Recent advances in single-cell sequencing technology have revolutionized our ability to acquire whole transcriptome data. However, uncovering the underlying transcriptional drivers and nonequilibrium driving forces of cell function directly from these data remains challenging. We address this by learning cell state vector fields from discrete single-cell RNA velocity to quantify the single-cell global nonequilibrium driving forces as landscape and flux. From single-cell data, we quantified the Waddington landscape, showing that optimal paths for differentiation and reprogramming deviate from the naively expected landscape gradient paths and may not pass through landscape saddles at finite fluctuations, challenging conventional transition state estimation of kinetic rate for cell fate decisions due to the presence of the flux. A key insight from our study is that stem/progenitor cells necessitate greater energy dissipation for rapid cell cycles and self-renewal, maintaining pluripotency. We predict optimal developmental pathways and elucidate the nucleation mechanism of cell fate decisions, with transition states as nucleation sites and pioneer genes as nucleation seeds. The concept of loop flux quantifies the contributions of each cycle flux to cell state transitions, facilitating the understanding of cell dynamics and thermodynamic cost, and providing insights into optimizing biological functions. We also infer cell-cell interactions and cell-type-specific gene regulatory networks, encompassing feedback mechanisms and interaction intensities, predicting genetic perturbation effects on cell fate decisions from single-cell omics data. Essentially, our methodology validates the landscape and flux theory, along with its associated quantifications, offering a framework for exploring the physical principles underlying cellular differentiation and reprogramming and broader biological processes through high-throughput single-cell sequencing experiments.

Non-covalent chalcogen bond (ChB) interactions have found utility in many fields, including catalysis, organic semiconductors, and crystal engineering. In this study, the kinetic effects of ChB interactions of oxygen and sulfur were experimentally measured using a series of molecular rotors. The rotors were designed to form ChB interactions in the bond rotation transition states. This enabled their kinetic influences to be assessed by monitoring changes in rotational barriers. Despite forming weaker ChB interactions, the smaller chalcogens were able to stabilize transition states and had measurable kinetic effects. Sulfur stabilized the bond rotation transition state by as much as -7.2 kcal/mol without electron-withdrawing groups. The key was to design a system where the sulfur 𝜎-hole was aligned with the lone pairs of the chalcogen bond acceptor. Oxygen rotors also could form transition state stabilizing ChB interactions but required electron-withdrawing groups. For both oxygen and sulfur ChB interactions, a strong correlation was observed between transition state stabilizing abilities and electrostatic potential (ESP) of the chalcogen, providing a useful predictive parameter for the rational design of future ChB systems.

Here, TS-tools is presented, a Python package facilitating the automated localization of transition states (TS) based on a textual reaction SMILES input. TS searches can either be performed at xTB or DFT level of theory, with the former yielding guesses at marginal computational cost, and the latter directly yielding accurate structures at greater expense. On a benchmarking dataset of mono- and bimolecular reactions, TS-tools reaches an excellent success rate of 95% already at xTB level of theory. For tri- and multimolecular reaction pathways - which are typically not benchmarked when developing new automated TS search approaches, yet are relevant for various types of reactivity, cf. solvent- and autocatalysis and enzymatic reactivity - TS-tools retains its ability to identify TS geometries, though a DFT treatment becomes essential in many cases. Throughout the presented applications, a particular emphasis is placed on solvation-induced mechanistic changes, another issue that received limited attention in the automated TS search literature so far.

In this work, the Crystal  code, developed previously by the authors to find \"holes\" as well as legitimate transition states in existing potential energy surface (PES) functions [JPC Lett. 11, 6468 (2020)], is retooled to perform on-the-fly \"direct dynamics\"-type PES explorations, as well as automatic construction of new PES functions. In all of these contexts, the chief advantage of Crystal  over other methods is its ability to globally map the PES, thereby determining the most relevant regions of configuration space quickly and reliably-even when the dimensionality is rather large. Here, Crystal  is used to generate a uniformly spaced grid of density functional theory (DFT) or ab initio points, truncated over the relevant regions, which can then be used to either: (a) hone in precisely on PES features such as minima and transition states, or; (b) create a new PES function automatically, via interpolation. Proof of concept is demonstrated via application to three molecular systems: water (H 2 O), (reduced-dimensional) methane (CH 4 ), and methylene imine (CH 2 NH).

Resolving the reaction networks associated with biomass pyrolysis is central to understanding product selectivity and aiding catalyst design to produce more valuable products. However, even the pyrolysis network of relatively simple [Formula: see text]-D-glucose remains unresolved due to its significant complexity in terms of the depth of the network and the number of major products. Here, a transition-state-guided reaction exploration has been performed that provides complete pathways to most significant experimental pyrolysis products of [Formula: see text]-D-glucose. The resulting reaction network involves over 31,000 reactions and transition states computed at the semiempirical quantum chemistry level and approximately 7,000 kinetically relevant reactions and transition states characterized with density function theory, comprising the largest reaction network reported for biomass pyrolysis. The exploration was conducted using graph-based rules to explore the reactivities of intermediates and an adaption of the Dijkstra algorithm to identify kinetically relevant intermediates. This simple exploration policy surprisingly (re)identified pathways to most major experimental pyrolysis products, many intermediates proposed by previous computational studies, and also identified new low-barrier reaction mechanisms that resolve outstanding discrepancies between reaction pathways and yields in isotope labeling experiments. This network also provides explanatory pathways for the high yield of hydroxymethylfurfural and the reaction pathway that contributes most to the formation of hydroxyacetaldehyde during glucose pyrolysis. Due to the limited domain knowledge required to generate this network, this approach should also be transferable to other complex reaction network prediction problems in biomass pyrolysis.

