteries. Using data from the Material Project, comprising 309 entries. This study examines the prediction of properties such as average voltage, gravimetric capacity, volumetric capacity, gravimetric energy, and volumetric energy. The features used include formula, spacegroup, Nsites, E above hull, formation energy, volume, density, and bandgap. To obtain structural features, the elements in the formula feature were parsed using the Chemparse library. Three combinations of Artificial Neural Network models were applied: the first combination used material property features, the second combination used all property features and parsed elements, while the third combination used only the parsed elements. The performance of the models was evaluated using R2 score, Mean Squared Error, and Root Mean Squared Error metrics. The results indicate that the second combination, which utilized all features, achieved the best performance. Additionally, it was found that the structural features in the form of parsed elements were able to enhance the model's performance.
Keyword: Artificial Neural Network, Machine learning, Baterai Ion Natrium

Judul: Effects of NaMnN2 Material Phase Differences on the Properties and Performance of Sodium Ion Battery Cathodes
Abstrak: Kemajuan teknologi menyebabkan kebutuhan akan baterai isi ulang meningkat. Namun baterai ion litium sebagai baterai yang umum digunakan mengalami kenaikan harga yang tinggi karena kelangkaan litium di alam. Baterai ion natrium sebagai baterai alternatif dari baterai ion litium belum menemukan kandidat material katoda yang tepat. NaMnN2 dari keluarga Layered N-based TMDs-like diharapkan dapat menjadi material katoda baterai ion natrium yang murah dan baik. Density Functional Theory digunakan dalam penelitian ini untuk membuktikan pengaruh fasa terhadap sifat elektronik dan performansi material katoda NaMnN2, sehingga dapat diperoleh fasa terbaik bagi keluarga Layered N-based TMDs-like sebagai katoda baterai ion natrium. Hasil penelitian menunjukan bahwa secara teoretis NaMnN2 berpotensi sebagai katoda baterai ion natrium yang memiliki performansi baik. Selain itu, NaMnN2 fasa 2H memiliki keunggulan pada kestabilan siklus, memiliki konduktivitas yang sangat baik, serta mengalami difusivitas ion Na yang baik dengan energi penghalang sebesar 0,41 eV. Material NaMnN2 fasa 1T unggul dalam tegangan yang lebih tinggi, mencapai 3,93 V, serta memiliki rapat energi yang besarnya mencapai 995,22 Wh/kg., Technological advancements have caused the need for rechargeable batteries to increase. However, lithium ion battery as a commonly used battery has experienced a high price increase due to the scarcity of lithium in nature. Sodium ion battery as an alternative battery to lithium ion battery has not found the right cathode material candidate. NaMnN2 from the Layered N-based TMDs-like family is expected to be a cheap and good sodium ion battery cathode material. Density Functional Theory is used in this study to prove the effect of phase on the electronic properties and performance of NaMnN2 cathode material, so that the best phase can be obtained for the Layered N-based TMDs-like family as a sodium ion battery cathode. The results showed that theoretically NaMnN2 has the potential as a cathode of sodium ion battery which has good performance. In addition, 2H-phase of NaMnN2 has advantages in cycle stability, has excellent conductivity, and experiences good sodium ion diffusivity with a barrier energy of 0,41 eV. The 1T phase NaMnN2 material excels in higher voltages, reaching 3,93 V, and has an energy density of up to 995,22 Wh/kg.
Keyword: cathode, DFT, phase, Quantum Espresso, SIB

Judul: The Control and Eradication of Brucellosis in Cattle from the Standpoint of Human Health
Abstrak: Brucella abotus occurs boyh in man and cattle. The oragnism may trhrive wherever cattle re raised by man for breeding purpose. As such, spread to humans can easily be lessened if the diases is properly cotrolled an/or eradicated in cattle.
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