Gas-phase lithium cation affinity of glycine

PAL 2.0: a physics-driven bayesian optimization framework for material discovery

The lack of efficient discovery tools for advanced functional materials remains a major bottleneck to enabling advances in the next-generation energy, health, and sustainability technologies. One main factor contributing to this inefficiency is the large combinatorial space of materials (with respect to material compositions and processing conditions) that is typically redolent of such materials-centric applications. Searches of this large combinatorial space are often influenced by expert knowledge and clustered close to material configurations that are known to perform well, thus ignoring potentially high-performing candidates in unanticipated regions of the composition-space or processing protocol. Moreover, experimental characterization or first principles quantum mechanical calculations of all possible material candidates can be prohibitively expensive, making exhaustive approaches to determine the best candidates infeasible. As a result, there remains a need for the development of computational algorithms that can efficiently search a large parameter space for a given material application. Here, we introduce PAL 2.0, a method that combines a physics-based surrogate model with Bayesian optimization. The key contributing factor of our proposed framework is the ability to create a physics-based hypothesis using XGBoost and Neural Networks. This hypothesis provides a physics-based "prior" (or initial beliefs) to a Gaussian process model, which is then used to perform a search of the material design space. In this paper, we demonstrate the usefulness of our approach on three material test cases: (1) discovery of metal halide perovskites with desired photovoltaic properties, (2) design of metal halide perovskite-solvent pairs that produce the best solution-processed films and (3) design of organic thermoelectric semiconductors. Our results indicate that the novel PAL 2.0 approach outperforms other state-of-the-art methods in its efficiency to search the material design space for the optimal candidate. We also demonstrate the physics-based surrogate models constructed in PAL 2.0 have lower prediction errors for material compositions not seen by the model. To the best of our knowledge, there is no competing algorithm capable of this useful combination for materials discovery, especially those for which data are scarce.

Energetics and mechanisms for decomposition of cationized amino acids and peptides explored using guided ion beam tandem mass spectrometry

Fragmentation studies of cationized amino acids and small peptides as studied using guided ion beam tandem mass spectrometry (GIBMS) are reviewed. After a brief examination of the key attributes of the GIBMS approach, results for a variety of systems are examined, compared, and contrasted. Cationization of amino acids, diglycine, and triglycine with alkali cations generally leads to dissociations in which the intact biomolecule is lost. Exceptions include most lithiated species as well as a few examples for sodiated and one example for potassiated species. Like the lithiated species, cationization by protons leads to numerous dissociation channels. Results for protonated glycine, cysteine, asparagine, diglycine, and a series of tripeptides are reviewed, along with the thermodynamic consequences that can be gleaned. Finally, the important physiological process of the deamidation of asparagine (Asn) residues is explored by the comparison of five dipeptides in which the C-terminal partner (AsnXxx) is altered. The GIBMS thermochemistry is shown to correlate well with kinetic results from solution phase studies.

Sodium Binding Interactions with Aliphatic Amino Acids: A Guided Ion Beam and Computational Study

Metal binding affinities play a vital role in medicinal, biological, and industrial applications. In particular, metal cation-amino acid (AA) interactions contribute to protein stability such that analyzing analogous prototypical interactions is important. Here, we present a full description of the interactions of sodium cations (Na⁺) and six aliphatic amino acids (AA), where AA = glycine (Gly), alanine (Ala), homoalanine (hAla), valine (Val), leucine (Leu), and isoleucine (Ile). Experimentally, these interactions are evaluated using threshold collision-induced dissociation carried out in a guided ion beam tandem mass spectrometer, allowing for the determination of the kinetic-energy-dependent behavior of Na⁺-AA dissociation. Analysis of these dissociation cross sections, after accounting for multiple ion-molecule collisions, internal energy of reactant ions, and unimolecular decay rates, allows the determination of absolute Na⁺-AA bond dissociation energies (BDEs) in kJ/mol of Gly (164.0), Ala (166.9), hAla (167.9), Val (172.7), Leu (173.7), and Ile (174.6). These are favorably compared to quantum chemical calculations conducted at the B3LYP, B3P86, MP2(full), B3LYP-GD3BJ, and M06-2X levels of theory. Our combination of structural and energetic analyses provides information regarding the specific factors responsible for Na⁺ interactions with amino acids. Specifically, we find that the BDEs increase linearly with increasing polarizability of the amino acid.

Cationic Noncovalent Interactions: Energetics and Periodic Trends

In this review, noncovalent interactions of ions with neutral molecules are discussed. After defining the scope of the article, which excludes anionic and most protonated systems, methods associated with measuring thermodynamic information for such systems are briefly recounted. An extensive set of tables detailing available thermodynamic information for the noncovalent interactions of metal cations with a host of ligands is provided. Ligands include small molecules (H2, NH3, CO, CS, H2O, CH3CN, and others), organic ligands (O- and N-donors, crown ethers and related molecules, MALDI matrix molecules), π-ligands (alkenes, alkynes, benzene, and substituted benzenes), miscellaneous inorganic ligands, and biological systems (amino acids, peptides, sugars, nucleobases, nucleosides, and nucleotides). Hydration of metalated biological systems is also included along with selected proton-based systems: 18-crown-6 polyether with protonated peptides and base-pairing energies of nucleobases. In all cases, the literature thermochemistry is evaluated and, in many cases, reanchored or adjusted to 0 K bond dissociation energies. Trends in these values are discussed and related to a variety of simple molecular concepts.