The hydrogen-based direct reduction of iron ores is a disruptive routine used to mitigate the large amount of CO2 emissions produced by the steel industry. The reduction of iron oxides by H2 involves a variety of physicochemical phenomena from macroscopic to atomistic scales. Particularly at the atomistic scale, the underlying mechanisms of the interaction of hydrogen and iron oxides is not yet fully understood. In this study, density functional theory (DFT) was employed to investigate the adsorption behavior of hydrogen atoms and H2 on different crystal FeO surfaces to gain a fundamental understanding of the associated interfacial adsorption mechanisms. It was found that H2 molecules tend to be physically adsorbed on the top site of Fe atoms, while Fe atoms on the FeO surface act as active sites to catalyze H2 dissociation. The dissociated H atoms were found to prefer to be chemically bonded with surface O atoms. These results provide a new insight into the catalytic effect of the studied FeO surfaces, by showing that both Fe (catalytic site) and O (binding site) atoms contribute to the interaction between H2 and FeO surfaces.

The chain-of-states (CoS) constant advance replicas (CAR) method and its climbing image variant (CI-CAR) for locating minimum energy paths (MEPs) and transition states are reported. The CAR algorithm applies the Lagrange multiplier method for imposing holonomic constraints on a chain-of-replicas, aiming to maintain equal mass-weighted/scaled root-mean-square (RMS) distances between the adjacent replicas by removing the sliding-down displacements contributed by the potential gradients during path optimization. Two contextual regularization schemes with clear geometrical interpretations are implemented to jointly promote high convergence and numerical robustness of the CAR algorithm. We show that the constrained reaction path can be solved normally within 5 steps of Lagrange multiplier updates with remarkably high numerical precision via the CAR approach. The efficacy of the CAR methods is demonstrated by testing on multiple analytical, classical, and quantum mechanical transition paths: the Müller potential, the alanine dipeptide isomerization, the helix unwinding of the VIVITLVMLKKK 12-mer peptide, and the Baker set of reactions. We also explore the potential of applying adaptive momentum (AdaM) optimizers for locating optimal transition paths under complex conformational changes. Most importantly, we discuss extensively the differences and connections between our newly proposed CAR methods and several related methods, with focuses on the reaction path with holonomic constraints (RPCons) approach of Brokaw et al. [J. Chem. Theory Comput. 2009, 5 (8), 2050-2061] and the state-of-the-art string method (SM) of E et al. [J. Chem. Phys. 2007, 126 (16), 164103]. The CAR approach represents a latest update to the general theoretical framework of reaction path finding algorithms in the two-ended CoS regime.

The human brain is divided into various anatomical regions that control and coordinate unique functions. The prefrontal cortex (PFC) is a large brain region that comprises a range of neuronal and non-neuronal cell types, sharing extensive interconnections with subcortical areas, and plays a critical role in cognition and memory. A timely appearance of distinct cell types through embryonic development is crucial for an anatomically perfect and functional brain. Direct tracing of cell fate development in the human brain is not possible, but single-cell transcriptome sequencing (scRNA-seq) datasets provide the opportunity to dissect cellular heterogeneity and its molecular regulators. Here, using scRNA-seq data of human PFC from fetal stages, we elucidate distinct transient cell states during PFC development and their underlying gene regulatory circuitry. We further identified that distinct intermediate cell states consist of specific gene regulatory modules essential to reach terminal fate using discrete developmental paths. Moreover, using in silico gene knock-out and over-expression analysis, we validated crucial gene regulatory components during the lineage specification of oligodendrocyte progenitor cells. Our study illustrates unique intermediate states and specific gene interaction networks that warrant further investigation for their functional contribution to typical brain development and discusses how this knowledge can be harvested for therapeutic intervention in challenging neurodevelopmental disorders.

The [3s,5s]-sigmatropic shift is an example of an orbital-symmetry forbidden pericyclic reaction that is outcompeted by the allowed [3s,3s]-sigmatropic shift. Density functional theory calculations are used to show that PdII -complexed systems with strategically placed substituents engaging in key stereoelectronic effects can select for the [3s,5s] process, thereby outcompeting both orbital-symmetry-allowed [3s,3s]- and [3s,5a]-shifts.

Triacetone triperoxide (TATP) and hexamethylene triperoxide diamine (HMTD) are cyclic peroxides that exhibit atropisomerism resulting from restricted rotation around three peroxide bonds. As a result, one pair of enantiomers with D3 symmetry and another pair of enantiomers with C2 symmetry can be identified. Previous studies, based on mass spectrometry data and computational results, have shown that conformations of TATP with D3 and C2 symmetry can be isolated. Assuming that enantiomer samples of TATP and HMTD can be obtained with sufficient enantiopurity, we investigated their chiroptical properties, namely, optical rotatory dispersion (ORD), vibrational circular dichroism (VCD), and Raman optical activity (VROA). ORD curves and VCD spectra are seen to be very similar for D3 - and C2 -symmetric atropisomers with the same overall helicity. Predicted VROA results, however, show significant differences between D3 - and C2 -symmetric atropisomers with the same overall helicity. The D3 -symmetric atropisomer is predicted to exhibit considerably larger magnitude vibrational optical activity signals than the C2 -symmetric atropisomer.