Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships by Janet, Jon Paul, Kulik, Heather J.

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Predicting electronic structure properties of transition metal complexes with neural networks by Janet, Jon Paul, Kulik, Heather Janine

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Communication: Recovering the flat-plane condition in electronic structure theory at semi-local DFT cost by Bajaj, Akash, Janet, Jon Paul, Kulik, Heather Janine

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Enumeration of de novo inorganic complexes for chemical discovery and machine learning by Gugler, Stefan, Janet, Jon Paul, Kulik, Heather J.

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Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network by Janet, Jon Paul, Chan, Lydia C., Kulik, Heather Janine

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Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization by Janet, Jon Paul, Ramesh, Sahasrajit, Duan, Chenru, Kulik, Heather J.

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Density functional theory for modelling large molecular adsorbate–surface interactions: a mini-review and worked example by Janet, Jon Paul, Zhao, Qing, Ioannidis, Efthymios Ioannis, Kulik, Heather Janine

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Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models by Duan, Chenru, Janet, Jon Paul, Liu, Fang, Nandy, Aditya, Kulik, Heather Janine

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A quantitative uncertainty metric controls error in neural network-driven chemical discovery by Janet, Jon Paul, Duan, Chenru, Yang, Tzuhsiung, Nandy, Aditya, Kulik, Heather J

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A quantitative uncertainty metric controls error in neural network-driven chemical discovery by Janet, Jon Paul, Duan, Chenru, Yang, Tzuhsiung, Nandy, Aditya, Kulik, Heather J.

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Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry by Nandy, Aditya, Duan, Chenru, Janet, Jon Paul, Gugler, Stefan O, Kulik, Heather Janine

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Machine Learning in Chemistry / by Janet, Jon Paul, author 652506, Kulik, Heather J., author 652507, American Chemical Society (Online service)

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Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal–Oxo Intermediate Formation by Nandy, Aditya, Zhu, Jiazhou, Janet, Jon Paul, Duan, Chenru, Getman, Rachel B., Kulik, Heather Janine

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Representations and strategies for transferable machine learning improve model performance in chemical discovery by Harper, Daniel R, Nandy, Aditya, Arunachalam, Naveen, Duan, Chenru, Janet, Jon Paul, Kulik, Heather J

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Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design by Janet, Jon Paul, Gani, Terry Zhi Hao, Steeves, Adam H., Ioannidis, Efthymios Ioannis, Kulik, Heather Janine

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DesDesigning in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry by Janet, Jon Paul, Liu, Fang, Nandy, Aditya, Duan, Chenru, Yang, Tzuhsiung, Lin, Sean, Kulik, Heather Janine

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Seeing Is Believing: Experimental Spin States from Machine Learning Model Structure Predictions by Taylor, Michael D., Yang, Tzuhsiung, Lin, Sean, Nandy, Aditya, Janet, Jon Paul, Duan, Chenru, Kulik, Heather Janine

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Understanding the diversity of the metal-organic framework ecosystem by Moosavi, Seyed Mohamad, Nandy, Aditya, Jablonka, Kevin Maik, Ongari, Daniele, Janet, Jon Paul, Boyd, Peter G, Lee, Yongjin, Smit, Berend, Kulik, Heather J

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Ligand additivity relationships enable efficient exploration of transition metal chemical space by Arunachalam, Naveen, Gugler, Stefan, Taylor, Michael G, Duan, Chenru, Nandy, Aditya, Janet, Jon Paul, Meyer, Ralf, Oldenstaedt, Jonas, Chu, Daniel BK, Kulik, Heather J

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