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Register a new userMolassembler: Molecular graph construction, modification and conformer generation for inorganic and organic molecules
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We present the graph-based molecule software Molassembler for building organic and inorganic molecules. Molassembler provides algorithms for the construction of molecules built from any set of elements from the periodic table. In particular, poly-nuclear transition metal complexes and clusters can be considered. Structural information is encoded as a graph. Stereocenter configurations are interpretable from Cartesian coordinates into an abstract index of permutation for an extensible set of polyhedral shapes. Substituents are distinguished through a ranking algorithm. Graph and stereocenter representations are freely modifiable and chiral state is propagated where possible through incurred ranking changes. Conformers are generated with full stereoisomer control by four spatial dimension Distance Geometry with a refinement error function including dihedral terms. Molecules are comparable by an extended graph isomorphism and their representation is canonicalizeable. Molassembler is written in C++ and provides Python bindings.
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Density functional theory (DFT) is one of the main methods in Quantum Chemistry that offers an attractive trade off between the cost and accuracy of quantum chemical computations. The electron density plays a key role in DFT. In this work, we explore whether machine learning - more specifically, deep neural networks (DNNs) - can be trained to predict electron densities faster than DFT. First, we choose a practically efficient combination of a DFT functional and a basis set (PBE0/pcS-3) and use it to generate a database of DFT solutions for more than 133,000 organic molecules from a previously published database QM9. Next, we train a DNN to predict electron densities and energies of such molecules. The only input to the DNN is an approximate electron density computed with a cheap quantum chemical method in a small basis set (HF/cc-VDZ). We demonstrate that the DNN successfully learns differences in the electron densities arising both from electron correlation and small basis set artifacts in the HF computations. All qualitative features in density differences, including local minima on lone pairs, local maxima on nuclei, toroidal shapes around C-H and C-C bonds, complex shapes around aromatic and cyclopropane rings and CN group, etc. are captured by the DNN. Accuracy of energy predictions by the DNN is ~ 1 kcal/mol, on par with other models reported in the literature, while those models do not predict the electron density. Computations with the DNN, including HF computations, take much less time that DFT computations (by a factor of ~20-30 for most QM9 molecules in the current version, and it is clear how it could be further improved).
We study the formation of C$_{18}$H and C$_{18}$H$_2$ by irradiating a cyclo[$18$]carbon molecule with atomic and molecular hydrogen at impact energy, $E$, in the range of 0.5-25 eV. We utilize the density-functional tight-binding method to perform molecular dynamics simulations to emulate the interaction of a carbon ring when colliding with atomic or molecular hydrogen. From our results, the formation of the C$_{18}$H molecules is likely to occur upon irradiating by H atoms at $E < 10$ eV and by H$_2$ molecules at $2 < E < 15$ eV center of mass energy. Formation of C$_{18}$H$_2$ molecules is only observed at around $E = 2$ eV. Our results show that the absorption of hydrogen is more prone in atomic than in molecular hydrogen atmosphere. Thus, we find that the probability of physio-absorption reaches up to 80 % for atomic projectiles with $E < 5$ eV but only up to 10 % for the molecular ones. Our analysis shows that the deformation of the carbon ring due to the hydrogen bonding produces transition from $sp$ to $sp^2$ hybridization. The angle between the carbon atoms at the locations near to the H bond in the resulting ring is not 120$^o$ but instead 110$^o$ degrees. No molecular fragmentation of the C$_{18}$ ring is observed.
Aqueous zinc-air batteries (ZABs) are a low-cost, safe, and sustainable technology for stationary energy storage. ZABs with pH-buffered near-neutral electrolytes have the potential for longer lifetime compared to traditional alkaline ZABs due to the slower absorption of carbonates at non-alkaline pH values. However, existing near-neutral electrolytes often contain halide salts, which are corrosive and threaten the precipitation of ZnO as the dominant discharge product. This paper presents a method for designing halide-free aqueous ZAB electrolytes using thermodynamic descriptors to computationally screen components. The dynamic performance of a ZAB with one possible halide-free aqueous electrolyte based on organic salts is simulated using an advanced method of continuum modeling, and the results are validated by experiments. XRD, SEM, and EDS measurements of Zn electrodes show that ZnO is the dominant discharge product, and operando pH measurements confirm the stability of the electrolyte pH during cell cycling. Long-term full cell cycling tests are performed, and RRDE measurements elucidate the mechanism of ORR and OER. Our analysis shows that aqueous electrolytes containing organic salts could be a promising field of research for zinc-based batteries, due to their Zn$^{2+}$ chelating and pH buffering properties. We discuss the remaining challenges including the electrochemical stability of the electrolyte components.
The problem of accelerating drug discovery relies heavily on automatic tools to optimize precursor molecules to afford them with better biochemical properties. Our work in this paper substantially extends prior state-of-the-art on graph-to-graph translation methods for molecular optimization. In particular, we realize coherent multi-resolution representations by interweaving the encoding of substructure components with the atom-level encoding of the original molecular graph. Moreover, our graph decoder is fully autoregressive, and interleaves each step of adding a new substructure with the process of resolving its attachment to the emerging molecule. We evaluate our model on multiple molecular optimization tasks and show that our model significantly outperforms previous state-of-the-art baselines.
An accurate force field is the key to the success of all molecular mechanics simulations on organic polymers and biomolecules. Accuracy beyond density functional theory is often needed to describe the intermolecular interactions, while most correlated wavefunction (CW) methods are prohibitively expensive for large molecules. Therefore, it posts a great challenge to develop an extendible ab initio force field for large flexible organic molecules at CW level of accuracy. In this work, we face this challenge by combining the physics-driven nonbonding potential with a data-driven subgraph neural network bonding model (named sGNN). Tests on polyethylene glycol polymer chains show that our strategy is highly accurate and robust for molecules of different sizes. Therefore, we can develop the force field from small molecular fragments (with sizes easily accessible to CW methods) and safely transfer it to large polymers, thus opening a new path to the next-generation organic force fields.
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