Research Group Patrick Rinke

The investigation of magnetic energy landscapes and the search for ground states of magnetic materials using ab initio methods like density functional theory (DFT) is a challenging task. Complex interactions, such as superexchange and spin-orbit coupling, make these calculations computationally expensive and often lead to non-trivial energy landscapes. Consequently, a comprehensive and systematic investigation of large magnetic configuration spaces is often impractical. We approach this problem by utilizing Bayesian Optimization, an active machine learning scheme that has proven to be efficient in modeling unknown functions and finding global minima. Using this approach we can obtain the magnetic contribution to the energy as a function of one or more spin canting angles with relatively small numbers of DFT calculations. To assess the capabilities and the efficiency of the approach we investigate the noncollinear magnetic energy landscapes of selected materials containing 3d, 5d and 5f magnetic ions: Ba3MnNb2O9, LaMn2Si2, β-MnO2, Sr2IrO4, UO2 and Ba2NaOsO6. By comparing our results to previous ab initio studies that followed more conventional approaches, we observe significant improvements in efficiency.

Lead-based perovskite solar cells have reached high efficiencies, but toxicity and lack of stability hinder their wide-scale adoption. These issues have been partially addressed through compositional engineering of perovskite materials, but the vast complexity of the perovskite materials space poses a significant obstacle to exploration. We previously demonstrated how machine learning (ML) can accelerate property predictions for the CsPb⁢(Cl/Br)3 perovskite alloy. However, the substantial computational demand of density functional theory (DFT) calculations required for model training prevents applications to more complex materials. Here, we introduce a data-efficient scheme to facilitate model training, validated initially on CsPb⁢(Cl/Br)3 data and extended to the ternary alloy CsSn⁢(Cl/Br/I)3. Our approach employs clustering to construct a compact yet diverse initial dataset of atomic structures. We then apply a two-stage active learning approach to first improve the reliability of the ML-based structure relaxations and then refine accuracy near equilibrium structures. Tests for CsPb⁢(Cl/Br)3 demonstrate that our scheme reduces the number of required DFT calculations during the different parts of our proposed model training method by up to 20% and 50%. The fitted model for CsSn⁢(Cl/Br/I)3 is robust and highly accurate, evidenced by the convergence of all ML-based structure relaxations in our tests and an average relaxation error of only 0.5 meV/atom.

Quaternary mixed-metal M(II)2M(III)Ch2X3 chalcohalides are an emerging material class for photovoltaic absorbers that combines the beneficial optoelectronic properties of lead-based halide perovskites with the stability of metal chalcogenides. Inspired by the recent discovery of lead-free mixed-metal chalcohalides materials, we utilized a combination of density functional theory and machine learning to determine compositional trends and chemical design rules in the lead-free and lead-based materials spaces. We explored a total of 54 M(II)2M(III)Ch2X3 materials with M(II) = Sn, Pb, M(III) = In, Sb, Bi, Ch = S, Se, Te, and X = Cl, Br, I per phase (Cmcm, Cmc21 , and P21/c). The P21/c phase is the equilibrium phase at low temperatures, followed by Cmc21 and Cmcm. The fundamental band gaps in Cmcm and Cmc21 are smaller than those in P21/c, but direct band gaps are more common in Cmcm and Cmc21. The effective electron masses in P21/c are significantly larger compared to Cmcm and Cmc21, while the effective hole masses are nearly the same across all three phases. Using random forest regression, we found that the two electron acceptor sites (Ch and X) are crucial in shaping the properties of mixed-metal chalcohalide compounds. Furthermore, the electron donor sites (M(II) and M(III)) can be used to finetune the material properties to desired applications. These design rules enable precise tailoring of mixed-metal chalcohalide compounds for a variety of applications.

Chemical ionization mass spectrometry (CIMS) is widely used in atmospheric chemistry studies. However, due to the complex interactions between reagent ions and target compounds, chemical understanding remains limited and compound identification difficult. In this study, we apply machine learning to a reference dataset of pesticides in two standard solutions to build a model that can provide insights from CIMS analyses in atmospheric science. The CIMS measurements were performed with an Orbitrap mass spectrometer coupled to a thermal desorption multi-scheme chemical ionization inlet unit (TD-MION-MS) with both negative and positive ionization modes utilizing Br−, , H3O+ and (CH3)2COH+ (AceH+) as reagent ions. We then trained two machine learning methods on these data: (1) random forest (RF) for classifying if a pesticide can be detected with CIMS and (2) kernel ridge regression (KRR) for predicting the expected CIMS signals. We compared their performance on five different representations of the molecular structure: the topological fingerprint (TopFP), the molecular access system keys (MACCS), a custom descriptor based on standard molecular properties (RDKitPROP), the Coulomb matrix (CM) and the many-body tensor representation (MBTR). The results indicate that MACCS outperforms the other descriptors. Our best classification model reaches a prediction accuracy of 0.85 ± 0.02 and a receiver operating characteristic curve area of 0.91 ± 0.01. Our best regression model reaches an accuracy of 0.44 ± 0.03 logarithmic units of the signal intensity. Subsequent feature importance analysis of the classifiers reveals that the most important sub-structures are NH and OH for the negative ionization schemes and nitrogen-containing groups for the positive ionization schemes.

Active learning (AL) has shown promise to be a particularly data-efficient machine learning approach. Yet, its performance depends on the application, and it is not clear when AL practitioners can expect computational savings. Here, we carry out a systematic AL performance assessment for three diverse molecular datasets and two common scientific tasks: compiling compact, informative datasets and targeted molecular searches. We implemented AL with Gaussian processes (GP) and used the many-body tensor as molecular representation. For the first task, we tested different data acquisition strategies, batch sizes, and GP noise settings. AL was insensitive to the acquisition batch size, and we observed the best AL performance for the acquisition strategy that combines uncertainty reduction with clustering to promote diversity. However, for optimal GP noise settings, AL did not outperform the randomized selection of data points. Conversely, for targeted searches, AL outperformed random sampling and achieved data savings of up to 64%. Our analysis provides insight into this task-specific performance difference in terms of target distributions and data collection strategies. We established that the performance of AL depends on the relative distribution of the target molecules in comparison to the total dataset distribution, with the largest computational savings achieved when their overlap is minimal.

Transforming CO2 into methanol represents a crucial step towards closing the carbon cycle, with thermoreduction technology nearing industrial application. However, obtaining high methanol yields and ensuring the stability of heterocatalysts remain significant challenges. Herein, we present a sophisticated computational framework to accelerate the discovery of novel thermal heterogeneous catalysts, using machine-learned force fields. We propose a new catalytic descriptor, termed adsorption energy distribution, that aggregates the binding energies for different catalyst facets, binding sites, and adsorbates. The descriptor is versatile and can easily be adjusted to a specific reaction through careful choice of the key-step reactants and reaction intermediates. By applying unsupervised machine learning and statistical analysis to a dataset comprising nearly 160 metallic alloys, we offer a powerful tool for catalyst discovery. Finally, we propose new promising candidate materials such as ZnRh and ZnPt3, which to our knowledge, have not yet been tested, and discuss their possible advantage in terms of stability.