Computational chemistry future developments

A general trend which could be noticed over the last few years and which may be expected to develop further in the near future involves a closer coupling between the use of general tools of computational chemistry (ab initio and semi-empirical quantum chemistry, statistical-mechanical simulations) and relaxation theory. When applied to model systems, the computational chemistry methods have the potential of providing new insights on how to develop theoretical models, as well as of yielding estimates of the parameters occurring in the models. 

Future developments in the field of optimization will undoubtedly be influenced by recent interest and rapid developments in new technologies— powerful vector and parallel machines. Indeed, their exploitation for algorithm design and solution of grand challenge applications15 16 is expected to bring new advances in the field of computational chemistry, in particular. 

There are so many developments in the field of computational chemistry that it is difficult to keep track of them. For that reason we established this review series. As in previous volumes, we attempt to treat computational chemistry as broadly and evenhandedly as possible. It should be obvious that not all facets of computational chemistry can be covered in each and every volume. Eventually, however, the existing and future volumes of Reviews in Computational Chemistry, when taken in toto, should constitute a useful guide to the field. 

This has provided computational chemistry with a unique opportunity to influence the development of silicon chemistry not only by providing interpretations to experimental findings but more importantly by making predictions and directing future experiments. Indeed in the last decade theoretical calculations played a major and sometimes even a crucial role in the development of silicon chemistry [5]. In this paper we hope to demonstrate the importance of theory for understanding and predicting the properties and chemistry of silylenes and of disilenes. 

Progress in theory, availability of software and development of computer technology have created highly sophisticated systems for performing complex calculations on various chemical compounds. Computational methods are routinely used nowadays not only by theoretical chemists but also by experimentalists (cf. QCLDB bibliography [1]). There is no doubt that the future of computational methods is bright. However, two fundamental problems face further development of computational chemistry. 

This review of semiempirical quantum-chemical methods outlines their development over the past 40 years. After a survey of the established methods such as MNDO, AMI, and PM3, recent methodological advances are described including the development of improved semiempirical models, new general-purpose and special-purpose parametriza-tions, and linear scaling approaches. Selected recent applications are presented covering examples from biochemistry, medicinal chemistry, and nanochemistry as well as direct reaction dynamics and electronically excited states. The concluding remarks address the current and future role of semiempirical methods in computational chemistry. 

In addition to the development and implementation of PTC processes, future advances of PTC in organic synthesis are expected in the field of supercritical-fluid PTC and from the incorporation of modern computational chemistry in the design of new, inexpensive chiral phase transfer catalysts. 

The second difference between molecular and solid-state fields is the lack, in the latter, of a reference theoretical method. Post-HF techniques in molecular quantum chemistry can yield results with a controlled degree of accuracy. In the absence of experimental data, the results obtained with different DFT functionals could be compared against those calculated with the reference computational technique. Recent developments in wavefunction methods [9], GW techniques [38], and quantum Monte Carlo (QMC) [39] for solid-state systems aim at filling this gap, and are promising for future work, but at present they still suffer from a limited applicability. 

---